BACKGROUND: Coronary artery disease (CAD) is a major global health burden characterized by complex pathophysiological mechanisms. Programmed cell death (PCD) pathways, including apoptosis, autophagy, necroptosis, and pyroptosis, have been implicated in the development and progression of CAD. Recent research has demonstrated that these forms of PCD interact through highly regulated and interconnected molecular networks, ultimately shaping disease progression in the cardiovascular system. However, the prognostic significance of these PCD mechanisms in CAD remains unclear. This study aims to comprehensively analyze the involvement of various PCD pathways in CAD and to identify prognostic biomarkers by integrating gene expression data and machine learning approaches.
METHODS: Gene expression analysis from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets and the single-sample Gene Set Enrichment Analysis (ssGSEA) assessed PCD pathway activity. Machine learning identified core PCD genes and developed the PCDscore. Immune cell infiltration and pan-cancer analysis were conducted, along with single-cell RNA sequencing (scRNA-seq).
RESULTS: Differential gene expression in CAD samples and varied PCD pathway activities were observed. Core genes (,,,, and) were identified, with the PCDscore effectively stratifying CAD patients. Immune cell differences and correlations with key genes were noted. Pan-cancer analysis and single-cell data provided further insights. GZMB CXCR4SFN ATP6V0A4GSDMA
CONCLUSIONS: The study highlights the importance of PCD pathways in CAD and the prognostic value of the PCDscore, offering a comprehensive tool for personalized treatment strategies.
