Integrative analysis of promising molecular biomarkers and pathways for coronary artery disease using WGCNA and MetaDE methods

Abstract

The present study aimed to examine the molecular mechanisms of coronary artery disease (CAD). A total of four microarray datasets (training dataset no. GSE12288; validation dataset nos. GSE20680, GSE20681 and GSE42148) were downloaded from the Gene Expression Omnibus database, which included CAD and healthy samples. Weighted gene co-expression network analysis was applied to identify highly preserved modules across the four datasets. Differentially expressed genes (DEGs) with significant consistency in the four datasets were selected using the MetaDE method. The overlapping genes amongst the DEGs with significant consistency and in the preserved modules were used to construct a protein-protein interaction (PPI) network, followed by functional enrichment analysis. A total of 11 modules were established in the training dataset, and five of them were highly preserved across all four datasets, including 873 genes. There was a total of 836 DEGs with significant consistency in the four datasets. A total of 177 overlapping genes were selected, with which a PPI network was constructed. The top five genes of the PPI network were identified based on their degrees: LCK proto-oncogene, Src family tyrosine kinase (LCK), euchromatic histone lysine methyltransferase 2 (EHMT2), inosine monophosphate dehydrogenase 2 (IMPDH2), protein phosphatase 4 catalytic subunit (PPP4C) and ζ-chain of T-cell receptor associated protein kinase 70 (ZAP70). Genes in the PPI network were significantly involved in a number of Kyoto Encyclopedia Genes and Genomes pathways, including the ‘natural killer cell mediated cytotoxicity’, ‘primary immunodeficiency’ and ‘Fc gamma R-mediated phagocytosis’ pathways. LCK, EHMT2, IMPDH2, PPP4C and ZAP70 are suggested as promising molecular biomarkers for CAD. The ‘natural killer cell mediated cytotoxicity’, ‘primary immunodeficiency’ and ‘Fc gamma R-mediated phagocytosis’ pathways may serve important roles in CAD.

Transcriptome Analysis Reveals the Genes Involved in Bifidobacterium Longum FGSZY16M3 Biofilm Formation

“…Despite 1296 non-redundant genes that were differentially expressed, the final PPI network only had 228 genes, which means that the role of most genes in biofilm formation is still unknown. Using the key genes selected from the PPI network as references, the biofilm related genes in the strains were further mined through WGCNA [ 51 ]. WGCNA was performed to decompose 2032 coding genes into nine functional modules ( Figure 5 a).…”

Src-family Protein Tyrosine Kinases: A promising target for treating Cardiovascular Diseases

“…downloaded the comprehensive database of gene expression of the sample population to analyze the correlation between genes and CVDs, the analysis of overlapping genes showed that Lck is one of the top three genes in a protein-protein interaction network. Nevertheless, related diagnostic techniques have not become popular without a large number of clinical trial data 86 . The accuracy, feasibility and effectiveness of using SFKs in the diagnosis of CVDs need more clinical trials to be tested.…”

Analysis of potential genetic biomarkers and molecular mechanism of smoking-related postmenopausal osteoporosis using weighted gene co-expression network analysis and machine learning

“…With the rapid development of high-throughput microarray technologies, the identification of genomic variations and biological mechanisms has improved our understanding of disease pathogenesis and treatment [ 16 , 17 ]. Weighted gene co-expression network analysis (WGCNA) is widely used to analyze gene expression microarray data, identify functional gene modules, and discover relationships between gene modules and disease traits [ 18 – 20 ].…”