Dataset Information

MiR-195 restrains lung adenocarcinoma by regulating CD4+ T cell activation via the CCDC88C/Wnt signaling pathway: a study based on the Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) and bioinformatic analysis.

ABSTRACT: Background:To systematically identity microRNA signatures, as well as miRNA-gene axes, for lung adenocarcinoma (LUAD) and to explore the potential biomarkers and mechanisms associated with the LUAD immune responses. Methods:LUAD-related data were obtained from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA), and these data were then used to identify the differentially expressed miRNAs that were downregulated in tumor tissues. Summary receiver operating characteristic curve analysis, survival analysis and meta-analysis were applied to evaluate the clinical significance and diagnostic value of the identified miRNAs. The presumed targets of the integrated-signature miRNAs were identified via 3 different target prediction algorithms: TargetScan, miRDB and DIANA-TarBase. Immunologic signature gene sets were enriched by gene set enrichment analysis (GSEA). Tumor-infiltrating lymphocytes were profiled by the Tumor IMmune Estimation Resource (TIMER). After pathway enrichment analysis using the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases, pathway-gene networks were constructed using Cytoscape software. Results:After integrated analysis of 4 GEO data sets (GSE48414, GSE51853, GSE63805 and GSE74190) and TCGA databases, miR-195 was identified as a potential clinical diagnostic marker. A total of 287 miR-195 target genes were screened, and 3 functional gene sets (GSE13485, GSE21379 and GSE29164) were enriched. GSE21379 was associated with the upregulation of CD4+ T cells in tumors, and the core genes were validated via the TIMER database. The CCDC88C expression level was significantly correlated with CD4+ T cell activation (partial.cor =0.437, P<0.001). Enrichment analysis revealed that CCDC88C was significantly enriched in the Wnt signaling pathway. Conclusions:MiR-195, as a suppressor of lung adenocarcinoma, regulates CD4+ T cell activation via CCDC88C.

SUBMITTER: Yuan C

PROVIDER: S-EPMC6614311 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

MiR-195 restrains lung adenocarcinoma by regulating CD4+ T cell activation via the CCDC88C/Wnt signaling pathway: a study based on the Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) and bioinformatic analysis.

Yuan Cheng C Xiang Liyang L Bai Rui R Cao Kuo K Gao Yanping Y Jiang Xueping X Zhang Nannan N Gong Yan Y Xie Conghua C

Annals of translational medicine 20190601 12

<h4>Background</h4>To systematically identity microRNA signatures, as well as miRNA-gene axes, for lung adenocarcinoma (LUAD) and to explore the potential biomarkers and mechanisms associated with the LUAD immune responses.<h4>Methods</h4>LUAD-related data were obtained from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA), and these data were then used to identify the differentially expressed miRNAs that were downregulated in tumor tissues. Summary receiver operating charact ...[more]

PMID: 31355230

Similar Datasets

Project description:BackgroundClear cell renal cell carcinoma (ccRCC) is a commonly occurring tumor. Through a deeper understanding of the immune regulatory mechanisms in the tumor microenvironment, immunotherapy may serve as a potential treatment for cancer patients. This study aimed at identifying the survival-related immune cells and hub genes, which could be potential targets for immunotherapy in ccRCC.MethodsThe gene expression profiles and clinical data of ccRCC patients were extracted from UCSC Xena and Gene Expression Omnibus (GEO) databases. Kaplan-Meier (KM) survival and Least Absolute Shrinkage and Selection Operator (LASSO) regression analyses were utilized to select the survival-related tumor-infiltrating immune cells. Multivariate Cox regression was utilized to develop a signature based on the tumor-infiltrating immune cells (TIICs). Based on the signature, the risk score was calculated, following which the samples were divided into high-risk and low-risk groups. Differentially expressed genes (DEGs) between the two risk groups were identified. Functional enrichment analysis was performed and cytoHubba plug-in of Cytoscape was used to identify the hub genes. Multiple datasets were utilized to validate these hub genes, including the Gene Expression Profiling Interactive Analysis (GEPIA), UALCAN, and the Human Protein Atlas (HPA), and the GEO datasets. Finally, a correlation analysis was performed to evaluate the relationship between the hub genes and TIICs.ResultsFour immune survival-related cells, including T cell CD4 memory-activated, T cell regulatory (Tregs), eosinophils, and mast cell resting were identified. Nine immune-specific hub genes were identified, which included APOE, CASR, CTLA4, CXCL8, EGF, F2, KNG1, MMP9, and IL6. Furthermore, these hub genes were significantly correlated with clinical traits and closely associated with some TIICs.ConclusionA total of four survival-related immune cell types and nine hub genes were found to be closely associated with ccRCC. These findings may have implications for the development of novel potential immunotherapeutic targets for ccRCC.

Project description:BackgroundBladder cancer (BLCA) is the ninth most common cancer globally, as well as the fourth most common cancer in men, with an incidence of 7%. However, few effective prognostic biomarkers or models of BLCA are available at present.MethodsThe prognostic genes of BLCA were screened from one cohort of The Cancer Genome Atlas (TCGA) database through univariate Cox regression analysis and functionally annotated by Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. The intersecting genes of the BLCA gene set and focal adhesion-related gene were obtained and subjected to the least absolute shrinkage and selection operator regression (LASSO) to construct a prognostic model. Gene set enrichment analysis (GSEA) of high- and low-risk patients was performed to explore further the biological process related to focal adhesion genes. Univariate and multivariate Cox analysis, receiver operating characteristic (ROC) curve analysis, and Kaplan-Meier survival analysis (KM) were used to evaluate the prognostic model. DNA methylation analysis was presented to explore the relationship between prognosis and gene methylation. Furthermore, immune cell infiltration was assessed by CIBERSORT, ESTIMATE, and TIMER. The model was verified in an external GSE32894 cohort of the Gene Expression Omnibus (GEO) database, and the Prognoscan database presented further validation of genes. The HPA database validated the related protein level, and functional experiments verified significant risk factors in the model.ResultsVCL, COL6A1, RAC3, PDGFD, JUN, LAMA2, and ITGB6 were used to construct a prognostic model in the TCGA-BLCA cohort and validated in the GSE32894 cohort. The 7-gene model successfully stratified the patients into both cohorts' high- and low-risk groups. The higher risk score was associated with a worse prognosis.ConclusionsThe 7-gene prognostic model can classify BLCA patients into high- and low-risk groups based on the risk score and predict the overall survival, which may aid clinical decision-making.

Dataset Information

MiR-195 restrains lung adenocarcinoma by regulating CD4+ T cell activation via the CCDC88C/Wnt signaling pathway: a study based on the Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) and bioinformatic analysis.

Publications

MiR-195 restrains lung adenocarcinoma by regulating CD4+ T cell activation via the CCDC88C/Wnt signaling pathway: a study based on the Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) and bioinformatic analysis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets