{"title":"利用机器学习算法识别动脉粥样硬化的新型诊断标志物。","authors":"Yanshuang Cheng, Yang Shao","doi":"10.29271/jcpsp.2025.05.574","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>To outline immune-cell infiltration and identify diagnostic genes for atherosclerosis (AS) to better understand the potential molecular processes involved in AS development.</p><p><strong>Study design: </strong> 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.</p><p><strong>Methodology: </strong>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.</p><p><strong>Results: </strong>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).</p><p><strong>Conclusion: </strong>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.</p><p><strong>Key words: </strong>Atherosclerosis, Inflammatory reaction, Machine-learning algorithms, Bioinformatic.</p>","PeriodicalId":54905,"journal":{"name":"Jcpsp-Journal of the College of Physicians and Surgeons Pakistan","volume":"35 5","pages":"574-579"},"PeriodicalIF":0.7000,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Identification of Novel Diagnostic Markers for Atherosclerosis Using Machine-Learning Algorithms.\",\"authors\":\"Yanshuang Cheng, Yang Shao\",\"doi\":\"10.29271/jcpsp.2025.05.574\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objective: </strong>To outline immune-cell infiltration and identify diagnostic genes for atherosclerosis (AS) to better understand the potential molecular processes involved in AS development.</p><p><strong>Study design: </strong> 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.</p><p><strong>Methodology: </strong>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.</p><p><strong>Results: </strong>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).</p><p><strong>Conclusion: </strong>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.</p><p><strong>Key words: </strong>Atherosclerosis, Inflammatory reaction, Machine-learning algorithms, Bioinformatic.</p>\",\"PeriodicalId\":54905,\"journal\":{\"name\":\"Jcpsp-Journal of the College of Physicians and Surgeons Pakistan\",\"volume\":\"35 5\",\"pages\":\"574-579\"},\"PeriodicalIF\":0.7000,\"publicationDate\":\"2025-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Jcpsp-Journal of the College of Physicians and Surgeons Pakistan\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.29271/jcpsp.2025.05.574\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"MEDICINE, GENERAL & INTERNAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Jcpsp-Journal of the College of Physicians and Surgeons Pakistan","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.29271/jcpsp.2025.05.574","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MEDICINE, GENERAL & INTERNAL","Score":null,"Total":0}
Identification of Novel Diagnostic Markers for Atherosclerosis Using Machine-Learning Algorithms.
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.
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.
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