Dataset Information

FunDMDeep-m6A: identification and prioritization of functional differential m6A methylation genes.

ABSTRACT: MOTIVATION:As the most abundant mammalian mRNA methylation, N6-methyladenosine (m6A) exists in >25% of human mRNAs and is involved in regulating many different aspects of mRNA metabolism, stem cell differentiation and diseases like cancer. However, our current knowledge about dynamic changes of m6A levels and how the change of m6A levels for a specific gene can play a role in certain biological processes like stem cell differentiation and diseases like cancer is largely elusive. RESULTS:To address this, we propose in this paper FunDMDeep-m6A a novel pipeline for identifying context-specific (e.g. disease versus normal, differentiated cells versus stem cells or gene knockdown cells versus wild-type cells) m6A-mediated functional genes. FunDMDeep-m6A includes, at the first step, DMDeep-m6A a novel method based on a deep learning model and a statistical test for identifying differential m6A methylation (DmM) sites from MeRIP-Seq data at a single-base resolution. FunDMDeep-m6A then identifies and prioritizes functional DmM genes (FDmMGenes) by combing the DmM genes (DmMGenes) with differential expression analysis using a network-based method. This proposed network method includes a novel m6A-signaling bridge (MSB) score to quantify the functional significance of DmMGenes by assessing functional interaction of DmMGenes with their signaling pathways using a heat diffusion process in protein-protein interaction (PPI) networks. The test results on 4 context-specific MeRIP-Seq datasets showed that FunDMDeep-m6A can identify more context-specific and functionally significant FDmMGenes than m6A-Driver. The functional enrichment analysis of these genes revealed that m6A targets key genes of many important context-related biological processes including embryonic development, stem cell differentiation, transcription, translation, cell death, cell proliferation and cancer-related pathways. These results demonstrate the power of FunDMDeep-m6A for elucidating m6A regulatory functions and its roles in biological processes and diseases. AVAILABILITY AND IMPLEMENTATION:The R-package for DMDeep-m6A is freely available from https://github.com/NWPU-903PR/DMDeepm6A1.0. SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.

SUBMITTER: Zhang SY

PROVIDER: S-EPMC6612877 | biostudies-other | 2019 Jul

REPOSITORIES: biostudies-other

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FunDMDeep-m6A: identification and prioritization of functional differential m6A methylation genes.

Zhang Song-Yao SY Zhang Shao-Wu SW Fan Xiao-Nan XN Zhang Teng T Meng Jia J Huang Yufei Y

Bioinformatics (Oxford, England) 20190701 14

<h4>Motivation</h4>As the most abundant mammalian mRNA methylation, N6-methyladenosine (m6A) exists in >25% of human mRNAs and is involved in regulating many different aspects of mRNA metabolism, stem cell differentiation and diseases like cancer. However, our current knowledge about dynamic changes of m6A levels and how the change of m6A levels for a specific gene can play a role in certain biological processes like stem cell differentiation and diseases like cancer is largely elusive.<h4>Resul ...[more]

PMID: 31510685

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Project description:Background:N6-methyladenosine (m6A) modification holds an important position in tumorigenesis and metastasis because it can change gene expression and even function in multiple levels including RNA splicing, stability, translocation and translation. In present study, we aim to conducted comprehensive investigation on m6A RNA methylation regulators and m6A-related genes in pancreatic cancer and their association with survival time. Methods:Based on Univariate Cox regression analysis, protein-protein interaction analysis, LASSO Cox regression, a risk prognostic model, STRING, Spearman and consensus clustering analysis, data from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) database was used to analyze 15 m6A RNA methylation regulators that were widely reported and 1,393 m6A-related genes in m6Avar. Results:We found that 283 candidate m6A RNA methylation-related genes and 4 m6A RNA methylation regulatory factors, including RNA binding motif protein 15 (RBM15), methyltransferase like 14 (METTL14), fat mass and obesity-associated protein (FTO), and ?-ketoglutarate-dependent dioxygenase AlkB homolog 5 (ALKBH5), differed significantly among different stages of the American Joint Committee on Cancer (AJCC) staging system. Protein-protein interaction analysis indicated epidermal growth factor receptor (EGFR), plectin-1 (PLEC), BLM RecQ like helicase (BLM), and polo like kinase 1 (PLK1) were closely related to other genes and could be considered as hub genes in the network. The results of LASSO Cox regression and the risk prognostic model indicated that AJCC stage, stage T and N, KRAS mutation status and x8q23.3 CNV fragment mutation differed significantly between the high-risk and the low-risk subgroups. The AUCs of 1 to 5 years after surgery were all more than 0.7 and increased year by year. Finally, we found KRAS mutation status and AJCC stage differed significantly among these groups after TCGA samples divided into subgroups with k=7. Moreover, we identified four m6A RNA methylation related genes expressed significantly differently among these seven subgroups, including collagen type VII alpha 1 chain (COL7A1), branched chain amino acid transaminase 1 (BCAT1), zinc finger protein 596 (ZNF596), and PLK1. Conclusions:Our study systematically analyzed the m6A RNA methylation related genes, including expression, protein-protein interaction, potential function, and prognostic value and provides important clues to further research on the function of RNA m6A methylation and its related genes in pancreatic cancer.

Project description:m6A RNA methylation regulators can regulate the growth, progression, and invasion of glioma cells by regulating their target genes, which provides a reliable support for the m6A regulator-target axes as the novel therapeutic targets and clinical prognostic signature in glioma. This study aimed to explore the role and prognostic value of m6A RNA methylation regulators and their targets. Expression profiles and clinicopathological data were obtained from the Chinese Glioma Genome Atlas (CGGA), The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and Clinical Proteome Tumor Analysis Consortium (CPTAC) datasets. Differential expression and correlation analyses were performed between normal and glioma tissues at mRNA and protein levels. Univariate Cox regression, survival, and Lasso Cox regression analyses were conducted to identify and establish the prognostic gene signature. Kaplan-Meier curve, multivariate Cox regression analysis, and ROC were utilized to evaluate the prognostic capacity of the prognostic gene signature. The correlation analysis, systematic bioinformatics analysis, and cell experiment were performed to further understand the potential underlying molecular mechanisms and drug sensitivity. Our results suggested that IGF2BP2, KIAA1429, METTL16, and METTL3, as well as 208 targets are involved in the occurrence of glioma, GBM, and LGG. YTHDF1 and 78 targets involved the occurrence of glioma and GBM, not LGG, among which 181 genes were associated with overall survival. From other findings and our cell experiment results, we demonstrated that METTL3 can activate Notch pathway and facilitate glioma occurrence through regulating its direct targets NOTCH3, DLL3, and HES1, and Notch pathway genes may serve as the potential treatment targets for glioma. Our study established and validated a seven-gene signature comprising METTL3, COL18A1, NASP, PHLPP2, TIMP1, U2AF2, and VEGFA, with a good capability for predicting glioma survival, which may guide therapeutic customization and clinical decision-making. These genes were identified to influence 81 anticancer drug responses, which further contributes to the early phase clinical trials of drug development.

Dataset Information

FunDMDeep-m6A: identification and prioritization of functional differential m6A methylation genes.

Publications

FunDMDeep-m6A: identification and prioritization of functional differential m6A methylation genes.

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