Dataset Information

Comprehensive Identification of Key Genes Involved in Development of Diabetes Mellitus-Related Atherogenesis Using Weighted Gene Correlation Network Analysis.

ABSTRACT: Coronary heart disease (CHD) is common in patients with diabetes mellitus (DM), however, the relevant mechanism remains elusive. The whole blood gene expression profiles of healthy control, patients with DM, patients with DM and CHD (DMCHD) were used to performed weight gene correlation network analysis (WGCNA) to identify the gene modules associated with DM-related atherogenesis. The candidate module was significantly involved in immune- and T cell activity-related biological process. GSEA results suggested that lysosome and apoptosis were enriched in DM and DMCHD samples. The protein-protein-KEGG pathway network may reveal the potential transcriptional regulatory network involving in DM-related atherosclerosis. Nineteen genes (RTKN, DCP1B, PDZD4, CACNA2D2, TSEN54, PVRIG, PLEKHF1, NKG7, ZAP70, NUDCD3, SLAMF6, CCDC107, NAG6, ZDHHC14, EOMES, VIL2, WDR54, DMAP1, and PMPCA) were considered as DM-related atherogenesis genes (DRAGs). The Gene Set Variation Analysis (GSVA) score of the DRAG set gradually increased in the control, DM and DMCHD. ROC curve analysis showed that ZAP70, TSEN54, and PLEKHF1 may be potential blood circulation biomarkers for DMCHD in patients with DM. In conclusion, we identified nineteen hallmark genes involving in DM-related atherogenesis and constructed a potential transcriptional regulatory network involving in DM-related atherosclerosis.

SUBMITTER: Huang Q

PROVIDER: S-EPMC7655645 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Comprehensive Identification of Key Genes Involved in Development of Diabetes Mellitus-Related Atherogenesis Using Weighted Gene Correlation Network Analysis.

Huang Qi Q Deng Guoxiong G Wei Rongguo R Wang Qiaoye Q Zou Donghua D Wei Jinru J

Frontiers in cardiovascular medicine 20201028

Coronary heart disease (CHD) is common in patients with diabetes mellitus (DM), however, the relevant mechanism remains elusive. The whole blood gene expression profiles of healthy control, patients with DM, patients with DM and CHD (DMCHD) were used to performed weight gene correlation network analysis (WGCNA) to identify the gene modules associated with DM-related atherogenesis. The candidate module was significantly involved in immune- and T cell activity-related biological process. GSEA resu ...[more]

PMID: 33195466

Similar Datasets

Project description:BACKGROUND:Left-sided heart failure (HF) is documented as a key prognostic factor in HF. However, the relative molecular mechanisms underlying left-sided HF is unknown. The purpose of this study is to unearth significant modules, pivotal genes and candidate regulatory components governing the progression of left-sided HF by bioinformatical analysis. METHODS:A total of 319 samples in GSE57345 dataset were used for weighted gene correlation network analysis (WGCNA). ClusterProfiler package in R was used to conduct functional enrichment for genes uncovered from the modules of interest. Regulatory networks of genes were built using Cytoscape while Enrichr database was used for identification of transcription factors (TFs). The MCODE plugin was used for identifying hub genes in the modules of interest and their validation was performed based on GSE1869 dataset. RESULTS:A total of six significant modules were identified. Notably, the blue module was confirmed as the most crucially associated with left-sided HF, ischemic heart disease (ISCH) and dilated cardiomyopathy (CMP). Functional enrichment conveyed that genes belonging to this module were mainly those driving the extracellular matrix-associated processes such as extracellular matrix structural constituent and collagen binding. A total of seven transcriptional factors, including Suppressor of Zeste 12 Protein Homolog (SUZ12) and nuclear factor erythroid 2 like 2 (NFE2L2), adrenergic receptor (AR), were identified as possible regulators of coexpression genes identified in the blue module. A total of three key genes (OGN, HTRA1 and MXRA5) were retained after validation of their prognostic value in left-sided HF. The results of functional enrichment confirmed that these key genes were primarily involved in response to transforming growth factor beta and extracellular matrix. CONCLUSION:We uncovered a candidate gene signature correlated with HF, ISCH and CMP in the left ventricle, which may help provide better prognosis and therapeutic decisions and in HF, ISCH and CMP patients.

Project description:PurposeThe specific mechanisms and biomarkersunderlying the progression of stable coronary artery disease (CAD) to acute myocardial infarction (AMI) remain unclear. The current study aims to explore novel gene biomarkers associated with CAD progression by analyzing the transcriptomic sequencing data of peripheral blood monocytes in different stages of CAD.Material and methodsA total of 24 age- and sex- matched patients at different CAD stages who received coronary angiography were enrolled, which included 8 patients with normal coronary angiography, 8 patients with angiographic intermediate lesion, and 8 patients with AMI. The RNA from peripheral blood monocytes was extracted and transcriptome sequenced to analyze the gene expression and the differentially expressed genes (DEG). A Gene Oncology (GO) enrichment analysis was performed to analyze the biological function of genes. Weighted gene correlation network analysis (WGCNA) was performed to classify genes into several gene modules with similar expression profiles, and correlation analysis was carried out to explore the association of each gene module with a clinical trait. The dynamic network biomarker (DNB) algorithm was used to calculate the key genes that promote disease progression. Finally, the overlapping genes between different analytic methods were explored.ResultsWGCNA analysis identified a total of nine gene modules, of which two modules have the highest positive association with CAD stages. GO enrichment analysis indicated that the biological function of genes in these two gene modules was closely related to inflammatory response, which included T-cell activation, cell response to inflammatory stimuli, lymphocyte activation, cytokine production, and the apoptotic signaling pathway. DNB analysis identified a total of 103 genes that may play key roles in the progression of atherosclerosis plaque. The overlapping genes between DEG/WGCAN and DNB analysis identified the following 13 genes that may play key roles in the progression of atherosclerosis disease: SGPP2, DAZAP2, INSIG1, CD82, OLR1, ARL6IP1, LIMS1, CCL5, CDK7, HBP1, PLAU, SELENOS, and DNAJB6.ConclusionsThe current study identified a total of 13 genes that may play key roles in the progression of atherosclerotic plaque and provides new insights for early warning biomarkers and underlying mechanisms underlying the progression of CAD.

Dataset Information

Comprehensive Identification of Key Genes Involved in Development of Diabetes Mellitus-Related Atherogenesis Using Weighted Gene Correlation Network Analysis.

Publications

Comprehensive Identification of Key Genes Involved in Development of Diabetes Mellitus-Related Atherogenesis Using Weighted Gene Correlation Network Analysis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure