Screening of key pathogenic genes of type 1 diabetes in children.

Abstract

The etiology of type 1 diabetes mellitus (T1DM) in pediatric populations remains poorly understood. The key to precise prevention and treatment of T1DM in identifying crucial pathogenic genes. These key pathogenic genes can serve as biological markers for early diagnosis and classification, as well as therapeutic targets. However, there is currently a lack of relevant research on screening key pathogenic genes based on sequencing data and efficient algorithms. The transcriptome sequencing results of peripheral blood mononuclear cells (PBMCs) of children with T1DM (GSE156035) were downloaded from the Gene Expression Omnibus (GEO) database. The data set contained 20 T1DM samples and 20 control samples. Differentially expressed genes (DEGs) in children with T1DM were selected based on fold change (FC) >1.5 times and adjusted P value <0.05. The weighted gene co-expression network was constructed. Hub genes were screened as modular membership (MM) >0.8 and gene significance (GS) >0.5. Intersection genes of DEGs and hub genes were defined as key pathogenic genes. The diagnostic efficacy of key pathogenic genes was evaluated using receiver operator characteristic (ROC) curves. A total of 293 DEGs were selected. Compared with the control group, 94 genes were down-regulated and 199 genes were up-regulated in the treatment group. Black modules (Cor =0.52, P=2e-12) were positively correlated with diabetic traits, whereas brown modules (Cor =-0.51, P=5e-12) and pink modules (Cor =-0.53, P=5e-13) were negatively correlated with diabetic traits. The black module contained 15 hub genes, the pink gene module contained 9 hub genes, and the brown module contained 52 hub genes. The intersection of hub genes and DEGs contained 2 genes, CCL25 and EGFR. The expression of CCL25 and EGFR was low in control samples and high in the test group (P<0.001). The areas under ROC curves (AUCs) of CCL25 and EGFR were 0.852 and 0.867, respectively (P<0.05). Weighted correlation network analysis (WGCNA) was used to identify the key pathogenic genes of T1DM in children, including CCL25 and EGFR, which have good diagnostic efficacy for T1DM in children.