Athero-oncology perspective: identifying hub genes for atherosclerosis diagnosis using machine learning

Area of Science:

• Cardiovascular Biology
• Cancer Biology
• Bioinformatics

Background:

• Atherosclerosis involves smooth muscle cell (SMC) phenotypic transformation, sharing oncological pathways with cancer.
• Precise molecular mechanisms linking DNA damage and oncogenic pathways in SMCs to atherosclerosis remain unclear.

Purpose of the Study:

• To integrate cancer gene sets using bioinformatics to identify key hub genes in atherosclerosis.
• To explore the immune molecular mechanisms associated with these hub genes.
• To develop a diagnostic model for early atherosclerosis detection based on identified genes.

Main Methods:

• Differential gene expression analysis (Limma, WGCNA) on GEO datasets.
• Machine learning algorithms (SVM-RFE, LASSO, random forest) for hub gene identification.
• Single-cell RNA sequencing and validation in human and mouse models.

Main Results:

• Identified four cancer-related hub genes: IRF7, FHOD1, TNF, and ZSWIM3.
• Developed a diagnostic nomogram using IRF7, FHOD1, and TNF with high accuracy.
• Observed altered immune microenvironments and SMC-derived macrophage-like cells expressing IRF7 and FHOD1.

Conclusions:

• Key hub genes associated with atherosclerosis and cancer parallels were identified.
• A reliable diagnostic tool for early atherosclerosis detection was established.
• Insights into SMC phenotypic switching and immune modulation provide potential therapeutic targets.