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Liu, Jie, Wenmin Deng, Zhiwen Xiao, Xiaofeng Huang, Minmin Lin, and Zhen Long. "Identification of RNA Modification-Associated Alternative Splicing Signature as an Independent Factor in Head and Neck Squamous Cell Carcinoma." Journal of Immunology Research 2022 (September 13, 2022): 1–19. http://dx.doi.org/10.1155/2022/8976179.
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Objective. Head and neck squamous cell carcinoma (HNSCC) is a highly heterotopic malignant tumor. Alternative splicing (AS) and RNA modification have been reported to be involved in tumorigenesis. Therefore, we constructed RNA modification-associated AS (RMA-AS) signature model to predict the prognosis of HNSCC. Methods. AS events and RNA-modified gene expression information were downloaded from TCGA-HNSCC database. Univariate Cox regression analysis was employed for analyzing prognosis-related AS events. RMA-AS events were obtained by constructing a coexpression network between RNA modification-associated genes and AS events using WGCNA package. The prognostic signatures were analyzed by LASSO, univariate Cox, and multivariate Cox regression. Kaplan-Meier survival analysis, proportional hazard model, and ROC curve were performed to verify the prognostic value. “ESTIMATE” R package, ssGSEA algorithm, and CIBERSORT method were used to detect immune microenvironment in HNSCC. Cytoscape was utilized to build a regulatory network of splicing factor-regulated RMA-AS. Results. There were 16,574 prognostic AS events and 4 differentially expressed RNA modification-associated genes in HNSCC. Based on RMA-AS events, we obtained a risk model consisting of 14 AS events, named RMA-AS_Score. The samples were divided into RMA-AS_Score high- and RMA-AS_Score low-risk groups, according to the risk score. The RMA-AS_Score high group was related to poor prognosis. Moreover, the RMA-AS_Score signature was an independent prognostic predictor and was related to tumor grade. Meanwhile, the AUC value of RMA-AS_Score was 0.652, which is better than other clinical characteristics. Besides, a nomogram prediction model of quantitative prognosis has also been developed, which has robust effectiveness in predicting prognosis. In addition, the prognostic signature was observably related to immune microenvironment and immune checkpoint. Finally, 14 splicing factors were identified and constructed into a network of splicing factor-regulated RMA-AS. Conclusion. We identified the RMA-AS signature of HNSCC. This signature could be treated as an independent prognostic predictor.
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Stupnikov, Alexey, Paul G. O’Reilly, Caitriona E. McInerney, Aideen C. Roddy, Philip D. Dunne, Alan Gilmore, Hayley P. Ellis, et al. "Impact of Variable RNA-Sequencing Depth on Gene Expression Signatures and Target Compound Robustness: Case Study Examining Brain Tumor (Glioma) Disease Progression." JCO Precision Oncology, no. 2 (November 2018): 1–17. http://dx.doi.org/10.1200/po.18.00014.
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Purpose Gene expression profiling can uncover biologic mechanisms underlying disease and is important in drug development. RNA sequencing (RNA-seq) is routinely used to assess gene expression, but costs remain high. Sample multiplexing reduces RNA-seq costs; however, multiplexed samples have lower cDNA sequencing depth, which can hinder accurate differential gene expression detection. The impact of sequencing depth alteration on RNA-seq–based downstream analyses such as gene expression connectivity mapping is not known, where this method is used to identify potential therapeutic compounds for repurposing. Methods In this study, published RNA-seq profiles from patients with brain tumor (glioma) were assembled into two disease progression gene signature contrasts for astrocytoma. Available treatments for glioma have limited effectiveness, rendering this a disease of poor clinical outcome. Gene signatures were subsampled to simulate sequencing alterations and analyzed in connectivity mapping to investigate target compound robustness. Results Data loss to gene signatures led to the loss, gain, and consistent identification of significant connections. The most accurate gene signature contrast with consistent patient gene expression profiles was more resilient to data loss and identified robust target compounds. Target compounds lost included candidate compounds of potential clinical utility in glioma (eg, suramin, dasatinib). Lost connections may have been linked to low-abundance genes in the gene signature that closely characterized the disease phenotype. Consistently identified connections may have been related to highly expressed abundant genes that were ever-present in gene signatures, despite data reductions. Potential noise surrounding findings included false-positive connections that were gained as a result of gene signature modification with data loss. Conclusion Findings highlight the necessity for gene signature accuracy for connectivity mapping, which should improve the clinical utility of future target compound discoveries.
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Albitar, Maher, Sally Agersborg, Ahmad Charifa, Hong Zhang, Andrew Ip, Katherine Linder, Andrew L. Pecora, Jamie Koprivnikar, Andre Goy, and James McCloskey. "Establishing Distinct Cytokine Signatures Differentiating between Acute Myeloid Leukemia, Myelodysplastic Syndrome, and Chip Using Bone Marrow RNA or Cell-Free RNA (cfRNA)." Blood 144, Supplement 1 (November 5, 2024): 4295. https://doi.org/10.1182/blood-2024-203870.
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Cytokines are essential for various immune functions and the overall inflammatory response to myeloid neoplasms in bone marrow. They play a major role in bone marrow microenvironment in normal and abnormal hematopoiesis. Cytokines exert their functions by interacting with their receptors, and full evaluation of the cytokines' roles requires evaluating their receptors as well. Using next generation sequencing (NGS) and machine learning, we measured the expression of 36 cytokines/chemokines and cytokine receptors in the bone marrow (BM) of patients with acute myeloid leukemia (AML), myelodysplastic syndrome (MDS), and clonal hematopoiesis of indeterminate potential (CHIP), and established cytokine signatures that differentiate between these diseases. We also explored if peripheral blood cell-free RNA (cfRNA) reflects BM environment. Methods: RNA was extracted from the bone marrow (BM) samples of patients with AML (N=515), MDS (N=825), and CHIP (N=915). cfRNA was extracted from the peripheral blood of patients with AML (N=30), MDS (N=184), and CHIP (N=502). BM RNA and cfRNA were sequenced using a 1500-gene targeted RNA next generation sequencing (NGS) panel. More than 80 million reads and a percentage of spliced reads above 20% were required for acceptable evaluation. The expression levels of 36 cytokines/chemokines were used in this analysis. Using Bayesian statistics, each of the 36 biomarkers was ranked based on its sensitivity and specificity of distinguishing between two diagnostic classes with 10-fold cross validation by leave-one-out. Random forest algorithms were developed using two-thirds of the BM samples and top-ranked biomarkers to build signatures that distinguished between two diagnostic classes. One-third of the bone marrow samples were used for testing these algorithms. Each model was then used to test if cfRNA samples showed the same results obtained from BM samples. Results: In distinguishing between AML and MDS, we first used Bayesian statistics with 10-fold cross validation to rank the studied 36 cytokines/chemokines and receptors. After ranking, random forest showed that a cytokine signature of 20 top-ranked biomarkers can reliably distinguish between BM with MDS from BM with AML (AUC: 0.874, CI: 0.843-.906). The biomarkers in the signature are: TNFRSF10D, TNFAIP3, TNFRSF4, IL3RA, IL8, TGFBR3, CXXC4, IL1RAP, IL7R, IFNG, TNFRSF10B, IL2, TNF, TGFBR2, CXCR4, TNFRSF14, CTLA4, IL12RB2, TGFBI, IL21R. Using the same algorithm and the same biomarkers but as measured using peripheral blood cfRNA, AML was distinguishable from MDS (AUC: 0.706, CI: 0.617-0.795). Using a similar approach, we were able to distinguish between BM with MDS and BM with CHIP using random forest and a cytokine signature of 20 biomarkers (AUC: 0.761, CI: 0.722-0.800). The biomarkers in this signature are TGFBR2, TNFRSF14, CXCR4, IL1RAP, TNFRSF10D, TNFAIP3, IL8, IL12RB2, IL1B, CTLA4, IL7R, IL21R, TNFRSF9, TNF, IL3, TNFRSF17, TGFBR3, TNFRSF4, TNFRSF10B, and IL13RA2, in order. Using the same signature and biomarkers but as measured using peripheral blood cfRNA from patients with MDS or CHIP, we were able to distinguish between the two diseases with AUC of 0.712 (CI: 0.668-0.756). While both signatures share multiple biomarkers (IL8, CTLA4, CXCR4...), the signature distinguishing AML from MDS is uniquely using IL3, IL1B and TNFRSF17 while the signature for distinguishing MDS from CHIP is uniquely using CXX4, IL2, and TGFB1. Conclusions: There are unique cytokines/chemokines and receptor signatures for each of the AML, MDS, and CHIP. This indicates that these biomarkers are crucial for defining each of these diseases. Furthermore, our data shows that cfRNA is reliable in reflecting bone marrow findings and can be used as an alternative to bone marrow samples for measuring and monitoring these signatures.
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Al Mahi, Naim, Erik Y. Zhang, Susan Sherman, Jane J. Yu, and Mario Medvedovic. "Connectivity Map Analysis of a Single-Cell RNA-Sequencing -Derived Transcriptional Signature of mTOR Signaling." International Journal of Molecular Sciences 22, no. 9 (April 22, 2021): 4371. http://dx.doi.org/10.3390/ijms22094371.
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In the connectivity map (CMap) approach to drug repositioning and development, transcriptional signature of disease is constructed by differential gene expression analysis between the diseased tissue or cells and the control. The negative correlation between the transcriptional disease signature and the transcriptional signature of the drug, or a bioactive compound, is assumed to indicate its ability to “reverse” the disease process. A major limitation of traditional CMaP analysis is the use of signatures derived from bulk disease tissues. Since the key driver pathways are most likely dysregulated in only a subset of cells, the “averaged” transcriptional signatures resulting from bulk analysis lack the resolution to effectively identify effective therapeutic agents. The use of single-cell RNA-seq (scRNA-seq) transcriptomic assay facilitates construction of disease signatures that are specific to individual cell types, but methods for using scRNA-seq data in the context of CMaP analysis are lacking. Lymphangioleiomyomatosis (LAM) mutations in TSC1 or TSC2 genes result in the activation of the mTOR complex 1 (mTORC1). The mTORC1 inhibitor Sirolimus is the only FDA-approved drug to treat LAM. Novel therapies for LAM are urgently needed as the disease recurs with discontinuation of the treatment and some patients are insensitive to the drug. We developed methods for constructing disease transcriptional signatures and CMaP analysis using scRNA-seq profiling and applied them in the analysis of scRNA-seq data of lung tissue from naïve and sirolimus-treated LAM patients. New methods successfully implicated mTORC1 inhibitors, including Sirolimus, as capable of reverting the LAM transcriptional signatures. The CMaP analysis mimicking standard bulk-tissue approach failed to detect any connection between the LAM signature and mTORC1 signaling. This indicates that the precise signature derived from scRNA-seq data using our methods is the crucial difference between the success and the failure to identify effective therapeutic treatments in CMaP analysis.
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Lu, Zhihao, Huan Chen, Shuang Li, Xi Jiao, Lihong Wu, Jianing Yu, Lin Shen, and Henghui Zhang. "A RNA signature predicts outcomes in immune checkpoint blockade treated gastrointestinal cancer patients." Journal of Clinical Oncology 37, no. 15_suppl (May 20, 2019): e14071-e14071. http://dx.doi.org/10.1200/jco.2019.37.15_suppl.e14071.
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e14071 Background: Cancer therapy has been greatly revolutionized in recent years by the conceptual developments in the field of cancer immunology. Growing evidence support the utility of immune checkpoint inhibition (ICB) in gastrointestinal (GI) cancer. However, a central question lies in understanding how therapeutic responsiveness is predicted. Methods: To address this, we evaluated tumor FFPE specimens from 97 patients who received ICB treatment. All patients were randomly assigned into discovery (60%) and validation (40%) cohorts. Tumor RNA before ICB treatment was analyzed on a multiplex RNA immune oncology (RNA IO) profiling sequencing panel. Results: We show that four immune-related gene expression signatures were upregulated in responders versus non-responders in the discovery cohort. Three of the four signatures showed significant correlation with clinic response and disease control rates. However, two previously reported RNA signatures, PD-L1 expression and MMR status revealed less predictive values in GI cancers. More importantly, we identified that higher levels of a 19-gene signature were remarkably associated with favorable overall survival (OS) and progression-free survival (PFS) when compared to patients with lower levels of signature in both the discovery and validation cohorts. Of note, a joint biomarker of tumor mutation burden (TMB) and the 19-gene signature may better stratify responders from non-responders in GI cancer patients. Conclusions: Our data provide evidence that a responsive feature, defined by a multi-gene expression pattern across different GI cancer types, can be obtained via a RNA quantitative strategy and may be explored as a future pan-cancer biomarker.
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Li, Chenyang, Thinh T. Nguyen, Jian-Rong Li, Xingzhi Song, Ignacio I. Wistuba, Andy Futureal, Jianhua Zhang, et al. "Abstract 97: Multiregional profiling revealed intra-tumor transcriptomic heterogeneity associated with the prognosis in non-small cell lung cancer." Cancer Research 83, no. 7_Supplement (April 4, 2023): 97. http://dx.doi.org/10.1158/1538-7445.am2023-97.
