Athero-Oncology Perspective: Identifying Hub Genes for Atherosclerosis Diagnosis Using Machine Learning

Liyan Zhao^1,2Xuzhen Lv³Wen Chen⁴Xinru Li³Jie Zhou⁴Qi Ai¹Qinhui Tuo^5*

• ¹School of Basic Medicine, Ningxia Medical University, Yinchuan, Ningxia, China
• ²Department of Anesthesiology, People's Hospital of Ningxia Hui Autonomous Region,, Ningxia Medical University, Yinchuan, Ningxia, China
• ³Key Laboratory for Quality Evaluation of Bulk Herbs of Hunan Province, School of Pharmacy, Hunan University of Chinese Medicine, Changsha, China
• ⁴Key Laboratory of Vascular Biology and Translational Medicine, Medical School,, Hunan University of Chinese Medicine, Changsha, China
• ⁵Key Laboratory of Vascular Biology and Translational Medicine, Medical School；Key Laboratory for Quality Evaluation of Bulk Herbs of Hunan Province, School of Pharmacy,, Hunan University of Chinese Medicine, Changsha, China

Background: The transformation of smooth muscle cells (SMCs) into alternative phenotypes is a key process in atherosclerosis pathogenesis. Recent studies have revealed oncological parallels between atherosclerosis and cancer, such as DNA damage and oncogenic pathway activation in SMCs, but the precise molecular mechanisms remain poorly understood. This study integrates cancer gene sets using bioinformatics to identify key hub genes associated with atherosclerosis and explores their immune molecular mechanisms. Methods: Datasets from the Gene Expression Omnibus (GEO) were analyzed to identify differentially expressed genes (DEGs) and module genes using Limma and WGCNA. Machine learning algorithms (SVM-RFE, LASSO regression, and random forest) were employed to identify cancer-related hub genes for early atherosclerosis diagnosis. A diagnostic model was constructed and validated. UMAP plots from single-cell RNA sequencing data were used to analyze the expression patterns of hub genes, particularly focusing on macrophage-like SMCs in SMC lineage-traced mouse models. Biomarker expression was validated in both human and mouse experiments. Results: Four cancer-related hub genes (CRGs) were identified: Interferon Regulatory Factor 7 (IRF7), Formin Homology 2 Domain Containing 1 (FHOD1), Tumor Necrosis Factor (TNF), and Zinc Finger SWIM Domain Containing 3 (ZSWIM3). A diagnostic nomogram using IRF7, FHOD1, and TNF demonstrated high accuracy and reliability in both training and validation datasets. Immune microenvironment analysis revealed significant differences between atherosclerosis and control groups. Spearman correlation analysis highlighted associations between hub genes and immune cell infiltration. Single-cell RNA sequencing identified distinct SMC-derived cell clusters and phenotypic transitions, with increased expression of IRF7 and FHOD1 in macrophages potentially derived from SMCs in both human carotid plaques and mouse models. Conclusion: This study integrates cancer gene sets to identify key hub genes in atherosclerosis, emphasizing its parallels with cancer. The diagnostic nomogram based on IRF7, FHOD1, and TNF provides a reliable tool for early diagnosis, while insights into SMC phenotypic switching and immune microenvironment modulation offer potential therapeutic targets.