Identifying Interaction Clusters for MiRNA and MRNA Pairs in TCGA Network

Dai, Xinqing; Ding, Lizhong; Liu, Hannah; Xu, Zhou; Jiang, Hui; Handelman, Samuel K.; Bai, Yongsheng

doi:10.3390/genes10090702

Cited by 16 publications

(24 citation statements)

References 31 publications

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“…Anti-correlated expressions of 15 cancer types’ miRNA–mRNA pairs from a previous study [ 2 ] were extracted from TCGA ( ), and a total of 65,535 miRNA–mRNA pairs were taken as candidates to be used in subsequent analyses. Because miRNA can repress the expression of its targets, we hypothesize that these anti-correlated miRNA–mRNA pairs are likely to be dysregulated in the observed cancer types, which highlight their potential functional importance.…”

Section: Methodsmentioning

confidence: 99%

“…Thus, in this study, we utilized such anti-correlated miRNA–mRNA pairs identified in a recent study, [ 2 ] to cross-check with publicly available databases—namely dbMTS (database of miRNA target site SNVs) [ 3 ] and dbNSFP (database for nonsynonymous SNPs’ functional predictions) [ 4 ]—for functionally annotated SNVs. This process validated existing cancer related SNVs and identified and prioritized potential SNVs for future functional and experimental validation.…”

Section: Introductionmentioning

confidence: 99%

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

Han

et al. 2020

Genes

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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.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Han

et al. 2020

Genes

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“…Since PBC is a liver-related trait, we searched the TCGA LIHC dataset [ 19 ] and checked anti-correlated pairs for PTEN targeted miRNA: hsa-mir-590 . We then used hsa-mir-590 to identify its targeted genes in the anti-correlated pair list and obtained 22 additional target genes.…”

Section: Resultsmentioning

confidence: 99%

Pinpointing miRNA and genes enrichment over trait-relevant tissue network in Genome-Wide Association Studies

Li¹,

Dong

et al. 2020

BMC Med Genomics

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Background Understanding gene regulation is important but difficult. Elucidating tissue-specific gene regulation mechanism is even more challenging and requires gene co-expression network assembled from protein–protein interaction, transcription factor and gene binding, and post-transcriptional regulation (e.g., miRNA targeting) information. The miRNA binding affinity could therefore be changed by SNP(s) located at the 3′ untranslated regions (3′UTR) of the target messenger RNA (mRNA) which miRNA(s) interacts with. Genome-wide association study (GWAS) has reported significant numbers of loci hosting SNPs associated with many traits. The goal of this study is to pinpoint GWAS functional variants located in 3′UTRs and elucidate if the genes harboring these variants along with their targeting miRNAs are associated with genetic traits relevant to certain tissues. Methods By applying MIGWAS, CoCoNet, ANNOVAR, and DAVID bioinformatics software and utilizing the gene expression database (e.g. GTEx data) to study GWAS summary statistics for 43 traits from 28 GWAS studies, we have identified a list of miRNAs and targeted genes harboring 3′UTR variants, which could contribute to trait-relevant tissue over miRNA-target gene network. Results Our result demonstrated that strong association between traits and tissues exists, and in particular, the Primary Biliary Cirrhosis (PBC) trait has the most significant p-value for all 180 tissues among all 43 traits used for this study. We reported SNPs located in 3′UTR regions of genes (SFMBT2, ZC3HAV1, and UGT3A1) targeted by miRNAs for PBC trait and its tissue association network. After employing Gene Ontology (GO) analysis for PBC trait, we have also identified a very important miRNA targeted gene over miRNA-target gene network, PFKL, which encodes the liver subunit of an enzyme. Conclusions The non-coding variants identified from GWAS studies are casually assumed to be not critical to translated protein product. However, 3′ untranslated regions (3′UTRs) of genes harbor variants can often change the binding affinity of targeting miRNAs playing important roles in protein translation degree. Our study has shown that GWAS variants could play important roles on miRNA-target gene networks by contributing the association between traits and tissues. Our analysis expands our knowledge on trait-relevant tissue network and paves way for future human disease studies.