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Abstract Introduction: Intratumor heterogeneity (ITH) describes the distinct tumor cell populations and microenvironments within the same tumor, which may profoundly impact cancer evolution and clinical outcomes. Non-small cell lung cancer (NSCLC) is not only a genetically diverse disease but also has high transcriptomic heterogeneity (RNA-ITH). The RNA-ITH limits the reproducibility of expression-based prognostic models, which is poorly understood. Methods: To address the issue, we investigated the effect of RNA-ITH on prognosis at both gene and signature levels using multiregional RNA-seq data from 45 NSCLC patients (145 regions) in the TRACERx study. We also performed multiregional RNA-seq of 25 NSCLC tumors (64 regions) for independent validation. Results: At the gene level, we found that the maximal expression of hazardous genes (Hazard Ration (HR) > 1) and the minimal expression of protective (HR < 1) genes across different regions within a tumor are more prognostic than their average expression. As for prognostic signatures, we first designed five different functions to transform the multiregional expression of signature genes into patient-level values. To calculate individual risk scores, we applied them to assist two existing prognostic gene signatures, ORACLE (Outcome Risk Associated Clonal Lung Expression) and WTGS (whole-transcriptomic gene signature). As a result, the best performance was achieved using the combination of maximal hazardous signature expressions and minimal protective signature expressions. We next developed a new signature called PACEG (Prognosis-Associated Clonally Expressed Genes) and proposed a multiregional assay for higher prognostic accuracy in NSCLC. We demonstrated significant improvement in PACEG performance by leveraging RNA-ITH captured by multiregional expression of signature genes. Finally, we utilized the same strategy to study the impact of tumor immune microenvironment ITH on patient prognosis. Consistently, the minimal/maximal infiltration of protective/hazardous immune cells across tumor regions was the best measurement associated with prognosis in NSCLC. These results were independently validated by our local datasets. Conclusions: The prognosis of NSCLC patients is often driven by the most aggressive tumor subclones. Our study proposed a novel strategy to incorporate RNA-ITH with expression-based prognostic models. Multiple distinct tumor regions should be considered to overcome the ITH issue for better prognostic evaluation, e.g., using the minimal/maximal expression of protective/hazardous signature genes across all regions to calculate the risk score in individuals. We also developed the PACEG panel composed of 26 genes that could be potentially applied in clinical specimens to identify high-risk NSCLC patients who may benefit from intensified adjuvant therapy. Citation Format: Chenyang Li, Thinh T. Nguyen, Jian-Rong Li, Xingzhi Song, Ignacio I. Wistuba, Andy Futureal, Jianhua Zhang, Shawna M. Hubert, Jia Wu, Jianjun Zhang, Chao Cheng. Multiregional profiling revealed intra-tumor transcriptomic heterogeneity associated with the prognosis in non-small cell lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 97.
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Wen, Huaming, Ryan A. Gallo, Xiaosheng Huang, Jiamin Cai, Shaoyi Mei, Ammad Ahmad Farooqi, Jun Zhao, and Wensi Tao. "Incorporating Differential Gene Expression Analysis with Predictive Biomarkers to Identify Novel Therapeutic Drugs for Fuchs Endothelial Corneal Dystrophy." Journal of Ophthalmology 2021 (June 28, 2021): 1–8. http://dx.doi.org/10.1155/2021/5580595.
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Purpose. Based on the differential gene expression analysis for predictive biomarkers with RNA-Sequencing data from Fuchs endothelial corneal dystrophy (FECD) patients, we are aiming to evaluate the efficacy of Library of Integrated Network-based Cellular Signatures (LINCS) perturbagen prediction software to identify novel pharmacotherapeutic targets that can revert the pathogenic gene expression signatures and reverse disease phenotype in FECD. Methods. A publicly available RNA-seq dataset was used to compare corneal endothelial specimens from controls and patients with FECD. Based on the differential gene expression analysis for predictive biomarkers, we evaluated the efficacy of LINCS perturbagen prediction software to identify novel therapeutic targets that can revert the pathogenic gene expression signatures and reverse disease phenotypes in FECD. Results. The RNA-seq dataset of the corneal endothelial cells from FECD patients revealed the differential gene expression signatures of FECD. Many of the differential expressed genes are related to canonical pathways of the FECD pathogenesis, such as extracellular matrix reorganization and immunological response. The expression levels of genes VSIG2, IL18, and ITGB8 were significantly increased in FECD compared with control. Meanwhile, the expression levels of CNGA3, SMOX, and CERS1 were significantly lower in the FECD than in control. We employed LINCS L1000 Characteristic Direction Signature Search Engine (L1000-CDS2) to investigate pathway-based molecular treatment. L1000-CDS2 predicted that small molecule drugs such as histone deacetylase (HDAC) inhibitors might be a potential candidate to reverse the pathological gene expression signature in FECD. Conclusions. Based on differential gene expression signatures, several candidate drugs have been identified to reverse the disease phenotypes in FECD. Gene expression signature with LINCS small molecule prediction software can discover novel preclinical drug candidates for FECD.
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Wang, Kang, Yajing Zhu, Ioannis Zerdes, Emmanouil Sifakis, Georgios Manikis, Dimitrios Salgkamis, Nikolaos Tsiknakis, et al. "Abstract PO2-07-06: Multimodal learning predictor of HER2-positive breast cancer therapy response in the randomized PREDIX HER2 trial." Cancer Research 84, no. 9_Supplement (May 2, 2024): PO2–07–06—PO2–07–06. http://dx.doi.org/10.1158/1538-7445.sabcs23-po2-07-06.
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Abstract Background: The PREDIX HER2 trial, compared six courses of docetaxel, trastuzumab, and pertuzumab (DTP) vs. trastuzumab emtansine (T-DM1) as neoadjuvant treatment for HER2-positive breast cancer (BC). Similar rates of pathologic complete response (pCR) were seen. Methods: Clinicopathological, shallow whole-genome sequencing (CUTseq, n=176), whole exome sequencing (WES, n=192), and RNA-sequencing (RNA-seq, n=187) data were generated using fresh-frozen baseline core biopsies. Potential tumor intrinsic resistance factors and microenvironment components were quantified by multi-omics analysis, including BC-specific somatic mutations and copy number alterations (CNA), COSMIC mutational signatures, CNA-based chromosomal instability signatures (CIN), subclone percentage, PAM50 subtype, GGI/PIK3CA score, HER2DX score, immune profiles (Danaher signature score, TIDE score and immune repertoires). We assessed the association of biomarkers with pCR in each treatment arm using logistic regression adjusting for hormone receptor (HR) status, and evaluated their predictive value by adding the interaction term (biomarker x treatment arm). In addition, a machine learning (ML) analysis was conducted from different classifiers, comprising unimodal ML-based models from clinical, RNA and DNA information, respectively. Model performance was assessed using the mean and standard deviation (mean ± std) of the area under receiver operator characteristic curve (AUC), positive predictive value (PPV) and negative predictive value (NPV) using a nested stratified cross-validation (CV) schema of 200 outer shuffle splits and 100 inner 5-fold CV to mitigate potential risk of overfitting. Results: In DTP arm, patients with higher ERBB2 copy ratio (ORadj=1.98, p=0.004) or mRNA (ORadj=3.08, p< 0.001) or HER2-enriched subtype (PAM50) (ORadj=1.78, p=0.02) had higher pCR rates, while ESR1 gene expression (ORadj=0.59, p=0.07) predicted treatment resistance despite adjustment for HR status. Conversely, response to T-DM1 was less likely to depend on ERBB2 profiles and only PAM50 HER2 enriched subtype (ORadj=1.53, p=0.1) showed higher pCR rate (52% vs. 25%) than other subtypes. Both ESR1 (ORadj=0.4, p=0.008) and PGR (ORadj=0.5, p=0.03) gene expression were independent predictors of T-DM1 resistance. Pre-treatment immune exclusion metrics could predict resistance to DTP (endothelial cell, ORadj=0.67, p=0.07) and T-DM1 (neutrophils, ORadj=0.54, p=0.02; mast cells, ORadj=0.57, p=0.02; cancer-associated fibroblasts, ORadj=0.67, p=0.09)), respectively. Predefined metrics such as PIK3CA signature score (ORadj =1.67, p=0.04) and Taxane response score (ORadj =1.64, p=0.03) were positively related to pCR in DTP arm. Genome instability, involving CIN CX2 signature (impaired homologous recombination) (ORadj=1.71, p=0.05), COSMIC signature6 (ORadj =1.53, p=0.07) and signature13 (ORadj =1.57, p=0.05), predicted benefit from DTP. The biomarker-treatment interaction tests were significant for HER2DX (pinteraction=0.004) and COSMIC signature15 (defective DNA mismatch repair) (pinteraction=0.007): lower HERDX score (ORadj =0.73, p=0.14) or higher COSMIC signature15 score (ORadj =1.51, p=0.1) could identify patients benefiting from T-DM1, while being resistant to DTP (HERDX: ORadj =1.46, p=0.13; signature15: ORadj =0.70, p=0.1). In the ML models, clinical information yielded an AUC=0.62±0.07, PPV=0.64±0.12 and NPV=0.64±0.06; for DNA data, AUC was equal to 0.70±0.08, PPV=0.72±0.09 and NPV=0.71±0.07; an adaptive boosting ensemble learning on RNA reported slightly increased pCR prediction performance (AUC=0.72±0.06, PPV=0.64±0.06, NPV=0.80±0.09). Conclusion: This study demonstrates that antibody–drug conjugates and standard treatment harbor strikingly distinctive biomarkers across tumor ecosystem. Further external validation and integrated ML model comprising all unimodal models are ongoing. Citation Format: Kang Wang, Yajing Zhu, Ioannis Zerdes, Emmanouil Sifakis, Georgios Manikis, Dimitrios Salgkamis, Nikolaos Tsiknakis, Luuk Harbers, Nicola Crosetto, Jonas Bergh, Alexios Matikas, Thomas Hatschek, Theodoros Foukakis. Multimodal learning predictor of HER2-positive breast cancer therapy response in the randomized PREDIX HER2 trial [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-07-06.
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Erbe, Rossin, Michelle M. Stein, Tim A. Rand, and Justin Guinney. "Abstract 2281: A tumor-intrinsic signature involving immunosuppression via MIF-CD74 signaling is associated with overall survival in ICT-treated lung adenocarcinoma." Cancer Research 84, no. 6_Supplement (March 22, 2024): 2281. http://dx.doi.org/10.1158/1538-7445.am2024-2281.
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Abstract Introduction: Immune checkpoint therapies (ICT) have changed cancer care, yielding robust and durable responses in a subset of patients. Identifying patients who are likely to respond to ICT remains an ongoing challenge. In addition, only a portion of patients with clinical biomarkers respond to therapy. Signatures of RNA expression have been developed to predict response, the majority of which focus on T-cell and cytotoxicity markers, yet have been unable to substantially improve outcome predictions. Here, we present a RNA signature that instead describes tumor-intrinsic immune resistance and a potential mechanism of immunosuppression via tumor signaling on macrophages, derived from single-cell RNA-sequencing (scRNA-seq). Methods: We performed dimensionality reduction using a Variational Autoencoder (VAE) on 30 scRNA-seq samples from 15 lung adenocarcinoma (LUAD) patients, comprising a total of 183,873 cells from the Cell Ranger pipeline. The VAE model was trained on each sample for 250 iterations, yielding 20 signatures from each patient, for a total of 300 signatures. The relationship of each signature with real-world overall survival (rwOS) across 1,983 bulk RNA-sequenced LUAD patients treated with an FDA approved ICT was assessed via a Cox proportional hazards model with risk set adjustment, using TMB and line of therapy as covariates. The NATMI python package was used to identify putative active ligand-receptor interactions between tumor cells and the immune environment. Results: Among the VAE encodings, we identified an immune resistance signature significantly associated with decreased rwOS across the patient cohort (HR = 4.2 [2.8-6.3], adjusted p = 4.3e-10). This signature was derived from a small cluster of neoplastic cells (4.4% of cells) in a patient sample that was otherwise dominated by immune cells, including a substantial fraction of cytotoxic CD8 T cells (24.5%). The strongest predicted ligand-receptor interactions were found between the neoplastic cells and macrophages, via the MIF-CD74 interaction, an interaction we found upregulated in multiple single-cell tumor samples. Further, MIF RNA expression alone was significantly associated with rwOS across the 1,983 patient LUAD cohort (HR = 1.5 [1.1-2.1], p = 0.004). Discussion: MIF-CD74 is a known immunosuppressive interaction and MIF signaling from tumor cells on macrophages has been previously shown to have immunosuppressive effects in a mouse model of melanoma that were largely reversible via MIF-CD74 blockade. Taken together, these results identify a signature of tumor intrinsic immune suppression that can indicate patients likely to experience reduced benefit from ICT. In addition, this signature provides evidence to support blockade of the MIF-CD74 axis as a means to enhance anti-tumor immune responses in LUAD. Citation Format: Rossin Erbe, Michelle M. Stein, Tim A. Rand, Justin Guinney. A tumor-intrinsic signature involving immunosuppression via MIF-CD74 signaling is associated with overall survival in ICT-treated lung adenocarcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2281.
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Qiu, Lipeng, Tao Wang, Qi Ge, Han Xu, Yihang Wu, Qi Tang, and Keping Chen. "Circular RNA Signature in Hepatocellular Carcinoma." Journal of Cancer 10, no. 15 (2019): 3361–72. http://dx.doi.org/10.7150/jca.31243.
