Dataset Information

Identification of MicroRNA-Related Tumorigenesis Variants and Genes in the Cancer Genome Atlas (TCGA) Data.

ABSTRACT: MicroRNAs (miRNAs) are a class of small non-coding RNA that can down-regulate their targets by selectively binding to the 3' untranslated region (3'UTR) of most messenger RNAs (mRNAs) in the human genome. Single nucleotide variants (SNVs) located in miRNA target sites (MTS) can disrupt the binding of targeting miRNAs. Anti-correlated miRNA-mRNA pairs between normal and tumor tissues obtained from The Cancer Genome Atlas (TCGA) can reveal important information behind these SNVs on MTS and their associated oncogenesis. In this study, using previously identified anti-correlated miRNA-mRNA pairs in 15 TCGA cancer types and publicly available variant annotation databases, namely dbNSFP (database for nonsynonymous SNPs' functional predictions) and dbMTS (database of miRNA target site SNVs), we identified multiple functional variants and their gene products that could be associated with various types of cancers. We found two genes from dbMTS and 33 from dbNSFP that passed our stringent filtering criteria (e.g., pathogenicity). Specifically, from dbMTS, we identified 23 candidate genes, two of which (BMPR1A and XIAP) were associated with diseases that increased the risk of cancer in patients. From dbNSFP, we identified 65 variants located in 33 genes that were likely pathogenic and had a potential causative relationship with cancer. This study provides a novel way of utilizing TCGA data and integrating multiple publicly available databases to explore cancer genomics.

SUBMITTER: Li C

PROVIDER: S-EPMC7565843 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Identification of MicroRNA-Related Tumorigenesis Variants and Genes in the Cancer Genome Atlas (TCGA) Data.

Li Chang C Wu Brian B Han Han H Zhao Jeff J Bai Yongsheng Y Liu Xiaoming X

Genes 20200819 9

MicroRNAs (miRNAs) are a class of small non-coding RNA that can down-regulate their targets by selectively binding to the 3' untranslated region (3'UTR) of most messenger RNAs (mRNAs) in the human genome. Single nucleotide variants (SNVs) located in miRNA target sites (MTS) can disrupt the binding of targeting miRNAs. Anti-correlated miRNA-mRNA pairs between normal and tumor tissues obtained from The Cancer Genome Atlas (TCGA) can reveal important information behind these SNVs on MTS and their a ...[more]

PMID: 32824926

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Project description:BackgroundRecurrence is common in bladder cancer, with a hypoxic tumor microenvironment (TME) playing a role in genetic instability and prognosis of bladder cancer. However, we still lack practical hypoxia related model for predicting the prognosis of bladder cancer. In this study, we identified new prognosis-related hypoxia genes and established a new hypoxia score related signature.MethodsThe Gene Set Variation Analysis (GSVA) algorithm was utilized to calculate the hypoxia score of bladder cancer cases found on the The Cancer Genome Atlas (TCGA) database on the gene expression profiles. The cases were first divided into low- and high-hypoxia score groups and then differentially expressed genes (DEGs) expression analysis was conducted. Hypoxia-related genes were identified using weighted gene co-expression network analysis (WGCNA). We then conducted a protein-protein interaction (PPI) network and carried out functional enrichment analysis of the genes that overlapped between DEGs and hypoxia-related genes. LASSO Cox regression analysis was used to establish a hypoxia-related prognostic signature, which was validated using the GSE69795 dataset downloaded from GEO database.ResultsResults from Kaplan-Meier analysis showed that patients with a high hypoxia score had significantly poor overall survival compared to patients with low hypoxia score. We selected 270 DEGs between low- and high-hypoxia score groups, while WGCNA analysis identified 1,313 genes as hypoxia-related genes. A total of 170 genes overlapped between DEGs and hypoxia-related genes. LASSO algorithms identified 29 genes associated with bladder cancer prognosis, which were used to construct a novel 29-gene signature model. The prognostic risk model performed well, since the receiver operating characteristic (ROC) curve showed an accuracy of 0.802 (95% CI: 0.759-0.844), and Cox proportional hazards regression analysis proved the model an independent predictor with hazard ratio (HR) =1.789 (95% CI: 1.585-2.019) (P<0.001). The low-risk score patients had remarkably longer overall survival than patients with a higher score (survival rate 71.06% vs. 23.66%) in the The Cancer Genome Atlas (TCGA) cohort (P<0.0001) and in the dataset GSE69795 (P=0.0079).ConclusionsWe established a novel 29-gene hypoxia-related signature model to predict the prognosis of bladder cancer cases. This model and identified hypoxia-related genes may further been used as biomarkers, assisting the evaluation of prognosis of bladder cancer cases and decision making in clinical practice.

Dataset Information

Identification of MicroRNA-Related Tumorigenesis Variants and Genes in the Cancer Genome Atlas (TCGA) Data.

Publications

Identification of MicroRNA-Related Tumorigenesis Variants and Genes in the Cancer Genome Atlas (TCGA) Data.

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