Journal articles: 'Immune-related gene'

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Marta, M., P. Stridh, K. Becanovic, A. Gillett, J. Öckinger, J. C. Lorentzen, M. Jagodic, and T. Olsson. "Multiple loci comprising immune-related genes regulate experimental neuroinflammation." Genes & Immunity 11, no. 1 (August 13, 2009): 21–36. http://dx.doi.org/10.1038/gene.2009.62.

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Bottini, Fulvia Gloria, Neri A, Saccucci P, Manca Bitti M L, Rapini N, Magrini A, and Bottini E. "IMMUNE RELATED DISEASES AND THEIR RELATIONSHIP WITH THE GENETIC VARIABILITY WITHIN THE ADENOSINE DEAMINASE GENE." Journal of Advances In Allergy & Immunologic Diseases 3, no. 1 (2018): 1–8. http://dx.doi.org/10.25177/jaaid.3.1.1.

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Singh, Dhirendra P., Prathyusha Bagam, Malaya K. Sahoo, and Sanjay Batra. "Immune-related gene polymorphisms in pulmonary diseases." Toxicology 383 (May 2017): 24–39. http://dx.doi.org/10.1016/j.tox.2017.03.020.

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Hamidi, Yeganeh, Elaheh Aliasgari, Paria Basimi, Mansour Sajadipour, and Kazem Baesi. "Immune-Related Gene Profile in HIV-Infected Patients with Discordant Immune Response." Iranian Biomedical Journal 26, no. 6 (October 1, 2022): 485–91. http://dx.doi.org/10.52547/ibj.3750.

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Tziastoudi, Maria, Christos Cholevas, Ioannis Stefanidis, and Theoharis C. Theoharides. "Immune-Related Gene Polymorphisms and Pharmacogenetic Studies in Nephrology." Clinical Therapeutics 43, no. 12 (December 2021): 2148–53. http://dx.doi.org/10.1016/j.clinthera.2021.09.020.

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UMEDA, YUKIO. "Inhibition of Immune Responses by Calcitonin Gene-Related Peptide." Annals of the New York Academy of Sciences 657, no. 1 Calcitonin Ge (June 1992): 552–54. http://dx.doi.org/10.1111/j.1749-6632.1992.tb22832.x.

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Wang, Huiling, Shuo You, Meng Fang, and Qian Fang. "Recognition of Immune Microenvironment Landscape and Immune-Related Prognostic Genes in Breast Cancer." BioMed Research International 2020 (November 15, 2020): 1–16. http://dx.doi.org/10.1155/2020/3909416.

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Background. Breast cancer (BC) is the most common malignant tumor in women. The immunophenotype of tumor microenvironment (TME) has shown great therapeutic potential in tumor. Method. The transcriptome was obtained from TCGA and GEO data. Immune infiltration was analyzed by single-sample gene set enrichment (ssGSEA). The immune feature was constructed by Cox regression analysis. In addition, the coexpression of differential expression genes (DEGs) was identified. Through enrichment analysis, the function and pathway of module genes were identified. The somatic mutations related to immune characteristics were analyzed by Maftools. By using the consistency clustering algorithm, the molecular subtypes were constructed, and the overall survival time (OS) was predicted. Results. Immune landscape can be divided into low immune infiltration and high immune infiltration. Cox regression analysis identified seven immune cells as protective factors of BC. In the coexpression modules for DEGs of high and low immune infiltration, module 1 was related to T cells and high immune infiltration. In particular, the area under the curve (AUC) value of hub gene SASH3 was the highest, and the correlation with T cells was stronger in the high immune infiltration. Enrichment analysis found that oxidative stress, T cell aggregation, and apoptosis were observed in high immune infiltration. In addition, TP53 was identified as the most important somatic gene mutation related to immune characteristics. Importantly, we also constructed seven immune cell-based breast cancer subtypes to predict OS. Conclusion. We evaluated the immune landscape of BC and constructed the gene characteristics related to the immune landscape. The potential of T cells and SASH3 in immunotherapy of BC was revealed, which may guide the development of new clinical treatment strategies.

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Stoll, Gautier, David Enot, Bernhard Mlecnik, Jérôme Galon, Laurence Zitvogel, and Guido Kroemer. "Immune-related gene signatures predict the outcome of neoadjuvant chemotherapy." OncoImmunology 3, no. 3 (February 27, 2014): e27884. http://dx.doi.org/10.4161/onci.27884.

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Jiang, Bitao, Qingsen Sun, Yao Tong, Yuzhuo Wang, Haifen Ma, Xuefei Xia, Yu Zhou, Xingguo Zhang, Feng Gao, and Peng Shu. "An immune-related gene signature predicts prognosis of gastric cancer." Medicine 98, no. 27 (July 2019): e16273. http://dx.doi.org/10.1097/md.0000000000016273.

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Liu, K. Y. P., X. J. D. Lu, Y. Zhu, S. Yip, and C. F. Poh. "Altered Immune-Related Gene Expressions Indicate Oral Cancer Nodal Disease." Journal of Dental Research 97, no. 6 (February 28, 2018): 709–16. http://dx.doi.org/10.1177/0022034518758045.

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Lymph nodal disease (LN+) is the most significant prognostic factor of oral squamous cell carcinoma (OSCC). Current risk indicator(s) for guiding elective neck dissection (END) is insufficient for clinically node-negative (cN0) patients, resulting in under- or overtreatment. While the role of immunological events in tumorigenesis and metastasis is evident, the prognostic implication in OSCC remains unclear. The study objective was to investigate large-scale immune-related gene expression and determine its prognostic value on node-free survival (NFS). We analyzed patients who received intent-to-cure surgery with at least 3 y of follow-up and known outcome of LN through a pan-Canadian surgical trial. Total RNA was extracted from surgical tissues with >70% tumor content and analyzed on a 730-gene panel (NanoString nCounter® PanCancer Immune Panel). We first profiled gene expression in a fresh-frozen (FF) discovery set to identify differentially expressed (DE) genes, which were then used in unsupervised clustering analysis to identify patient subgroups. The prognostic value of the identified DE genes was then validated on formalin-fixed, paraffin-embedded (FFPE) samples. A total of 177 RNA samples were derived from 89 FF and 88 FFPE surgical tissues, of which 45 (51%) and 40 (45%), respectively, were from patients who developed LN+. We identified 6 DE genes overexpressed in LN+ tumors (false discovery rate <0.001; log2 fold change >1). Clustering analysis separated the patients into 2 subgroups (CM1, CM2), with CM2 exhibiting significantly increased expression and worse 5-y NFS rate (28%; P < 0.001). The prognostic value of these 6 candidate genes was validated on FFPE samples, which were also separated into 2 distinct prognostic groups, confirming the association between increased gene expression and poor 5-y NFS (CM1, 70.3%; CM2, 43.3%; P = 0.01). This is the first study identifying a panel of immune-related genes associated with NFS that can potentially be used clinically stratifying the risk of LN+ at the time of OSCC diagnosis.

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Mocchegiani, Eugenio, and Marco Malavolta. "Zinc–gene interaction related to inflammatory/immune response in ageing." Genes & Nutrition 3, no. 2 (June 20, 2008): 61–75. http://dx.doi.org/10.1007/s12263-008-0085-2.

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Lin, Jinding, Haifeng Tang, Zhitong Xu, and Rongdong Zeng. "Detection of Immune Microenvironment Changes and Immune-Related Regulators in Langerhans Cell Histiocytosis Bone Metastasis." BioMed Research International 2023 (January 19, 2023): 1–10. http://dx.doi.org/10.1155/2023/1447435.