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Pačínková, Anna, and Vlad Popovici. "Cross-platform Data Analysis Reveals a Generic Gene Expression Signature for Microsatellite Instability in Colorectal Cancer." BioMed Research International 2019 (March 17, 2019): 1–9. http://dx.doi.org/10.1155/2019/6763596.
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The dysfunction of the DNA mismatch repair system results in microsatellite instability (MSI). MSI plays a central role in the development of multiple human cancers. In colon cancer, despite being associated with resistance to 5-fluorouracil treatment, MSI is a favourable prognostic marker. In gastric and endometrial cancers, its prognostic value is not so well established. Nevertheless, recognising the MSI tumours may be important for predicting the therapeutic effect of immune checkpoint inhibitors. Several gene expression signatures were trained on microarray data sets to understand the regulatory mechanisms underlying microsatellite instability in colorectal cancer. A wealth of expression data already exists in the form of microarray data sets. However, the RNA-seq has become a routine for transcriptome analysis. A new MSI gene expression signature presented here is the first to be valid across two different platforms, microarrays and RNA-seq. In the case of colon cancer, its estimated performance was (i) AUC = 0.94, 95% CI = (0.90 – 0.97) on RNA-seq and (ii) AUC = 0.95, 95% CI = (0.92 – 0.97) on microarray. The 25-gene expression signature was also validated in two independent microarray colon cancer data sets. Despite being derived from colorectal cancer, the signature maintained good performance on RNA-seq and microarray gastric cancer data sets (AUC = 0.90, 95% CI = (0.85 – 0.94) and AUC = 0.83, 95% CI = (0.69 – 0.97), respectively). Furthermore, this classifier retained high concordance even when classifying RNA-seq endometrial cancers (AUC = 0.71, 95% CI = (0.62 – 0.81). These results indicate that the new signature was able to remove the platform-specific differences while preserving the underlying biological differences between MSI/MSS phenotypes in colon cancer samples.
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Allen, Hermione E., Andrew McIntyre, Jesus Gutierrez-Abril, Amy A. Kirkwood, Daniel Leongamornlert, Rodothea Amerikanou, Emily A. Cutler, et al. "The Role of the Bone Marrow Immune Niche in Preventing Relapse in Adult B-ALL Following Reduced Intensity Conditioning Allogeneic HSCT." Blood 144, Supplement 1 (November 5, 2024): 1454. https://doi.org/10.1182/blood-2024-205516.
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Reduced intensity-conditioned allogeneic haematopoietic stem cell transplants (alloHSCT) improved event-free survival for patients with B-cell acute lymphoblastic leukaemia (B-ALL) >40 years old, as established by the UKALL14 trial (Marks et al. 2022) The main factor predicting outcome post alloHSCT was persistence of minimal residual disease (MRD). Other studies also find that MRD to be a strong predictor of relapse after alloHSCT in ALL (Della Starza et al., 2019) which is associated with an ineffective graft-versus-leukaemia (GVL) response by donor T cells (Rosko et al., 2016); (Lin et al., 2000). We hypothesised that we would find a different T cell ‘signature’ in patients who relapsed and those who did not following alloHSCT in the UKALL14 study. We used post-transplant samples to assess the bone marrow (BM) immune cell content. Twenty-one BM samples from 12 patients (aged 41-60, median = 48 years old) were analysed by bulk RNA-sequencing (RNA-seq), ranging from 8-109 weeks post-alloHSCT. Six (50%) patients were female and six (50%) male, 2 (17%) had BCR::ABL+ B-ALL, and 2 (17%) had other UKALL14 high-risk cytogenetics. Samples were analysed in 2 groups: A) patients whose disease was in continuous remission and remained in remission long term (CR → CR); B) patients whose disease was in complete remission but relapsed in the future (CR → Rel). When comparing 1 sample per patient (13-55 weeks post-alloHSCT, median = 38 weeks), we did not detect any differences in the T cell subpopulation representation. However, neutrophil-expressed genes were significantly upregulated (L2FC ≥ 1, p-value ≤ 0.05), and gene set enrichment analysis (GSEA) revealed an enrichment of pathways associated with neutrophil degranulation in CR → Rel, compared to CR → CR. Longitudinal sampling, used to assess how the BM composition changes temporally, post-alloHSCT, did not identify any significant differences across the cohort over time. To characterise this myeloid signature at greater resolution, single cell RNA-sequencing was performed on BM samples from 10 patients (aged 36-59, median = 44.5 years old), in the same analysis groups as previously described, with samples taken between 19-82 weeks post-alloHSCT (median = 31 weeks). Seven (58%) were male and 5 (42%) female, 3 had BCR::ABL+ B-ALL, and an additional 5 (42%) had high-risk cytogenetics. Investigation of this cohort revealed increased cytotoxic CD8+ T cells and NK cells in CR → CR, with CD4+ naïve and central memory populations more prominent in CR → Rel. A neutrophil population with a gene expression signature comparable to that seen in the bulk RNA-seq was also enriched in CR → Rel. Cell-cell interactome analysis revealed stronger putative interactions in CR → Rel specimens, most notably involving the CD4+ naïve T cell and neutrophil populations. To determine whether this myeloid signature was transplant-dependent, we analysed data from bulk RNA-seq of 7 BM diagnostic samples, from patients with samples analysed post-alloHSCT, for the presence of the same signature. Analysis using DESeq2 and GSEA identified a myeloid gene signature that was associated with subsequent relapse after alloHSCT; this gene signature overlapped with the myeloid signature found in post-transplant CR → Rel (hypergeometric test p = 8.5x10-36). Six myeloid signatures were generated from the post-alloHSCT and diagnostic bulk RNA-seq and used to generate normalised enrichment scores (NES) for a cohort of 150 UKALL14 patients at diagnosis. Interrogation of this cohort showed that increased NES of all six signatures were significantly correlated with age and event-free survival. To summarise, we have identified BM immune signatures associated with continuous remission versus future relapse after alloHSCT for B-ALL. Our data suggest that a neutrophil signature is enriched in both the diagnostic and post-transplant samples of patients destined to relapse, which is supported by its correlation with age and EFS in a large diagnostic cohort. To corroborate whether this myeloid population predicts eventual treatment failure post-alloHSCT in an independent cohort, the same 6 signatures will be used to interrogate bulk RNA-seq of patients at diagnosis from the GRALL trial. Further work is being performed to evaluate the profile of the neutrophil-like populations in the bone marrow samples of relapsing patients and to determine how they might regulate anti-tumour immune surveillance.
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Narjala, Anushree, Ashwin Nair, Varsha Tirumalai, G. Vivek Hari Sundar, and Padubidri V. Shivaprasad. "A conserved sequence signature is essential for robust plant miRNA biogenesis." Nucleic Acids Research 48, no. 6 (February 6, 2020): 3103–18. http://dx.doi.org/10.1093/nar/gkaa077.
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Abstract Micro (mi)RNAs are 20–22nt long non-coding RNA molecules involved in post-transcriptional silencing of targets having high base-pair complementarity. Plant miRNAs are processed from long Pol II-transcripts with specific stem-loop structures by Dicer-like (DCL) 1 protein. Although there were reports indicating how a specific region is selected for miRNA biogenesis, molecular details were unclear. Here, we show that the presence of specific GC-rich sequence signature within miRNA/miRNA* region is required for the precise miRNA biogenesis. The involvement of GC-rich signatures in precise processing and abundance of miRNAs was confirmed through detailed molecular and functional analysis. Consistent with the presence of the miRNA-specific GC signature, target RNAs of miRNAs also possess conserved complementary sequence signatures in their miRNA binding motifs. The selection of these GC signatures was dependent on an RNA binding protein partner of DCL1 named HYL1. Finally, we demonstrate a direct application of this discovery for enhancing the abundance and efficiency of artificial miRNAs that are popular in plant functional genomic studies.
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Farias Amorim, Camila, Fernanda O. Novais, Ba T. Nguyen, Mauricio T. Nascimento, Jamile Lago, Alexsandro S. Lago, Lucas P. Carvalho, Daniel P. Beiting, and Phillip Scott. "Localized skin inflammation during cutaneous leishmaniasis drives a chronic, systemic IFN-γ signature." PLOS Neglected Tropical Diseases 15, no. 4 (April 1, 2021): e0009321. http://dx.doi.org/10.1371/journal.pntd.0009321.
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Cutaneous leishmaniasis is a localized infection controlled by CD4+ T cells that produce IFN-γ within lesions. Phagocytic cells recruited to lesions, such as monocytes, are then exposed to IFN-γ which triggers their ability to kill the intracellular parasites. Consistent with this, transcriptional analysis of patient lesions identified an interferon stimulated gene (ISG) signature. To determine whether localized L. braziliensis infection triggers a systemic immune response that may influence the disease, we performed RNA sequencing (RNA-seq) on the blood of L. braziliensis-infected patients and healthy controls. Functional enrichment analysis identified an ISG signature as the dominant transcriptional response in the blood of patients. This ISG signature was associated with an increase in monocyte- and macrophage-specific marker genes in the blood and elevated serum levels IFN-γ. A cytotoxicity signature, which is a dominant feature in the lesions, was also observed in the blood and correlated with an increased abundance of cytolytic cells. Thus, two transcriptional signatures present in lesions were found systemically, although with a substantially reduced number of differentially expressed genes (DEGs). Finally, we found that the number of DEGs and ISGs in leishmaniasis was similar to tuberculosis–another localized infection–but significantly less than observed in malaria. In contrast, the cytolytic signature and increased cytolytic cell abundance was not found in tuberculosis or malaria. Our results indicate that systemic signatures can reflect what is occurring in leishmanial lesions. Furthermore, the presence of an ISG signature in blood monocytes and macrophages suggests a mechanism to limit systemic spread of the parasite, as well as enhance parasite control by pre-activating cells prior to lesion entry.
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McDermott, David F., Jae-Lyun Lee, Georg A. Bjarnason, James M. G. Larkin, Rustem Airatovich Gafanov, Mark D. Kochenderfer, Jahangeer Malik, et al. "Evaluation of RNA-sequencing (RNA-seq) signatures with pembrolizumab (pembro) in patients (pts) with renal cell carcinoma (RCC) from KEYNOTE-427 cohort A." Journal of Clinical Oncology 38, no. 6_suppl (February 20, 2020): 729. http://dx.doi.org/10.1200/jco.2020.38.6_suppl.729.
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729 Background: We evaluated the association (assoc) of baseline RNA-seq–based signatures with response/resistance to pembro from the phase 2 KEYNOTE-427 study (NCT02853344) in pts with advanced clear cell RCC enrolled in cohort A (n = 110). Methods: In pembro-treated pts with RNA-seq and clinical data (N = 78), we analyzed the assoc of signatures (18-gene tumor T-cell–inflamed GEP; 10 non–T-cell–inflamed GEP canonical signatures [angiogenesis, gMDSC, glycolysis, hypoxia, mMDSC, MYC, proliferation, RAS, stromal/EMT/TGFβ, WNT]) quantifying the TME with clinical outcomes. Canonical signatures were derived from 2 databases (TCGA, Moffit) using an algorithm that included genes based on their correlation to reference signatures in the literature. Signature definitions were finalized before linking to the clinical data, and significance was prespecified at 0.10 given the potential for limited power. Canonical signatures were also analyzed through regression testing of response and the residuals of consensus signatures after adjusting for T-cell–inflamed GEP and IMDC scores in the model. P values were adjusted for multiplicity. Database cutoff date for clinical data: March 12, 2019. Results: Pt characteristics for this analysis were similar to those of the overall cohort A population. T-cell–inflamed GEP was statistically significantly assoc with ORR ( P = 0.021) but not PFS ( P = 0.116). When adjusting for PD-L1 expression (CPS by IHC) and IMDC scores, T-cell–inflamed GEP remained statistically significantly assoc with ORR ( P = 0.059). The angiogenesis signature was not assoc with response. PD-L2 RNA-seq expression was not assoc with response with multiplicity-adjusted P values. Like PD-L1 IHC, PD-L2 RNA-seq expression moderately correlated with T-cell–inflamed GEP and did not show independent predictive value when adjusted for T-cell–inflamed GEP and IMDC scores. Conclusions: RNA-seq–based, T-cell–inflamed GEP was assoc with ORR in pts with clear cell RCC receiving first-line pembro monotherapy; no canonical signatures showed statistical significance. Future directions for these data include whole exome sequencing analysis. Clinical trial information: NCT02853344.
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Dolgosheina, E. V., R. D. Morin, G. Aksay, S. C. Sahinalp, V. Magrini, E. R. Mardis, J. Mattsson, and P. J. Unrau. "Conifers have a unique small RNA silencing signature." RNA 14, no. 8 (June 20, 2008): 1508–15. http://dx.doi.org/10.1261/rna.1052008.
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Chai, Rui-Chao, tao jiang, and Yong-Zhi Wang. "GENE-49. RNA PROCESSING GENES CHARACTERIZE RNA SPLICING AND FURTHER STRATIFY LOWER-GRADE GLIOMA." Neuro-Oncology 21, Supplement_6 (November 2019): vi108. http://dx.doi.org/10.1093/neuonc/noz175.451.
