Dataset Information

Commonly integrated epigenetic modifications of differentially expressed genes lead to adaptive resistance in cancer.

ABSTRACT: Aim: To investigate the integrated epigenetic regulation of acquired drug resistance in cancer. Materials & methods: Our gene expression data of five induced drug-tolerant cell models, one resistant cell line and one publicly available drug-resistant dataset were integrated to identify common differentially expressed genes and pathways. ChIP-seq and DNA methylation by HM450K beadchip were used to study the epigenetic profile of differential expressed genes. Results & conclusion: Integrated transcriptomic analysis identified a common 'viral mimicry' related gene signature in induced drug-tolerant cells and the resistant state. Analysis of the epigenetic regulation revealed a common set of down-regulated genes, which are marked and regulated by a concomitant loss of H3K4me3, gain of H3K9me3 and increment of regional DNA methylation levels associated with tumor suppressor genes and apoptotic signaling.

SUBMITTER: Emran AA

PROVIDER: S-EPMC6595545 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Commonly integrated epigenetic modifications of differentially expressed genes lead to adaptive resistance in cancer.

Emran Abdullah Al AA Marzese Diego M DM Menon Dinoop R DR Hammerlindl Heinz H Ahmed Farzana F Richtig Erika E Duijf Pascal P Hoon Dave Sb DS Schaider Helmut H

Epigenomics 20190509 7

Aim: To investigate the integrated epigenetic regulation of acquired drug resistance in cancer. Materials & methods: Our gene expression data of five induced drug-tolerant cell models, one resistant cell line and one publicly available drug-resistant dataset were integrated to identify common differentially expressed genes and pathways. ChIP-seq and DNA methylation by HM450K beadchip were used to study the epigenetic profile of differential expressed genes. Results & conclusion: ...[more]

PMID: 31070054

Similar Datasets

Project description:ObjectiveWound healing is a complex process involving the coordinated interaction of various genes and molecularpathways. The study aimed to uncover novel therapeutic targets, biomarkers and candidate genes for drug developmentto improve successful wound repair interventions.Materials and Methods: This study is a network-meta analysis study. Nine wound healing microarray datasets obtainedfrom the Gene Expression Omnibus (GEO) database were used for this study. Differentially expressed genes (DEGs)were described using the Limma package and shared genes were used as input for weighted gene co-expressionnetwork analysis. The Gene Ontology analysis was performed using the EnrichR web server, and construction of aprotein-protein interaction (PPI) network was achieved by the STRING and Cytoscape.Results: A total of 424 DEGs were determined. A co-expression network was constructed using 7692 shared genesbetween nine data sets, resulting in the identification of seven modules. Among these modules, those with the top 20genes of up and down-regulation were selected. The top down-regulated genes, including TJP1, SEC61A1, PLEK,ATP5B, PDIA6, PIK3R1, SRGN, SDC2, and RBBP7, and the top up-regulated genes including RPS27A, EEF1A1,HNRNPA1, CTNNB1, POLR2A, CFL1, CSNk1E, HSPD1, FN1, and AURKB, which can potentially serve as therapeutictargets were identified. The KEGG pathway analysis found that the majority of the genes are enriched in the "Wntsignaling pathway".Conclusion: In our study of nine wound healing microarray datasets, we identified DEGs and co-expressed modulesusing WGCNA. These genes are involved in important cellular processes such as transcription, translation, and posttranslationalmodifications. We found nine down-regulated genes and ten up-regulated genes, which could serve aspotential therapeutic targets for further experimental validation. Targeting pathways related to protein synthesis and celladhesion and migration may enhance wound healing, but additional experimental validation is needed to confirm theeffectiveness and safety of targeted interventions.

Project description:Nasopharyngeal carcinoma (NPC) is a common malignancy of the head and neck. The aim of the present study was to conduct an integrated bioinformatics analysis of differentially expressed genes (DEGs) and to explore the molecular mechanisms of NPC. Two profiling datasets, GSE12452 and GSE34573, were downloaded from the Gene Expression Omnibus database and included 44 NPC specimens and 13 normal nasopharyngeal tissues. R software was used to identify the DEGs between NPC and normal nasopharyngeal tissues. Distributions of DEGs in chromosomes were explored based on the annotation file and the CYTOBAND database of DAVID. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were applied. Additionally, a protein-protein interaction (PPI) network, constructed using the STRING database and visualized by Cytoscape, was used to identify hub genes, key modules and important transcription factors (TFs). A total of 906 DEGs were identified; 434 (47.90%) DEGs were upregulated and 472 (52.10%) were downregulated. The DEGs were demonstrated to be enriched in chromosome 7p15-p14, 2q31, 1q21-q22, 1q21, 4q21 and 1p31-p22. DEGs were mainly enriched for the following GO terms: 'Cilium movement', 'microtubule bundle formation' and 'axoneme assembly'. KEGG pathway enrichment analysis revealed that pathways for 'cell cycle', 'DNA replication', 'interleukin-17 signaling', 'amoebiasis' and 'glutathione metabolism' were enriched. In addition, a PPI network comprising 867 nodes and 1,241 edges was constructed. Finally, five hub genes (aurora kinase A, cell division cycle 6, mitotic arrest deficient 2-like 1, DNA topoisomerase 2α and TPX2 microtubule nucleation factor), 8 modules, and 14 TFs were identified. Modules analysis revealed that cyclin-dependent kinase 1 and exportin 1 were involved in the pathway of Epstein-Barr virus infection. In summary, the hub genes, key modules and TFs identified in this study may promote our understanding of the pathogenesis of NPC and require further in-depth investigation.

Project description:Coronary artery disease (CAD) is a major cause of end-stage cardiac disease. Although profound efforts have been made to illuminate the pathogenesis, the molecular mechanisms of CAD remain to be analyzed. To identify the candidate genes in the advancement of CAD, microarray dataset GSE23766 was downloaded from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) were identified, and pathway and gene ontology (GO) enrichment analyses were performed. The protein-protein interaction network was constructed and the module analysis was performed using the Biological General Repository for Interaction Datasets (BioGRID) and Cytoscape. Additionally, target genes-miRNA regulatory network and target genes-TF regulatory network were constructed and analyzed. There were 894 DEGs between male human CAD samples and female human CAD samples, including 456 up regulated genes and 438 down regulated genes. Pathway enrichment analyses revealed that DEGs (up and down regulated) were mostly enriched in the superpathway of steroid hormone biosynthesis, ABC transporters, oxidative ethanol degradation III and Complement and coagulation cascades. Similarly, geneontology enrichment analyses revealed that DEGs (up and down regulated) were mostly enriched in the forebrain neuron differentiation, filopodium membrane, platelet degranulation and blood microparticle. In the PPI network and modules (up and down regulated), MYC, NPM1, TRPC7, UBC, FN1, HEMK1, IFT74 and VHL were hub genes. In the target genes-miRNA regulatory network and target genes-TF regulatory network (up and down regulated), TAOK1, KHSRP, HSD17B11 and PAH were target genes. In conclusion, the pathway and GO ontology enriched by DEGs may reveal the molecular mechanism of CAD. Its hub and target genes, MYC, NPM1, TRPC7, UBC, FN1, HEMK1, IFT74, VHL, TAOK1, KHSRP, HSD17B11 and PAH were expected to be new targets for CAD. Our finding provided clues for exploring molecular mechanism and developing new prognostics, diagnostic and therapeutic strategies for CAD.

Dataset Information

Commonly integrated epigenetic modifications of differentially expressed genes lead to adaptive resistance in cancer.

Publications

Commonly integrated epigenetic modifications of differentially expressed genes lead to adaptive resistance in cancer.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets