Identification and validation of lactylation-related diagnostic biomarkers for type 2 diabetes by WGCNA

Lactylation, a novel post-translational histone modification, has emerged as a critical regulatory mechanism in various metabolic disorders. However, its role in the pathogenesis of type 2 diabetes (T2D) remains poorly understood. This study aims to investigate the potential of lactylation-related genes as diagnostic biomarkers for T2D. Differential analysis and weighted gene co-expression network analysis (WGCNA) were performed on the GSE164416 dataset. Genes obtained from these analyses were intersected with the lactylation-related genes to screen candidate genes. The LASSO, SVM-RFE and random forest algorithms were applied to screen the characteristic genes, and their diagnostic efficacy was verified in the independent cohort. The functions and immune associations were analyzed by GSVA, ssGSEA, and TF-miRNA regulatory network analysis, and qRT-PCR, Western blot and CCK-8 experiments were conducted in the T2D cell model for verification. Lactylation-related IKZF1, S100A4, and VIM were identified as potential diagnostic markers for T2D. These three genes were significantly upregulated in T2D samples and exhibited excellent diagnostic performance (AUC >0.80) in both the training set and validation set. The GSVA analysis revealed that these three genes were involved in key biological processes such as immune regulation, transcriptional modification, metabolic homeostasis and cytoskeleton remodeling. Cell experiments demonstrated that the three genes were upregulated in T2D cell models and knockdown of their expression could promote cell viability. This study identified and validated three potential diagnostic markers related to lactylation for T2D, providing new molecular evidence for the early diagnosis and mechanism research of this disease.