Identification of biomarkers for childhood obesity based on expressional correlation and functional similarity

Abstract

The aim of the current study was to identify potential biomarkers of childhood obesity, and investigate molecular mechanisms and candidate agents in order to improve therapeutic strategies for childhood obesity. The GSE9624 gene expression profile was downloaded from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) in omental adipose tissues were analyzed with limma package by comparing samples from obese and normal control children. Two-way hierarchical clustering was applied using the pheatmap package. The co-expression (CE) analysis was performed using online CoExpress software. Subsequent to functional classification via the GOSim package, the gene network enriched by DEGs was visualized using the Cytoscape package. The codon usage bias of the DEGs was then examined using the CAI program from the European Molecular Biology Open Software Suite. In total, 583 DEGs (273 upregulated genes and 310 downregulated genes) were observed in the omental adipose tissues between samples from obese and normal control children. Hierarchical clustering identified a significant difference between samples from obesea nd normal control children. Subsequent to CE analysis, 130 DEGs, which were classified into 4 clusters, were selected. The following 3 upregulated and 2 downregulated genes were identified to be significant: Upregulated genes, microtubule-associated protein tau (MAPT), destrin (actin depolymerizing factor) (DSTN) and spectrin, β, non-erythrocytic 1 (SPTBN1); downregulated genes, Rho/Rac guanine nucleotide exchange factor 2 (ARHGEF2) and spindle and kinetochore associated complex subunit 1 (SKA1). The top 3 amino acids were identified to be glycine, leucine and serine with a high bias. The DEGs MAPT, DSTN, SPTBN1, ARHGEF2 and SKA1 are suggested to be candidate biomarkers for childhood obesity.