Development and assessment of diabetic nephropathy prediction model using hub genes identified by weighted correlation network analysis

Diabetic nephropathy (DN) is one microvascular complication of diabetes. About 30% of diabetic patients can develop DN, which is closely related to the high incidence and mortality of heart diseases, and then develop end-stage renal diseases. Therefore, early detection and screening of high-risk patients with DN is important. Herein, we explored the differences of serum transcriptomics between DN and non-DN in type II diabetes mellitus (T2DM) patients. We obtained 110 target genes using weighted correlation network analysis. Gene Ontology enrichment analysis indicates these target genes are mainly related to membrane adhesion, alpha-amino acid biosynthesis, metabolism, and binding, terminus, inhibitory synapse, clathrinid-sculpted vesicle, kinase activity, hormone binding, receptor activity, and transporter activity. Kyoto Encyclopedia of Genes and Genomes analysis indicates the process of DN in diabetic patients can involve synaptic vesicle cycle, cysteine and methionine metabolism, N-Glycan biosynthesis, osteoclast differentiation, and cAMP signaling pathway. Next, we detected the expression levels of hub genes in a retrospective cohort. Then, we developed a risk score tool included in the prediction model for early DN in T2DM patients. The prediction model was well applied into clinical practice, as confirmed by internal validation and several other methods. A novel DN risk model with relatively high prediction accuracy was established based on clinical characteristics and hub genes of serum detection. The estimated risk score can help clinicians develop individualized intervention programs for DN in T2DM. External validation data are required before individualized intervention measures.