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Abstract Aberrant expression of RNA processing genes may drive the alterative RNA profile in lower-grade gliomas (LGGs). Thus, we aimed to further stratify LGGs based on the expression of RNA processing genes. This study included 446 LGGs from The Cancer Genome Atlas (TCGA, training set) and 171 LGGs from the Chinese Glioma Genome Atlas (CGGA, validation set). The least absolute shrinkage and selection operator (LASSO) Cox regression algorithm was conducted to develop a risk-signature. The ROC curves and Kaplan–Meier curves were used to study the prognosis value of the risk-signature. Among the tested 784 RNA processing genes, 276 were significantly correlated with the OS of LGGs. Further LASSO Cox regression identified a 19-gene risk-signature, whose risk score was also an independently prognosis factor (P< 0.0001, multiplex Cox regression) in the validation dataset. The signature had better prognostic value than the traditional factors “age”, “grade” and “WHO 2016 classification” for 3‐ and 5‐year survival both two datasets (AUCs > 85%). Importantly, the risk-signature could further stratify the survival of LGGs in specific subgroups of WHO 2016 classification. Furthermore, alternative splicing events for genes such as EGFR and FGFR were found to be associated with the risk score. RNA expression levels for genes, which participated in cell proliferation and other processes, were significantly correlated to the risk score. Our results highlight the role of RNA processing genes for further stratifying the survival of patients with LGGs and provide insight into the alternative splicing events underlying this role.
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BYKHOVSKI, ALEXEI, TATIANA GLOBUS, TATYANA KHROMOVA, BORIS GELMONT, and DWIGHT WOOLARD. "AN ANALYSIS OF THE THZ FREQUENCY SIGNATURES IN THE CELLULAR COMPONENTS OF BIOLOGICAL AGENTS." International Journal of High Speed Electronics and Systems 17, no. 02 (June 2007): 225–37. http://dx.doi.org/10.1142/s012915640700445x.
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The development of an effective biological (bio) agent detection capability based upon terahertz (THz) frequency absorption spectra will require insight into how the constituent cellular components contribute to the overall THz signature. In this work, the specific contribution of ribonucleic acid (RNA) to THz spectra is analyzed in detail. Previously, it has only been possible to simulate partial fragments of the RNA (or DNA) structures due to the excessive computational demands. For the first time, the molecular structure of the entire transfer RNA (tRNA) molecule of E. coli was simulated and the associated THz signature was derived theoretically. The tRNA that binds amino acid tyrosine (tRNAtyr) was studied. Here, the molecular structure was optimized using the potential energy minimization and molecular dynamical (MD) simulations. Solvation effects (water molecules) were also included explicitly in the MD simulations. To verify that realistic molecular signatures were simulated, a parallel experimental study of tRNAs of E. coli was also conducted. Two very similar molecules, valine and tyrosine tRNA were investigated experimentally. Samples were prepared in the form of water solutions with the concentrations in the range 0.01-1 mg/ml. A strong correlation of the measured THz signatures associated with valine tRNA and tyrosine tRNA was observed. These findings are consistent with the structural similarity of the two tRNAs. The calculated THz signature of the tyrosine tRNA of E. coli reproduces many features of our measured spectra, and, therefore, provides valuable new insights into bio-agent detection.
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Davanian, Haleh, Anangi Balasiddaiah, Robert Heymann, Magnus Sundström, Poppy Redenström, Mikael Silfverberg, David Brodin, et al. "Ameloblastoma RNA profiling uncovers a distinct non-coding RNA signature." Oncotarget 8, no. 3 (December 10, 2016): 4530–42. http://dx.doi.org/10.18632/oncotarget.13889.
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Zhou, Yao, Xingju Zheng, Zhucheng Sun, and Bo Wang. "Analysis of Bladder Cancer Staging Prediction Using Deep Residual Neural Network, Radiomics, and RNA-Seq from High-Definition CT Images." Genetics Research 2024 (April 30, 2024): 1–11. http://dx.doi.org/10.1155/2024/4285171.
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Bladder cancer has recently seen an alarming increase in global diagnoses, ascending as a predominant cause of cancer-related mortalities. Given this pressing scenario, there is a burgeoning need to identify effective biomarkers for both the diagnosis and therapeutic guidance of bladder cancer. This study focuses on evaluating the potential of high-definition computed tomography (CT) imagery coupled with RNA-sequencing analysis to accurately predict bladder tumor stages, utilizing deep residual networks. Data for this study, including CT images and RNA-Seq datasets for 82 high-grade bladder cancer patients, were sourced from the TCIA and TCGA databases. We employed Cox and lasso regression analyses to determine radiomics and gene signatures, leading to the identification of a three-factor radiomics signature and a four-gene signature in our bladder cancer cohort. ROC curve analyses underscored the strong predictive capacities of both these signatures. Furthermore, we formulated a nomogram integrating clinical features, radiomics, and gene signatures. This nomogram’s AUC scores stood at 0.870, 0.873, and 0.971 for 1-year, 3-year, and 5-year predictions, respectively. Our model, leveraging radiomics and gene signatures, presents significant promise for enhancing diagnostic precision in bladder cancer prognosis, advocating for its clinical adoption.
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Li, Xinyu, Shuqiao Zhang, Shijun Zhang, Weihong Kuang, and Chunzhi Tang. "Inflammatory Response-Related Long Non-Coding RNA Signature Predicts the Prognosis of Hepatocellular Carcinoma." Journal of Oncology 2022 (March 17, 2022): 1–13. http://dx.doi.org/10.1155/2022/9917244.
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Background. Hepatocellular carcinoma (HCC) is a high mortality malignant tumor with genetic and phenotypic heterogeneity, making predicting prognosis challenging. Meanwhile, the inflammatory response is an indispensable player in the tumorigenesis process and regulates the tumor microenvironment, which can affect the prognosis of tumor patients. Methods. Using HCC samples in the TCGA-LIHC dataset, we explored lncRNA expression profiles associated with the inflammatory response. The inflammatory response-related lncRNA signature was constructed by univariate Cox regression, LASSO regression, and multivariate Cox regression methods based on inflammatory response-related differentially expressed lncRNAs in HCC. Results. Seven inflammatory response-related lncRNA signatures were identified in predicting HCC prognosis. Kaplan–Meier (K-M) survival analysis indicated that high-risk group HCC patients were associated with poor prognosis. The utility of the inflammatory response-related lncRNA signatures was proved by the AUC and DCA analysis. The nomogram further confirmed the accuracy of the novel signature in predicting HCC patients’ prognoses. In validation, our novel signature is more accurate than traditional clinicopathological performance for prognosis prediction of HCC patients. GSEA analysis further elucidated the underlying mechanisms and pathways of HCC progression in the low- and high-risk groups. Moreover, immune cells infiltration responses and immune function analyses revealed a significant difference between high- and low-risk groups in cytolytic activity, MHC class I, type I INF response, type II INF response, inflammation-promoting, and T cell coinhibition. Finally, HHLA2, NRP1, CD276, TNFRSF9, TNFSF4, CD80, and VTCN1 were expressed higher in high-risk groups in the immune checkpoint analysis. Conclusions. A novel inflammatory response-related lncRNA signature (AC145207.5, POLHAS1, AL928654.1, MKLN1AS, AL031985.3, PRRT3AS1, and AC023157.2) is capable of predicting the prognosis of HCC patients and providing new immune targeted therapies insight.
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Baranov, Oleg, Polina Turova, Vladimir Kushnarev, Linda Balabanian, Nikita Kotlov, Konstantin Chernyshov, Nathan Hale Fowler, and Jochen K. Lennerz. "Closing classification gaps in luminal breast cancer with single-cell RNA-seq insights from normal breast lineages." Journal of Clinical Oncology 42, no. 16_suppl (June 1, 2024): e13168-e13168. http://dx.doi.org/10.1200/jco.2024.42.16_suppl.e13168.
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e13168 Background: Almost two decades ago, invasive breast cancer was classified into five subgroups based on expression patterns, each with distinct biological and prognostic characteristics. Advances in single-cell RNA sequencing offers insights into the expression profiles of normal luminal epithelial cells. Here, we investigate the connection between breast cancer (BC) classification and luminal expression profiles. Methods: We applied single-cell data from recent studies (Table) to compile gene signatures corresponding to expression profiles of luminal epithelial cell clusters in normal breast tissue. Normalized enrichment scores (ESs) of these signatures were calculated for bulk RNA-seq gene expression data from open source cohorts (TCGA-BRCA, SCAN-B, METABRIC). The BC molecular subtype of each tumor sample was predicted based on a modified PAM50 classification using gene expression profiling (1); LumA and LumB subtypes were combined into Luminal. ESs of signatures were compared between the BC subtypes (Table). Results: The ESs of all signatures differed significantly across BC subtypes (p < 0.001, Kruskal-Wallis test, Table 1). The ESs of signatures for LumSec-major and Luminal progenitor clusters are the highest in subtypes HER2-low and Basal-like (p < 0.001, Dwass-Steel-Critchlow-Fligner test [DSCF]). Basal-like also had the highest enrichment of the ductal signature (p < 0.001, DSCF). Luminal subtype showed the highest ESs of signatures corresponding to HR+ clusters of mature luminal cells and of the TDLU signature (p < 0.001, DSCF). HER2-low subtype showed the highest ES of the LumSec-lac signature (p < 0.001, DSCF). Conclusions: Our analyses challenge traditional classifications by showing that luminal breast cancers are less similar to most luminal epithelial clusters in normal breast tissue than other breast cancer subtypes. These findings suggest traditional expression classification may not fully capture the complexity of this largest subset of luminal-type breast cancers. 1. Turova et al., SABCS, 2023. [Table: see text]
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Li, Xuanyi, Ben Ho Park, Justin M. Balko, and Douglas Buckner Johnson. "Pathway enrichment analysis in tumors with high mutation burdens and interferon-γ signature expression: A pancancer analysis." Journal of Clinical Oncology 41, no. 16_suppl (June 1, 2023): 3138. http://dx.doi.org/10.1200/jco.2023.41.16_suppl.3138.
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3138 Background: Tumor mutation burden (TMB) correlates with immunotherapy response, but outcomes still vary in high TMB cancers, and many high TMB tumors lack T cell infiltration. Here, we assessed gene expression signatures of high TMB, including both inflamed and non-inflamed tumors (defined by interferon-γ [IFN-γ] gene signatures). Methods: Gene set enrichment analysis (GSEA) was assessed from RNA-sequencing data in tumors in TCGA. TMB and IFN-γ signature score were assessed by previously published methods. We performed GSEA on high vs low TMB tumors, and further stratified by high vs low IFN-γ signature expression within high TMB tumors. We validated results in Tempus RNA-sequencing data from 264 patients at Vanderbilt. Results: 4020 TCGA cases in non-small cell lung, melanoma, bladder, head and neck, kidney, and colorectal cancer were included. Major signatures upregulated in the high TMB group included cell cycle signaling, nuclear protein/mRNA export, cholesterol biosynthetics, and ubiquitin signaling. High TMB/high IFN-γ signature tumors were enriched in immune-related pathways (neutrophil and monocyte chemotaxis, T cell activation and proliferation, and interleukin-1 secretion), and non-immune pathways including cAMP metabolic process and phosphatidylinositol signaling. High TMB/low IFN-γ signature tumors had enriched pathways in nonsense mediated decay, electron transport chain/oxidative phosphorylation, and mitochondrial translational disassembly. 264 tumors from Vanderbilt were used for validation; pathways including cholesterol biosynthetics also correlated with immunotherapy response. Conclusions: High TMB tumors were enriched in pathways such as ubiquitin signaling, which could regulate immune cell function in antitumor immunity. Within high TMB cancers, tumors with higher IFN-γ signatures have enriched immune-related, and metabolic pathways, which could suggest novel therapeutic targets to make high TMB/ low IFN-γ tumors more immunogenic.
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Kenarangi, Taiebe, Enayatolah Bakhshi, Kolsoum Inanloo Rahatloo, and Akbar Biglarian. "Identifying Gene Signature in RNA Sequencing Multiple Sclerosis Data." Iranian Rehabilitation Journal 20, no. 2 (June 1, 2022): 217–24. http://dx.doi.org/10.32598/irj.20.2.1606.1.
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Objectives: Multiple Sclerosis (MS) is a complex central nervous system disease; it is the result of a combination of genetic predispositions and a nongenetic trigger. This study aims to find the gene signatures using a Pareto optimization algorithm for MS RNA sequencing (RNA-seq) data. Methods: This case-control study involved 50 samples (25 MS patients and 25 age-matched healthy individuals) and their GSE profiles (GSE123496) were selected from the National Center for Biotechnology Information Gene Expression Omnibus database. We used Pareto-optimal cluster size identification to find the gene signatures in the RNA-seq data. After prefiltering and normalizing the data, we used the Limma package to find the differentially expressed genes (DEGs). The Pareto-optimal cluster size for these DEGs was then determined using the technique, multi-objective optimization for collecting the clusters alternatives. Afterward, the RNA-seq data were clustered via k-means with suitable cluster size. The best cluster, as a signature, was found by calculating the mean of the Spearman correlation coefficients (SCCs) of whole genes in the module in a pairwise manner. All analysis was performed in the R software, 4.1.1 package, under virtual space with 100 GB RAM. Results: In total, 960 DEGs were identified by the Limma analysis. Among them, 720 were up-regulated genes and 240 were down-regulated genes. Meanwhile, 6 Pareto-optimal clusters were obtained. Two clusters that had the greatest average SCCs score (0.88 and 0.74, respectively) were chosen as the gene signatures. Discussion: A total of 9 metabolic prognostic genes and 3 biological pathways were identified. These can provide more potent prognostic information for MS patients.
