ORIGINAL RESEARCH article
Front. Cardiovasc. Med.
Sec. Atherosclerosis and Vascular Medicine
Establishing an Atherosclerosis Diagnostic Model Based on WGCNA and Machine Learning Algorithms with Key Genes in Cholesterol Metabolism and Ferroptosis, and Revealing the Regulatory Role of HMOX1 in Cellular Ferroptosis
Zengguang Fan 1
Caihui Liu 2
Yiwen Liu 2
Zijian Hong 2
Jianming Zhong 2
Bei Yang 2
Ye Yuan 1
1. Affiliated Hospital of Jiangxi University of Traditional Chinese Medicine, Nanchang, China
2. Jiangxi University of Traditional Chinese Medicine, Nanchang, China
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Abstract
Background: As the primary pathological basis for cardiovascular diseases, atherosclerosis (AS) arises from pathogenesis closely linked to dysregulated cholesterol metabolism and ferroptosis. This study seeks to develop an AS diagnostic model and identify potential biomarkers. Methods: AS-related transcriptomic datasets were obtained from the GEO database. Differentially expressed cholesterol metabolism- and ferroptosis-related genes (DE-CM-FRGs) were screened by integrating WGCNA module genes, AS-related differentially expressed genes, cholesterol metabolism-related genes, and ferroptosis-related genes. Consensus clustering was performed to subtype AS patients. Hub genes were refined using three machine learning algorithms: Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine-Recursive Feature Elimination (SVM-RFE) and Boruta. A logistic regression diagnostic model based on filtered genes was established and evaluated with ROC curves. A nomogram was constructed and evaluated through calibration, decision, and impact curves, followed by building a diagnostic gene-based regulatory network. Single-cell RNA sequencing analyzed HMOX1-expressing cells. In vitro, HMOX1 knockdown effects on proliferation, ROS, MDA, iron content, and mRNA expression of SLC7A11, GPX4, and ACSL4 were assessed in ox-LDL-induced THP-1 cells. Results: The identified five core feature genes (CD36, DPP4, HMOX1, IL1B, NFIL3) exhibited robust diagnostic relevance and auxiliary discriminant value across both training and validation sets. The diagnostic model based on these five genes exhibited strong discriminatory ability in both sets. Regulatory network analysis revealed interactions between the diagnostic genes and transcription factors, miRNAs, and compounds. HMOX1 knockdown suppressed ox-LDL-induced THP-1 cell proliferation, lowered intracellular ROS, MDA, and iron levels, upregulated GPX4 and SLC7A11 expression, and downregulated ACSL4. Conclusion: By systematically identifying key genes in AS-associated cholesterol metabolism and ferroptosis, this study constructs a robust diagnostic model and identifies potential biomarkers and therapeutic targets for AS diagnosis.
Summary
Keywords
Atherosclerosis, cholesterol metabolism, ferroptosis, machine learning, WGCNA
Received
30 September 2025
Accepted
17 February 2026
Copyright
© 2026 Fan, Liu, Liu, Hong, Zhong, Yang and Yuan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Ye Yuan
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