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The inflammation/immune response pathway is considered a key contributor to the development of Langerhans cell histiocytosis (LCH) bone metastasis. However, the dynamic changes in the immune microenvironment of LCH bone metastasis and critical regulators are still unclear. Expression profiling by arrays of GSE16395, GSE35340, and GSE122476 was applied to detect the immune microenvironment changes in the development of LCH bone metastasis. The single-cell high-throughput sequencing of GSE133704, involved in LCH bone lesions, was analyzed. The online database Metascape and gene set variation analysis (GSVA) algorithms were used to detect the gene function of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). The protein–protein interaction (PPI) network of hub regulators was constructed by the STRING database. In these results, key immune cells, such as Tem cells, NK T cells, CD8(+) T cells, and Th1 cells, were identified in LCH bone metastasis. These genes, which include LAG3, TSPAN5, LPAR5, VEGFA, CXCL16, CD74, and MARCKS, may significantly correlate with the cellular infiltration of B cells, aDCs, pDCs, cytotoxic cells, T cells, CD8+ T cells, T helper cells, and Tcm cells. In conclusion, our study constructed an atlas of the immune microenvironment of LCH bone metastasis. Genes including LAG3, TSPAN5, LPAR5, VEGFA, CXCL16, CD74, and MARCKS may be involved in the development of LCH bone metastasis. The hub gene-immune cell interactive map may be a potential prognostic biomarker for the progression of LCH bone metastasis and synergetic targets for immunotherapy in LCH patients.

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Xu, Lin, Xiaoyan Su, Zhongcheng Liu, and Aihong Zhou. "Predicted Immune-Related Genes and Subtypes in Systemic Lupus Erythematosus Based on Immune Infiltration Analysis." Disease Markers 2022 (July 12, 2022): 1–25. http://dx.doi.org/10.1155/2022/8911321.

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Objective. The present investigation is aimed at identifying key immune-related genes linked with SLE and their roles using integrative analysis of publically available gene expression datasets. Methods. Four gene expression datasets pertaining to SLE, 2 from whole blood and 2 experimental PMBC, were sourced from GEO. Shared differentially expressed genes (DEG) were determined as SLE-related genes. Immune cell infiltration analysis was performed using CIBERSORT, and case samples were subjected to k -means cluster analysis using high-abundance immune cells. Key immune-related SLE genes were identified using correlation analysis with high-abundance immune cells and subjected to functional enrichment analysis for enriched Gene Ontology Biological Processes and KEGG pathways. A PPI network of genes interacting with the key immune-related SLE genes was constructed. LASSO regression analysis was performed to identify the most significant key immune-related SLE genes, and correlation with clinicopathological features was examined. Results. 309 SLE-related genes were identified and found functionally enriched in several pathways related to regulation of viral defenses and T cell functions. k -means cluster analysis identified 2 sample clusters which significantly differed in monocytes, dendritic cell resting, and neutrophil abundance. 65 immune-related SLE genes were identified, functionally enriched in immune response-related signaling, antigen receptor-mediated signaling, and T cell receptor signaling, along with Th17, Th1, and Th2 cell differentiation, IL-17, NF-kappa B, and VEGF signaling pathways. LASSO regression identified 9 key immune-related genes: DUSP7, DYSF, KCNA3, P2RY10, S100A12, SLC38A1, TLR2, TSR2, and TXN. Imputed neutrophil percentage was consistent with their expression pattern, whereas anti-Ro showed the inverse pattern as gene expression. Conclusions. Comprehensive bioinformatics analyses revealed 9 key immune-related genes and their associated functions highly pertinent to SLE pathogenesis, subtypes, and identified valuable candidates for experimental research.

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Ma, Chao, Feng Li, Ziming Wang, and Huan Luo. "A Novel Immune-Related Gene Signature Predicts Prognosis of Lung Adenocarcinoma." BioMed Research International 2022 (April 9, 2022): 1–16. http://dx.doi.org/10.1155/2022/4995874.

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Background. Lung adenocarcinoma (LUAD) is the most common form of lung cancer, accounting for 30% of all cases and 40% of all non-small-cell lung cancer cases. Immune-related genes play a significant role in predicting the overall survival and monitoring the status of the cancer immune microenvironment. The present study was aimed at finding an immune-related gene signature for predicting LUAD patient outcomes. Methods. First, we chose the TCGA-LUAD project in the TCGA database as the training cohort for model training. For model validating, we found the datasets of GSE72094 and GSE68465 in the GEO database and took them as the candidate cohorts. We obtained 1793 immune-related genes from the ImmPort database and put them into a univariate Cox proportional hazard model to initially look for the genes with potential prognostic ability using the data of the training cohort. These identified genes then entered into a random survival forests-variable hunting algorithm for the best combination of genes for prognosis. In addition, the LASSO Cox regression model tested whether the gene combination can be further shrinkage, thereby constructing a gene signature. The Kaplan-Meier, Cox model, and ROC curve were deployed to examine the gene signature’s prognosis in both cohorts. We conducted GSEA analysis to study further the mechanisms and pathways that involved the gene signature. Finally, we performed integrating analyses about the 22 TICs, fully interpreted the relationship between our signature and each TIC, and highlighted some TICs playing vital roles in the signature’s prognostic ability. Results. A nine-gene signature was produced from the data of the training cohort. The Kaplan-Meier estimator, Cox proportional hazard model, and ROC curve confirmed the independence and predictive ability of the signature, using the data from the validation cohort. The GSEA analysis results illustrated the gene signature’s mechanism and emphasized the importance of immune-related pathways for the gene signature. 22 TICs immune infiltration analysis revealed resting mast cells’ key roles in contributing to gene signature’s prognostic ability. Conclusions. This study discovered a novel immune-related nine-gene signature (BTK, CCR6, S100A10, SEMA3C, GPI, SCG2, TNFRSF11A, CCL20, and DKK1) that predicts LUAD prognosis precisely and associates with resting mast cells strongly.

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Childers, Eva, Elijah F. W. Bowen, C. Harker Rhodes, and Richard Granger. "Immune-Related Genomic Schizophrenic Subtyping Identified in DLPFC Transcriptome." Genes 13, no. 7 (July 4, 2022): 1200. http://dx.doi.org/10.3390/genes13071200.

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Well-documented evidence of the physiologic, genetic, and behavioral heterogeneity of schizophrenia suggests that diagnostic subtyping may clarify the underlying pathobiology of the disorder. Recent studies have demonstrated that increased inflammation may be a prominent feature of a subset of schizophrenics. However, these findings are inconsistent, possibly due to evaluating schizophrenics as a single group. In this study, we segregated schizophrenic patients into two groups (“Type 1”, “Type 2”) by their gene expression in the dorsolateral prefrontal cortex and explored biological differences between the subgroups. The study included post-mortem tissue samples that were sequenced in multiple, publicly available gene datasets using different sequencing methods. To evaluate the role of inflammation, the expression of genes in multiple components of neuroinflammation were examined: complement cascade activation, glial cell activation, pro-inflammatory mediator secretion, blood–brain barrier (BBB) breakdown, chemokine production and peripheral immune cell infiltration. The Type 2 schizophrenics showed widespread abnormal gene expression across all the neuroinflammation components that was not observed in Type 1 schizophrenics. Our results demonstrate the importance of separating schizophrenic patients into their molecularly defined subgroups and provide supporting evidence for the involvement of the immune-related pathways in a schizophrenic subset.

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Taylor, Jude M., Amy Li, and Craig S. McLachlan. "Immune cell profile and immune‐related gene expression of obese peripheral blood and liver tissue." FEBS Letters 596, no. 2 (December 8, 2021): 199–210. http://dx.doi.org/10.1002/1873-3468.14248.

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Xiong, Hui, Hui Gao, Jinding Hu, Yun Dai, Hanbo Wang, Min Fu, Taiguo Qi, et al. "Prognostic Implications of Immune-Related Gene Pairs Signatures in Bladder Cancer." Journal of Oncology 2021 (July 26, 2021): 1–20. http://dx.doi.org/10.1155/2021/5345181.

