Journal articles on the topic 'The Cancer Genome Atlas (TCGA) dataset'
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Tu, Juchuanli, Xiaolu Li, and Jianjun Wang. "Characterization of bidirectional gene pairs in The Cancer Genome Atlas (TCGA) dataset." PeerJ 7 (June 17, 2019): e7107. http://dx.doi.org/10.7717/peerj.7107.
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The “bidirectional gene pair” indicates a particular head-to-head gene organization in which transcription start sites of two genes are located on opposite strands of genomic DNA within a region of one kb. Despite bidirectional gene pairs are well characterized, little is known about their expression profiles and regulation features in tumorigenesis. We used RNA-seq data from The Cancer Genome Atlas (TCGA) dataset for a systematic analysis of the expression profiles of bidirectional gene pairs in 13 cancer datasets. Gene pairs on the opposite strand with transcription end site distance within one kb or on the same strand with the distance of two genes between 1–10 kb and gene pairs comprising two randomly chosen genes were used as control gene pairs (CG1, CG2, and random). We identified and characterized up-/down-regulated genes by comparing the expression level between tumors and adjacent normal tissues in 13 TCGA datasets. There were no consistently significant difference in the percentage of up-/down-regulated genes between bidirectional and control/random genes in most of TCGA datasets. However, the percentage of bidirectional gene pairs comprising two up- or two down-regulated genes was significantly higher than gene pairs from CG1/2 in 12/11 analyzed TCGA datasets and the random gene pairs in all 13 TCGA datasets. Then we identified the methylation correlated bidirectional genes to explore the regulatory mechanism of bidirectional genes. Like the differentially expressed gene pairs, the bidirectional genes in a pair were significantly prone to be both hypo- or hyper-methylation correlated genes in 12/13 TCGA datasets when comparing to the CG2/random gene pairs despite no significant difference between the percentages of hypo-/hyper-methylation correlated genes in bidirectional and CG2/random genes in most of TCGA datasets. Finally, we explored the correlation between bidirectional genes and patient’s survival, identifying prognostic bidirectional genes and prognostic bidirectional gene pairs in each TCGA dataset. Remarkably, we found a group of prognostic bidirectional gene pairs in which the combination of two protein coding genes with different expression level correlated with different survival prognosis in survival analysis for OS. The percentage of these gene pairs in bidirectional gene pair were significantly higher than the gene pairs in controls in COAD datasets and lower in none of 13 TCGA datasets.
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Neapolitan, Richard, and Xia Jiang. "Inferring Aberrant Signal Transduction Pathways in Ovarian Cancer from TCGA Data." Cancer Informatics 13s1 (January 2014): CIN.S13881. http://dx.doi.org/10.4137/cin.s13881.
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This paper concerns a new method for identifying aberrant signal transduction pathways (STPs) in cancer using case/control gene expression-level datasets, and applying that method and an existing method to an ovarian carcinoma dataset. Both methods identify STPs that are plausibly linked to all cancers based on current knowledge. Thus, the paper is most appropriate for the cancer informatics community. Our hypothesis is that STPs that are altered in tumorous tissue can be identified by applying a new Bayesian network (BN)-based method (causal analysis of STP aberration (CASA)) and an existing method (signaling pathway impact analysis (SPIA)) to the cancer genome atlas (TCGA) gene expression-level datasets. To test this hypothesis, we analyzed 20 cancer-related STPs and 6 randomly chosen STPs using the 591 cases in the TCGA ovarian carcinoma dataset, and the 102 controls in all 5 TCGA cancer datasets. We identified all the genes related to each of the 26 pathways, and developed separate gene expression datasets for each pathway. The results of the two methods were highly correlated. Furthermore, many of the STPs that ranked highest according to both methods are plausibly linked to all cancers based on current knowledge. Finally, CASA ranked the cancer-related STPs over the randomly selected STPs at a significance level below 0.05 ( P = 0.047), but SPIA did not ( P = 0.083).
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Kim, In Ah, and Bum Sup Jang. "TMIC-52. RELATIONSHIP BETWEEN MACROPHAGE AND RADIOSENSITIVITY IN HUMAN PRIMARY AND RECURRENT GLIOBLASTOMA: IN SILICO ANALYSIS WITH PUBLICLY AVAILABLE DATASETS." Neuro-Oncology 24, Supplement_7 (November 1, 2022): vii283. http://dx.doi.org/10.1093/neuonc/noac209.1096.
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Abstract The glioblastoma microenvironment predominantly contains tumor-associated macrophages that support tumor growth and invasion. We investigated the relationship between tumor radiosensitivity and infiltrating M1/M2 macrophage profiles in public datasets of primary and recurrent glioblastoma. We estimated the radiosensitivity index (RSI) score based on gene expression rankings. Macrophages were profiled using the deconvolution algorithm CIBERSORTx. Samples from The Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), the Ivy Glioblastoma Atlas Project dataset, a single-cell RNA sequencing dataset (GSE84465), Glioma Longitudinal Analysis Consortium (GLASS), and an immunotherapy trial dataset (GSE121810) were included. RSI-high radioresistant tumors were associated with worse overall survival in TCGA and CGGA than RSI-low tumors. M1/M2 macrophage ratios and RSI scores were inversely associated, indicating that radioresistant glioblastoma tumor microenvironments contain more M2 than M1 macrophages. In the single-cell RNA sequencing dataset, the mean RSI of neoplastic cells was positively correlated with high M2 macrophages proportions. A favorable response to programmed cell death protein 1 (PD-1) therapy was observed in recurrent glioblastomas with high M1/M2 macrophage ratios and low RSI scores. In patients with recurrent glioblastoma, fewer M2 macrophages and low RSI scores were associated with improved overall survival. High M2 macrophage proportions may be involved in radioresistant glioblastoma.
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Jang, Bum-Sup, and In Ah Kim. "Relationship between Macrophage and Radiosensitivity in Human Primary and Recurrent Glioblastoma: In Silico Analysis with Publicly Available Datasets." Biomedicines 10, no. 2 (January 27, 2022): 292. http://dx.doi.org/10.3390/biomedicines10020292.
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The glioblastoma microenvironment predominantly contains tumor-associated macrophages that support tumor growth and invasion. We investigated the relationship between tumor radiosensitivity and infiltrating M1/M2 macrophage profiles in public datasets of primary and recurrent glioblastoma. We estimated the radiosensitivity index (RSI) score based on gene expression rankings. Macrophages were profiled using the deconvolution algorithm CIBERSORTx. Samples from The Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), the Ivy Glioblastoma Atlas Project dataset, a single-cell RNA sequencing dataset (GSE84465), Glioma Longitudinal Analysis Consortium (GLASS), and an immunotherapy trial dataset (GSE121810) were included. RSI-high radioresistant tumors were associated with worse overall survival in TCGA and CGGA than RSI-low tumors. M1/M2 macrophage ratios and RSI scores were inversely associated, indicating that radioresistant glioblastoma tumor microenvironments contain more M2 than M1 macrophages. In the single-cell RNA sequencing dataset, the mean RSI of neoplastic cells was positively correlated with high M2 macrophages proportions. A favorable response to programmed cell death protein 1 (PD-1) therapy was observed in recurrent glioblastomas with high M1/M2 macrophage ratios and low RSI scores. In patients with recurrent glioblastoma, fewer M2 macrophages and low RSI scores were associated with improved overall survival. High M2 macrophage proportions may be involved in radioresistant glioblastoma.
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Torcivia, John, Kawther Abdilleh, Fabian Seidl, Owais Shahzada, Rebecca Rodriguez, David Pot, and Raja Mazumder. "Whole Genome Variant Dataset for Enriching Studies across 18 Different Cancers." Onco 2, no. 2 (June 17, 2022): 129–44. http://dx.doi.org/10.3390/onco2020009.
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Whole genome sequencing (WGS) has helped to revolutionize biology, but the computational challenge remains for extracting valuable inferences from this information. Here, we present the cancer-associated variants from the Cancer Genome Atlas (TCGA) WGS dataset. This set of data will allow cancer researchers to further expand their analysis beyond the exomic regions of the genome to the entire genome. A total of 1342 WGS alignments available from the consortium were processed with VarScan2 and deposited to the NCI Cancer Cloud. The sample set covers 18 different cancers and reveals 157,313,519 pooled (non-unique) cancer-associated single-nucleotide variations (SNVs) across all samples. There was an average of 117,223 SNVs per sample, with a range from 1111 to 775,470 and a standard deviation of 163,273. The dataset was incorporated into BigQuery, which allows for fast access and cross-mapping, which will allow researchers to enrich their current studies with a plethora of newly available genomic data.
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Martino, Francesco, Domenico D. Bloisi, Andrea Pennisi, Mulham Fawakherji, Gennaro Ilardi, Daniela Russo, Daniele Nardi, Stefania Staibano, and Francesco Merolla. "Deep Learning-Based Pixel-Wise Lesion Segmentation on Oral Squamous Cell Carcinoma Images." Applied Sciences 10, no. 22 (November 23, 2020): 8285. http://dx.doi.org/10.3390/app10228285.
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Oral squamous cell carcinoma is the most common oral cancer. In this paper, we present a performance analysis of four different deep learning-based pixel-wise methods for lesion segmentation on oral carcinoma images. Two diverse image datasets, one for training and another one for testing, are used to generate and evaluate the models used for segmenting the images, thus allowing to assess the generalization capability of the considered deep network architectures. An important contribution of this work is the creation of the Oral Cancer Annotated (ORCA) dataset, containing ground-truth data derived from the well-known Cancer Genome Atlas (TCGA) dataset.
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Zhou, Weige, Shijing Zhang, Zheyou Cai, Fei Gao, Wenhui Deng, Yi Wen, Zhen-wen Qiu, Zheng-kun Hou, and Xin-Lin Chen. "A glycolysis-related gene pairs signature predicts prognosis in patients with hepatocellular carcinoma." PeerJ 8 (September 29, 2020): e9944. http://dx.doi.org/10.7717/peerj.9944.
