Identification of Novel Diagnostic Markers for Atherosclerosis Using Machine-Learning Algorithms.

IF 0.7 4区医学 Q3 MEDICINE, GENERAL & INTERNAL

Jcpsp-Journal of the College of Physicians and Surgeons Pakistan Pub Date : 2025-05-01 DOI:10.29271/jcpsp.2025.05.574

Yanshuang Cheng, Yang Shao

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引用次数: 0

Abstract

Objective: To outline immune-cell infiltration and identify diagnostic genes for atherosclerosis (AS) to better understand the potential molecular processes involved in AS development.

Study design: Descriptive study. Place and Duration of the Study: Department of Cardiology, The First Hospital of China Medical University, Shenyang, Liaoning, China, from 10th June to 8th October 2024.

Methodology: Relevant datasets were collected from the Gene Expression Omnibus database. Gene set enrichment analysis was conducted on differentially expressed genes (DEGs). Subsequently, three machine-learning algorithms were used to identify the core genes. Receiver operating characteristic (ROC) curves were used to analyse the clinical diagnostic value of the core genes.

Results: A Total of 3,307 DEGs, which were found primarily enriched in inflammation-related pathways. Further analysis of the core genes using three machine-learning algorithms revealed four intersecting genes, IBSP, PI16, MYOC, and IGLL5, which are all inflammation-related genes; they also showed good clinical diagnostic abilities, which were verified using ROC curves (area under the curve: 0.959, 0.946, 0.931, and 0.880, respectively).

Conclusion: IBSP, PI16, MYOC, and IGLL5 participate in AS pathogenesis by regulating inflammatory reactions. These are novel diagnostic markers and are expected to become potential targets for AS-targeted therapies.

Key words: Atherosclerosis, Inflammatory reaction, Machine-learning algorithms, Bioinformatic.

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利用机器学习算法识别动脉粥样硬化的新型诊断标志物。

目的：概述动脉粥样硬化（AS）的免疫细胞浸润和诊断基因，以更好地了解参与AS发展的潜在分子过程。研究设计：描述性研究。研究地点和时间：2024年6月10日至10月8日，中国辽宁省沈阳市中国医科大学第一医院心内科。方法：相关数据集来自Gene Expression Omnibus数据库。对差异表达基因（DEGs）进行基因集富集分析。随后，使用三种机器学习算法来识别核心基因。采用受试者工作特征（ROC）曲线分析核心基因的临床诊断价值。结果：共有3307个deg，主要富集于炎症相关途径。使用三种机器学习算法对核心基因进行进一步分析，发现了四个交叉基因，IBSP, PI16， MYOC和IGLL5，它们都是炎症相关基因；通过ROC曲线（曲线下面积分别为0.959、0.946、0.931、0.880）验证其临床诊断能力较好。结论：IBSP、PI16、MYOC、IGLL5通过调节炎症反应参与AS发病。这些都是新的诊断标记物，有望成为as靶向治疗的潜在靶点。关键词：动脉粥样硬化，炎症反应，机器学习算法，生物信息学

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来源期刊

Jcpsp-Journal of the College of Physicians and Surgeons Pakistan MEDICINE, GENERAL & INTERNAL-

CiteScore

1.40

自引率

0.00%

发文量

453

审稿时长

3-6 weeks

期刊介绍： Journal of College of Physicians and Surgeons Pakistan (JCPSP), is the prestigious, peer reviewed monthly biomedical journal of the country published regularly since 1991. Established with the primary aim of promotion and dissemination of medical research and contributed by scholars of biomedical sciences from Pakistan and abroad, it carries original research papers, , case reports, review articles, articles on medical education, commentaries, short communication, new technology, editorials and letters to the editor. It covers the core biomedical health science subjects, basic medical sciences and emerging community problems, prepared in accordance with the “Uniform requirements for submission to bio-medical journals” laid down by International Committee of Medical Journals Editors (ICMJE). All publications of JCPSP are peer reviewed by subject specialists from Pakistan and locally and abroad.