Identification of lactate metabolism-related genes as kidney fibrosis biomarkers through integrated transcriptomic and experimental analyses

Kidney fibrosis, the final pathological outcome of chronic kidney disease, lacks reliable biomarkers, and the role of lactylation in its pathogenesis remains poorly defined. We integrated multi-omics, machine learning, and experimental validation to identify lactylation-associated biomarkers using two public transcriptomic datasets. After batch correction and differential expression analysis, 13 overlapping lactylation-modified genes were obtained, and eight hub genes were identified via 113 model combinations. Downregulated hub genes regulate core renal metabolic pathways including TCA cycle and oxidative phosphorylation, while upregulated genes mediate immune-inflammatory activation and cytokine signaling. Immune infiltration analysis showed significant immune cell enrichment in fibrotic tissues, with hub genes correlated with T cell subsets. Six hub genes were validated in UUO mouse models and human fibrotic kidney tissues. A five-gene diagnostic panel exhibited robust diagnostic performance with AUCs of 0.89–0.90. This study establishes a lactylation-related diagnostic signature, reveals metabolic-immune crosstalk, and supports early clinical diagnosis of kidney fibrosis.