Integrating machine learning and immune infiltration analysis to identify core genes and construct a diagnostic model for type 2 diabetes mellitus

Background Type 2 diabetes mellitus (T2DM) is a prevalent metabolic disorder, and identifying robust biomarkers is crucial for improving diagnosis and understanding its pathogenesis. Methods We analyzed the gene expression dataset GSE250283 from the GEO database to identify differentially expressed genes (DEGs). Functional enrichment analyses (GO and KEGG) were performed. A comprehensive evaluation of 113 machine learning algorithm combinations was conducted to select an optimal model for hub gene identification and diagnostic prediction. The expression of key genes was validated using independent datasets and quantitative real-time PCR (qRT-PCR). Immune infiltration analysis, gene regulatory network prediction, and drug interaction analysis were also carried out. Results A total of 393 DEGs were identified, primarily enriched in immune-related functions and pathways. The LASSO+GBM hybrid model demonstrated superior relative performance among the tested algorithms and pinpointed six hub genes: LY96, CCR1, BLVRB, TCF3, LILRA2, and NCF1. A logistic regression model based on these genes showed promising predictive accuracy (AUC 0.75) in both training and testing sets. Validation confirmed that BLVRB and NCF1 were significantly dysregulated. Immune infiltration revealed significant alterations in the immune cell landscape of T2DM patients, with BLVRB and NCF1 showing substantial correlations with various immune cells. Regulatory network analysis suggested hsa-miR-127-5p as a potential upstream regulator of BLVRB, and methylene blue was identified as a potential targeting drug. Conclusion This study identifies novel immune-related candidate genes, particularly BLVRB and NCF1, for T2DM. The constructed diagnostic model shows potential for further development and the findings offer new insights into the immune mechanisms and potential therapeutic avenues for T2DM.