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Li, Yang, Rongrong Sun, Rui Li, Yonggang Chen, and He Du. "Prognostic Nomogram Based on Circular RNA-Associated Competing Endogenous RNA Network for Patients with Lung Adenocarcinoma." Oxidative Medicine and Cellular Longevity 2021 (August 28, 2021): 1–13. http://dx.doi.org/10.1155/2021/9978206.
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Evidence is increasingly indicating that circular RNAs (circRNAs) are closely involved in tumorigenesis and cancer progression. However, the function and application of circRNAs in lung adenocarcinoma (LUAD) are still unknown. In this study, we constructed a circRNA-associated competitive endogenous RNA (ceRNA) network to investigate the regulatory mechanism of LUAD procession and further constructed a prognostic signature to predict overall survival for LUAD patients. Differentially expressed circRNAs (DEcircRNAs), differentially expressed miRNAs (DEmiRNAs), and differentially expressed mRNAs (DEmRNAs) were selected to construct the ceRNA network. Based on the TargetScan prediction tool and Pearson correlation coefficient, we constructed a circRNA-associated ceRNA network including 11 DEcircRNAs, 8 DEmiRNAs, and 49 DEmRNAs. GO and KEGG enrichment indicated that the ceRNA network might be involved in the regulation of GTPase activity and endothelial cell differentiation. After removing the discrete points, a PPI network containing 12 DEmRNAs was constructed. Univariate Cox regression analysis showed that three DEmRNAs were significantly associated with overall survival. Therefore, we constructed a three-gene prognostic signature for LUAD patients using the LASSO method in the TCGA-LUAD training cohort. By applying the signature, patients could be categorized into the high-risk or low-risk subgroups with significant survival differences (HR: 1.62, 95% CI: 1.12-2.35, log-rank p = 0.009 ). The prognostic performance was confirmed in an independent GEO cohort (GSE42127, HR: 2.59, 95% CI: 1.32-5.10, log-rank p = 0.004 ). Multivariate Cox regression analysis proved that the three-gene signature was an independent prognostic factor. Combining the three-gene signature with clinical characters, a nomogram was constructed. The primary and external verification C -indexes were 0.717 and 0.716, respectively. The calibration curves for the probability of 3- and 5-year OS showed significant agreement between nomogram predictions and actual observations. Our findings provided a deeper understanding of the circRNA-associated ceRNA regulatory mechanism in LUAD pathogenesis and further constructed a useful prognostic signature to guide personalized treatment of LUAD patients.
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Singhal, Sandeep K., Sarmad Al-Marsoummi, Emilie E. Vomhof-DeKrey, Bo Lauckner, Trysten Beyer, and Marc D. Basson. "Schlafen 12 Slows TNBC Tumor Growth, Induces Luminal Markers, and Predicts Favorable Survival." Cancers 15, no. 2 (January 7, 2023): 402. http://dx.doi.org/10.3390/cancers15020402.
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The Schlafen 12 (SLFN12) protein regulates triple-negative breast cancer (TNBC) growth, differentiation, and proliferation. SLFN12 mRNA expression strongly correlates with TNBC patient survival. We sought to explore SLFN12 overexpression effects on in vivo human TNBC tumor xenograft growth and performed RNA-seq on xenografts to investigate related SLFN12 pathways. Stable SLFN12 overexpression reduced tumorigenesis, increased tumor latency, and reduced tumor volume. RNA-seq showed that SLFN12 overexpressing xenografts had higher luminal markers levels, suggesting that TNBC cells switched from an undifferentiated basal phenotype to a more differentiated, less aggressive luminal phenotype. SLFN12-overexpressing xenografts increased less aggressive BC markers, HER2 receptors ERBB2 and EGFR expression, which are not detectable by immunostaining in TNBC. Two cancer progression pathways, the NAD signaling pathway and the superpathway of cholesterol biosynthesis, were downregulated with SLFN12 overexpression. RNA-seq identified gene signatures associated with SLFN12 overexpression. Higher gene signature levels indicated good survival when tested on four independent BC datasets. These signatures behaved differently in African Americans than in Caucasian Americans, indicating a possible biological difference between these races that could contribute to the worse survival observed in African Americans with BC. These results suggest an increased SLFN12 expression modulates TNBC aggressiveness through a gene signature that could offer new treatment targets.
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Dungu, Kia Hee Schultz, Emma Louise Malchau Carlsen, Jonathan Peter Glenthøj, Lisbeth Samsø Schmidt, Inger Merete Jørgensen, Dina Cortes, Anja Poulsen, Nadja Hawwa Vissing, Frederik Otzen Bagger, and Ulrikka Nygaard. "Host RNA Expression Signatures in Young Infants with Urinary Tract Infection: A Prospective Study." International Journal of Molecular Sciences 25, no. 9 (April 29, 2024): 4857. http://dx.doi.org/10.3390/ijms25094857.
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Early diagnosis of infections in young infants remains a clinical challenge. Young infants are particularly vulnerable to infection, and it is often difficult to clinically distinguish between bacterial and viral infections. Urinary tract infection (UTI) is the most common bacterial infection in young infants, and the incidence of associated bacteremia has decreased in the recent decades. Host RNA expression signatures have shown great promise for distinguishing bacterial from viral infections in young infants. This prospective study included 121 young infants admitted to four pediatric emergency care departments in the capital region of Denmark due to symptoms of infection. We collected whole blood samples and performed differential gene expression analysis. Further, we tested the classification performance of a two-gene host RNA expression signature approaching clinical implementation. Several genes were differentially expressed between young infants with UTI without bacteremia and viral infection. However, limited immunological response was detected in UTI without bacteremia compared to a more pronounced response in viral infection. The performance of the two-gene signature was limited, especially in cases of UTI without bloodstream involvement. Our results indicate a need for further investigation and consideration of UTI in young infants before implementing host RNA expression signatures in clinical practice.
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Wang, Jiamin, Han Lin, Mingda Zhou, Qian Xiang, Yihan Deng, Lianmin Luo, Yangzhou Liu, Zhiguo Zhu, and Zhigang Zhao. "The m6A methylation regulator-based signature for predicting the prognosis of prostate cancer." Future Oncology 16, no. 30 (October 2020): 2421–32. http://dx.doi.org/10.2217/fon-2020-0330.
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Aim: To construct a survival prediction signature for prostate cancer (PC) based on the RNA N6-methyladenosine (m6A) methylation regulator. Materials & methods: This paper explores the interaction network of differentially expressed m6A RNA methylation regulators in PC by Pearson correlation analysis. Univariate Cox risk regression and LASSO regression analysis were used to construct a predictive signature of PC. Kaplan–Meier survival analysis compared the overall survival of the high- and low-risk groups. Results & Conclusion: We first constructed a prognostic two gene signature for PC based on the m6A RNA methylation regulators MRTTL14 and YTHDF2. The interaction network of m6A RNA methylation regulators in PC was also established.
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Parikesit, Arli Aditya, Imron Imron, Rizky Nurdiansyah, and David Agustriawan. "The Structural Annotations of The Mir-122 Non-Coding RNA from The Tilapia Fish (Oreochromis niloticus)." HAYATI Journal of Biosciences 29, no. 2 (January 17, 2022): 171–81. http://dx.doi.org/10.4308/hjb.29.2.171-181.
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Tilapia (Oreochromis niloticus) is an important fisheries commodity. Scientific efforts have been done to increase its quality. One of them is staging a premium diet such as a fat-enriched diet. The transcriptomics approach is able to provide the signatures of the diet outcomes by observing the micro(mi)RNA signature in transcriptional regulation. Hence, it was found that the availability of mir-122 is essential in the regulation of a high-fat diet in tilapia. However, this transcriptomics signature is lacking structural annotations and the complete interaction annotations with its silencing(si)RNA. RNAcentral website was navigated for the latest annotation of mir-122 from tilapia and other species as a comparison. MEGA X was employed to comprehend the miRNA evolutionary repertoire. The RNA secondary structure prediction tools from the Vienna RNA package and the RNA tertiary structure prediction tools from simRNA and modeRNA are secured with default parameters. The HNADOCK tools were leveraged to observe the interaction between mir-122 and its siRNA. The post-processing was conducted with the Chimera visualization tool. The secondary and tertiary structure of the mir-122 and its siRNA could be elucidated, docked, and visualized. In this end, further effort to develop a comprehensive molecular breeding tool could be secured with the structural annotation information.
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Albitar, Maher, Hong Zhang, Sally Agersborg, Ahmad Charifa, Pooja Phull, Noa Biran, David H. Vesole, et al. "Establishing a Distinct Cytokine Signature for Multiple Myeloma Using Bone Marrow RNA and Peripheral Blood Cell-Free RNA (cfRNA)." Blood 144, Supplement 1 (November 5, 2024): 3316. https://doi.org/10.1182/blood-2024-203774.
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Introduction: The combined effects of cytokines/chemokines and their receptors are believed to play a major role in determining the overall environment for plasma cell growth and the clinical course of multiple myeloma (MM). Cytokines also play a significant role in the immune response to the neoplastic plasma cells and are relevant to various therapeutic approaches. Numerous studies have evaluated various cytokines individually and correlated with clinical behavior. We used next generation sequencing (NGS) and RNA quantification along with machine learning algorithms to establish signatures based on the levels of cytokines/chemokines and their receptors that distinguish MM from other lymphoid neoplasms. Methods: RNA was extracted from the bone marrow samples of patients with MM (N=409), chronic lymphocytic leukemia (CLL) (N=184), and bone marrow samples without any molecular evidence of abnormalities (N=430). RNA was also extracted from lymph nodes with diffuse large B-cell lymphoma (DLBCL) (N=287). cfRNA was extracted from the peripheral blood of 430 normal individuals, 23 patients with MM, 19 patients with DLBCL, and 16 patients with CLL. Tissue RNA and cfRNA were sequenced using a 1500-gene targeted RNA next generation sequencing (NGS) panel. Two-thirds of the tissue samples were used for training and building the models and one third was used for testing the models. In every model, Bayesian with 10-fold cross validation using leave-one-out was used to evaluate the sensitivity and specificity of each biomarker in distinguishing between two classes. The biomarkers were ranked, and random forest was used to develop algorithms selecting top-ranked biomarkers. Each model was confirmed by one-third of the tissue samples. Each model was then used to test if cfRNA samples showed the same results obtained from tissue samples. We measured the RNA expression levels of 36 different cytokines/chemokines and their receptors in bone marrow (BM) from patients with MM, normal control, and patients with chronic lymphocytic leukemia (CLL). Lymph node biopsies were used for evaluating expression in patients with diffuse large B-cell lymphoma (DLBCL). We compared the signatures obtained from these tissues with those obtained from peripheral blood cfRNA. Results: In comparing MM bone marrow samples with normal bone marrow, Bayesian statistics selected and ranked the various cytokine/chemokine and receptor biomarkers. The random forest algorithm showed that a signature of 10 biomarkers reliably distinguished MM from normal (AUC= 0.915, CI: 0.891-0.939). The top 10 biomarkers are: TGFBR2, TNFRSF10D, CXCR4, TNFRSF14, TNFRSF17, TNFRSF10B, TNFAIP3, TGFBR3, IL1RAP, and IL12RB2. The same algorithm and the same biomarkers quantified in peripheral blood cfRNA also distinguished MM from normal with AUC of 0.743 (CI: 0.678-0.809). Using the same approach, BM with MM was distinguishable from BM with CLL using a signature of 10 different biomarkers (TGFBR2, TNFRSF10B, CTLA4, TNFRSF14, IL21R, TNFRSF10D, TGFBR3, CXXC4, TNFRSF9, and TGFBI) (AUC = 0.978, CI 0.952-1.00). Similarly, cfRNA showed high accuracy in predicting MM from CLL using the same signature and algorithm (AUC= 0.829, CI: 0.689-0.968). Distinguishing BM with MM from LN with DLBCL, a signature of 10 biomarkers (IL21R, TNFRSF9, TNFRSF4, IL2RA, TNFRSF10B, TNFRSF6B, TGFBR2, CTLA4, TGFB3, and TNFAIP3) was adequate (AUC of 0.981, CI: 0961-1.00). However, these biomarkers when quantified in cfRNA failed to distinguish between MM and DLBCL (AUC: 0.542, CI: 0.365-0.720). The three signatures are specifically relying on the TNF pathway. They shared TNF and TNFAIP3 and their receptors (TNFRSF10B, TNFRSF10D, TNFRSF14, TNFRSF17, TNFRSF4, TNFRSF6B, TNFRSF9). Sorted multiple myeloma cells using CD138 antibodies showed that all TNF receptors are expressed at significantly (P<0.0001) higher level than in the flow-through cells (macrophages and myeloid cells). In contrast, flow-through cells showed significantly (P<0.0001) higher levels of TNF and TNFAIP3 than in multiple myeloma cells. Conclusions: The findings suggest that the MM bone marrow microenvironment is unique and distinct from other lymphoid neoplasms. This uniqueness is mainly driven by the TNF pathway. The distinct cytokine signature of MM is commonly reflected and can be measured and monitored using cfRNA as possibly replacing the need for bone marrow samples.
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Luo, Yong, Xiaopeng Liu, Jingbo Lin, Weide Zhong, and Qingbiao Chen. "Development and validation of novel inflammatory response-related gene signature to predict prostate cancer recurrence and response to immune checkpoint therapy." Mathematical Biosciences and Engineering 19, no. 11 (2022): 11345–66. http://dx.doi.org/10.3934/mbe.2022528.
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<abstract> <p>The aim of this study is to construct an inflammatory response-related genes (IRRGs) signature to monitor biochemical recurrence (BCR) and treatment effects in prostate cancer patients (PCa). A gene signature for inflammatory responses was constructed on the basis of the data from the Cancer Genome Atlas (TCGA) database, and validated in external datasets. It was analyzed using receiver operating characteristic curve, BCR-free survival, Cox regression, and nomogram. Distribution analysis and external model comparison were utilized. Then, enrichment analysis, tumor mutation burden, tumor immune microenvironment, and immune cell infiltration signatures were investigated. The role of the signature in immunotherapy was evaluated. The expression patterns of core genes were verified by RNA sequencing. We identified an IRRGs signature in the TCGA-PRAD cohort and verified it well in two other independent external datasets. The signature was a robust and independent prognostic index for predicting the BCR of PCa. The high-risk group of our signature predicted a shortened BCR time and an aggressive disease progression. A nomogram was constructed to predict BCR-free time in clinical practices. Neutrophils and CD8+ T cells were in higher abundance among the low-risk individuals. Immune functions varied significantly between the two groups and immune checkpoint therapy worked better for the low-risk patients. The expression of four IRRGs showed significant differences between PCa and surrounding benign tissues, and were validated in BPH-1 and DU145 cell lines by RNA sequencing. Our signature served as a reliable and promising biomarker for predicting the prognosis and evaluating the efficacy of immunotherapy, facilitating a better outcome for PCa patients.</p> </abstract>
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Farias, Epitácio, Patrick Terrematte, and Beatriz Stransky. "Machine Learning Gene Signature to Metastatic ccRCC Based on ceRNA Network." International Journal of Molecular Sciences 25, no. 8 (April 11, 2024): 4214. http://dx.doi.org/10.3390/ijms25084214.
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Clear-cell renal-cell carcinoma (ccRCC) is a silent-development pathology with a high rate of metastasis in patients. The activity of coding genes in metastatic progression is well known. New studies evaluate the association with non-coding genes, such as competitive endogenous RNA (ceRNA). This study aims to build a ceRNA network and a gene signature for ccRCC associated with metastatic development and analyze their biological functions. Using data from The Cancer Genome Atlas (TCGA), we constructed the ceRNA network with differentially expressed genes, assembled nine preliminary gene signatures from eight feature selection techniques, and evaluated the classification metrics to choose a final signature. After that, we performed a genomic analysis, a risk analysis, and a functional annotation analysis. We present an 11-gene signature: SNHG15, AF117829.1, hsa-miR-130a-3p, hsa-mir-381-3p, BTBD11, INSR, HECW2, RFLNB, PTTG1, HMMR, and RASD1. It was possible to assess the generalization of the signature using an external dataset from the International Cancer Genome Consortium (ICGC-RECA), which showed an Area Under the Curve of 81.5%. The genomic analysis identified the signature participants on chromosomes with highly mutated regions. The hsa-miR-130a-3p, AF117829.1, hsa-miR-381-3p, and PTTG1 were significantly related to the patient’s survival and metastatic development. Additionally, functional annotation resulted in relevant pathways for tumor development and cell cycle control, such as RNA polymerase II transcription regulation and cell control. The gene signature analysis within the ceRNA network, with literature evidence, suggests that the lncRNAs act as “sponges” upon the microRNAs (miRNAs). Therefore, this gene signature presents coding and non-coding genes and could act as potential biomarkers for a better understanding of ccRCC.
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Ahmed, Yaman B., Obada E. Ababneh, Anas A. Al-Khalili, Abdullah Serhan, Zaid Hatamleh, Owais Ghammaz, Mohammad Alkhaldi, and Safwan Alomari. "Identification of Hypoxia Prognostic Signature in Glioblastoma Multiforme Based on Bulk and Single-Cell RNA-Seq." Cancers 16, no. 3 (February 1, 2024): 633. http://dx.doi.org/10.3390/cancers16030633.
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Glioblastoma (GBM) represents a profoundly aggressive and heterogeneous brain neoplasm linked to a bleak prognosis. Hypoxia, a common feature in GBM, has been linked to tumor progression and therapy resistance. In this study, we aimed to identify hypoxia-related differentially expressed genes (DEGs) and construct a prognostic signature for GBM patients using multi-omics analysis. Patient cohorts were collected from publicly available databases, including the Gene Expression Omnibus (GEO), the Chinese Glioma Genome Atlas (CGGA), and The Cancer Genome Atlas—Glioblastoma Multiforme (TCGA-GBM), to facilitate a comprehensive analysis. Hypoxia-related genes (HRGs) were obtained from the Molecular Signatures Database (MSigDB). Differential expression analysis revealed 41 hypoxia-related DEGs in GBM patients. A consensus clustering approach, utilizing these DEGs’ expression patterns, identified four distinct clusters, with cluster 1 showing significantly better overall survival. Machine learning techniques, including univariate Cox regression and LASSO regression, delineated a prognostic signature comprising six genes (ANXA1, CALD1, CP, IGFBP2, IGFBP5, and LOX). Multivariate Cox regression analysis substantiated the prognostic significance of a set of three optimal signature genes (CP, IGFBP2, and LOX). Using the hypoxia-related prognostic signature, patients were classified into high- and low-risk categories. Survival analysis demonstrated that the high-risk group exhibited inferior overall survival rates in comparison to the low-risk group. The prognostic signature showed good predictive performance, as indicated by the area under the curve (AUC) values for one-, three-, and five-year overall survival. Furthermore, functional enrichment analysis of the DEGs identified biological processes and pathways associated with hypoxia, providing insights into the underlying mechanisms of GBM. Delving into the tumor immune microenvironment, our analysis revealed correlations relating the hypoxia-related prognostic signature to the infiltration of immune cells in GBM. Overall, our study highlights the potential of a hypoxia-related prognostic signature as a valuable resource for forecasting the survival outcome of GBM patients. The multi-omics approach integrating bulk sequencing, single-cell analysis, and immune microenvironment assessment enhances our understanding of the intricate biology characterizing GBM, thereby potentially informing the tailored design of therapeutic interventions.
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Werner, Stephan, Lukas Schmidt, Virginie Marchand, Thomas Kemmer, Christoph Falschlunger, Maksim V. Sednev, Guillaume Bec, et al. "Machine learning of reverse transcription signatures of variegated polymerases allows mapping and discrimination of methylated purines in limited transcriptomes." Nucleic Acids Research 48, no. 7 (February 25, 2020): 3734–46. http://dx.doi.org/10.1093/nar/gkaa113.
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Abstract Reverse transcription (RT) of RNA templates containing RNA modifications leads to synthesis of cDNA containing information on the modification in the form of misincorporation, arrest, or nucleotide skipping events. A compilation of such events from multiple cDNAs represents an RT-signature that is typical for a given modification, but, as we show here, depends also on the reverse transcriptase enzyme. A comparison of 13 different enzymes revealed a range of RT-signatures, with individual enzymes exhibiting average arrest rates between 20 and 75%, as well as average misincorporation rates between 30 and 75% in the read-through cDNA. Using RT-signatures from individual enzymes to train a random forest model as a machine learning regimen for prediction of modifications, we found strongly variegated success rates for the prediction of methylated purines, as exemplified with N1-methyladenosine (m1A). Among the 13 enzymes, a correlation was found between read length, misincorporation, and prediction success. Inversely, low average read length was correlated to high arrest rate and lower prediction success. The three most successful polymerases were then applied to the characterization of RT-signatures of other methylated purines. Guanosines featuring methyl groups on the Watson-Crick face were identified with high confidence, but discrimination between m1G and m22G was only partially successful. In summary, the results suggest that, given sufficient coverage and a set of specifically optimized reaction conditions for reverse transcription, all RNA modifications that impede Watson-Crick bonds can be distinguished by their RT-signature.
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Fahey, Catherine, Anupama Reddy, Kristin Kathleen Ancell, Kerry Roe Schaffer, Brian I. Rini, and Katy Beckermann. "Examination of irAE and treatment discontinuation irAE in patients with RCC with T effector phenotype." Journal of Clinical Oncology 42, no. 16_suppl (June 1, 2024): 4549. http://dx.doi.org/10.1200/jco.2024.42.16_suppl.4549.
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4549 Background: The standard of care for renal cell carcinoma (RCC) is physician-choice of dual immunotherapy with ipilimumab/nivolumab (IO/IO) or a combination a VEGF inhibitor with immunotherapy (VEGF/IO). The IMmotion 151 trial identified gene expression signatures that differentiate likelihood of response to IO/IO (cluster 4 and 5 – T effector) versus VEGF/IO (cluster 1 and 2 - Angiogenic). Given that the T effector RNA-seq signature consists of higher expression of genes associated with inflammation, we hypothesized that patients with the T effector phenotype would have higher rates of immune-related adverse events (irAE) on IO-based therapy. Methods: Patients with metastatic RCC treated with systemic IO-based therapy had RNA sequencing completed on the primary tumor or metastatic site. Charts of patients who had a T effector RNA-seq signature were manually curated to identify development of irAE by reading the most recent clinic note and searching ‘steroids’, ‘irAE’, ‘rash’, ‘thyroid’. Hits from searches were investigated by manual chart review. Results: 118 patients underwent RNA sequencing and 105 passed quality control. Nineteen patients (18%) were assigned the T effector phenotype. Of the 17 metastatic patients, twelve of these patients received at least one dose of IO/IO in the first or second line of therapy, five were treated with VEGF/IO Among patients treated with IO/IO, 8 developed any grade irAE (66%; 41% grade 3+), 5 (41%) required steroid treatment, 5 (41%) required hospitalization, and 4 (33%) discontinued treatment due to irAE. Grade 3 toxicities in this group included colitis, adrenal insufficiency, and hypophysitis. Of the 5 patients treated with VEGF/IO, 3 developed any grade irAE (60%; 20% grade 3+), none were treated with steroids, and none were hospitalized. The grade 3 toxicity in this group was adrenal insufficiency. Conclusions: Patients with a T effector RNA-seq signature had higher rates ofirAE than historic controls when treated with IO/IO leading to high rates of steroid use and treatment discontinuation. VEGF/IO-treated patients with the T effector RNA-seq signature had similar rates of irAE to historic controls. The ongoing OPTIC clinical will expand on these results by studying treatment assignment in a prospective manner.
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Wang, Kai, Kai Song, Zhigang Ma, Yang Yao, Chao Liu, Jing Yang, Huiting Xiao, Jiashuai Zhang, Yanqiao Zhang, and Wenyuan Zhao. "Identification of EMT-related high-risk stage II colorectal cancer and characterisation of metastasis-related genes." British Journal of Cancer 123, no. 3 (May 21, 2020): 410–17. http://dx.doi.org/10.1038/s41416-020-0902-y.
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Abstract Background Our laboratory previously reported an individual-level prognostic signature for patients with stage II colorectal cancer (CRC). However, this signature was not applicable for RNA-sequencing datasets. In this study, we constructed a robust epithelial-to-mesenchymal transition (EMT)- related gene pair prognostic signature. Methods Based on EMT-related genes, metastasis-associated gene pairs were identified between metastatic and non-metastatic samples. Then, we selected prognosis-associated gene pairs, which were significantly correlated with disease-free survival of stage II CRC using multivariate Cox regression model, as the EMT-related prognosis signature. Results An EMT-related signature composed of fifty-one gene pairs (51-GPS) for prediction-relapse risk of patients with stage II CRC was developed, whose prognostic efficiency was validated in independent datasets. Moreover, 51-GPS achieved better predictive performance than other reported signatures, including a commercial signature Oncotype Dx colon cancer and an immune-related gene pair signature. Besides, EMT-related functional gene sets achieved high enrichment scores in high-risk samples. Especially, loss-of-function antisense approach showed that DEGs between the predicted two clusters were metastasis-related. Conclusions The EMT-related gene pair signature can identify the high relapse-risk patients with stage II CRC, which can facilitate individualised management of patients.
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Thiede, Stephanie, Matthew Berginski, Akash Mitra, Timothy J. Taxter, Michelle M. Stein, Rotem Ben-Shachar, Halla Nimeiri, Charu Aggarwal, and Jyoti D. Patel. "Homologous recombination deficiency (HRD) in non-small cell lung cancer: Genomic analysis using an RNA-based HRD algorithm." Journal of Clinical Oncology 41, no. 16_suppl (June 1, 2023): 3123. http://dx.doi.org/10.1200/jco.2023.41.16_suppl.3123.
