Machine learning selection of basement membrane-associated genes and development of a predictive model for kidney fibrosis.

This study investigates the role of basement membrane-related genes in kidney fibrosis, a significant factor in the progression of chronic kidney disease that can lead to end-stage renal failure. The authors aim to develop a predictive model using machine learning techniques due to the limitations of existing diagnostic methods, which often lack sensitivity and specificity. Utilizing gene expression data from the GEO database, the researchers applied LASSO, Random Forest, and SVM-RFE methods to identify five pivotal genes: ARID4B, EOMES, KCNJ3, LIF, and STAT1. These genes were analyzed across training and validation datasets, resulting in the development of a Nomogram prediction model. Performance metrics, including the area under the ROC curve (AUC), calibration curves, and decision curve analysis, indicated excellent predictive capabilities with an AUC of 0.923. Experimental validation through qRT-PCR in clinical samples and TGF-β-treated HK-2 cells corroborated the expression patterns identified in silico, showing upregulation of ARID4B, EOMES, LIF, and STAT1, and downregulation of KCNJ3. The findings emphasize the importance of basement membrane-related genes in kidney fibrosis and pave the way for enhanced early diagnosis and targeted therapeutic strategies.