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Background Hepatocellular carcinoma (HCC) is one of the most universal malignant liver tumors worldwide. However, there were no systematic studies to establish glycolysis‑related gene pairs (GRGPs) signatures for the patients with HCC. Therefore, the study aimed to establish novel GRGPs signatures to better predict the prognosis of HCC. Methods Based on the data from Gene Expression Omnibus, The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium databases, glycolysis-related mRNAs were used to construct GRGPs. Cox regression was applied to establish a seventeen GRGPs signature in TCGA dataset, which was verified in two validation (European and American, and Asian) datasets. Results Seventeen prognostic GRGPs (HMMR_PFKFB1, CHST1_GYS2, MERTK_GYS2, GPC1_GYS2, LDHA_GOT2, IDUA_GNPDA1, IDUA_ME2, IDUA_G6PD, IDUA_GPC1, MPI_GPC1, SDC2_LDHA, PRPS1_PLOD2, GALK1_IER3, MET_PLOD2, GUSB_IGFBP3, IL13RA1_IGFBP3 and CYB5A_IGFBP3) were identified to be significantly progressive factors for the patients with HCC in the TCGA dataset, which constituted a GRGPs signature. The patients with HCC were classified into low-risk group and high-risk group based on the GRGPs signature. The GRGPs signature was a significantly independent prognostic indicator for the patients with HCC in TCGA (log-rank P = 2.898e−14). Consistent with the TCGA dataset, the patients in low-risk group had a longer OS in two validation datasets (European and American: P = 1.143e−02, and Asian: P = 6.342e−08). Additionally, the GRGPs signature was also validated as a significantly independent prognostic indicator in two validation datasets. Conclusion The seventeen GRGPs and their signature might be molecular biomarkers and therapeutic targets for the patients with HCC.
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Miller, Marina, Eric Devor, Erin Salinas, Andreea Newtson, Michael Goodheart, Kimberly Leslie, and Jesus Gonzalez-Bosquet. "Population Substructure Has Implications in Validating Next-Generation Cancer Genomics Studies with TCGA." International Journal of Molecular Sciences 20, no. 5 (March 8, 2019): 1192. http://dx.doi.org/10.3390/ijms20051192.
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In the era of large genetic and genomic datasets, it has become crucially important to validate results of individual studies using data from publicly available sources, such as The Cancer Genome Atlas (TCGA). However, how generalizable are results from either an independent or a large public dataset to the remainder of the population? The study presented here aims to answer that question. Utilizing next generation sequencing data from endometrial and ovarian cancer patients from both the University of Iowa and TCGA, genomic admixture of each population was analyzed using STRUCTURE and ADMIXTURE software. In our independent data set, one subpopulation was identified, whereas in TCGA 4–6 subpopulations were identified. Data presented here demonstrate how different the genetic substructures of the TCGA and University of Iowa populations are. Validation of genomic studies between two different population samples must be aware of, account for and be corrected for background genetic substructure.
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Sorgini, Alana, Hugh Andrew Jinwook Kim, Peter Y. F. Zeng, Mushfiq Hassan Shaikh, Neil Mundi, Farhad Ghasemi, Eric Di Gravio, et al. "Analysis of the TCGA Dataset Reveals that Subsites of Laryngeal Squamous Cell Carcinoma Are Molecularly Distinct." Cancers 13, no. 1 (December 31, 2020): 105. http://dx.doi.org/10.3390/cancers13010105.
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Laryngeal squamous cell carcinoma (LSCC) from different subsites have distinct presentations and prognosis. In this study, we carried out a multiomic comparison of LSCC subsites. The Cancer Genome Atlas (TCGA) LSCC cohort was analyzed in the R statistical environment for differences between supraglottic and glottic cancers in single nucleotide variations (SNVs), copy number alterations (CNAs), mRNA abundance, protein abundance, pathway overrepresentation, tumor microenvironment (TME), hypoxia status, and patient outcome. Supraglottic cancers had significantly higher overall and smoking-associated SNV mutational load. Pathway analysis revealed upregulation of muscle related pathways in glottic cancer and neural pathways in supraglottic cancer. Proteins involved in cancer relevant signaling pathways including PI3K/Akt/mTOR, the cell cycle, and PDL1 were differentially abundant between subsites. Glottic and supraglottic tumors have different molecular profiles, which may partially account for differences in presentation and response to therapy.
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Jang, Bum-Sup, and In Ah Kim. "TAMI-32. CORRELATION BETWEEN RADIOSENSITIVITY INDEX AND M2 MACROPHAGE PROPORTION IN TUMOR MICROENVIRONMENT OF GLIOBLASTOMA." Neuro-Oncology 23, Supplement_6 (November 2, 2021): vi204—vi205. http://dx.doi.org/10.1093/neuonc/noab196.816.
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Abstract BACKGROUND Tumor-associated macrophages (TAMs) Macrophage are predominant in glioblastoma tumor microenvironment (TME), supporting for neoplastic cell expansion and invasion. We investigated the relationship between radiosensitivity of glioblastoma and M1/M2 macrophage profiles in bulk and single cell RNA sequencing datasets. METHODS We used radiosensitivity index (RSI) gene signature and estimated RSI score based on the ranking of genes by expression level. Two large glioma datasets – The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) – were employed to identify whether RSI is clinically predictive of overall survival following radiation therapy. To analyze the association between M1/M2 macrophages and RSI within spatial context, the Ivy Glioblastoma Atlas Project dataset was investigated and single cell RNA sequencing dataset (GSE84465) was analyzed as well. Macrophages were profiled using a deconvolution algorithm, CIBERSORTx. RESULTS The RSI-high group having radioresistant tumors showed worse overall survival than the RSI-low group in both the TCGA (HR=1.87, 95% CI=1.06-3.29, P=0.031) and the CGGA (HR=1.61, 95% CI=1.04-2.50, P=0.031) glioblastoma population. In the Ivy Glioblastoma Atlas Project dataset, radiosensitive tumor having lower RSI was significantly more found in more vascular region including hyperplastic and microvascular region (coefficient=-0.07, P=0.001), meanwhile, radioresistant tumor was significantly clustered in necrotic region including perinecrotic and pseudopalisading regions (coefficient=0.07, P< 0.001). The proportion of M1/M2 macrophage and RSI score showed an inverse relationship (coefficient=-0.23, P=0.015), indicating that radioresistant glioblastomas are related with TME having more M2 than M1 macrophage. In single cell RNA sequencing dataset composed of immune and tumor cells collected from four patients, mean RSI of neoplastic cells was positively correlated with high proportion of M2 macrophages. CONCLUSION RSI can predict radiation response in terms of overall survival in glioblastoma patients. High proportion of M2 macrophage may play an important role in TME of radioresistant glioblastoma.
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Liu, Sha, Jiazhong Shi, Yuting Liu, Liwei Wang, Jingqi Zhang, Yaqin Huang, Zhiwen Chen, and Jin Yang. "Analysis of mRNA expression differences in bladder cancer metastasis based on TCGA datasets." Journal of International Medical Research 49, no. 3 (March 2021): 030006052199692. http://dx.doi.org/10.1177/0300060521996929.
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Objective To investigate the metastatic mechanism of muscle invasive bladder cancer (MIBC), which accounts for approximately 30% of all bladder cancer cases, and is a considerable medical problem with high metastatic and mortality rates. Methods The mRNA levels of patients with metastatic MIBC and nonmetastatic MIBC from The Cancer Genome Atlas dataset were compared. An integrated bioinformatics analysis was performed of the differentially expressed genes (DEGs), and analyses of Gene Ontology, Kyoto Encyclopaedia of Genes and Genomes pathway, protein-protein interaction, and survival were performed to investigate differences between metastatic and nonmetastatic MIBC. Results Data from 264 patients were included (131 with, and 133 without, metastasis). A total of 385 significantly DEGs were identified, including 209 upregulated genes and 176 downregulated genes. Based on results using the STRING database and the MCODE plugin of Cytoscape software, two clusters were obtained. Moreover, two genes were identified that may be valuable for prognostic analysis: Keratin 38, type I ( KRT38) and Histone cluster 1, H3f ( HIST1H3F). Conclusion The KRT38 and HIST1H3F genes may be important in metastasis of MIBC.
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Archer, Kellie J., Anna Eames Seffernick, Shuai Sun, and Yiran Zhang. "ordinalbayes: Fitting Ordinal Bayesian Regression Models to High-Dimensional Data Using R." Stats 5, no. 2 (April 15, 2022): 371–84. http://dx.doi.org/10.3390/stats5020021.
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The stage of cancer is a discrete ordinal response that indicates the aggressiveness of disease and is often used by physicians to determine the type and intensity of treatment to be administered. For example, the FIGO stage in cervical cancer is based on the size and depth of the tumor as well as the level of spread. It may be of clinical relevance to identify molecular features from high-throughput genomic assays that are associated with the stage of cervical cancer to elucidate pathways related to tumor aggressiveness, identify improved molecular features that may be useful for staging, and identify therapeutic targets. High-throughput RNA-Seq data and corresponding clinical data (including stage) for cervical cancer patients have been made available through The Cancer Genome Atlas Project (TCGA). We recently described penalized Bayesian ordinal response models that can be used for variable selection for over-parameterized datasets, such as the TCGA-CESC dataset. Herein, we describe our ordinalbayes R package, available from the Comprehensive R Archive Network (CRAN), which enhances the runjags R package by enabling users to easily fit cumulative logit models when the outcome is ordinal and the number of predictors exceeds the sample size, P>N, such as for TCGA and other high-throughput genomic data. We demonstrate the use of this package by applying it to the TCGA cervical cancer dataset. Our ordinalbayes package can be used to fit models to high-dimensional datasets, and it effectively performs variable selection.
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Chon, H. S., R. Abdallah, J. M. Lancaster, R. M. Wenham, S. M. Apte, P. L. Judson Lancaster, M. M. K. Shahzad, and J. Gonzalez-Bosquet. "Association between endometrioid endometrial cancer (EC) risk classification and gene expression in The Cancer Genome Atlas (TCGA) dataset." Gynecologic Oncology 133 (June 2014): 138. http://dx.doi.org/10.1016/j.ygyno.2014.03.361.
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Nicolle, Remy, Jerome Raffenne, Valerie Paradis, Anne Couvelard, Aurelien de Reynies, Yuna Blum, and Jerome Cros. "Prognostic Biomarkers in Pancreatic Cancer: Avoiding Errata When Using the TCGA Dataset." Cancers 11, no. 1 (January 21, 2019): 126. http://dx.doi.org/10.3390/cancers11010126.
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Data from the Cancer Genome Atlas (TCGA) are now easily accessible through web-based platforms with tools to assess the prognostic value of molecular alterations. Pancreatic tumors have heterogeneous biology and aggressiveness ranging from the deadly adenocarcinoma (PDAC) to the better prognosis, neuroendocrine tumors. We assessed the availability of the pancreatic cancer TCGA data (TCGA_PAAD) from several repositories and investigated the nature of each sample and how non-PDAC samples impact prognostic biomarker studies. While the clinical and genomic data (n = 185) were fairly consistent across all repositories, RNAseq profiles varied from 176 to 185. As a result, 35 RNAseq profiles (18.9%) corresponded to a normal, inflamed pancreas or non-PDAC neoplasms. This information was difficult to obtain. By considering gene expression data as continuous values, the expression of the 5312 and 4221 genes were significantly associated with the progression-free and overall survival respectively. Considering the cohort was not curated, only 4 and 14, respectively, had prognostic value in the PDAC-only cohort. Similarly, mutations in key genes or well-described miRNA lost their prognostic significance in the PDAC-only cohort. Therefore, we propose a web-based application to assess biomarkers in the curated TCGA_PAAD dataset. In conclusion, TCGA_PAAD curation is critical to avoid important biological and clinical biases from non-PDAC samples.