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Compelling evidence indicates that immune function is correlated with the prognosis of bladder cancer (BC). Here, we aimed to develop a clinically translatable immune-related gene pairs (IRGPs) prognostic signature to estimate the overall survival (OS) of bladder cancer. From the 251 prognostic-related IRGPs, 37 prognostic-related IRGPs were identified using LASSO regression. We identified IRGPs with the potential to be prognostic markers. The established risk scores divided BC patients into high and low risk score groups, and the survival analysis showed that risk score was related to OS in the TCGA-training set ( p < 0.001 ; HR = 7.5 [5.3, 10]). ROC curve analysis showed that the AUC for the 1-year, 3-year, and 5-year follow-up was 0.820, 0.883, and 0.879, respectively. The model was verified in the TCGA-testing set and external dataset GSE13507. Multivariate analysis showed that risk score was an independent prognostic predictor in patients with BC. In addition, significant differences were found in gene mutations, copy number variations, and gene expression levels in patients with BC between the high and low risk score groups. Gene set enrichment analysis showed that, in the high-risk score group, multiple immune-related pathways were inhibited, and multiple mesenchymal phenotype-related pathways were activated. Immune infiltration analysis revealed that immune cells associated with poor prognosis of BC were upregulated in the high-risk score group, whereas immune cells associated with a better prognosis of BC were downregulated in the high-risk score group. Other immunoregulatory genes were also differentially expressed between high and low risk score groups. A 37 IRGPs-based risk score signature is presented in this study. This signature can efficiently classify BC patients into high and low risk score groups. This signature can be exploited to select high-risk BC patients for more targeted treatment.

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Takahashi, Naoki, Yutaka Matano, Akihiko Tsujibata, Kozue Murayama, Shoichi Miyazawa, TOMOHIRO MATSUSHIMA, Satoshi Shimizu, et al. "Comparison of immune-related gene expression between primary and metastatic site in advanced gastric cancer patients with peritoneal dissemination." Journal of Clinical Oncology 38, no. 4_suppl (February 1, 2020): 423. http://dx.doi.org/10.1200/jco.2020.38.4_suppl.423.

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423 Background: Immune checkpoints inhibitor (ICI) is effective and approved in some solid tumors including advanced gastric cancer (GC). Peritoneal dissemination is known as a poor prognostic factor and was reported to be associated with the resistance to ICI according to the previous reports. The aim of our study is to compare the immune-related gene expression between primary site and peritoneal lesion in advanced GC patients. Methods: Among advanced GC patients, we selected those who underwent surgical resection for both primary and peritoneal lesions simultaneously. Formalin-fixed paraffin-embedded (FFPE) tumor tissues of primary and peritoneal lesions were prepared and RNA was extracted by Maxwell RSC RNA FFPE kit (Promega). Immune-related gene expression was evaluated by using nCounter Max Analysis System (NanoString). We used nCounter PanCancer Immune Profiling Panel Kit which includes 770 immune-related genes. Results: Immune-related gene expression was evaluated by using twenty-four FFPE tumor tissues in twelve GC patients. Scatter plot and hierarchical clustering analyses showed that the pattern of immune-related gene expression was not much different between primary and peritoneal lesions beyond the individual differences. Regarding the T cell function, high expression of Immune-related genes was widely detected in patients with EB virus-positive (n = 2) and HER2-positive (n = 1). Gene expressions such as CD70, FAS, MAF and IL-3 were higher in peritoneal lesion compared with primary lesion (p < 0.05). Whereas, expressions of F2RL1 and IL-11 were lower in peritoneal lesion compared with primary lesion (p < 0.05). Conclusions: Our study indicated that there was not much difference of Immune-related gene expression between primary and peritoneal lesion in advanced GC patients. Positive status of EB virus and HER2 may be associated with high expression of immune-related genes. Further analysis to evaluate immune-related gene expression between primary and metastatic site may contribute the further understanding of cancer immunity in advanced GC.

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Trejo Flores, Jose, Antonio Luna Gonzalez, Pindaro Alvarez Ruiz, Ruth Escamilla Montes, Jesus Fierro Coronado, Viridiana Peraza Gomez, Maria Flores Miranda, Genaro Diarte Plata, and Arturo Rubio Castro. "Immune related gene expression expression in Penaeus vannamei fed Aloe vera." Latin American Journal of Aquatic Research 46, no. 4 (September 10, 2018): 756–64. http://dx.doi.org/10.3856/vol46-issue4-fulltext-13.

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Zhao, Jianli, Ying Wang, Zengding Lao, Siting Liang, Jingyi Hou, Yunfang Yu, Herui Yao, Na You, and Kai Chen. "Prognostic immune-related gene models for breast cancer: a pooled analysis." OncoTargets and Therapy Volume 10 (September 2017): 4423–33. http://dx.doi.org/10.2147/ott.s144015.

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Shen, Bingbing, Guanqi Zhang, Yunxun Liu, Jianguo Wang, and Jianxin Jiang. "Identification and Analysis of Immune-Related Gene Signature in Hepatocellular Carcinoma." Genes 13, no. 10 (October 11, 2022): 1834. http://dx.doi.org/10.3390/genes13101834.

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Background: Hepatocellular carcinoma (HCC) originates from the hepatocytes and accounts for 90% of liver cancer. The study intends to identify novel prognostic biomarkers for predicting the prognosis of HCC patients based on TCGA and GSE14520 cohorts. Methods: Differential analysis was employed to obtain the DEGs (Differentially Expressed Genes) of the TCGA-LIHC-TPM cohort. The lasso regression analysis was applied to build the prognosis model through using the TCGA cohort as the training group and the GSE14520 cohort as the testing group. Next, based on the prognosis model, we performed the following analyses: the survival analysis, the independent prognosis analysis, the clinical feature analysis, the mutation analysis, the immune cell infiltration analysis, the tumor microenvironment analysis, and the drug sensitivity analysis. Finally, the survival time of HCC patients was predicted by constructing nomograms. Results: Through the lasso regression analysis, we obtained a prognosis model of ten genes including BIRC5 (baculoviral IAP repeat containing 5), CDK4 (cyclin-dependent kinase 4), DCK (deoxycytidine kinase), HSPA4 (heat shock protein family A member 4), HSP90AA1 (heat shock protein 90 α family class A member 1), PSMD2 (Proteasome 26S Subunit Ubiquitin Receptor, Non-ATPase 2), IL1RN (interleukin 1 receptor antagonist), PGF (placental growth factor), SPP1 (secreted phosphoprotein 1), and STC2 (stanniocalcin 2). First, we found that the risk score is an independent prognosis factor and is related to the clinical features of HCC patients, covering AFP (α-fetoprotein) and stage. Second, we observed that the p53 mutation was the most obvious mutation between the high-risk and low-risk groups. Third, we also discovered that the risk score is related to some immune cells, covering B cells, T cells, dendritic, macrophages, neutrophils, etc. Fourth, the high-risk group possesses a lower TIDE score, a higher expression of immune checkpoints, and higher ESTIMATE score. Finally, nomograms include the clinical features and risk signatures, displaying the clinical utility of the signature in the survival prediction of HCC patients. Conclusions: Through the comprehensive analysis, we constructed an immune-related prognosis model to predict the survival of HCC patients. In addition to predicting the survival time of HCC patients, this model significantly correlates with the tumor microenvironment. Furthermore, we concluded that these ten immune-related genes (BIRC5, CDK4, DCK, HSPA4, HSP90AA1, PSMD2, IL1RN, PGF, SPP1, and STC2) serve as novel targets for antitumor immunity. Therefore, this study plays a significant role in exploring the clinical application of immune-related genes.

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Ren, Haoyu, Alexandr V. Bazhin, Elise Pretzsch, Sven Jacob, Haochen Yu, Jiang Zhu, Markus Albertsmeier, et al. "A novel immune-related gene signature predicting survival in sarcoma patients." Molecular Therapy - Oncolytics 24 (March 2022): 114–26. http://dx.doi.org/10.1016/j.omto.2021.12.007.

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Narverud, I., J. J. Christensen, S. S. Bakke, S. M. Ulven, A. Rundblad, P. Aukrust, T. Espevik, et al. "Profiling of immune‐related gene expression in children with familial hypercholesterolaemia." Journal of Internal Medicine 287, no. 3 (November 12, 2019): 310–21. http://dx.doi.org/10.1111/joim.13001.

