Disulfidptosis related immune genes drive prognostic model development and tumor microenvironment characterization in bladder urothelial carcinoma.

The intricate nature and varied forms of bladder urothelial carcinoma (BLCA) highlight the need for new indicators to define tumor prognosis. Disulfidptosis, a novel form of cell death, is closely linked to BLCA progression, prognosis, and treatment outcomes. Our current goal is to develop a novel disulfidptosis-related immune prognostic model to enhance BLCA treatment strategies.

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.