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“…We obtained the mRNA-miRNA pairs in the significant clusters (FDR < 0.1) identified for 15 TCGA cancer types from Dai et al [ 12 ]. The 15 TCGA cancer types studied are bladder urothelial carcinoma (BLCA), breast invasive carcinoma (BRCA or BC), colon adenocarcinoma (COAD), esophageal carcinoma (ESCA), head and neck squamous cell carcinoma (HNSC), kidney chromophobe (KICH), kidney renal clear cell carcinoma (KIRC), kidney renal papillary cell carcinoma (KIRP), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), prostate adenocarcinoma (PRAD), stomach adenocarcinoma (STAD), thyroid carcinoma (THCA), and uterine corpus endometrial carcinoma (UCEC).…”

Section: Methodsmentioning

confidence: 99%

“…In this study, we utilized anti-correlated mRNA-miRNA pairs identified in a recent publication [ 12 ] and human sex-biased genes and miRNAs published to date [ 13 , 14 ] to unravel the targeting pattern of sex-biased genes and miRNAs and their potential roles in tumorigenesis and tumor invasion. The prioritized miRNAs and their targeting female-biased genes which have positive correlations with six different immune cell abundance levels could serve as alternative therapeutic cancer markers to design personalized immunotherapy.…”

Section: Introductionmentioning

confidence: 99%

Computational Identification of Sex-Biased Biomarker MicroRNAs and Genes Associated with Immune Infiltration in Breast Cancer

Li¹,

Bai

2021

Genes

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MicroRNAs (miRNAs) perform their functions through targeting messenger RNAs (mRNAs). X chromosome-located (X-linked) miRNAs have a broad role in cell lineage determination, immune regulation, and oncogenesis. The regulating roles of miRNAs in cancer and immunity are often altered when aberrant expression happens. Sex-biased genes could contribute to cancer sex bias in the context of their expression change due to targeting miRNAs. How biological roles and associations with immune cell abundance levels for sex-biased gene-miRNA pairs in gender-related cancer (e.g., breast cancer) change due to the alteration of their expression pattern to identify candidate therapeutic markers has not been investigated thoroughly. Upon analyzing anti-correlated genes and miRNAs within significant clusters of 12 The Cancer Genome Atlas (TCGA) cancer types and the list of sex-biased genes and miRNAs reported from previous studies, 125 sex-biased genes (11 male-biased and 114 female-biased) were identified in breast cancer (BC). Seventy-three sex-biased miRNAs (40 male-biased and 33 female-biased) were identified across 5 out of 12 cancers (head and neck squamous cell carcinoma (HNSC), kidney chromophobe (KICH), kidney renal clear cell carcinoma (KIRC), kidney renal papillary cell carcinoma (KIRP), and lung adenocarcinoma (LUAD)). Correlation between the BC sex-biased genes and tumor infiltrating immune cell types was further evaluated. We found eight genes having high correlation with immune infiltration. Fifteen candidate female-biased BC genes targeted by 3 X-linked miRNAs (has-mir-18hashsa-mir-221, and hsa-mir-224) were pinpointed in this study. Our computational result indicates that many identified female-biased genes which have positive associations with immune cell abundance levels could serve as alternative therapeutic markers. Our analysis suggests that female-biased expression of BC candidate genes is likely influenced by their targeting miRNA(s).

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Identifying Interaction Clusters for MiRNA and MRNA Pairs in TCGA Network

Cited by 16 publications

References 31 publications

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

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

Pinpointing miRNA and genes enrichment over trait-relevant tissue network in Genome-Wide Association Studies

Computational Identification of Sex-Biased Biomarker MicroRNAs and Genes Associated with Immune Infiltration in Breast Cancer

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