Bioinformatics analysis to decode diabetes biomarkers and related molecular mechanisms

The molecular basis of diabetes Mellitus is yet to be fully elucidated. We aimed to identify the most frequently reported and differentially expressed genes (DEGs) in diabetes using bioinformatics approaches. Text mining analysis included 40,225 abstracts that were analyzed through Python programming language. Three Gene Expression Omnibus (GEO) datasets (44 patients and 57 controls) were analyzed using ImaGEO software. Gene enrichment analysis was conducted using ShinyGo, gprofiler, and Uniprot databases. Protein-protein interaction (PPI) was established using STRING database. Gene expression and text mining results were represented using R-ggplot2 and GeneSyno according to the human genome data.A total of 5939 genes were identified through text mining, of which 112 genes were mentioned in more than 50 articles. Among these genes, HNF4A, PPARA, VEGFA, TCF7L2, HLA-DRB1, PPARG, NOS3, KCNJ11, PRKAA2, and HNF1A were mentioned in more than 200 articles. Analysis of GEO datasets revealed 135 DEGs. CEACAM6, ENPP4, HDAC5, HPCAL1, PARVG, STYXL1, VPS28, ZBTB33, ZFP37 and CCDC58 were the top ten DEGs. The gene enrichment analysis revealed that a significant number of these genes were enriched in aerobic respiration, T-Cell antigen receptor pathway, Tricarboxylic acid metabolic process, vitamin D receptor pathway, Toll-like receptor signaling, and ER unfolded protein response. The PPI analysis showed few genes with high interaction activity, including ATP5G3, LCN2, MDH1, and NDUFB5. Our data mining and gene expression analysis have provided useful information about potential biomarkers in diabetes.