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Zhou, Ranran, Jingjing Liang, Hu Tian, Qi Chen, Cheng Yang, and Cundong Liu. "An Immunosenescence-Related Gene Signature to Evaluate the Prognosis, Immunotherapeutic Response, and Cisplatin Sensitivity of Bladder Cancer." Disease Markers 2022 (March 2, 2022): 1–34. http://dx.doi.org/10.1155/2022/2143892.
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Immunosenescence refers to the immune system undergoing a series of degenerative changes with advancing age and is tightly associated with the initiation and progression of cancers. However, the immunosenescence-related genes as critical biomarkers for bladder cancer (BLCA) have not been systematically analyzed. We retrieved the immunosenescence-related genes from the public database and verified their association with hallmarks of immunosenescence based on The Cancer Genome Atlas (TCGA) cohort. Through gene pairing, Lasso, and univariate Cox regression, an 8-gene pair model was constructed to evaluate the overall survival of BLCA, which was then validated in the training cohort ( P < 0.001 , n = 396 ), two external validation cohorts ( P < 0.05 , n = 165 ; P < 0.001 , n = 224 ), and local samples ( P < 0.05 , n = 10 ). We also downloaded the clinical information and gene expression matrices of other 32 different cancers from TCGA. The established model showed significant predictive value for the prognosis in 15 cancers ( P < 0.05 ). The risk model could also serve as a promising predictor for immunotherapeutic response, which has been verified by the TIDE algorithm ( P < 0.05 ), IMvigor210 dataset ( P < 0.01 , n = 298 ), and other two datasets correlated with immunotherapy ( P < 0.05 , n = 56 ; P = 0.17 , n = 27 ). The TCGA dataset, in vitro cell experiments, and pan-cancer analysis displayed that the gene signature was associated with cisplatin sensitivity ( P < 0.05 ). Overall, we proposed a novel immunosenescence-related gene signature to predict prognosis, immunotherapeutic response, and cisplatin sensitivity of BLCA, which were validated in different independent cohorts, local samples, and pan-cancer analyses.
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Suteau, Valentine, Mathilde Munier, Rym Ben Boubaker, Méline Wery, Daniel Henrion, Patrice Rodien, and Claire Briet. "Identification of Dysregulated Expression of G Protein Coupled Receptors in Endocrine Tumors by Bioinformatics Analysis: Potential Drug Targets?" Cells 11, no. 4 (February 17, 2022): 703. http://dx.doi.org/10.3390/cells11040703.
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Background: Many studies link G protein-coupled receptors (GPCRs) to cancer. Some endocrine tumors are unresponsive to standard treatment and/or require long-term and poorly tolerated treatment. This study explored, by bioinformatics analysis, the tumoral profiling of the GPCR transcriptome to identify potential targets in these tumors aiming at drug repurposing. Methods: We explored the GPCR differentially expressed genes (DEGs) from public datasets (Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas (TCGA)). The GEO datasets were available for two medullary thyroid cancers (MTCs), eighty-seven pheochromocytomas (PHEOs), sixty-one paragangliomas (PGLs), forty-seven pituitary adenomas and one-hundred-fifty adrenocortical cancers (ACCs). The TCGA dataset covered 92 ACCs. We identified GPCRs targeted by approved drugs from pharmacological databases (ChEMBL and DrugBank). Results: The profiling of dysregulated GPCRs was tumor specific. In MTC, we found 14 GPCR DEGs, including an upregulation of the dopamine receptor (DRD2) and adenosine receptor (ADORA2B), which were the target of many drugs. In PGL, seven GPCR genes were downregulated, including vasopressin receptor (AVPR1A) and PTH receptor (PTH1R), which were targeted by approved drugs. In ACC, PTH1R was also downregulated in both the GEO and TCGA datasets and was the target of osteoporosis drugs. Conclusions: We highlight specific GPCR signatures across the major endocrine tumors. These data could help to identify new opportunities for drug repurposing.
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Yang, Liuqing, Saisai Tian, Yun Chen, Chenyun Miao, Ying Zhao, Ruye Wang, and Qin Zhang. "Ferroptosis-Related Gene Model to Predict Overall Survival of Ovarian Carcinoma." Journal of Oncology 2021 (January 13, 2021): 1–14. http://dx.doi.org/10.1155/2021/6687391.
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Background. Ovarian cancer (OC) is the eighth most common cause of cancer death and the second cause of gynecologic cancer death in women around the world. Ferroptosis, an iron-dependent regulated cell death, plays a vital role in the development of many cancers. Applying expression of ferroptosis-related gene to forecast the cancer progression is helpful for cancer treatment. However, the relationship between ferroptosis-related genes and OC patient prognosis is still vastly unknown, making it still a challenge for developing ferroptosis therapy for OC. Methods. The Cancer Genome Atlas (TCGA) data of OC were obtained and the datasets were randomly divided into training and test datasets. A novel ferroptosis-related gene signature associated with overall survival (OS) was constructed according to the training cohort. The test dataset and ICGC dataset were used to validate this signature. Results. We constructed a model containing nine ferroptosis-related genes, namely, LPCAT3, ACSL3, CRYAB, PTGS2, ALOX12, HSBP1, SLC1A5, SLC7A11, and ZEB1, and predicted the OS of OC in TCGA. At a suitable cutoff, patients were divided into low risk and high risk groups. The OS curves of the two groups of patients had significant differences, and the time-dependent receiver operating characteristics (ROCs) were as high as 0.664, respectively. Then, the test dataset and the ICGC dataset were used to evaluate our model, and the ROCs of test dataset were 0.667 and 0.777, respectively. In addition, functional analysis and correlation analysis showed that immune-related pathways were significantly enriched. Meanwhile, we also integrated with other clinical factors and we found the synthesized clinical factors and ferroptosis-related gene signature improved prognostic accuracy relative to the ferroptosis-related gene signature alone. Conclusion. The ferroptosis-related gene signature could predict the OS of OC patients and improve therapeutic decision-making.
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Chamorro Petronacci, Cintia M., Abel García García, Elena Padín Iruegas, Berta Rivas Mundiña, Alejandro I. Lorenzo Pouso, and Mario Pérez Sayáns. "Identification of Prognosis Associated microRNAs in HNSCC Subtypes Based on TCGA Dataset." Medicina 56, no. 10 (October 13, 2020): 535. http://dx.doi.org/10.3390/medicina56100535.
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Background and Objectives: Head and Neck Squamous Cell Carcinoma (HNSCC) includes cancers from the oral cavity, larynx, and oropharynx and is the sixth-most common cancer worldwide. MicroRNAs are small non-coding RNAs for which altered expression has been demonstrated in pathological processes, such as cancer. The objective of our study was to evaluate the different expression profile in HNSCC subtypes and the prognostic value that one or several miRNAs may have. Materials and Methods: Data from The Cancer Genome Atlas Program-Head and Neck Squamous Cell Carcinoma (TCGA-HNSCC) patients were collected. Differential expression analysis was conducted by edge R-powered TCGAbiolinks R package specific function. Enrichment analysis was developed with Diana Tool miRPath 3.0. Kaplan-Meier survival estimators were used, followed by log-rank tests to compute significance. Results: A total of 127 miRNAs were identified with differential expression level in HNSCC; 48 of them were site-specific and, surprisingly, only miR-383 showed a similar deregulation in all locations studied (tonsil, mouth, floor of mouth, cheek mucosa, lip, tongue, and base of tongue). The most probable affected pathways based on miRNAs interaction levels were protein processing in endoplasmic reticulum, proteoglycans in cancer (p < 0.01), Hippo signaling pathway (p < 0.01), and Transforming growth factor-beta (TGF-beta) signaling pathway (p < 0.01). The survival analysis highlighted 38 differentially expressed miRNAs as prognostic biomarkers. The miRNAs with a greater association between poor prognosis and altered expression (p < 0.001) were miR-137, miR-125b-2, miR-26c, and miR-1304. Conclusions: In this study we have determined miR-137, miR-125b-2, miR-26c, and miR-1304 as novel powerful prognosis biomarkers. Furthermore, we have depicted the miRNAs expression patterns in tumor patients compared with normal subjects using the TCGA-HNSCC cohort.
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Wei, Yunzhen, Limeng Zhou, Yingzhang Huang, and Dianjing Guo. "Integrated Dissection of lncRNA-Perturbated Triplets Reveals Novel Prognostic Signatures Across Cancer Types." International Journal of Molecular Sciences 21, no. 17 (August 24, 2020): 6087. http://dx.doi.org/10.3390/ijms21176087.
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Long noncoding RNA (lncRNA)/microRNA(miRNA)/mRNA triplets contribute to cancer biology. However, identifying significative triplets remains a major challenge for cancer research. The dynamic changes among factors of the triplets have been less understood. Here, by integrating target information and expression datasets, we proposed a novel computational framework to identify the triplets termed as “lncRNA-perturbated triplets”. We applied the framework to five cancer datasets in The Cancer Genome Atlas (TCGA) project and identified 109 triplets. We showed that the paired miRNAs and mRNAs were widely perturbated by lncRNAs in different cancer types. LncRNA perturbators and lncRNA-perturbated mRNAs showed significantly higher evolutionary conservation than other lncRNAs and mRNAs. Importantly, the lncRNA-perturbated triplets exhibited high cancer specificity. The pan-cancer perturbator OIP5-AS1 had higher expression level than that of the cancer-specific perturbators. These lncRNA perturbators were significantly enriched in known cancer-related pathways. Furthermore, among the 25 lncRNA in the 109 triplets, lncRNA SNHG7 was identified as a stable potential biomarker in lung adenocarcinoma (LUAD) by combining the TCGA dataset and two independent GEO datasets. Results from cell transfection also indicated that overexpression of lncRNA SNHG7 and TUG1 enhanced the expression of the corresponding mRNA PNMA2 and CDC7 in LUAD. Our study provides a systematic dissection of lncRNA-perturbated triplets and facilitates our understanding of the molecular roles of lncRNAs in cancers.
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Cavicchioli, Maria Vittoria, Mariangela Santorsola, Nicola Balboni, Daniele Mercatelli, and Federico Manuel Giorgi. "Prediction of Metabolic Profiles from Transcriptomics Data in Human Cancer Cell Lines." International Journal of Molecular Sciences 23, no. 7 (March 31, 2022): 3867. http://dx.doi.org/10.3390/ijms23073867.
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The Metabolome and Transcriptome are mutually communicating within cancer cells, and this interplay is translated into the existence of quantifiable correlation structures between gene expression and metabolite abundance levels. Studying these correlations could provide a novel venue of understanding cancer and the discovery of novel biomarkers and pharmacological strategies, as well as laying the foundation for the prediction of metabolite quantities by leveraging information from the more widespread transcriptomics data. In the current paper, we investigate the correlation between gene expression and metabolite levels in the Cancer Cell Line Encyclopedia dataset, building a direct correlation network between the two molecular ensembles. We show that a metabolite/transcript correlation network can be used to predict metabolite levels in different samples and datasets, such as the NCI-60 cancer cell line dataset, both on a sample-by-sample basis and in differential contrasts. We also show that metabolite levels can be predicted in principle on any sample and dataset for which transcriptomics data are available, such as the Cancer Genome Atlas (TCGA).
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Dong, Shu, Zhimin Ding, Hao Zhang, and Qiwen Chen. "Identification of Prognostic Biomarkers and Drugs Targeting Them in Colon Adenocarcinoma: A Bioinformatic Analysis." Integrative Cancer Therapies 18 (January 2019): 153473541986443. http://dx.doi.org/10.1177/1534735419864434.
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Objective: To identify prognostic biomarkers and drugs that target them in colon adenocarcinoma (COAD) based on the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus databases. Methods: The TCGA dataset was used to identify the top 50 upregulated differentially expressed genes (DEGs), and Gene Expression Omnibus profiles were used for validation. Survival analyses were conducted with the TCGA dataset using the RTCGAToolbox package in the R software environment. Drugs targeting the candidate prognostic biomarkers were searched in the DrugBank and herbal databases. Results: Among the top 50 upregulated DEGs in patients with COAD in the TCGA dataset, the Wnt signaling pathway and cytokine-cytokine receptor interactions and pathways in cancer Kyoto Encyclopedia of Genes and Genomes pathway analysis were enriched in DEGs. Tissue development and regulation of cell proliferation were the main Gene Ontology biological processes associated with upregulated DEGs. MYC and KLK6 were overexpressed in tumors validated in the TCGA, GSE41328, and GSE113513 databases (all P < .001) and were significantly associated with overall survival in patients with COAD ( P = .021 and P = .047). Nadroparin and benzamidine were identified as inhibitors of MYC and KLK6 in DrugBank, and 8 herbs targeting MYC, including Da Huang ( Radix Rhei Et Rhizome), Hu Zhang ( Polygoni Cuspidati Rhizoma Et Radix), Huang Lian ( Coptidis Rhizoma), Ban Xia ( Arum Ternatum Thunb), Tu Fu Ling ( Smilacis Glabrae Rhixoma), Lei Gong Teng ( Tripterygii Radix), Er Cha ( Catechu), and Guang Zao ( Choerospondiatis Fructus), were identified. Conclusion: MYC and KLK6 may serve as candidate prognostic predictors and therapeutic targets in patients with COAD.
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Li, Xiaoyong, Jiaqong Lin, Yuguo pan, Peng Cui, and Jintang Xia. "Identification of a Liver Progenitor Cell-Related Genes Signature Predicting Overall Survival for Hepatocellular Carcinoma." Technology in Cancer Research & Treatment 20 (January 2021): 153303382110414. http://dx.doi.org/10.1177/15330338211041425.
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Background: Liver progenitor cells (LPCs) play significant roles in the development and progression of hepatocellular carcinoma (HCC). However, no studies on the value of LPC-related genes for evaluating HCC prognosis exist. We developed a gene signature of LPC-related genes for prognostication in HCC. Methods: To identify LPC-related genes, we analyzed mRNA expression arrays from a dataset (GSE57812 & GSE 37071) containing LPCs, mature hepatocytes, and embryonic stem cell samples. HCC RNA-Seq data from The Cancer Genome Atlas (TCGA) were used to explore the differentially expressed genes (DEGs) related to prognosis through DEG analysis and univariate Cox regression analysis. Lasso and multivariate Cox regression analyses were performed to construct the LPC-related gene prognostic model in the TCGA training dataset. This model was validated in the TCGA testing set and an external dataset (International Cancer Genome Consortium [ICGC] dataset). Finally, we investigated the relationship between this prognostic model with tumor-node-metastasis stage, tumor grade, and vascular invasion of HCC. Results: Overall, 1770 genes were identified as LPC-related genes, of which 92 genes were identified as DEGs in HCC tissues compared with normal tissues. Furthermore, we randomly assigned patients from the TCGA dataset to the training and testing cohorts. Twenty-six DEGs correlated with overall survival (OS) in the univariate Cox regression analysis. Lasso and multivariate Cox regression analyses were performed in the TCGA training set, and a 3-gene signature was constructed to stratify patients into 2 risk groups: high-risk and low-risk. Patients in the high-risk group had significantly lower OS than those in the low-risk group. Receiver operating characteristic curve analysis confirmed the signature's predictive capacity. Moreover, the risk score was confirmed to be an independent predictor for patients with HCC. Conclusion: We demonstrated that the LPC-related gene signature can be used for prognostication in HCC. Thus, targeting LPCs may serve as a therapeutic alternative for HCC.
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Baxter, Eva, Karolina Windloch, Greg Kelly, Jason S. Lee, Frank Gannon, and Donal J. Brennan. "Molecular basis of distinct oestrogen responses in endometrial and breast cancer." Endocrine-Related Cancer 26, no. 1 (January 2019): 31–46. http://dx.doi.org/10.1530/erc-17-0563.
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Up to 80% of endometrial and breast cancers express oestrogen receptor alpha (ERα). Unlike breast cancer, anti-oestrogen therapy has had limited success in endometrial cancer, raising the possibility that oestrogen has different effects in both cancers. We investigated the role of oestrogen in endometrial and breast cancers using data from The Cancer Genome Atlas (TCGA) in conjunction with cell line studies. Using phosphorylation of ERα (ERα-pSer118) as a marker of transcriptional activation of ERα in TCGA datasets, we found that genes associated with ERα-pSer118 were predominantly unique between tumour types and have distinct regulators. We present data on the alternative and novel roles played by SMAD3, CREB-pSer133 and particularly XBP1 in oestrogen signalling in endometrial and breast cancer.
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Pop-Bica, Cecilia, Cristina Alexandra Ciocan, Cornelia Braicu, Antonia Haranguș, Marioara Simon, Andreea Nutu, Laura Ancuta Pop, et al. "Next-Generation Sequencing in Lung Cancer Patients: A Comparative Approach in NSCLC and SCLC Mutational Landscapes." Journal of Personalized Medicine 12, no. 3 (March 13, 2022): 453. http://dx.doi.org/10.3390/jpm12030453.
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Background: Lung cancer remains one of the most diagnosed malignancies, being the second most diagnosed cancer, while still being the leading cause of cancer-related deaths. Late diagnosis remains a problem, alongside the high mutational burden encountered in lung cancer. Methods: We assessed the genetic profile of cancer genes in lung cancer using The Cancer Genome Atlas (TCGA) datasets for mutations and validated the results in a separate cohort of 32 lung cancer patients using tumor tissue and whole blood samples for next-generation sequencing (NGS) experiments. Another separate cohort of 32 patients was analyzed to validate some of the molecular alterations depicted in the NGS experiment. Results: In the TCGA analysis, we identified the most commonly mutated genes in each lung cancer dataset, with differences among the three histotypes analyzed. NGS analysis revealed TP53, CSF1R, PIK3CA, FLT3, ERBB4, and KDR as being the genes most frequently mutated. We validated the c.1621A>C mutation in KIT. The correlation analysis indicated negative correlation between adenocarcinoma and altered PIK3CA (r = −0.50918; p = 0.0029). TCGA survival analysis indicated that NRAS and IDH2 (LUAD), STK11 and TP53 (LUSC), and T53 (SCLC) alterations are correlated with the survival of patients. Conclusions: The study revealed differences in the mutational landscape of lung cancer histotypes.
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Kim, Hugh Andrew Jinwook, Mushfiq Hassan Shaikh, Mark Lee, Peter Y. F. Zeng, Alana Sorgini, Temitope Akintola, Xiaoxiao Deng, et al. "3p Arm Loss and Survival in Head and Neck Cancer: An Analysis of TCGA Dataset." Cancers 13, no. 21 (October 22, 2021): 5313. http://dx.doi.org/10.3390/cancers13215313.
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Loss of the 3p chromosome arm has previously been reported to be a biomarker of poorer outcome in both human papillomavirus (HPV)-positive and HPV-negative head and neck cancer. However, the precise operational measurement of 3p arm loss is unclear and the mutational profile associated with the event has not been thoroughly characterized. We downloaded the clinical, single nucleotide variation (SNV), copy number aberration (CNA), RNA sequencing, and reverse phase protein assay (RPPA) data from The Cancer Genome Atlas (TCGA) and The Cancer Proteome Atlas HNSCC cohorts. Survival data and hypoxia scores were downloaded from published studies. In addition, we report the inclusion of an independent Memorial Sloan Kettering cohort. We assessed the frequency of loci deletions across the 3p arm separately in HPV-positive and -negative disease. We found that deletions on chromosome 3p were almost exclusively an all or none event in the HPV-negative cohort; patients either had <1% or >97% of the arm deleted. 3p arm loss, defined as >97% deletion in HPV-positive patients and >50% in HPV-negative patients, had no impact on survival (p > 0.05). However, HPV-negative tumors with 3p arm loss presented at a higher N-category and overall stage and developed more distant metastases (p < 0.05). They were enriched for SNVs in TP53, and depleted for point mutations in CASP8, HRAS, HLA-A, HUWE1, HLA-B, and COL22A1 (false discovery rate, FDR < 0.05). 3p arm loss was associated with CNAs across the whole genome (FDR < 0.1), and pathway analysis revealed low lymphoid–non-lymphoid cell interactions and cytokine signaling (FDR < 0.1). In the tumor microenvironment, 3p arm lost tumors had low immune cell infiltration (FDR < 0.1) and elevated hypoxia (FDR < 0.1). 3p arm lost tumors had lower abundance of proteins phospho-HER3 and ANXA1, and higher abundance of miRNAs hsa-miR-548k and hsa-miR-421, which were all associated with survival. There were no molecular differences by 3p arm status in HPV-positive patients, at least at our statistical power level. 3p arm loss is largely an all or none phenomenon in HPV-negative disease and does not predict poorer survival from the time of diagnosis in TCGA cohort. However, it produces tumors with distinct molecular characteristics and may represent a clinically useful biomarker to guide treatment decisions for HPV-negative patients.
