Identification of lipid metabolism related immune markers in atherosclerosis through machine learning and experimental analysis

Aug 5, 2026

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Atherosclerosis drives major cardiovascular morbidity, and conventional diagnostics often miss early-stage disease. This study integrates public atherosclerosis datasets with ssGSEA, WGCNA, differential expression, and multiple machine-learning algorithms to derive lipid metabolism–associated diagnostic genes. Immune infiltration relationships are assessed via ssGSEA immune cell scoring, while molecular docking evaluates binding potential between key markers and therapeutic agents. Experimental validation confirms the expression changes of the identified pivotal genes, supporting immune-relevant biomarker and drug-target value.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages16

Reading Time40 min

AccessFree to download

Background

Methods

Results

Conclusion

Introduction

TYPE Original Research PUBLISHED 25 February 2025 DOI 10.3389/fimmu.2025.1549150 OPEN ACCESS EDITED BY Minghua Ren, First Afﬁliated Hospital of Harbin Medical University, China REVIEWED BY Chen Li, Free University of Berlin, Germany Chuanlong Zhang, Capital Medical University, China Wu Mianhua, Nanjing University of Chinese Medicine, China *CORRESPONDENCE Dazhi Li [21918363@zju.edu.cn](21918363@zju.edu.cn)[ ](21918363@zju.edu.cn)Weicheng Zhao [873709201@qq.com](873709201@qq.com) †These authors have contributed equally to this work RECEIVED 20 December 2024 ACCEPTED 05 February 2025 PUBLISHED 25 February 2025 CITATION Chen H, Wu B, Guan K, Chen L, Chai K, Ying M, Li D and Zhao W (2025) Identiﬁcation of lipid metabolism related immune markers in atherosclerosis through machine learning and experimental analysis. Front. Immunol. 16:1549150 . doi: 10.3389/fimmu.2025.1549150 COPYRIGHT © 2025 Chen, Wu, Guan, Chen, Chai, Ying, Li and Zhao. 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) and the copyright owner(s) 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. Identiﬁcation of lipid metabolism related immune markers in atherosclerosis through machine learning and experimental analysis Hang Chen 1†, Biao Wu 2,3†, Kunyu Guan 4†, Liang Chen 2, Kangjie Chai2, Maoji Ying5, Dazhi Li 2* and Weicheng Zhao 6* 1 Department of Thyroid Breast Vascular Surgery, Banan Hospital of Chongqing Medical University, Chongqing, China, 2 Department of Vascular Surgery, Changhai Hospital Afﬁliated to Naval Medical University, Shanghai, China, 3Cancer Research Centre Nantong, Nantong Tumor Hospital, Nantong, China, 4 Pediatrics, Changhai Hospital Afﬁliated to Naval Medical University, Shanghai, China, 5General Practice, Changhai Hospital Afﬁliated to Naval Medical University, Shanghai, China, 6 Department of Interventional, Shenzhen Traditional Chinese Medicine Hospital, Shenzhen, China Background: Atherosclerosis is a signiﬁcant contributor to cardiovascular disease, and conventional diagnostic methods frequently fall short in the timely and accurate detection of early-stage atherosclerosis. Abnormal lipid metabolism plays a critical role in the development of atherosclerosis. Consequently, the identiﬁcation of new diagnostic markers is essential for the precise diagnosis of this condition. Method: The datasets related to atherosclerosis utilized in this research were obtained from the GEO database (GSE2470, GSE24495, GSE100927 and GSE43292) . The ssGSEA technique was ﬁrst utilized to assess lipid metabolism scores in samples affected by atherosclerosis, thereby aiding in the discovery of important regulatory genes linked to lipid metabolism via WGCNA. Following this, differential expression analysis and functional evaluations were carried out, after which various machine learning approaches were employed to determine signiﬁcant diagnostic genes for atherosclerosis. A diagnostic model was then developed and validated through several machine learning algorithms. Furthermore, molecular docking studies were conducted to analyze the binding afﬁnity of these key markers with therapeutic agents for atherosclerosis. The ssGSEA technique was also used to measure immune cell scores in atherosclerotic samples, aiding the exploration of the connection between key diagnostic markers and immune cells. Finally, the expression variations of the identiﬁed pivotal genes were conﬁrmed through experimental validation. Result: WGCNA identiﬁed 302 lipid metabolism-related genes in atherosclerotic samples, and functional analysis revealed that these genes are associated with multiple immune pa

FAQs

What datasets and computational approaches were used to discover candidate markers?

Datasets were retrieved from the GEO database, with ssGSEA to quantify lipid metabolism scores, WGCNA to find regulatory genes, differential expression analysis for screening, and machine-learning methods to determine diagnostic genes.

Which genes were identified as key diagnostic markers for atherosclerosis?

APLNR, PCDH12, PODXL, SLC40A1, TM4SF18, and TNFRSF25 were identified as key diagnostic genes after machine-learning-based screening.

How were immune cell relationships and therapeutic targeting potential evaluated?

ssGSEA was used to estimate immune cell scores and link the diagnostic genes to immune infiltration, and molecular docking assessed binding affinity between the markers and therapeutic agents.

Aug 5, 2026

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