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3123 Background: Recent evidence has suggested that some patients with non-small cell lung cancer (NSCLC) harbor a HRD signature that represents a distinct genomic subtype that could be targeted by PARP inhibitors (PARPi). However, there is little data on HRD prevalence in NSCLC or its genomic associations. Here, we evaluated the co-occurrence of driver mutations and established immune biomarkers with an RNA-based HRD signature in a large, real-world NSCLC cohort. Methods: We analyzed data from 5119 NSCLC patients that underwent sequencing via the Tempus xT test (DNA-seq of 648 genes; RNA-seq with whole exome capture). HRD status was predicted by the Tempus HRD-RNA test, a pan-cancer logistic regression classifier that uses an RNA gene expression signature optimized to distinguish between BRCA-biallelic loss and homologous recombination repair (HRR)-WT samples (Leibowitz et al, 2022). Cohort samples were excluded from model training. All comparisons were tested via chi-squared or Fisher’s exact tests. Results: An RNA-derived signature of HRD (HRD-RNA+) was observed in 3.53% (n=181/5119) of patients. HRD-RNA+ prevalence was higher in squamous cell carcinoma (84/1331, 6.3%) relative to adenocarcinoma (68/3015, 2.3%; p < 0.001). The prevalence of select alterations by HRD-RNA status are shown in Table. Alterations in BRCA1/2 and HRR genes (inclusive of BRCA1/2) were enriched in HRD-RNA+ vs. HRD-RNA- cases (8.8% vs. 2.5%, p < 0.001; 22% vs. 15%, p = 0.008 respectively). Notably, 141 (78%) HRD-RNA+ patients had no alterations in HRR genes. Of all NCCN targetable driver mutations assessed, KRAS G12C and ALK fusions were the only targetable drivers with significantly different prevalence in HRD-RNA+ vs. HRD-RNA- patients. Across the entire cohort, NCCN driver mutations were depleted in HRD RNA+ patients (18% in HRD-RNA+ vs. 30% in HRD-RNA-, p < 0.001). Immune biomarkers (TMB, PD-L1) did not vary by HRD-RNA status. Conclusions: Compared to HRD-RNA- NSCLC, HRD-RNA+ NSCLC represents a unique, molecularly defined subset that has a decreased prevalence of NCCN-driver mutations and is not enriched for TMB-H or PD-L1 expression. Further, this signature increases the number of patients classified as HRD-RNA+ compared to HRR gene alterations alone. Functional characterization (e.g. RAD51 foci immunofluorescence assay) and clinical benefit of targeted HRD therapies such as PARPi should be explored in this HRD-RNA+ population. [Table: see text]
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Reimann, Maja, Korkut Avsar, Andrew DiNardo, Torsten Goldmann, Gunar Günther, Michael Hoelscher, Elmira Ibraim, et al. "The TB27 Transcriptomic Model for Predicting Mycobacterium tuberculosis Culture Conversion." Pathogens and Immunity 10, no. 1 (January 29, 2025): 120–39. https://doi.org/10.20411/pai.v10i1.770.
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Rationale: Treatment monitoring of tuberculosis patients is complicated by a slow growth rate of Mycobacterium tuberculosis. Recently, host RNA signatures have been used to monitor the response to tuberculosis treatment. Objective: Identifying and validating a whole blood-based RNA signature model to predict microbiological treatment responses in patients on tuberculosis therapy. Methods: Using a multi-step machine learning algorithm to identify an RNA-based algorithm to predict the remaining time to culture conversion at flexible time points during anti-tuberculosis therapy. Results: The identification cohort included 149 patients split into a training and a test cohort, to develop a multistep algorithm consisting of 27 genes (TB27) for predicting the remaining time to culture conversion (TCC) at any given time. In the test dataset, predicted TCC and observed TCC achieved a correlation coefficient of r=0.98. An external validation cohort of 34 patients shows a correlation between predicted and observed days to TCC also of r=0.98. Conclusion: We identified and validated a whole blood-based RNA signature (TB27) that demonstrates an excellent agreement between predicted and observed times to M. tuberculosis culture conversion during tuberculosis therapy. TB27 is a potential useful biomarker for anti-tuberculosis drug development and for prediction of treatment responses in clinical practice.
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Motzer, Robert J., Camillo Porta, Masatoshi Eto, Thomas E. Hutson, Sun Young Rha, Jaime R. Merchan, Eric Winquist, et al. "Biomarker analyses in patients with advanced renal cell carcinoma (aRCC) from the phase 3 CLEAR trial." Journal of Clinical Oncology 42, no. 16_suppl (June 1, 2024): 4504. http://dx.doi.org/10.1200/jco.2024.42.16_suppl.4504.
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4504 Background: In the primary analysis of CLEAR, lenvatinib + pembrolizumab (L+P) significantly improved efficacy vs sunitinib (S) in treatment-naïve patients with aRCC (Motzer 2021). Results were confirmed at the final prespecified OS analysis (Motzer 2024). We report biomarker analyses from CLEAR. Methods: PD-L1 IHC 22C3 pharmDx and NGS assays (ImmunoID NeXT platform: WES and RNA-Seq) were performed on archival tumor specimens. To identify somatic alterations including mutations and copy-number variations, paired PBMC samples were used as reference. For RNA-Seq/IHC-derived analyses, a continuous value analysis was performed adjusting by KPS score for: each gene-signature score (T-cell inflamed gene-expression profile [GEP], and non-GEP signatures including proliferation, angiogenesis, hypoxia, MYC, WNT, and other signatures [Cristescu 2022]) vs best overall response (BOR); non-GEP signatures vs BOR adjusted by GEP; and PD-L1 CPS vs BOR. Cutoff analyses were performed for biomarkers that showed significant association in the continuous value analysis. Cutoff values (1st tertile of GEP, or median of non-GEP, signatures) were determined based on combined L+P and S arms. WES analyses were descriptively summarized if TMB/INDEL burden and mutation status of key RCC driver genes were associated with BOR. Results: There were no notable differences in baseline characteristics and tumor responses in biomarker analysis sets vs the ITT population. In the L+P arm, the continuous GEP signature score was not associated with BOR. The MYC signature score was negatively associated with BOR (2-sided test, significance criteria 0.1; FDR-adjusted p=0.013/0.012 with/without adjustment by GEP signature score, respectively). The ORRs (95% CI) for the MYC-high and -low groups were 66.3% (56.1-75.6) and 84.0% (75.0-90.8), respectively. In the S arm, the continuous GEP signature score was positively associated with BOR (2-sided test, significance criteria 0.05; p=0.010). The ORRs (95% CI) for the GEP-high and -low groups were 46.9% (38.1-55.9) and 28.8% (18.3-41.3), respectively. The angiogenesis signature was positively associated with BOR (2-sided test, significance criteria 0.1; FDR-adjusted p=0.046/0.088 with/without adjustment by GEP signature score, respectively). The ORRs (95% CI) for the angiogenesis-high and -low groups were 52.1% (41.6-62.5) and 30.4% (21.7-40.3), respectively. PD-L1 CPS and TMB/INDEL burden were not associated with BOR in L+P or S arms. ORR was higher with L+P vs S, regardless of the deleterious mutation status of BAP1, VHL, PBRM1, SETD2, and KDM5C—frequently mutated genes in RCC. Conclusions: The superiority of L+P vs S in ORR does not appear to be impacted by gene-expression signatures for tumor-induced proliferation, angiogenesis, hypoxia, MYC, or WNT, or by PD-L1 status, TMB/INDEL burden or mutation status of RCC driver genes. Clinical trial information: NCT02811861 .
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Lan, Yunyun, Juan Su, Yaxin Xue, Lulu Zeng, Xun Cheng, and Liyi Zeng. "Analysing a Novel RNA-Binding-Protein-Related Prognostic Signature Highly Expressed in Breast Cancer." Journal of Healthcare Engineering 2021 (October 18, 2021): 1–15. http://dx.doi.org/10.1155/2021/9174055.
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Background. Breast cancer (BRCA) is one of the most common cancers and the leading cause of cancer-related death in women. RNA-binding proteins (RBPs) play an important role in the emergence and pathogenesis of tumors. The target RNAs of RBPs are very diverse; in addition to binding to mRNA, RBPs also bind to noncoding RNA. Noncoding RNA can cause secondary structures that can bind to RBPs and regulate multiple processes such as splicing, RNA modification, protein localization, and chromosomes remodeling, which can lead to tumor initiation, progression, and invasion. Methods. (1) BRCA data were downloaded from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) databases and were used as training and testing datasets, respectively. (2) The prognostic RBPs-related genes were screened according to the overlapping differentially expressed genes (DEGs) from the TCGA database. (3) Univariate Cox proportional hazard regression was performed to identify the genes with significant prognostic value. (4) Further, we used the LASSO regression to construct a prognostic signature and validated the signature in the TCGA and ICGC cohort. (5) Besides, we also performed prognostic analysis, expression level verification, immune cell correlation analysis, and drug correlation analysis of the genes in the model. Results. Four genes (MRPL13, IGF2BP1, BRCA1, and MAEL) were identified as prognostic gene signatures. The prognostic model has been validated in the TCGA and ICGC cohorts. The risk score calculated with four genes signatures could largely predict overall survival for 1, 3, and 5 years in patients with BRCA. The calibration plot demonstrated outstanding consistency between the prediction and actual observation. The findings of online database verification revealed that these four genes were significantly highly expressed in tumors. Also, we observed their significant correlations with some immune cells and also potential correlations with some drugs. Conclusion. We constructed a 4-RBPs-based prognostic signature to predict the prognosis of BRCA patients, and it has the potential for treating and diagnosing BRCA.
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Zhong, Shanliang, Zhenzhong Lin, Huanwen Chen, Ling Mao, Jifeng Feng, and Siying Zhou. "The m6A-related gene signature for predicting the prognosis of breast cancer." PeerJ 9 (June 4, 2021): e11561. http://dx.doi.org/10.7717/peerj.11561.
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N6-methyladenosine (m6A) modification has been shown to participate in tumorigenesis and metastasis of human cancers. The present study aimed to investigate the roles of m6A RNA methylation regulators in breast cancer. We used LASSO regression to identify m6A-related gene signature predicting breast cancer survival with the datasets downloaded from Gene Expression Omnibus and The Cancer Genome Atlas (TCGA). RNA-Seq data of 3409 breast cancer patients from GSE96058 and 1097 from TCGA were used in present study. A 10 m6A-related gene signature associated with prognosis was identified from 22 m6A RNA methylation regulators. The signature divided patients into low- and high-risk group. High-risk patients had a worse prognosis than the low-risk group. Further analyses indicated that IGF2BP1 may be a key m6A RNA methylation regulator in breast cancer. Survival analysis showed that IGF2BP1 is an independent prognostic factor of breast cancer, and higher expression level of IGF2BP1 is associated with shorter overall survival of breast cancer patients. In conclusion, we identified a 10 m6A-related gene signature associated with overall survival of breast cancer. IGF2BP1 may be a key m6A RNA methylation regulator in breast cancer.
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Tian, Ye, Jing Dong, and Lin Li. "Bridging Pyroptosis and Immunity: A Comprehensive Study of the Pyroptosis-Related Long Non-Coding RNA Signature in Breast Cancer." Life 13, no. 7 (July 21, 2023): 1599. http://dx.doi.org/10.3390/life13071599.
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Breast cancer continuously poses serious clinical challenges to human health due to its intrinsic heterogenicity and evolving drug resistance. Recently, increasing evidence has shown that pyroptosis, known as a programmed and inflammatory form of cell death, participates in tumorigenesis, progression, and remodeling of the tumor immune microenvironment (TIME). However, a comprehensive insight into pyroptosis-related signatures for breast cancer remains elusive. The current study established a pyroptosis-related lncRNA signature using transcriptome data and corresponding clinical information from The Cancer Genome Atlas (TCGA). Pyroptosis-related gene clusters, the associated differential expression in breast cancer patients’ subtypes, and the potential mechanisms were all discussed. This integrative analysis revealed a unique signature underpinning the dichotomy of breast cancer progression and survival outcomes. Interestingly, the pyroptosis-related lncRNA signature was revealed as closely intertwined with the TIME. A correlation was established between the pyroptosis-related LncRNA signature and the TIME, underlying the mutual effect between pyroptosis and the immune responses implicated in breast cancer. The findings in this work underline the critical role exerted by pyroptosis in breast cancer, providing new insights into disease progression, prognosis, and therapeutic potential. This work has been poised to provide new avenues for personalized, immune-based cancer therapeutics by enhancing our understanding of pyroptosis in breast cancer.
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Zhou, J., and H. Ma. "OS1.2 Development and Validation of a RNA-Seq Based Prognostic Signature in Neuroblastoma." Neuro-Oncology 21, Supplement_3 (August 2019): iii5. http://dx.doi.org/10.1093/neuonc/noz126.015.
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Abstract BACKGROUND Credible prognostic stratification remains a challenge for neuroblastoma (NBL) with variable clinical manifestations. RNA expression signatures might predict the outcomes; notwithstanding, independent cross-platform validation is still rare. MATERIAL AND METHODS expression data were obtained from NBL patients and then analyzed. In TARGET-NBL data, an RNA-based prognostic signature was developed and validated. Survival prediction was assessed using a time-dependent receiver operating characteristic (ROC) curve. Functional enrichment analysis of the RNAs was conducted using bioinformatics methods. RESULTS A total of 1,119 differentially expressed RNAs and 149 prognosis-related RNAs were identified sequentially. Then, in the training cohort, 12 RNAs were identified as significantly associated with overall survival (OS) and were combined to develop a model that stratified NBL patients into low- and high-risk groups. Twelve RNA signature high-risk patients had poorer OS in the training cohort (n = 105, Hazard Ratios (HR)= 0.10 (0.05–0.20), P < 0.001) and in the validation cohort (n = 44, HR = 0.25 (0.09–0.69), P = 0.008). ROC curve analysis also showed that both the training and validation cohorts performed well in predicting OS (12-month AUC values of 0.852 and 0.438, 36-month AUC values of 0.824 and 0.737, and 60-month AUC values of 0.802 and 0.702, respectively). Moreover, these 12 RNAs may be involved in certain events that are known to be associated with NBL through functional enrichment analysis. CONCLUSION This study identified and validated a novel 12-RNA prognostic signature to reliably distinguish NBL patients at low and high risk of death. Further larger, multicenter prospective studies are desired to validate this model.