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Scaria, George S., Betsy T. Kren, and Mark A. Klein. "Cyclin-Dependent Kinase and Antioxidant Gene Expression in Cancers with Poor Therapeutic Response." Pharmaceuticals 13, no. 2 (February 5, 2020): 26. http://dx.doi.org/10.3390/ph13020026.
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Pancreatic cancer, hepatocellular carcinoma (HCC), and mesothelioma are treatment-refractory cancers, and patients afflicted with these cancers generally have a very poor prognosis. The genomics of these tumors were analyzed as part of The Cancer Genome Atlas (TCGA) project. However, these analyses are an overview and may miss pathway interactions that could be exploited for therapeutic targeting. In this study, the TCGA Pan-Cancer datasets were queried via cBioPortal for correlations among mRNA expression of key genes in the cell cycle and mitochondrial (mt) antioxidant defense pathways. Here we describe these correlations. The results support further evaluation to develop combination treatment strategies that target these two critical pathways in pancreatic cancer, hepatocellular carcinoma, and mesothelioma.
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Zhong, Shanliang, Huanwen Chen, Sujin Yang, Jifeng Feng, and Siying Zhou. "Identification and validation of prognostic signature for breast cancer based on genes potentially involved in autophagy." PeerJ 8 (July 27, 2020): e9621. http://dx.doi.org/10.7717/peerj.9621.
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We aimed to identify prognostic signature based on autophagy-related genes (ARGs) for breast cancer patients. The datasets of breast cancer were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Least absolute shrinkage and selection operator (LASSO) Cox regression was conducted to construct multiple-ARG risk signature. In total, 32 ARGs were identified as differentially expressed between tumors and adjacent normal tissues based on TCGA. Six ARGs (IFNG, TP63, PPP1R15A, PTK6, EIF4EBP1 and NKX2-3) with non-zero coefficient were selected from the 32 ARGs using LASSO regression. The 6-ARG signature divided patients into high-and low-risk group. Survival analysis indicated that low-risk group had longer survival time than high-risk group. We further validated the 6-ARG signature using dataset from GEO and found similar results. We analyzed the associations between ARGs and breast cancer survival in TCGA and nine GEO datasets, and obtained 170 ARGs with significant associations. EIF4EBP1, FOS and FAS were the top three ARGs with highest numbers of significant associations. EIF4EBP1 may be a key ARG which had a higher expression level in patients with more malignant molecular subtypes and higher grade breast cancer. In conclusion, our 6-ARG signature was of significance in predicting of overall survival of patients with breast cancer. EIF4EBP1 may be a key ARG associated with breast cancer survival.
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Tiong, Khong-Loon, Nardnisa Sintupisut, Min-Chin Lin, Chih-Hung Cheng, Andrew Woolston, Chih-Hsu Lin, Mirrian Ho, Yu-Wei Lin, Sridevi Padakanti, and Chen-Hsiang Yeang. "An integrated analysis of the cancer genome atlas data discovers a hierarchical association structure across thirty three cancer types." PLOS Digital Health 1, no. 12 (December 20, 2022): e0000151. http://dx.doi.org/10.1371/journal.pdig.0000151.
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Cancer cells harbor molecular alterations at all levels of information processing. Genomic/epigenomic and transcriptomic alterations are inter-related between genes, within and across cancer types and may affect clinical phenotypes. Despite the abundant prior studies of integrating cancer multi-omics data, none of them organizes these associations in a hierarchical structure and validates the discoveries in extensive external data. We infer this Integrated Hierarchical Association Structure (IHAS) from the complete data of The Cancer Genome Atlas (TCGA) and compile a compendium of cancer multi-omics associations. Intriguingly, diverse alterations on genomes/epigenomes from multiple cancer types impact transcriptions of 18 Gene Groups. Half of them are further reduced to three Meta Gene Groups enriched with (1) immune and inflammatory responses, (2) embryonic development and neurogenesis, (3) cell cycle process and DNA repair. Over 80% of the clinical/molecular phenotypes reported in TCGA are aligned with the combinatorial expressions of Meta Gene Groups, Gene Groups, and other IHAS subunits. Furthermore, IHAS derived from TCGA is validated in more than 300 external datasets including multi-omics measurements and cellular responses upon drug treatments and gene perturbations in tumors, cancer cell lines, and normal tissues. To sum up, IHAS stratifies patients in terms of molecular signatures of its subunits, selects targeted genes or drugs for precision cancer therapy, and demonstrates that associations between survival times and transcriptional biomarkers may vary with cancer types. These rich information is critical for diagnosis and treatments of cancers.
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Lee, Wan-Ping, Jiantao Wu, and Gabor T. Marth. "Toolbox for Mobile-Element Insertion Detection on Cancer Genomes." Cancer Informatics 13s4 (January 2014): CIN.S13979. http://dx.doi.org/10.4137/cin.s13979.
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Mobile elements constitute greater than 45% of the human genome as a result of repeated insertion events during human genome evolution. Although most of mobile elements are fixed within the human population, some elements (including ALU, long interspersed elements (LINE) 1 (L1), and SVA) are still actively duplicating and may result in life-threatening human diseases such as cancer, motivating the need for accurate mobile-element insertion (MEI) detection tools. We developed a software package, TANGRAM, for MEI detection in next-generation sequencing data, currently serving as the primary MEI detection tool in the 1000 Genomes Project. TANGRAM takes advantage of valuable mapping information provided by our own MOSAIK mapper, and until recently required MOSAIK mappings as its input. In this study, we report a new feature that enables TANGRAM to be used on alignments generated by any mainstream short-read mapper, making it accessible for many genomic users. To demonstrate its utility for cancer genome analysis, we have applied TANGRAM to the TCGA (The Cancer Genome Atlas) mutation calling benchmark 4 dataset. TANGRAM is fast, accurate, easy to use, and open source on https://github.com/jiantao/Tangram .
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Lee, Wan-Ping, Jiantao Wu, and Gabor T. Marth. "Toolbox for Mobile-Element Insertion Detection on Cancer Genomes." Cancer Informatics 14s1 (January 2015): CIN.S24657. http://dx.doi.org/10.4137/cin.s24657.
Full textAbstract:
Mobile elements constitute greater than 45% of the human genome as a result of repeated insertion events during human genome evolution. Although most of mobile elements are fixed within the human population, some elements (including ALU, long interspersed elements (LINE) 1 (L1), and SVA) are still actively duplicating and may result in life-threatening human diseases such as cancer, motivating the need for accurate mobile-element insertion (MEI) detection tools. We developed a software package, TANGRAM, for MEI detection in next-generation sequencing data, currently serving as the primary MEI detection tool in the 1000 Genomes Project. TANGRAM takes advantage of valuable mapping information provided by our own MOSAIK mapper, and until recently required MOSAIK mappings as its input. In this study, we report a new feature that enables TANGRAM to be used on alignments generated by any mainstream short-read mapper, making it accessible for many genomic users. To demonstrate its utility for cancer genome analysis, we have applied TANGRAM to the TCGA (The Cancer Genome Atlas) mutation calling benchmark 4 dataset. TANGRAM is fast, accurate, easy to use, and open source on https://github.com/jiantao/Tangram .
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Zhuang, Liping, Yuan Zhang, Zhiqiang Meng, and Zongguo Yang. "Oncogenic Roles of RAD51AP1 in Tumor Tissues Related to Overall Survival and Disease-Free Survival in Hepatocellular Carcinoma." Cancer Control 27, no. 1 (January 1, 2020): 107327482097714. http://dx.doi.org/10.1177/1073274820977149.
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Objective: This study aimed to investigate the associations between RAD51AP1 and the outcomes of hepatocellular carcinoma (HCC). Methods: RAD51AP1 expression levels were compared in Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) datasets. The Liver Hepatocellular Carcinoma (TCGA, Provisional) and GSE36376 datasets were used for survival analysis. RAD51AP1 associations with clinicopathological features were determined with the GSE36376 dataset. Results: RAD51AP1 mRNA expression was significantly upregulated in advanced liver fibrosis samples (S3-4 vs. S0-2 and G3-4 vs. G0-2) from hepatitis B virus (HBV)-related liver fibrosis patients and in tumor tissues and peripheral blood mononuclear cells (PBMCs) from HCC patients (all P < 0.05). HCC patients with high RAD51AP1 expression had significantly worse overall survival (OS) and disease-free survival (DFS) than those with low RAD51AP1 expression ( P = 0.0034 and P = 0.0012, respectively) in the TCGA dataset, and these findings were validated with the GSE36376 dataset ( P = 0.0074 and P = 0.0003, respectively). A Cox regression model indicated that RAD51AP1 was a risk factor for OS and DFS in HCC patients in GSE36376 (HR = 1.54, 95% CI = 1.02-2.32, P = 0.04 and HR = 1.71, 95% CI = 1.22-2.39, P = 0.002, respectively). Moreover, RAD51AP1 mRNA expression increased gradually with increasing tumor stage, including stratification by American Joint Committee on Cancer (AJCC) stages, Barcelona Clinic Liver Cancer (BCLC) stages and Edmondson grades. In addition, RAD51AP1 was overexpressed in HCC patients with intrahepatic metastasis, major portal vein invasion, vascular invasion and/or an alpha-fetoprotein (AFP) level > 300 ng/ml. Conclusions: Contributing to an advanced tumor stage, intrahepatic metastasis, vascular invasion and AFP level elevation, RAD51AP1 upregulation was significantly associated with OS and DFS in HCC patients.
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Wagner, Erin K., Satyajeet Raje, Liz Amos, Jessica Kurata, Abhijit S. Badve, Yingquan Li, and Ben Busby. "Extending TCGA queries to automatically identify analogous genomic data from dbGaP." F1000Research 6 (March 24, 2017): 319. http://dx.doi.org/10.12688/f1000research.9837.1.
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Data sharing is critical to advance genomic research by reducing the demand to collect new data by reusing and combining existing data and by promoting reproducible research. The Cancer Genome Atlas (TCGA) is a popular resource for individual-level genotype-phenotype cancer related data. The Database of Genotypes and Phenotypes (dbGaP) contains many datasets similar to those in TCGA. We have created a software pipeline that will allow researchers to discover relevant genomic data from dbGaP, based on matching TCGA metadata. The resulting research provides an easy to use tool to connect these two data sources.
