Integrated machine learning and multi-omics analysis identifies ALOX5 as a potential therapeutic target for tubulointerstitial inflammation in diabetic kidney disease
摘要
Diabetic kidney disease (DKD) is a leading cause of renal failure. Inflammation of the renal tubules and interstitium is a critical factor in the progression of DKD; however, the key regulatory genes and characteristics of the immune microenvironment remain poorly understood. This study aims to identify key inflammatory biomarkers in the renal tubule tissues of DKD patients and to elucidate their potential immunoregulatory mechanisms. By integrating multiple GEO transcriptome datasets and employing differential expression analysis, weighted gene co-expression network analysis (WGCNA), and machine learning algorithms (LASSO, Random Forest), we identified arachidonate 5-lipoxygenase (ALOX5) as a crucial feature gene of renal tubular inflammation in DKD. Clinical correlation analysis revealed that ALOX5 is significantly upregulated in DKD tissues, with high expression closely associated with decreased glomerular filtration rate and infiltration of M1 macrophages. Additionally, combining single-cell sequencing pseudotime analysis and multiplex immunohistochemistry (mIHC), we demonstrated that ALOX5 and its partner protein ALOX5AP are primarily expressed in CD68