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Zhong, Cheng, Dongliang Yang, Liping Zhong, Weixing Xie, Guodong Sun, Daxiang Jin, and Yuming Li. "Single-cell and bulk RNA sequencing reveals Anoikis related genes to guide prognosis and immunotherapy in osteosarcoma." Scientific Reports 13, no. 1 (November 18, 2023). http://dx.doi.org/10.1038/s41598-023-47367-3.
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AbstractAnoikis resistance, a notable factor in osteosarcoma, plays a significant role in tumor invasion and metastasis. This study seeks to identify a distinct gene signature that is specifically associated with the anoikis subcluster in osteosarcoma. Clinical, single-cell, and transcriptional data from TARGET and GEO datasets were used to develop a gene signature for osteosarcoma based on the anoikis subcluster. Univariate Cox and LASSO regression analyses were employed. The signature's predictive value was evaluated using time-dependent ROC and Kaplan–Meier analyses. Functional enrichment analyses and drug sensitivity analyses were conducted. Validation of three modular genes was performed using RT-qPCR and Western blotting. Signature (ZNF583, CGNL1, CXCL13) was developed to predict overall survival in osteosarcoma patients, targeting the anoikis subcluster. The signature demonstrated good performance in external validation. Stratification based on the signature revealed significantly different prognoses. The signature was an independent prognostic factor. The low-risk group showed enhanced immune cell infiltration and improved immune function. Drug sensitivity analysis indicated efficacy of chemotherapy agents. Prognostic nomograms incorporating the signature provided greater predictive accuracy and clinical utility. Signatures related to the anoikis subcluster play a significant role in osteosarcoma progression. Incorporating these findings into clinical decision-making can improve osteosarcoma treatment and patient outcomes.
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Zhao, Xudong, Tong Liu, and Guohua Wang. "Ensemble classification based signature discovery for cancer diagnosis in RNA expression profiles across different platforms." Briefings in Bioinformatics, May 24, 2022. http://dx.doi.org/10.1093/bib/bbac185.
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Abstract Molecular signatures have been excessively reported for diagnosis of many cancers during the last 20 years. However, false-positive signatures are always found using statistical methods or machine learning approaches, and that makes subsequent biological experiments fail. Therefore, signature discovery has gradually become a non-mainstream work in bioinformatics. Actually, there are three critical weaknesses that make the identified signature unreliable. First of all, a signature is wrongly thought to be a gene set, each component of which keeps differential expressions between or among sample groups. Second, there may be many false-positive genes expressed differentially found, even if samples derived from cancer or normal group can be separated in one-dimensional space. Third, cross-platform validation results of a discovered signature are always poor. In order to solve these problems, we propose a new feature selection framework based on ensemble classification to discover signatures for cancer diagnosis. Meanwhile, a procedure for data transform among different expression profiles across different platforms is also designed. Signatures are found on simulation and real data representing different carcinomas across different platforms. Besides, false positives are suppressed. The experimental results demonstrate the effectiveness of our method.
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Sahu, Divya, Shinn-Ying Ho, Hsueh-Fen Juan, and Hsuan-Cheng Huang. "High-risk, Expression-Based Prognostic Long Noncoding RNA Signature in Neuroblastoma." JNCI Cancer Spectrum 2, no. 2 (April 1, 2018). http://dx.doi.org/10.1093/jncics/pky015.
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Abstract Background Current clinical risk factors stratify patients with neuroblastoma (NB) for appropriate treatments, yet patients with similar clinical behaviors evoke variable responses. MYCN amplification is one of the established drivers of NB and, when combined with high-risk displays, worsens outcomes. Growing high-throughput transcriptomics studies suggest long noncoding RNA (lncRNA) dysregulation in cancers, including NB. However, expression-based lncRNA signatures are altered by MYCN amplification, which is associated with high-risk, and patient prognosis remains limited. Methods We investigated RNA-seq-based expression profiles of lncRNAs in MYCN status and risk status in a discovery cohort (n = 493) and validated them in three independent cohorts. In the discovery cohort, a prognostic association of lncRNAs was determined by univariate Cox regression and integrated into a signature using the risk score method. A novel risk score threshold selection criterion was developed to stratify patients into risk groups. Outcomes by risk group and clinical subgroup were assessed using Kaplan-Meier survival curves and multivariable Cox regression. The performance of lncRNA signatures was evaluated by receiver operating characteristic curve. All statistical tests were two-sided. Results In the discovery cohort, 16 lncRNAs that were differentially expressed (fold change ≥ 2 and adjusted P ≤ 0.01) integrated into a prognostic signature. A high risk score group of lncRNA signature had poor event-free survival (EFS; P < 1E-16). Notably, lncRNA signature was independent of other clinical risk factors when predicting EFS (hazard ratio = 3.21, P = 5.95E–07). The findings were confirmed in independent cohorts (P = 2.86E-02, P = 6.18E-03, P = 9.39E-03, respectively). Finally, the lncRNA signature had higher accuracy for EFS prediction (area under the curve = 0.788, 95% confidence interval = 0.746 to 0.831). Conclusions Here, we report the first (to our knowledge) RNA-seq 16-lncRNA prognostic signature for NB that may contribute to precise clinical stratification and EFS prediction.
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Ren, Xin, Zhuxiao Feng, Xiaodong Ma, Lijuan Huo, Huiying Zhou, Ayu Bai, Shujie Feng, Ying Zhou, Xuchu Weng, and Changhe Fan. "m6A/m1A/m5C-Associated Methylation Alterations and Immune Profile in MDD." Molecular Neurobiology, March 8, 2024. http://dx.doi.org/10.1007/s12035-024-04042-6.
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AbstractMajor depressive disorder (MDD) is a prevalent psychiatric condition often accompanied by severe impairments in cognitive and functional capacities. This research was conducted to identify RNA modification-related gene signatures and associated functional pathways in MDD. Differentially expressed RNA modification-related genes in MDD were first identified. And a random forest model was developed and distinct RNA modification patterns were discerned based on signature genes. Then, comprehensive analyses of RNA modification-associated genes in MDD were performed, including functional analyses and immune cell infiltration. The study identified 29 differentially expressed RNA modification-related genes in MDD and two distinct RNA modification patterns. TRMT112, MBD3, NUDT21, and IGF2BP1 of the risk signature were detected. Functional analyses confirmed the involvement of RNA modification in pathways like phosphatidylinositol 3-kinase signaling and nucleotide oligomerization domain (NOD)-like receptor signaling in MDD. NUDT21 displayed a strong positive correlation with type 2 T helper cells, while IGF2BP1 negatively correlated with activated CD8 T cells, central memory CD4 T cells, and natural killer T cells. In summary, further research into the roles of NUDT21 and IGF2BP1 would be valuable for understanding MDD prognosis. The identified RNA modification-related gene signatures and pathways provide insights into MDD molecular etiology and potential diagnostic biomarkers.
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Deng, Zhili, Fangfen Liu, Mengting Chen, Chuchu Huang, Wenqin Xiao, Sini Gao, Dan Jian, et al. "Keratinocyte-Immune Cell Crosstalk in a STAT1-Mediated Pathway: Novel Insights Into Rosacea Pathogenesis." Frontiers in Immunology 12 (July 5, 2021). http://dx.doi.org/10.3389/fimmu.2021.674871.
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Rosacea is a common chronic inflammatory condition that mainly affects the central face. However, the molecular background of the normal central face and the transcriptional profiling and immune cell composition of rosacea lesions remain largely unknown. Here, we performed whole-skin and epidermal RNA-seq of central facial skin from healthy individuals, lesions and matched normal skin from rosacea patients. From whole-skin RNA-seq, the site-specific gene signatures for central facial skin were mainly enriched in epithelial cell differentiation, with upregulation of the activator protein-1 (AP1) transcription factor (TF). We identified the common upregulated inflammatory signatures and diminished keratinization signature for rosacea lesions. Gene ontology, pathway, TF enrichment and immunohistochemistry results suggested that STAT1 was the potential core of the critical TF networks connecting the epithelial–immune crosstalk in rosacea lesions. Epidermal RNA-seq and immunohistochemistry analysis further validated the epithelial-derived STAT1 signature in rosacea lesions. The epidermal STAT1/IRF1 signature was observed across ETR, PPR, and PhR subtypes. Immune cell composition revealed that macrophages were common in all 3 subtypes. Finally, we described subtype-specific gene signatures and immune cell composition correlated with phenotypes. These findings reveal the specific epithelial differentiation in normal central facial skin, and epithelial–immune crosstalk in lesions providing insight into an initial keratinocyte pattern in the pathogenesis of rosacea.
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El-Sharkawy, Yasser H., Sherif Elbasuney, Sara M. Radwan, Mostafa A. Askar, Samar H. Rizk, and Gharieb S. El-Sayyad. "The potentials of nonlinear polarization with hyperspectral imaging of RNA for hepatocellular carcinoma early diagnosis." Egyptian Journal of Medical Human Genetics 25, no. 1 (June 22, 2024). http://dx.doi.org/10.1186/s43042-024-00541-2.
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Abstract Background Most cancers acquire numerous genetic changes in proto-oncogenes as well as tumor-suppressor genes. Cancer's early diagnosis remains a challenge. Recently, nonlinear polarization has revealed the potential as a promising tool for early cancer diagnosis. Laser-induced nonlinear polarization can offer a novel fingerprint signature. Methods In this study, nonlinear polarization was adopted for the characterization of both DNA and RNA samples from healthy volunteers. Total DNA and RNA were illuminated with a 656-nm LED source, and the resonance frequencies (scattered and re-emitted signals) were captured and recorded using a hyperspectral camera. Results Changes in signal frequency as well as phase shift offered a potent means to differentiate DNA (control) from RNA (control). DNA (control) demonstrated characteristic resonance frequencies that differ from total RNA (control) at the 2nd, 3rd, 4th, and 5th harmonics. While DNA demonstrated a phase shift dominating at 0.88 GHz, RNA dominates at 0.106 GHz. The resonance spectral signature of RNA samples from people with hepatocellular carcinoma (HCC) was compared to that of RNA (control). RNA (HCC) demonstrated distinctive frequency signals at 0.014, 0.021, 0.032, and 0.072 GHz. These characteristics feature could facilitate early HCC diagnosis. While RNA (control) dominates at 0.014 and 0.072 MHz, RNA (HCC) dominates at 0.021 and 0.032. Conclusion As far as we are aware, this is the initial investigation into the use of simple nonlinear polarization to generate spectral fingerprinting signatures of total DNA and RNA. Furthermore, RNA mutations due to HCC were identified via characteristic nonlinear spectral signature.
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Zhao, Chunxia, Yulu Wang, Famei Tu, Shuai Zhao, Xiaoying Ye, Jing Liu, Juan Zhang, and Zifeng Wang. "A Prognostic Autophagy-Related Long Non-coding RNA (ARlncRNA) Signature in Acute Myeloid Leukemia (AML)." Frontiers in Genetics 12 (June 30, 2021). http://dx.doi.org/10.3389/fgene.2021.681867.
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BackgroundSome studies have proven that autophagy and lncRNA play important roles in AML. Several autophagy related lncRNA signatures have been shown to affect the survival of patients in some other cancers. However, the role of autophagy related lncRNA in AML has not been explored yet. Hence, this study aims to find an autophagy related lncRNA signature that can affect survival for AML patients.MethodA Pearson correlation analysis, a Kaplan–Meier survival curve, a univariate cox regression, and a multivariate cox regression were performed to establish an autophagy related lncRNA signature. A univariate cox regression, a multivariate cox regression, a Kaplan–Meier survival curve, and a ROC curve were applied to confirm if the signature is an independent prognosis for AML patients. The relationship between the signature and the clinical features was explored by using a T test. Gene Set Enrichment Analysis (GSEA) was used to investigate the potential tumor related pathways.ResultsA four-autophagy related lncRNA (MIR133A1HG, AL359715.1, MIRLET7BHG, and AL356752.1) signature was established. The high risk score based on signature was related to the short survival time of AML patients. The signature was an independent factor for the prognosis for AML patients (HR = 1.684, 95% CI = 1.324–2.142, P < 0.001). The signature was correlated with age, leukocyte numbers, and FAB (M3 or non-M3). The P53, IL6/JAK/STAT3, TNF-α, INF-γ, and IL2/STAT5 pathways might contribute to the differences between the risk groups based on signature in AML.ConclusionThe four autophagy related lncRNAs and their signature might be novel biomarkers for predicting the survival of AML patients. Some biological pathways might be the potential mechanisms of the signature for the survival of AML patients.