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Li, Jiao, Si Zheng, Hongyu Kang, Zhen Hou, and Qing Qian. "Identifying Scientific Project-generated Data Citation from Full-text Articles: An Investigation of TCGA Data Citation." Journal of Data and Information Science 1, no. 2 (September 1, 2017): 32–44. http://dx.doi.org/10.20309/jdis.201612.
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AbstractPurposeIn the open science era, it is typical to share project-generated scientific data by depositing it in an open and accessible database. Moreover, scientific publications are preserved in a digital library archive. It is challenging to identify the data usage that is mentioned in literature and associate it with its source. Here, we investigated the data usage of a government-funded cancer genomics project, The Cancer Genome Atlas (TCGA), via a full-text literature analysis.Design/methodology/approachWe focused on identifying articles using the TCGA dataset and constructing linkages between the articles and the specific TCGA dataset. First, we collected 5,372 TCGA-related articles from PubMed Central (PMC). Second, we constructed a benchmark set with 25 full-text articles that truly used the TCGA data in their studies, and we summarized the key features of the benchmark set. Third, the key features were applied to the remaining PMC full-text articles that were collected from PMC.FindingsThe amount of publications that use TCGA data has increased significantly since 2011, although the TCGA project was launched in 2005. Additionally, we found that the critical areas of focus in the studies that use the TCGA data were glioblastoma multiforme, lung cancer, and breast cancer; meanwhile, data from the RNA-sequencing (RNA-seq) platform is the most preferable for use.Research limitationsThe current workflow to identify articles that truly used TCGA data is labor-intensive. An automatic method is expected to improve the performance.Practical implicationsThis study will help cancer genomics researchers determine the latest advancements in cancer molecular therapy, and it will promote data sharing and data-intensive scientific discovery.Originality/valueFew studies have been conducted to investigate data usage by government-funded projects/programs since their launch. In this preliminary study, we extracted articles that use TCGA data from PMC, and we created a link between the full-text articles and the source data.
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Abildgaard, Cecilie, Luisa Matos do Canto, Cláudia Aparecida Rainho, Fabio Albuquerque Marchi, Naiade Calanca, Marianne Waldstrøm, Karina Dahl Steffensen, and Silvia Regina Rogatto. "The Long Non-Coding RNA SNHG12 as a Mediator of Carboplatin Resistance in Ovarian Cancer via Epigenetic Mechanisms." Cancers 14, no. 7 (March 25, 2022): 1664. http://dx.doi.org/10.3390/cancers14071664.
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Genetic and epigenetic changes contribute to intratumor heterogeneity and chemotherapy resistance in several tumor types. LncRNAs have been implicated, directly or indirectly, in the epigenetic regulation of gene expression. We investigated lncRNAs that potentially mediate carboplatin-resistance of cell subpopulations, influencing the progression of ovarian cancer (OC). Four carboplatin-sensitive OC cell lines (IGROV1, OVCAR3, OVCAR4, and OVCAR5), their derivative resistant cells, and two inherently carboplatin-resistant cell lines (OVCAR8 and Ovc316) were subjected to RNA sequencing and global DNA methylation analysis. Integrative and cross-validation analyses were performed using external (The Cancer Genome Atlas, TCGA dataset, n = 111 OC samples) and internal datasets (n = 39 OC samples) to identify lncRNA candidates. A total of 4255 differentially expressed genes (DEGs) and 14529 differentially methylated CpG positions (DMPs) were identified comparing sensitive and resistant OC cell lines. The comparison of DEGs between OC cell lines and TCGA-OC dataset revealed 570 genes, including 50 lncRNAs, associated with carboplatin resistance. Eleven lncRNAs showed DMPs, including the SNHG12. Knockdown of SNHG12 in Ovc316 and OVCAR8 cells increased their sensitivity to carboplatin. The results suggest that the lncRNA SNHG12 contributes to carboplatin resistance in OC and is a potential therapeutic target. We demonstrated that SNHG12 is functionally related to epigenetic mechanisms.
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Kong, Weihao, Xutong Li, Honghai Xu, and Yufeng Gao. "Development and validation of a m6A-related gene signature for predicting the prognosis of hepatocellular carcinoma." Biomarkers in Medicine 14, no. 13 (September 2020): 1217–28. http://dx.doi.org/10.2217/bmm-2020-0178.
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Background: This study aimed to investigate the prognostic role of m6A methylation regulators in hepatocellular carcinoma (HCC). Materials & methods: Gene expression matrices were downloaded from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium, and Gene Expression Omnibus databases. Univariate and multivariate regression analysis were utilized to determine the m6A risk genes. Results: Two m6A-related risk genes (YTHDF1, YTHDF2) were identified in the TCGA HCC cohort. The m6A-correlated risk score is an independent risk factor for the overall survival of the TCGA HCC cohort. Finally, we verified the reliability of our results using three external datasets. Conclusion: The m6A-correlated gene signature has prognostic value in HCC patients and thus provides guidance for the treatment of HCC.
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Ohashi, Schraml, Angori, Batavia, Rupp, Ohe, Otsuki, et al. "Classic Chromophobe Renal Cell Carcinoma Incur a Larger Number of Chromosomal Losses than Seen in the Eosinophilic Subtype." Cancers 11, no. 10 (October 3, 2019): 1492. http://dx.doi.org/10.3390/cancers11101492.
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Chromophobe renal cell carcinoma (chRCC) is a renal tumor subtype with a good prognosis, characterized by multiple chromosomal copy number variations (CNV). The World Health Organization (WHO) chRCC classification guidelines define a classic and an eosinophilic variant. Large cells with reticular cytoplasm and prominent cell membranes (pale cells) are characteristic for classic chRCC. Classic and eosinophilic variants were defined in 42 Swiss chRCCs, 119 Japanese chRCCs and in whole-slide digital images of 66 chRCCs from the Cancer Genome Atlas (TCGA) kidney chromophobe (KICH) dataset. 32 of 42 (76.2%) Swiss chRCCs, 90 of 119 (75.6%) Japanese chRCCs and 53 of 66 (80.3%) TCGA-KICH were classic chRCCs. There was no survival difference between eosinophilic and classic chRCC in all three cohorts. To identify a genotype/phenotype correlation, we performed a genome-wide CNV analysis using Affymetrix OncoScan® CNV Assay (Affymetrix/Thermo Fisher Scientific, Waltham, MA, USA) in 33 Swiss chRCCs. TCGA-KICH subtypes were compared with TCGA CNV data. In the combined Swiss and TCGA-KICH cohorts, losses of chromosome 1, 2, 6, 10, 13, and 17 were significantly more frequent in classic chRCC (p < 0.05, each), suggesting that classic chRCC are characterized by higher chromosomal instability. This molecular difference justifies the definition of two chRCC variants. Absence of pale cells could be used as main histological criterion to define the eosinophilic variant of chRCC.
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Sun, Qingqing, Jun Du, Jie Dong, Shuaikang Pan, Hongwei Jin, Xinghua Han, and Jinguo Zhang. "Systematic Investigation of the Multifaceted Role of SOX11 in Cancer." Cancers 14, no. 24 (December 12, 2022): 6103. http://dx.doi.org/10.3390/cancers14246103.
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SRY-box transcription factor 11 (SOX11), as a member of the SOX family, is a transcription factor involved in the regulation of specific biological processes and has recently been found to be a prognostic marker for certain cancers. However, the roles of SOX11 in cancer remain controversial. Our study aimed to explore the various aspects of SOX11 in pan-cancer. The expression of SOX11 was investigated by the Genotype Tissue-Expression (GTEX) dataset and the Cancer Genome Atlas (TCGA) database. The protein level of SOX11 in tumor tissues and tumor-adjacent tissues was verified by human pan-cancer tissue microarray. Additionally, we used TCGA pan-cancer data to analyze the correlations among SOX11 expression and survival outcomes, clinical features, stemness, microsatellite instability (MSI), tumor mutation burden (TMB), mismatch repair (MMR) related genes and the tumor immune microenvironment. Furthermore, the cBioPortal database was applied to investigate the gene alterations of SOX11. The main biological processes of SOX11 in cancers were analyzed by Gene Set Enrichment Analysis (GSEA). As a result, aberrant expression of SOX11 has been implicated in 27 kinds of cancer types. Aberrant SOX11 expression was closely associated with survival outcomes, stage, tumor recurrence, MSI, TMB and MMR-related genes. In addition, the most frequent alteration of the SOX11 genome was mutation. Our study also showed the correlations of SOX11 with the level of immune infiltration in various cancers. In summary, our findings underline the multifaceted role and prognostic value of SOX11 in pan-cancer.
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Xiao, Huiting, Kun Wang, Dan Li, Ke Wang, and Min Yu. "Evaluation of FGFR1 as a diagnostic biomarker for ovarian cancer using TCGA and GEO datasets." PeerJ 9 (February 3, 2021): e10817. http://dx.doi.org/10.7717/peerj.10817.
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Background Malignant ovarian cancer is associated with the highest mortality of all gynecological tumors. Designing therapeutic targets that are specific to OC tissue is important for optimizing OC therapies. This study aims to identify different expression patterns of genes related to FGFR1 and the usefulness of FGFR1 as diagnostic biomarker for OC. Methods We collected data from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. In the TCGA cohort we analyzed clinical information according to patient characteristics, including age, stage, grade, longest dimension of the tumor and the presence of a residual tumor. GEO data served as a validation set. We obtained data on differentially expressed genes (DEGs) from the two microarray datasets. We then used gene set enrichment analysis (GSEA) to analyze the DEG data in order to identify enriched pathways related to FGFR1. Results Differential expression analysis revealed that FGFR1 was significantly downregulated in OC specimens. 303 patients were included in the TCGA cohort. The GEO dataset confirmed these findings using information on 75 Asian patients. The GSE105437 and GSE12470 database highlighted the significant diagnostic value of FGFR1 in identifying OC (AUC = 1, p = 0.0009 and AUC = 0.8256, p = 0.0015 respectively). Conclusions Our study examined existing TCGA and GEO datasets for novel factors associated with OC and identified FGFR1 as a potential diagnostic factor. Further investigation is warranted to characterize the role played by FGFR1 in OC.
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Wu, Yilin, Eric Zander, Andrew Ardeleanu, Ryan Singleton, and Barnabas Bede. "An Overview of Mathematical Models for RNA Sequence-based Glioblastoma Subclassification." Artificial Intelligence in Oncology 3, no. 1 (June 1, 2021): 001–7. http://dx.doi.org/10.52454/aio.v3i1.11.
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Molecular marker-based glioblastoma (GBM) subclassification is emerging as a key factor in personalized GBM treatment planning. Multiple genetic alterations, including methylation status and mutations, have been proposed in GBM subclassification. RNA-Sequence (RNA-Seq)-based molecular profiling of GBM is widely implemented and readily quantifiable. Machine learning (ML) algorithms have been reported as an applicable method that can consistently subgroup GBM. In this study, we systematically studied the applicability of the commonly used ML algorithms based on The Cancer Genome Atlas Glioblastoma Multiforme (TCGA-GBM) dataset and cross-validated in the Chinese Glioma Genome Atlas (CGGA) dataset. ML algorithms studied include Binomial and multinomial Logistic Regression, Linear discriminant analysis, Decision trees, K-Nearest Neighbors, Gaussian Naive Bayes, Support Vector Machines, Gradient Boosting, Voting Ensemble, Multi-Layer Perceptron. RNA-Seq data of 44 biomarkers were passed through the algorithms for performance evaluation. We found ML algorithms Support Vector Machines, Multi-Layer Perceptron s, and Voting Ensemble are best equipped in assigning GBM to correct molecular subgroups of GBM without histological studies.
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Kim, Dong Hyeok, and Kyung Eun Lee. "Discovering Breast Cancer Biomarkers Candidates through mRNA Expression Analysis Based on The Cancer Genome Atlas Database." Journal of Personalized Medicine 12, no. 10 (October 21, 2022): 1753. http://dx.doi.org/10.3390/jpm12101753.
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Background: Research on the discovery of tumor biomarkers based on big data analysis is actively being conducted. This study aimed to secure foundational data for identifying new biomarkers of breast cancer via breast cancer datasets in The Cancer Genome Atlas (TCGA). Methods: The mRNA profiles of 526 breast cancer and 60 adjacent non-cancerous breast tissues collected from TCGA datasets were analyzed via MultiExperiment Viewer and GraphPad Prism. Diagnostic performance was analyzed by identifying the pathological grades of the selected differentially expressed (DE) mRNAs and the expression patterns of molecular subtypes. Results: Via DE mRNA profile analysis, we selected 14 mRNAs with downregulated expression (HADH, CPN2, ADAM33, TDRD10, SNF1LK2, HBA2, KCNIP2, EPB42, PYGM, CEP68, ING3, EMCN, SYF2, and DTWD1) and six mRNAs with upregulated expression (ZNF8, TOMM40, EVPL, EPN3, AP1M2, and SPINT2) in breast cancer tissues compared to that in non-cancerous tissues (p < 0.001). Conclusions: In total, 20 DE mRNAs had an area under cover of 0.9 or higher, demonstrating excellent diagnostic performance in breast cancer. Therefore, the results of this study will provide foundational data for planning preliminary studies to identify new tumor biomarkers.
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Chen, Ssu-Han, Hong-Han Lin, Yao-Feng Li, Wen-Chiuan Tsai, and Dueng-Yuan Hueng. "Clinical Significance and Systematic Expression Analysis of the Thyroid Receptor Interacting Protein 13 (TRIP13) as Human Gliomas Biomarker." Cancers 13, no. 10 (May 12, 2021): 2338. http://dx.doi.org/10.3390/cancers13102338.
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The prognosis of malignant gliomas such as glioblastoma multiforme (GBM) has remained poor due to limited therapeutic strategies. Thus, it is pivotal to determine prognostic factors for gliomas. Thyroid Receptor Interacting Protein 13 (TRIP13) was found to be overexpressed in several solid tumors, but its role and clinical significance in gliomas is still unclear. Here, we conducted a comprehensive expression analysis of TRIP13 to determine the prognostic values. Gene expression profiles of the Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA) and GSE16011 dataset showed increased TRIP13 expression in advanced stage and worse prognosis in IDH-wild type lower-grade glioma. We performed RT-PCR and Western blot to validate TRIP13 mRNA expression and protein levels in GBM cell lines. TRIP13 co-expressed genes via database screening were regulated by essential cancer-related upstream regulators (such as TP53 and FOXM1). Then, TCGA analysis revealed that more TRIP13 promoter hypomethylation was observed in GBM than in low-grade glioma. We also inferred that the upregulated TRIP13 levels in gliomas could be regulated by dysfunction of miR-29 in gliomas patient cohorts. Moreover, TRIP13-expressing tumors not only had higher aneuploidy but also tended to reduce the ratio of CD8+/Treg, which led to a worse survival outcome. Overall, these findings demonstrate that TRIP13 has with multiple functions in gliomas, and they may be crucial for therapeutic potential.
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Seok, Jungirl, Chang Hwan Ryu, Junsun Ryu, Ji-Hyun Kim, Sang-Jin Lee, Weon Seo Park, and Yuh-Seog Jung. "Prognostic Implication of SOX2 Expression Associated with p16 in Oropharyngeal Cancer: A Study of Consecutive Tissue Microarrays and TCGA." Biology 9, no. 11 (November 9, 2020): 387. http://dx.doi.org/10.3390/biology9110387.
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For oropharyngeal squamous cell carcinoma (OPSCC), there are not enough additional robust biomarkers for subgrouping after the distinct classification using p16. As SOX2 is an emerging biomarker for cancer treatment, its clinical implication in OPSCC was evaluated using a consecutive tissue microarray (TMA) cohort consisting of 111 patients who underwent surgery as an initial treatment from May 2002 to December 2016 and 79 patients in The Cancer Genome Atlas (TCGA) dataset. In both datasets, p16+/SOX2High (HPV+/SOX2High in TCGA) showed the best prognosis among the four groups classified by SOX2 and p16 for 5-year overall survival (OS) and recurrence (all p < 0.05), but SOX2 did not make a significant difference in the prognosis of the p16− group. In the TMA cohort, SOX2High was significantly correlated with response to radiotherapy and lower pathologic T classification in the p16+ group (p = 0.001). In TCGA, correlations between SOX2 and tumor stage classification or radiotherapy were not observed; however, HPV+/SOX2High had a significantly low tumor mutation burden among the four groups (all p < 0.05). In summary, SOX2 was proven to be a potential marker to predict overall survival and recurrence in p16+ OPSCC. However, the role of SOX2 has not yet been confirmed in p16− OPSCC patients.
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Yip, Shun H., Michael Diamreyan, Edmund Wong, Raghuraman Ramamurthy, Sidney Tobias, Hao Cheng, Victor Solovyev, and Cheuk Ying Tang. "Enabling tandem repeat sequence genotype analysis across capture kits." Journal of Clinical Oncology 40, no. 16_suppl (June 1, 2022): e18700-e18700. http://dx.doi.org/10.1200/jco.2022.40.16_suppl.e18700.
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e18700 Background: Previous studies on mutation calling have documented capture kit batch effects in Whole Exome Sequencing (WES) data from The Cancer Genome Atlas (TCGA) database, hindering direct comparison between samples from different capture kits. For example, in classification, a cancer type exclusively sampled by a specific capture kit in the training set would have very low accuracy if the testing set was sampled by another capture kit. To enable cross-capture-kit between-cancer genotype analyses with the TCGA dataset, a novel read count transformation algorithm is developed to remove capture kit batch effects. This algorithm was tested with our Machine Learning model which uses Tandem Repeat Sequence (TRS) mutation markers as training features. Methods: The proposed algorithm transforms TRS read count data to remove low quality samples, read depth differences, and capture kit batch effects from the dataset. Results: 1) TRS read count of WES samples are investigated. Particularly, we show that TRS site read counts do not correlate across capture kits but correlate within capture kits. This suggests that WES read count is largely independent from an exon’s location in the genome and is more strongly correlated with capture kit probes. 2) TRS detection rate for each sample within each capture kit is found to be normally distributed. Outliers with very low TRS detection rate can be used for quality filtering. 3) The transformation algorithm effectively removes capture kit batch effects from the dataset. At the same time, it retains cancer-specific signals in the samples. Before applying the transformation algorithm, cancer type classification accuracy is low (̃0-25%) if the testing data set uses a different capture kit from the training data set. We show that applying the transformation algorithm allows cancer type classification accuracy to improve by over 65%. Conclusions: We demonstrated that direct comparison of WES TRS read count data across capture kits is possible after application of our transformation algorithm. This opens the path to cross-capture-kit between-cancer genotype analyses with the TCGA dataset, which were previously unfeasible due to capture kit batch effects.
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Gao, Chao, Guangxu Jin, Elizabeth Forbes, Lingegowda S. Mangala, Yingmei Wang, Cristian Rodriguez-Aguayo, Paola Amero, et al. "Inactivating Mutations of the IK Gene Weaken Ku80/Ku70-Mediated DNA Repair and Sensitize Endometrial Cancer to Chemotherapy." Cancers 13, no. 10 (May 20, 2021): 2487. http://dx.doi.org/10.3390/cancers13102487.
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IK is a mitotic factor that promotes cell cycle progression. Our previous investigation of 271 endometrial cancer (EC) samples from the Cancer Genome Atlas (TCGA) dataset showed IK somatic mutations were enriched in a cluster of patients with high-grade and high-stage cancers, and this group had longer survival. This study provides insight into how IK somatic mutations contribute to EC pathophysiology. We analyzed the somatic mutational landscape of IK gene in 547 EC patients using expanded TCGA dataset. Co-immunoprecipitation and mass spectrometry were used to identify protein interactions. In vitro and in vivo experiments were used to evaluate IK’s role in EC. The patients with IK-inactivating mutations had longer survival during 10-year follow-up. Frameshift and stop-gain were common mutations and were associated with decreased IK expression. IK knockdown led to enrichment of G2/M phase cells, inactivation of DNA repair signaling mediated by heterodimerization of Ku80 and Ku70, and sensitization of EC cells to cisplatin treatment. IK/Ku80 mutations were accompanied by higher mutation rates and associated with significantly better overall survival. Inactivating mutations of IK gene and loss of IK protein expression were associated with weakened Ku80/Ku70-mediated DNA repair, increased mutation burden, and better response to chemotherapy in patients with EC.
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Mahmood, Tahir, Muhammad Owais, Kyoung Jun Noh, Hyo Sik Yoon, Ja Hyung Koo, Adnan Haider, Haseeb Sultan, and Kang Ryoung Park. "Accurate Segmentation of Nuclear Regions with Multi-Organ Histopathology Images Using Artificial Intelligence for Cancer Diagnosis in Personalized Medicine." Journal of Personalized Medicine 11, no. 6 (June 4, 2021): 515. http://dx.doi.org/10.3390/jpm11060515.
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Accurate nuclear segmentation in histopathology images plays a key role in digital pathology. It is considered a prerequisite for the determination of cell phenotype, nuclear morphometrics, cell classification, and the grading and prognosis of cancer. However, it is a very challenging task because of the different types of nuclei, large intraclass variations, and diverse cell morphologies. Consequently, the manual inspection of such images under high-resolution microscopes is tedious and time-consuming. Alternatively, artificial intelligence (AI)-based automated techniques, which are fast and robust, and require less human effort, can be used. Recently, several AI-based nuclear segmentation techniques have been proposed. They have shown a significant performance improvement for this task, but there is room for further improvement. Thus, we propose an AI-based nuclear segmentation technique in which we adopt a new nuclear segmentation network empowered by residual skip connections to address this issue. Experiments were performed on two publicly available datasets: (1) The Cancer Genome Atlas (TCGA), and (2) Triple-Negative Breast Cancer (TNBC). The results show that our proposed technique achieves an aggregated Jaccard index (AJI) of 0.6794, Dice coefficient of 0.8084, and F1-measure of 0.8547 on TCGA dataset, and an AJI of 0.7332, Dice coefficient of 0.8441, precision of 0.8352, recall of 0.8306, and F1-measure of 0.8329 on the TNBC dataset. These values are higher than those of the state-of-the-art methods.
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Chen, Chaang-Ray, Rong-Shing Chang, and Chi-Shuo Chen. "Identification of Prognostic Genes in Gliomas Based on Increased Microenvironment Stiffness." Cancers 14, no. 15 (July 27, 2022): 3659. http://dx.doi.org/10.3390/cancers14153659.
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With a median survival time of 15 months, glioblastoma multiforme is one of the most aggressive primary brain cancers. The crucial roles played by the extracellular matrix (ECM) stiffness in glioma progression and treatment resistance have been reported in numerous studies. However, the association between ECM-stiffness-regulated genes and the prognosis of glioma patients remains to be explored. Thus, using bioinformatics analysis, we first identified 180 stiffness-dependent genes from an RNA-Seq dataset, and then evaluated their prognosis in The Cancer Genome Atlas (TCGA) glioma dataset. Our results showed that 11 stiffness-dependent genes common between low- and high-grade gliomas were prognostic. After validation using the Chinese Glioma Genome Atlas (CGGA) database, we further identified four stiffness-dependent prognostic genes: FN1, ITGA5, OSMR, and NGFR. In addition to high-grade glioma, overexpression of the four-gene signature also showed poor prognosis in low-grade glioma patients. Moreover, our analysis confirmed that the expression levels of stiffness-dependent prognostic genes in high-grade glioma were significantly higher than in low-grade glioma, suggesting that these genes were associated with glioma progression. Based on a pathophysiology-inspired approach, our findings illuminate the link between ECM stiffness and the prognosis of glioma patients and suggest a signature of four stiffness-dependent genes as potential therapeutic targets.
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Wang, Xue, Chundi Gao, Fubin Feng, Jing Zhuang, Lijuan Liu, Huayao Li, Cun Liu, et al. "Construction and Analysis of Competing Endogenous RNA Networks for Breast Cancer Based on TCGA Dataset." BioMed Research International 2020 (July 24, 2020): 1–10. http://dx.doi.org/10.1155/2020/4078596.
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Background. Long noncoding RNAs (lncRNAs) act as competing endogenous RNAs for microRNAs in cancer metastasis. However, the roles of lncRNA-mediated competing endogenous RNA (ceRNA) networks for breast cancer (BC) are still unclear. Material and Methods. The expression profiles of mRNAs, lncRNAs, and miRNAs with BC were extracted from The Cancer Genome Atlas database. Weighted gene coexpression network analysis was conducted to extract differentially expressed mRNAs (DEmRNAs) that might be core genes. Through miRWalk, TargetScan, and miRDB to predict the target genes, an abnormal lncRNA-miRNA-mRNA ceRNA network with BC was constructed. The survival possibilities of mRNAs, miRNAs, and lncRNAs for patients with BC were determined by Kaplan-Meier survival curves and Oncomine. Results. We identified 2134 DEmRNAs, 1059 differentially expressed lncRNAs (DElncRNAs), and 86 differentially expressed miRNAs (DEmiRNAs). We then compose a ceRNA network for BC, including 72 DElncRNAs, 8 DEmiRNAs, and 12 DEmRNAs. After verification, 2 lncRNAs (LINC00466, LINC00460), 1 miRNA (Hsa-mir-204), and 5 mRNAs (TGFBR2, CDH2, CHRDL1, FGF2, and CHL1) were meaningful as prognostic biomarkers for BC patients. In the ceRNA network, we found that three axes were present in 10 RNAs related to the prognosis of BC, namely, LINC00466-Hsa-mir-204-TGFBR2, LINC00466-Hsa-mir-204-CDH2, and LINC00466-Hsa-mir-204-CHRDL1. Conclusion. This study highlighted lncRNA-miRNA-mRNA ceRNA related to the pathogenesis of BC, which might be used for latent diagnostic biomarkers and therapeutic targets for BC.
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Spakowicz, Daniel, Rebecca Hoyd, Caroline E. Wheeler, Yousef Zakharia, Rebecca D. Dodd, Jennifer Ose, Sheetal Hardikar, et al. "Pan-cancer analysis of exogenous (microbial) sequences in tumor transcriptome data from the ORIEN consortium and their association with cancer and tumor microenvironment." Journal of Clinical Oncology 40, no. 16_suppl (June 1, 2022): 3113. http://dx.doi.org/10.1200/jco.2022.40.16_suppl.3113.
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3113 Background: The tumor microbiome holds great potential for its ability to characterize various aspects of cancer biology and as a target for rational manipulation. For many cancer types, little is known about the role of microbes and in what contexts they affect clinical outcomes. Non-human (i.e. exogenous) sequences can be observed in low abundance within high throughput sequencing data of tumors. Here, we describe a collaboration among members of The Oncology Research Information Exchange Network (ORIEN) to leverage tumor biopsy RNAseq data collected under a shared protocol and generated at a single site to better understand the tumor microbiome, its association with prognostic features of the tumor microenvironment (TME) such as hypoxia, and how it may be used to improve clinical outcomes. Methods: Tumor RNAseq samples from 10 primary source locations including the tissues colon, lung, pancreas, and skin from ORIEN and similar cancers from The Cancer Genome Atlas (TCGA) were processed through the exoTIC (exogenous sequencing in tumors and immune cells) pipeline to identify and count exogenous sequences, filter contaminants, and normalize across datasets. Gene expression signatures of the TME, such as hypoxia, were calculated using ‘tmesig’. Microbe relative abundances were modeled with primary tumor location and hypoxia score using a gamma-distributed generalized linear regression via the stats package in R. Results: We analyzed RNAseq data of 2892 and 2720 tumors from ORIEN and TCGA, respectively. Patients’ ages were significantly greater in the ORIEN than the TCGA dataset (62 vs 58 yo, t-test p<0.001). The ORIEN data contained more sarcoma samples than TCGA (n = 691 vs 259) with roughly equivalent numbers in other cancer types. Fewer microbes were significantly associated with the hypoxia score than with cancer type (n = 32 vs 210). This trend was observed in both the ORIEN and TCGA datasets. The largest effect sizes were observed between microbes and small cell lung cancer. Conclusions: We found microbial sequences in all ORIEN and TCGA tumor RNAseq samples tested. Cancer type showed more significant associations with microbes than a hypoxia signature. These observations merit further investigation into the interaction between microbes and the TME. [Table: see text]
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Zhao, Xiaoyu, Huimin Yan, Xueqing Yan, Zhilin Chen, and Rui Zhuo. "A Novel Prognostic Four-Gene Signature of Breast Cancer Identified by Integrated Bioinformatics Analysis." Disease Markers 2022 (February 27, 2022): 1–17. http://dx.doi.org/10.1155/2022/5925982.
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Molecular analysis facilitates the prediction of overall survival (OS) of breast cancer and decision-making of the treatment plan. The current study was designed to identify new prognostic genes for breast cancer and construct an effective prognostic signature with integrated bioinformatics analysis. Differentially expressed genes in breast cancer samples from The Cancer Genome Atlas (TCGA) dataset were filtered by univariate Cox regression analysis. The prognostic model was optimized by the Akaike information criterion and further validated using the TCGA dataset ( n = 1014 ) and Gene Expression Omnibus (GEO) dataset ( n = 307 ). The correlation between the risk score and clinical information was assessed by univariate and multivariate Cox regression analyses. Functional pathways in relation to high-risk and low-risk groups were analyzed using gene set enrichment analysis (GSEA). Four prognostic genes (EXOC6, GPC6, PCK2, and NFATC2) were screened and used to construct a prognostic model, which showed robust performance in classifying the high-risk and low-risk groups. The risk score was significantly related to clinical features and OS. We identified 19 functional pathways significantly associated with the risk score. This study constructed a new prognostic model with a high prediction performance for breast cancer. The four-gene prognostic signature could serve as an effective tool to predict prognosis and assist the management of breast cancer patients.
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Guo, Qian, Wei He, Dan Nie, Wuzhi Li, and Ping Zhan. "SPP1 is a biomarker of cervical cancer prognosis and involved in immune infiltration." Revista Romana de Medicina de Laborator 30, no. 3 (July 1, 2022): 281–92. http://dx.doi.org/10.2478/rrlm-2022-0028.
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Abstract Background: Cervical cancer is the fourth commonly occurred cancer in women around the world. However, it still lacks effective approaches to improve current prognosis of cervical cancer and prevent metastasis. Objective: We aim to discover a promising biomarker for cervical cancer prognosis by utilizing bioinformatics analysis. Methods: Gene expression was analyzed by the datasets from The Cancer Genome Atlas Program-Cervical squamous cell carcinoma and endocervical adenocarcinoma (TCGA-CESC) dataset and three independent patient cohort datasets. Biological process and pathway enrichment were performed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analysis. Immune infiltration was analyzed through TISIDB tool. Results: SPP1 gene was highly expressed in cervical cancer tissues. In addition, SPP1 was positively correlated to advanced CESC stages and nodal metastasis status. SPP1 co-expressed genes are mainly enriched in immunological processes. Furthermore, SPP1 expression is involved in immune infiltration level, in which several tumour infiltrating lymphocytes are correlated with SPP1. SPP1 overexpression promotes a wide spectrum of chemokines and immunoinhibiors which contribute to CESC progression. Conclusions: SPP1 is a promising biomarker and a prognostic factor of CESC. Tumour infiltrating lymphocytes are also possibly regulated by SPP1. Our study suggests that investigation on SPP1 is a new direction for CESC therapy.