Weighted gene co-expression network analysis to identify key modules and hub genes related to hyperlipidaemia

Abstract

Abstract Background The purpose of this study was to explore the potential molecular targets of hyperlipidaemia and the related molecular mechanisms. Methods The microarray dataset of GSE66676 obtained from patients with hyperlipidaemia was downloaded. Weighted gene co-expression network (WGCNA) analysis was used to analyse the gene expression profile, and the royal blue module was considered to have the highest correlation. Gene Ontology (GO) functional and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were implemented for the identification of genes in the royal blue module using the Database for Annotation, Visualization and Integrated Discovery (DAVID) online tool (version 6.8; http://david.abcc.ncifcrf.gov). A protein–protein interaction (PPI) network was established by using the online STRING tool. Then, several hub genes were identified by the MCODE and cytoHubba plug-ins in Cytoscape software. Results The significant module (royal blue) identified was associated with TC, TG and non-HDL-C. GO and KEGG enrichment analyses revealed that the genes in the royal blue module were associated with carbon metabolism, steroid biosynthesis, fatty acid metabolism and biosynthesis pathways of unsaturated fatty acids. SQLE (degree = 17) was revealed as a key molecule associated with hypercholesterolaemia (HCH), and SCD was revealed as a key molecule associated with hypertriglyceridaemia (HTG). RT-qPCR analysis also confirmed the above results based on our HCH/HTG samples. Conclusions SQLE and SCD are related to hyperlipidaemia, and SQLE/SCD may be new targets for cholesterol-lowering or triglyceride-lowering therapy, respectively.