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Li, Ju, Sai Ma, Qi Feng, Ming Hou, and Jun Peng. "Inflammation-Related Gene Polymorphisms Associated with Susceptibility to Primary Immune Thrombocytopenia." Blood 128, no. 22 (December 2, 2016): 3737. http://dx.doi.org/10.1182/blood.v128.22.3737.3737.

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Abstract INTRODUCTION Primary immune thrombocytopenia (ITP) is an acquired autoimmune disease characterized by a reduced platelet count and an increased risk of bleeding. Although immense research has improved our understanding of ITP and led to impressive therapeutics, the pathogenesis is still not completely clear and a large subset of patients are refractory to first- and second-line therapies.In this study, we investigated the involvement of a cluster of inflammation-related genes, including CD24, FCRL3, CD226, IL2, IRF5, ITGAM, NLRP3, CARD8, PTPN22, SH2B3, STAT4, TNFAIP3, and TRAF1 genes. METHODS In this case-control study, 261 ITP inpatients (92 males and 169 females) with a mean age of 39.59 ± 17.52 years were recruited from Qilu Hospital, Shandong University between January 2007 to April 2016. In addition, 154 healthy participants (60 males and 94 females) with a mean age of 44.82 ± 13.63 years were enrolled as a control group. Inflammation-related single nucleotide polymorphism (SNP) genotyping was performed on the Sequenom MassARRAY iPLEX platform. SNPs included CD24 rs8734/rs52812045, FCRL3 rs11264799, rs7528684, rs945635, rs3761959, CD226 rs763361, IL2 rs6822844, IRF5 rs2004640, rs2280714, rs10954213, ITGAM rs1143679, NLRP3 rs35829419, rs4353135, rs10754558, CARD8 rs2043211, PTPN22 rs33996649, rs1310182, SH2B3 rs3184504, STAT4 rs7574865, rs10181656, TNFAIP3 rs6920220, rs10499194, rs2230926, rs5029939, TRAF1 rs10818488. SPSS (Ver 22.0, Chicago, IL) was used to perform statistical analysis. The results of genotyping were analyzed with t or ¦Ö2 tests. A Fisher's exact test was performed when expected frequencies were less than 5. Univariate and multivariate analyses were then performed by non-conditional logistic regression adjusting for age and gender. P values < 0.05 were considered significant. The two-tailed significance level was set at 0.05. RESULTS Genotype frequencies of these inflammation-related SNPs in healthy controls were consistent with the Hardy-Weinberg equilibrium (p > 0.05). Genotype distributions in ITP patients and controls are detailed in Table 1. Statistical analysis revealed a significant difference in TNFAIP3 rs10499194 and CD24 rs52812045 between patients and controls (Table 1). Genotype frequencies of other SNPs were not significantly different (p > 0.05). However, when allelic frequencies were compared, significant differences were not found in any genotyped SNPs (p > 0.05). ITP is classified by duration into newly diagnosed (less than 3-month duration), persistent (3- to 12-month duration) and chronic (1-year duration). According to ordinal logistic regression, the genotype frequency of IRF5 rs2004640 was significantly different between the 3 ITP phases. Compared with heterozygous carriers, homozygous carriers of major allele were found in higher frequency in newly diagnosed patients. The OR for GG vs. GT was 0.472 (95%CI = 0.289-0.770, p = 0.003). Patients with clinically relevant bleeding who require mandated treatment or additional therapeutic intervention are defined as having severe ITP. The inflammation-related SNPs were not significantly different between severe and non-severe patients (p > 0.05). Interestingly, genotype frequency distribution of CARD8 rs2043211 was significantly higher in refractory patients than in non-refractory patients (OR = 4.191, 95%CI = 1.026-14.567, p = 0.040), while allelic frequencies were not significantly different. We also compared corticosteroid-sensitive and corticosteroid-resistant ITP patients. We found that the genotype frequency of NLRP3 rs4353135 was significantly different between the two groups, and heterozygous genotype was more frequent in corticosteroid-sensitive patients (OR = 0.413, 95%CI = 0.203-0.841, p = 0.015). No significant difference was observed in other polymorphisms. CONCLUSIONS Our study identifies SNPs that differ based on the presence of ITP (TNFAIP3 rs10499194 and CD24 rs52812045), the phase of ITP (IRF5 rs2004640), refractoriness (CARD8 rs2043211), and corticosteroid sensitivity (NLRP3 rs4353135) in Chinese Han population. These important findings may lead to more tailored treatment strategies for ITP patients. Further study will dissect how these SNPs exert their roles to affect the pathogenesis of ITP. Disclosures No relevant conflicts of interest to declare.

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Li, Jun, Yinglun Han, Ting Zhu, Yue Pang, and Qingwei Li. "Immune-related gene expression in the early development of lamprey larva." Acta Biochimica et Biophysica Sinica 50, no. 9 (July 19, 2018): 938–40. http://dx.doi.org/10.1093/abbs/gmy083.

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Kaufman, Jim. "Innate immune genes of the chicken MHC and related regions." Immunogenetics 74, no. 1 (October 26, 2021): 167–77. http://dx.doi.org/10.1007/s00251-021-01229-2.

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AbstractCompared to the major histocompatibility complex (MHC) of typical mammals, the chicken BF/BL region is small and simple, with most of the genes playing central roles in the adaptive immune response. However, some genes of the chicken MHC are almost certainly involved in innate immunity, such as the complement component C4 and the lectin-like receptor/ligand gene pair BNK and Blec. The poorly expressed classical class I molecule BF1 is known to be recognised by natural killer (NK) cells and, analogous to mammalian immune responses, the classical class I molecules BF1 and BF2, the CD1 homologs and the butyrophilin homologs called BG may be recognised by adaptive immune lymphocytes with semi-invariant receptors in a so-called adaptate manner. Moreover, the TRIM and BG regions next to the chicken MHC, along with the genetically unlinked Y and olfactory/scavenger receptor regions on the same chromosome, have multigene families almost certainly involved in innate and adaptate responses. On this chicken microchromosome, the simplicity of the adaptive immune gene systems contrasts with the complexity of the gene systems potentially involved in innate immunity.

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Hao, Zhiquan, Siqiao Wang, Zixuan Zheng, Jiehan Li, Wanting Fu, Donglin Han, Yinrou Huang, et al. "Prognostic Bone Metastasis-Associated Immune-Related Genes Regulated by Transcription Factors in Mesothelioma." BioMed Research International 2022 (January 27, 2022): 1–26. http://dx.doi.org/10.1155/2022/9940566.

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Mesothelioma (MESO) is a mesothelial originate neoplasm with high morbidity and mortality. Despite advancement in technology, early diagnosis still lacks effectivity and is full of pitfalls. Approaches of cancer diagnosis and therapy utilizing immune biomarkers and transcription factors (TFs) have attracted more and more attention. But the molecular mechanism of these features in MESO bone metastasis has not been thoroughly studied. Utilizing high-throughput genome sequencing data and lists of specific gene subsets, we performed several data mining algorithm. Single-sample Gene Set Enrichment Analysis (ssGSEA) was applied to identify downstream immune cells. Potential pathways involved in MESO bone metastasis were identified using Gene Oncology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, Gene Set Variation Analysis (GSVA), Gene Set Enrichment Analysis (GSEA), and Cox regression analysis. Ultimately, a model to help early diagnosis and to predict prognosis was constructed based on differentially expressed immune-related genes between bone metastatic and nonmetastatic MESO groups. In conclusion, immune-related gene SDC2, regulated by TFs TCF7L1 and POLR3D, had an important role on immune cell function and infiltration, providing novel biomarkers and therapeutic targets for metastatic MESO.

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Deng, Feng, Feng Tao, Zhili Xu, Jun Zhou, Xiaowei Gong, and Ruhu Zhang. "Construction of Prognostic Risk Model for Small Cell Lung Cancer Based on Immune-Related Genes." Computational and Mathematical Methods in Medicine 2022 (September 30, 2022): 1–20. http://dx.doi.org/10.1155/2022/7116080.

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Small cell lung cancer (SCLC) is a highly invasive and fatal malignancy. Research at the present stage implied that the expression of immune-related genes is associated with the prognosis in SCLC. Accordingly, it is essential to explore effective immune-related molecular markers to judge prognosis and treat SCLC. Our research obtained SCLC dataset from Gene Expression Omnibus (GEO) and subjected mRNAs in it to differential expression analysis. Differentially expressed mRNAs (DEmRNAs) were intersected with immune-related genes to yield immune-related differentially expressed genes (DEGs). The functions of these DEGs were revealed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Thereafter, we categorized 3 subtypes of immune-related DEGs via K-means clustering. Kaplan-Meier curves analyzed the effects of 3 subtypes on SCLC patients’ survival. Single-sample gene set enrichment analysis (ssGSEA) and ESTIMATE validated that the activation of different immune gene subtypes differed significantly. Finally, an immune-related-7-gene assessment model was constructed by univariate-Lasso-multiple Cox regression analyses. Riskscores, Kaplan-Meier curves, receiver operating characteristic (ROC) curves, and independent prognostic analyses validated the prognostic value of the immune-related-7-gene assessment model. As suggested by GSEA, there was a prominent difference in cytokine-related pathways between high- and low-risk groups. As the analysis went further, we discovered a statistically significant difference in the expression of human leukocyte antigen (HLA) proteins and costimulatory molecules expressed on the surface of CD274, CD152, and T lymphocytes in different groups. In a word, we started with immune-related genes to construct the prognostic model for SCLC, which could effectively evaluate the clinical outcomes and offer guidance for the treatment and prognosis of SCLC patients.

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Hijikata, M., I. Matsushita, N. T. Le Hang, P. H. Thuong, D. B. Tam, S. Maeda, S. Sakurada, V. C. Cuong, L. T. Lien, and N. Keicho. "Influence of the polymorphism of the DUSP14 gene on the expression of immune-related genes and development of pulmonary tuberculosis." Genes & Immunity 17, no. 4 (March 3, 2016): 207–12. http://dx.doi.org/10.1038/gene.2016.11.

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Zhang, Li, Jason Cham, James Cooley, Tao He, Katsunobu Hagihara, Hai Yang, Frances Fan, et al. "Cross-platform comparison of immune-related gene expression to assess intratumor immune responses following cancer immunotherapy." Journal of Immunological Methods 494 (July 2021): 113041. http://dx.doi.org/10.1016/j.jim.2021.113041.

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Jiang, Pan, Yanli Li, Zheng Xu, and Shengteng He. "A signature of 17 immune-related gene pairs predicts prognosis and immune status in HNSCC patients." Translational Oncology 14, no. 1 (January 2021): 100924. http://dx.doi.org/10.1016/j.tranon.2020.100924.

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Xu, Ying. "Immune-Related Gene Spatzle4 and Its Differential Immune Responses against Microbes in the Silkworm, Bombyx Mori." American Journal of Clinical and Experimental Medicine 3, no. 6 (2015): 344. http://dx.doi.org/10.11648/j.ajcem.20150306.14.

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Colborn, James M., Joni H. Ylöstalo, Ousmane A. Koita, Ousmane H. Cissé, and Donald J. Krogstad. "Human Gene Expression in UncomplicatedPlasmodium falciparumMalaria." Journal of Immunology Research 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/162639.

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To examine human gene expression during uncomplicatedP. falciparummalaria, we obtained three samples (acute illness, treatment, and recovery) from 10 subjects and utilized each subject’s recovery sample as their baseline. At the time of acute illness (day 1), subjects had upregulation of innate immune response, cytokine, and inflammation-related genes (IL-1β, IL-6, TNF, and IFN-γ), which was more frequent with parasitemias>100,000 perμL and body temperatures≥39∘C. Apoptosis-related genes (Fas, BAX, and TP53) were upregulated acutely and for several days thereafter (days 1–3). In contrast, the expression of immune-modulatory (transcription factor 7, HLV-DOA, and CD6) and apoptosis inhibitory (c-myc, caspase 8, and Fas Ligand G) genes was downregulated initially and returned to normal with clinical recovery (days 7–10). These results indicate that the innate immune response, cytokine, and apoptosis pathways are upregulated acutely in uncomplicated malaria with concomitant downregulation of immune-modulatory and apoptosis inhibitory genes.

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Giotta Lucifero, Alice, and Sabino Luzzi. "Immune Landscape in PTEN-Related Glioma Microenvironment: A Bioinformatic Analysis." Brain Sciences 12, no. 4 (April 14, 2022): 501. http://dx.doi.org/10.3390/brainsci12040501.

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Introduction: PTEN gene mutations are frequently found in the genetic landscape of high-grade gliomas since they influence cell proliferation, proangiogenetic pathways, and antitumoral immune response. The present bioinformatics analysis explores the PTEN gene expression profile in HGGs as a prognostic factor for survival, especially focusing on the related immune microenvironment. The effects of PTEN mutation on the susceptibility to conventional chemotherapy were also investigated. Methods: Clinical and genetic data of GBMs and normal tissue samples were acquired from The Cancer Genome Atlas (TCGA)-GBM and Genotype-Tissue Expression (GTEx) online databases, respectively. The genetic differential expressions were analyzed in both groups via the one-way ANOVA test. Kaplan–Meier survival curves were applied to estimate the overall survival (OS) and disease-free survival (DFS). The Genomics of Drug Sensitivity in Cancer platform was chosen to assess the response of PTEN-mutated GBMs to temozolomide (TMZ). p < 0.05 was fixed as statistically significant. On Tumor Immune Estimation Resource and Gene Expression Profiling Interactive Analysis databases, the linkage between immune cell recruitment and PTEN status was assessed through Spearman’s correlation analysis. Results: PTEN was found mutated in 22.2% of the 617 TCGA-GBMs patients, with a higher log2-transcriptome per million reads compared to the GTEx group (255 samples). Survival curves revealed a worse OS and DFS, albeit not significant, for the high-PTEN profile GBMs. Spearman’s analysis of immune cells demonstrated a strong positive correlation between the PTEN status and infiltration of Treg (ρ = 0.179) and M2 macrophages (ρ = 0.303). The half-maximal inhibitor concentration of TMZ was proven to be lower for PTEN-mutated GBMs compared with PTEN wild-types. Conclusions: PTEN gene mutations prevail in GBMs and are strongly related to poor prognosis and least survival. The infiltrating immune lymphocytes Treg and M2 macrophages populate the glioma microenvironment and control the mechanisms of tumor progression, immune escape, and sensitivity to standard chemotherapy. Broader studies are required to confirm these findings and turn them into new therapeutic perspectives.

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Xia, Fei, Zhilong Yu, Aijun Deng, and Guohong Gao. "Identification of molecular subtyping system and four-gene prognostic signature with immune-related genes for uveal melanoma." Experimental Biology and Medicine 247, no. 3 (November 7, 2021): 246–62. http://dx.doi.org/10.1177/15353702211053801.

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Immunotherapy is the most promising treatment for uveal melanoma patients with metastasis. Tumor microenvironment plays an essential role in tumor progression and greatly affects the efficacy of immunotherapy. This research constructed an immune-related subtyping system and discovered immune prognostic genes to further understand the immune mechanism in uveal melanoma. Immune-related genes were determined from literature. Gene expression profiles of uveal melanoma were clustered using consensus clustering based on immune-related genes. Subtypes were further divided by applying immune landscape, and weighted correlation network analysis was performed to construct immune gene modules. Univariate Cox regression analysis was conducted to generate a prognostic model. Enriched immune cells were determined after gene set enrichment analysis. Three major immune subtypes (IS1, IS2, and IS3) were identified, and IS2 could be further divided into IS2A and IS2B. The subtypes were closely associated with uveal melanoma prognosis. IS3 group had the most favorable prognosis and was sensitive to PD-1 inhibitor. Immune genes in IS1 group showed an overall higher expression than IS3 group. Six immune gene modules were identified, and the enrichment score of immune genes varied within immune subtypes. Four immune prognostic genes ( IL32, IRF1, SNX20, and VAV1) were found to be closely related to survival. This novel immune subtyping system and immune landscape provide a new understanding of immunotherapy in uveal melanoma. The four prognostic genes can predict prognosis of uveal melanoma patients and contribute to new development of targeted drugs.

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Prioli, R. A., R. A. Curi, L. A. Chardulo, V. N. Gomes, S. M. A. P. Prioli, and M. D. S. Mota. "Characterization of gene polymorphisms related to immune system physiology in Mangalarga horses." Arquivo Brasileiro de Medicina Veterinária e Zootecnia 64, no. 5 (October 2012): 1302–8. http://dx.doi.org/10.1590/s0102-09352012000500030.

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The objectives of this study were to standardize a PCR-RFLP genotyping method for the AY_731081:g.1900T>C SNP of the equine CD14 gene, and to characterize this SNP and two other polymorphisms (AY_005808: c.1530A>G of the TLR4 gene and AX_463789: g.133T>C of the Cε gene) in Mangalarga horses, in order to contribute to future studies investigating the association between DNA markers and traits related to immune system physiology in this breed. A total of 151 Mangalarga horses of both sexes and variable ages, representative of the population of São Paulo State, were used. PCR-RFLP was found to be adequate for genotyping of the AY_731081: g.1900T>C SNP of the equine CD14 gene. However, this polymorphism is probably not present in Mangalarga horses, thus impairing association studies using this marker in the breed. The population genetic parameters obtained for the TLR4 AY_005808:c.1530A>G and Cε AX_463789:g.133T>C polymorphisms suggest the use of these markers in association studies with immune system-related traits in Mangalarga horses.

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Xue, Feng, Lixue Yang, Binghua Dai, Hui Xue, Lei Zhang, Ruiliang Ge, and Yanfu Sun. "Bioinformatics profiling identifies seven immune-related risk signatures for hepatocellular carcinoma." PeerJ 8 (May 26, 2020): e8301. http://dx.doi.org/10.7717/peerj.8301.

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Background Density of tumor infiltrating lymphocytes (TIL) and expressions of certain immune-related genes have prognostic and predictive values in hepatocellular carcinoma (HCC); however, factors determining the immunophenotype of HCC patients are still unclear. In the current study, the transcript sequencing data of liver cancer were systematically analyzed to determine an immune gene marker for the prediction of clinical outcome of HCC. Methods RNASeq data and clinical follow-up information were downloaded from The Cancer Genome Atlas (TCGA), and the samples were assigned into high-stage and low-stage groups. Immune pathway-related genes were screened from the Molecular Signatures Database v4.0 (MsigDB) database. LASSO regression analysis was performed to identify robust immune-related biomarkers in predicting HCC clinical outcomes. Moreover, an immune gene-related prognostic model was established and validated by test sets and Gene Expression Omnibus (GEO) external validation sets. Results We obtained 319 immune genes from MsigDB, and the genes have different expression profiles in high-stage and low-stage of HCC. Univariate survival analysis found that 17 genes had a significant effect on HCC prognosis, among them, 13 (76.5%) genes were prognostically protective factors. Further lasso regression analysis identified seven potential prognostic markers (IL27, CD1D, NCOA6, CTSE, FCGRT, CFHR1, and APOA2) of robustness, most of which are related to tumor development. Cox regression analysis was further performed to establish a seven immune gene signature, which could stratify the risk of samples in training set, test set and external verification set (p < 0.01), and the AUC in both training set and test set was greater than 0.85, which also greater compared with previous studies. Conclusion This study constructed a 7-immunogenic marker as novel prognostic markers for predicting survival of HCC patients.

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Xu, Zhiquan, Ling Xiang, Linglong Peng, Haitao Gu, and Yaxu Wang. "Comprehensive Analysis of the Immune Implication of AKAP12 in Stomach Adenocarcinoma." Computational and Mathematical Methods in Medicine 2022 (September 13, 2022): 1–20. http://dx.doi.org/10.1155/2022/3445230.

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A kinase anchor protein 12 (AKAP12) as a tumor suppressor in various cancers has been extensively studied and confirmed. However, its immune implication in stomach adenocarcinoma (STAD) remains uncertain. Here, using The Cancer Genome Atlas (TCGA), Human Protein Atlas (HPA), Tumor Immune Estimation Resource (TIMER), Cancer Cell Line Encyclopedia (CCLE), integrated repository portal for tumor-immune system interactions (TISIDB), and Search Tool for the Retrieval of Interaction Gene/Proteins (STRING) database, we systematically analyzed the immune correlation of AKAP12 from three aspects including immune infiltration cells, immune-related pathways, and immunomodulators and developed a AKAP12-related 4-gene signature for prognosis prediction. Our results showed that AKAP12 mRNA and protein levels were downregulated in STAD patients, and its expression was positively related to CD4+ T cells and macrophages. In addition, the immune cell infiltration levels were associated with AKAP12 gene copy number deletion in STAD. Based on CCLE database, we found that AKAP12 coexpressed genes were enriched in several immune- and cancer-related pathways, which was further validated by Gene Set Enrichment Analysis (GSEA). Moreover, we identified 46 immunomodulators that were significantly related to AKAP12 expression using TISIDB database, and these immunomodulators were involved in immune-related pathways including Th17 cell differentiation and natural killer cell-mediated cytotoxicity. Additionally, based on the 46 AKAP12-related immunomodulators, a 4-gene risk prediction signature was developed using the Cox regression model. The risk signature was identified as an independent prognostic factor, which can accurately predict the prognosis of patients with STAD, showing good predictive performance. Furthermore, we constructed a prognostic nomogram and calibration to predict and assess patient survival probabilities by integrating the risk score and other clinical factors. In conclusion, our study provides strong evidence that AKAP12 is closely related to tumor immunity in STAD from three aspects: immune infiltration cells, immune pathways, and immunomodulators. More importantly, the AKAP12-related prognostic signature may have a good application prospect for clinical practice.

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Wang, Shuwen, Xiaoyu Zhang, Shaoqiu Leng, Qirui Xu, Zi Sheng, Yanqi Zhang, Jie Yu, et al. "Immune Checkpoint-Related Gene Polymorphisms Are Associated With Primary Immune Thrombocytopenia." Frontiers in Immunology 11 (January 5, 2021). http://dx.doi.org/10.3389/fimmu.2020.615941.

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Cancer immunotherapy by immune checkpoint blockade has been effective in the treatment of certain tumors. However, the association between immune checkpoints and autoimmune diseases remains elusive and requires urgent investigation. Primary immune thrombocytopenia (ITP), characterized by reduced platelet count and a consequent increased risk of bleeding, is an autoimmune disorder with a hyper-activated T cell response. Here, we investigated the contribution of immune checkpoint-related single-nucleotide polymorphisms (SNPs), including CD28, ICOS, PD1, TNFSF4, DNAM1, TIM3, CTLA4, and LAG3 to the susceptibility and therapeutic effects of ITP. In this case-control study, 307 ITP patients and 295 age-matched healthy participants were recruited. We used the MassARRAY system for genotyping immune checkpoint-related SNPs. Our results revealed that rs1980422 in CD28 was associated with an increased risk of ITP after false discovery rate correction (codominant, CT vs. TT, OR = 1.788, 95% CI = 1.178–2.713, p = 0.006). In addition, CD28 expression at both the mRNA and protein levels was significantly higher in patients with CT than in those with the TT genotype (p = 0.028 and p = 0.001, respectively). Furthermore, the T allele of PD1 rs36084323 was a risk factor for ITP severity and the T allele of DNAM1 rs763361 for corticosteroid-resistance. In contrast, the T allele of LAG3 rs870849 was a protective factor for ITP severity, and the T allele of ICOS rs6726035 was protective against corticosteroid-resistance. The TT/CT genotypes of PD1 rs36084323 also showed an 8.889-fold increase in the risk of developing refractory ITP. This study indicates that immune checkpoint-related SNPs, especially CD28 rs1980422, may be genetic factors associated with the development and treatment of ITP patients. Our results shed new light on prognosis prediction, disease severity, and discovering new therapeutic targets.

