Comprehensive Analysis Based on Genes Associated With Cuproptosis, Ferroptosis, and Pyroptosis for the Prediction of Diagnosis and Therapies in Coronary Artery Disease

Cardiovascular Therapeutics, Journal Year: 2025, Volume and Issue: 2025(1)

Published: Jan. 1, 2025

Coronary artery disease (CAD) is a complex condition influenced by genetic factors, lifestyle, and other risk factors that contribute to increased mortality. This study aimed at evaluating the diagnostic potential of genes associated with cuproptosis, ferroptosis, pyroptosis (CFP) using network modularization machine learning methods. CAD‐related datasets GSE42148, GSE20680, GSE20681 were sourced from GEO database, related CFP gathered MsigDB FerrDb literature. To identify linked these pathways, weighted gene coexpression analysis (WGCNA) was used isolate modules. The accuracy key in modules then assessed LASSO, SVM, random forest models. Immunity drug sensitivity correlation analyses subsequently performed investigate possible underlying mechanisms. function gene, STK17B, analyzed through western blot transwell assays. Two strong correlations identified validated. SVM model outperformed LASSO models, demonstrating superior discriminative power (AUC = 0.997 blue module AUC 1.000 turquoise module), nine identified: CTDSP2, DHRS7, NLRP1, MARCKS, PELI1, RILPL2, JUNB, SLC40A1. Knockdown STK17B inhibited cell migration invasion human umbilical vein endothelial cells. In summary, our findings suggest hold as biomarkers therapeutic targets, playing role CAD progression.

Language: Английский

Development of a machine learning-derived programmed cell death index for prognostic prediction and immune insights in colorectal cancer

Discover Oncology, Journal Year: 2025, Volume and Issue: 16(1)

Published: April 24, 2025

Language: Английский