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"O3.2 Immune-related gene polymorphisms in endometriosis susceptibility." European Journal of Obstetrics & Gynecology and Reproductive Biology 123 (September 2005): S7. http://dx.doi.org/10.1016/s0301-2115(05)80213-1.

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Guo, Caiyu, Fanye Zeng, Hui Liu, Jianlin Wang, Xue Huang, and Judong Luo. "Establish immune-related gene prognostic index for esophageal cancer." Frontiers in Genetics 13 (August 9, 2022). http://dx.doi.org/10.3389/fgene.2022.956915.

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Background: Esophageal cancer is a tumor type with high invasiveness and low prognosis. As immunotherapy has been shown to improve the prognosis of esophageal cancer patients, we were interested in the establishment of an immune-associated gene prognostic index to effectively predict the prognosis of patients. Methods: To establish the immune-related gene prognostic index of esophageal cancer (EC), we screened 363 upregulated and 83 downregulated immune-related genes that were differentially expressed in EC compared to normal tissues. By multivariate Cox regression and weighted gene coexpression network analysis (WGCNA), we built a prognostic model based on eight immune-related genes (IRGs). We confirmed the prognostic model in both TCGA and GEO cohorts and found that the low-risk group had better overall survival than the high-risk group. Results: In this study, we identified 363 upregulated IRGs and 83 downregulated IRGs. Next, we found a prognostic model that was constructed with eight IRGs (OSM, CEACAM8, HSPA6, HSP90AB1, PCSK2, PLXNA1, TRIB2, and HMGB3) by multivariate Cox regression analysis and WGCNA. According to the Kaplan–Meier survival analysis results, the model we constructed can predict the prognosis of patients with esophageal cancer. This result can be verified by the Gene Expression Omnibus (GEO). Patients were divided into two groups with different outcomes. IRGPI-low patients had better overall survival than IRGPI-high patients.Conclusion: Our findings indicated the potential value of the IRGPI risk model for predicting the prognosis of EC patients.

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Li, Ju, Sai Ma, Linlin Shao, Chunhong Ma, Chengjiang Gao, Xiao-hui Zhang, Ming Hou, and Jun Peng. "Inflammation-Related Gene Polymorphisms Associated With Primary Immune Thrombocytopenia." Frontiers in Immunology 8 (June 28, 2017). http://dx.doi.org/10.3389/fimmu.2017.00744.

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Zhang, Nijia, Ming Ge, Tao Jiang, Xiaoxia Peng, Hailang Sun, Xiang Qi, Zhewei Zou, and Dapeng Li. "An Immune-Related Gene Pairs Signature Predicts Prognosis and Immune Heterogeneity in Glioblastoma." Frontiers in Oncology 11 (April 13, 2021). http://dx.doi.org/10.3389/fonc.2021.592211.

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PurposeGlioblastoma is one of the most aggressive nervous system neoplasms. Immunotherapy represents a hot spot and has not been included in standard treatments of glioblastoma. So in this study, we aim to filtrate an immune-related gene pairs (IRGPs) signature for predicting survival and immune heterogeneity.MethodsWe used gene expression profiles and clinical information of glioblastoma patients in the TCGA and CGGA datasets, dividing into discovery and validation cohorts. IRGPs significantly correlative with prognosis were selected to conduct an IRGPs signature. Low and high risk groups were separated by this IRGPs signature. Univariate and multivariate cox analysis were adopted to check whether risk can be a independent prognostic factor. Immune heterogeneity between different risk groups was analyzed via immune infiltration and gene set enrichment analysis (GSEA). Some different expressed genes between groups were selected to determine their relationship with immune cells and immune checkpoints.ResultsWe found an IRGPs signature consisting of 5 IRGPs. Different risk based on IRGPs signature is a independent prognostic factor both in the discovery and validation cohorts. High risk group has some immune positive cells and more immune repressive cells than low risk group by means of immune infiltration. We discovered some pathways are more active in the high risk group, leading to immune suppression, drug resistance and tumor evasion. In two specific signaling, some genes are over expressed in high risk group and positive related to immune repressive cells and immune checkpoints, which indicate aggression and immunotherapy resistance.ConclusionWe identified a robust IRGPs signature to predict prognosis and immune heterogeneity in glioblastoma patients. Some potential targets and pathways need to be further researched to make different patients benefit from personalized immunotherapy.

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Zhang, Wenshuo, Pang Lyu, Darja Andreev, Yewei Jia, Fulin Zhang, and Aline Bozec. "Hypoxia-immune-related microenvironment prognostic signature for osteosarcoma." Frontiers in Cell and Developmental Biology 10 (December 12, 2022). http://dx.doi.org/10.3389/fcell.2022.974851.

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Introduction: Increasing evidences have shown that hypoxia and the immune microenvironment play vital roles in the development of osteosarcoma. However, reliable gene signatures based on the combination of hypoxia and the immune status for prognostic prediction of osteosarcoma have so far not been identified.Methods: The individual hypoxia and immune status of osteosarcoma patients were identified with transcriptomic profiles of a training cohort from the TARGET database using ssGSEA and ESTIMATE algorithms, respectively. Lasso regression and stepwise Cox regression were performed to develop a hypoxia-immune-based gene signature. An independent cohort from the GEO database was used for external validation. Finally, a nomogram was constructed based on the gene signature and clinical features to improve the risk stratification and to quantify the risk assessment for individual patients.Results: Hypoxia and the immune status were significantly associated with the prognosis of osteosarcoma patients. Seven hypoxia- and immune-related genes (BNIP3, SLC38A5, SLC5A3, CKMT2, S100A3, CXCL11 and PGM1) were identified to be involved in our prognostic signature. In the training cohort, the prognostic signature discriminated high-risk patients with osteosarcoma. The hypoxia-immune-based gene signature proved to be a stable and predictive method as determined in different datasets and subgroups of patients. Furthermore, a nomogram based on the prognostic signature was generated to optimize the risk stratification and to quantify the risk assessment. Similar results were validated in an independent GEO cohort, confirming the stability and reliability of the prognostic signature.Conclusion: The hypoxia-immune-based prognostic signature might contribute to the optimization of risk stratification for survival and personalized management of osteosarcoma patients.

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Su, Yang, Ruoshan Qi, Lanying Li, Xu Wang, Sijin Li, Xuan Zhao, Rui Hou, et al. "An immune-related gene prognostic risk index for pancreatic adenocarcinoma." Frontiers in Immunology 13 (July 26, 2022). http://dx.doi.org/10.3389/fimmu.2022.945878.

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ObjectiveOur goal is to construct an immune-related gene prognostic risk index (IRGPRI) for pancreatic adenocarcinoma (PAAD), and to clarify the immune and molecular features in IRGPRI-defined PAAD subgroups and the benefit of immune checkpoint inhibitors (ICIs) therapy.MethodThrough differential gene expression analysis, weighted gene co-expression network analysis (WGCNA), and univariate Cox regression analysis, 16 immune-related hub genes were identified using the Cancer Genome Atlas (TCGA) PAAD dataset (n = 182) and immune gene set. From these genes, we constructed an IRGPRI with the Cox regression method and the IRGPRI was verified based on the Gene Expression Omnibus (GEO) dataset (n = 45). Then, we analyzed the immune and molecular features and the benefit of ICI therapy in IRGPRI-defined subgroups.ResultsFive genes, including S100A16, CD40, VCAM1, TNFRSF4 and TRAF1 were used to construct IRGPRI. As with the results of the GEO cohort, the overall survival (OS) was more favorable in low IRGPRI patients versus high IRGPRI patients. The composite results pointed out that low IRGPRI was associated with immune response-related pathways, high level of CTLA4, low KRAS and TP53 mutation rate, more infiltration of activated memory CD4+ T cells, CD8+ T cells, and more benefits from ICIs therapy. In comparison, high IRGPRI was associated with cancer-related pathways, low expression of CTLA4, high KRAS and TP53 mutation rate, more infiltration of M2 macrophages, and less benefit from ICIs therapies.ConclusionThis IRGPRI is an encouraging biomarker to define the prognosis, immune and molecular features, and benefits from ICIs treatments in PAAD.

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Chen, Meng-Yu, Yue-Can Zeng, and Xi-He Zhao. "Chemotherapy- and Immune-Related Gene Panel in Prognosis Prediction and Immune Microenvironment of SCLC." Frontiers in Cell and Developmental Biology 10 (June 15, 2022). http://dx.doi.org/10.3389/fcell.2022.893490.

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Small-cell lung cancer (SCLC) is a highly proliferative, invasive lung cancer with poor prognosis. Chemotherapy is still the standard first-line treatment for SCLC, but many patients relapse due to chemoresistance. Along with advances in immunology, it is essential to investigate potential indicators of the immune response and the prognosis of SCLC. Using bioinformatics analysis, we identified 313 differentially expressed genes (DEGs) in SCLC and normal lung samples, and we found that four upregulated genes (TOP2A, CDKN2A, BIRC5, and MSH2) were associated with platinum resistance, while immune-related genes (HLA family genes) were downregulated in SCLC. Then, a prognostic prediction model was constructed for SCLC based on those genes. Immune cell infiltration analysis showed that antigen presentation was weak in SCLC, and TOP2A expression was negatively correlated with CD8+ T cells, while HLA-ABC expression was positively correlated with M1 macrophages, memory B cells, and CD8+ T cells. We also found that TOP2A was related to poor prognosis and inversely correlated with HLA-ABC, which was verified with immunohistochemical staining in 151 SCLC specimens. Our study findings indicated that TOP2A may be a potential prognosis indicator and a target to reverse the immunosuppressive tumor microenvironment of SCLC.

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Shi, Xiaoshun, Ruidong Li, Xiaoying Dong, Allen Menglin Chen, Xiguang Liu, Di Lu, Siyang Feng, He Wang, and Kaican Cai. "IRGS: an immune-related gene classifier for lung adenocarcinoma prognosis." Journal of Translational Medicine 18, no. 1 (February 4, 2020). http://dx.doi.org/10.1186/s12967-020-02233-y.

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Chen, Lin, Yong Wang, Juan Huang, Binbin Hu, and Wei Huang. "Identification of Immune-Related Hub Genes in Parkinson’s Disease." Frontiers in Genetics 13 (July 22, 2022). http://dx.doi.org/10.3389/fgene.2022.914645.

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Background: Parkinson’s disease (PD) is a common, age-related, and progressive neurodegenerative disease. Growing evidence indicates that immune dysfunction plays an essential role in the pathogenic process of PD. The objective of this study was to explore potential immune-related hub genes and immune infiltration patterns of PD.Method: The microarray expression data of human postmortem substantia nigra samples were downloaded from GSE7621, GSE20141, and GSE49036. Key module genes were screened via weighted gene coexpression network analysis, and immune-related genes were intersected to obtain immune-key genes. Functional enrichment analysis was performed on immune-key genes of PD. In addition to, immune infiltration analysis was applied by a single-sample gene set enrichment analysis algorithm to detect differential immune cell types in the substantia nigra between PD samples and control samples. Least absolute shrinkage and selection operator analysis was performed to further identify immune-related hub genes for PD. Receiver operating characteristic curve analysis of the immune-related hub genes was used to differentiate PD patients from healthy controls. Correlations between immune-related hub genes and differential immune cell types were assessed.Result: Our findings identified four hub genes (SLC18A2, L1CAM, S100A12, and CXCR4) and seven immune cell types (neutrophils, T follicular helper cells, myeloid-derived suppressor cells, type 1 helper cells, immature B cells, immature dendritic cells, and CD56 bright natural killer cells). The area under the curve (AUC) value of the four-gene-combined model was 0.92. The AUC values of each immune-related hub gene (SLC18A2, L1CAM, S100A12, and CXCR4) were 0.81, 0.78, 0.78, and 0.76, respectively.Conclusion: In conclusion, SLC18A2, L1CAM, S100A12, and CXCR4 were identified as being associated with the pathogenesis of PD and should be further researched.

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Wan, Yanhua, Yingcheng He, Qijun Yang, Yunqi Cheng, Yuqiu Li, Xue Zhang, Wenyige Zhang, et al. "Construction of a prognostic assessment model for colon cancer patients based on immune-related genes and exploration of related immune characteristics." Frontiers in Cell and Developmental Biology 10 (December 16, 2022). http://dx.doi.org/10.3389/fcell.2022.993580.

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Objectives: To establish a novel risk score model that could predict the survival and immune response of patients with colon cancer.Methods: We used The Cancer Genome Atlas (TCGA) database to get mRNA expression profile data, corresponding clinical information and somatic mutation data of patients with colon cancer. Limma R software package and univariate Cox regression were performed to screen out immune-related prognostic genes. GO (Gene ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) were used for gene function enrichment analysis. The risk scoring model was established by Lasso regression and multivariate Cox regression. CIBERSORT was conducted to estimate 22 types of tumor-infiltrating immune cells and immune cell functions in tumors. Correlation analysis was used to demonstrate the relationship between the risk score and immune escape potential.Results: 679 immune-related genes were selected from 7846 differentially expressed genes (DEGs). GO and KEGG analysis found that immune-related DEGs were mainly enriched in immune response, complement activation, cytokine-cytokine receptor interaction and so on. Finally, we established a 3 immune-related genes risk scoring model, which was the accurate independent predictor of overall survival (OS) in colon cancer. Correlation analysis indicated that there were significant differences in T cell exclusion potential in low-risk and high-risk groups.Conclusion: The immune-related gene risk scoring model could contribute to predicting the clinical outcome of patients with colon cancer.

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Zhang, Jia-An, Xu-Yue Zhou, Dan Huang, Chao Luan, Heng Gu, Mei Ju, and Kun Chen. "Development of an Immune-Related Gene Signature for Prognosis in Melanoma." Frontiers in Oncology 10 (January 21, 2021). http://dx.doi.org/10.3389/fonc.2020.602555.

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Abstract:

Melanoma remains a potentially deadly malignant tumor. The incidence of melanoma continues to rise. Immunotherapy has become a new treatment method and is widely used in a variety of tumors. Original melanoma data were downloaded from TCGA. ssGSEA was performed to classify them. GSVA software and the "hclust" package were used to analyze the data. The ESTIMATE algorithm screened DEGs. The edgeR package and Venn diagram identified valid immune-related genes. Univariate, LASSO and multivariate analyses were used to explore the hub genes. The "rms" package established the nomogram and calibrated the curve. Immune infiltration data were obtained from the TIMER database. Compared with that of samples in the high immune cell infiltration cluster, we found that the tumor purity of samples in the low immune cell infiltration cluster was higher. The immune score, ESTIMATE score and stromal score in the low immune cell infiltration cluster were lower. In the high immune cell infiltration cluster, the immune components were more abundant, while the tumor purity was lower. The expression levels of TIGIT, PDCD1, LAG3, HAVCR2, CTLA4 and the HLA family were also higher in the high immune cell infiltration cluster. Survival analysis showed that patients in the high immune cell infiltration cluster had shorter OS than patients in the low immune cell infiltration cluster. IGHV1-18, CXCL11, LTF, and HLA-DQB1 were identified as immune cell infiltration-related DEGs. The prognosis of melanoma was significantly negatively correlated with the infiltration of CD4+ T cells, CD8+ T cells, dendritic cells, neutrophils and macrophages. In this study, we identified immune-related melanoma core genes and relevant immune cell subtypes, which may be used in targeted therapy and immunotherapy of melanoma.

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Journal articles on the topic 'Immune-related gene'

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