{"title":"确定COPD和败血症之间的共同诊断生物标志物和治疗靶点:生物信息学和机器学习方法。","authors":"Xinyi Li, Yuyang Xiao, Meng Yang, Xupeng Zhang, Zhangchi Yuan, Zaiqiu Zhang, Hanyong Zhang, Lin Liu, Mingyi Zhao","doi":"10.2147/COPD.S510846","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Evidence suggests a bidirectional association between chronic obstructive pulmonary disease (COPD) and sepsis, but the underlying mechanisms remain unclear. This study aimed to explore shared diagnostic genes, potential mechanisms, and the role of immune cells in the COPD-sepsis relationship using Mendelian randomization (MR) and bioinformatics approaches, while also identifying potential therapeutic drugs.</p><p><strong>Methods: </strong>Two-sample MR analysis was performed using genome-wide association data to assess genetically predicted COPD and sepsis. Immune cell-mediated effects were quantified using a two-way two-sample MR analysis. Differential expression gene (DEG) analysis and weighted gene co-expression network analysis (WGCNA) were used to identify common genes. Functional enrichment analyses were conducted to explore the biological roles of these genes. LASSO and SVM-RFE algorithms identified shared diagnostic genes, which were evaluated using receiver operating characteristic (ROC) curves. Immune cell infiltration was analyzed with CIBERSORT, while transcription factor (TF) and miRNA networks were constructed using NetworkAnalyst. Drug predictions were made using DSigDB, and molecular docking validated potential drugs.</p><p><strong>Results: </strong>Three immune cell types were identified as mediators between COPD and sepsis, with genetically predicted effects mediated by these cells at rates of 6.5%, 12.8%, and 3.9%. A total of 33 overlapping genes were identified, and AIM2 and RNF125 were highlighted as key diagnostic genes. Immune infiltration analysis revealed dysregulated monocyte, macrophage, plasma, and dendritic cells. Regulatory network analysis identified nine key co-regulators. Ten potential drug targets were identified, with seven validated via molecular docking.</p><p><strong>Conclusion: </strong>AIM2 and RNF125 may serve as diagnostic biomarkers, and identified immune cell subsets could mediate the COPD-sepsis connection, offering insights into potential therapeutic targets.</p>","PeriodicalId":48818,"journal":{"name":"International Journal of Chronic Obstructive Pulmonary Disease","volume":"20 ","pages":"1761-1786"},"PeriodicalIF":2.7000,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12126980/pdf/","citationCount":"0","resultStr":"{\"title\":\"Identifying Common Diagnostic Biomarkers and Therapeutic Targets between COPD and Sepsis: A Bioinformatics and Machine Learning Approach.\",\"authors\":\"Xinyi Li, Yuyang Xiao, Meng Yang, Xupeng Zhang, Zhangchi Yuan, Zaiqiu Zhang, Hanyong Zhang, Lin Liu, Mingyi Zhao\",\"doi\":\"10.2147/COPD.S510846\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Evidence suggests a bidirectional association between chronic obstructive pulmonary disease (COPD) and sepsis, but the underlying mechanisms remain unclear. This study aimed to explore shared diagnostic genes, potential mechanisms, and the role of immune cells in the COPD-sepsis relationship using Mendelian randomization (MR) and bioinformatics approaches, while also identifying potential therapeutic drugs.</p><p><strong>Methods: </strong>Two-sample MR analysis was performed using genome-wide association data to assess genetically predicted COPD and sepsis. Immune cell-mediated effects were quantified using a two-way two-sample MR analysis. Differential expression gene (DEG) analysis and weighted gene co-expression network analysis (WGCNA) were used to identify common genes. Functional enrichment analyses were conducted to explore the biological roles of these genes. LASSO and SVM-RFE algorithms identified shared diagnostic genes, which were evaluated using receiver operating characteristic (ROC) curves. Immune cell infiltration was analyzed with CIBERSORT, while transcription factor (TF) and miRNA networks were constructed using NetworkAnalyst. Drug predictions were made using DSigDB, and molecular docking validated potential drugs.</p><p><strong>Results: </strong>Three immune cell types were identified as mediators between COPD and sepsis, with genetically predicted effects mediated by these cells at rates of 6.5%, 12.8%, and 3.9%. A total of 33 overlapping genes were identified, and AIM2 and RNF125 were highlighted as key diagnostic genes. Immune infiltration analysis revealed dysregulated monocyte, macrophage, plasma, and dendritic cells. Regulatory network analysis identified nine key co-regulators. Ten potential drug targets were identified, with seven validated via molecular docking.</p><p><strong>Conclusion: </strong>AIM2 and RNF125 may serve as diagnostic biomarkers, and identified immune cell subsets could mediate the COPD-sepsis connection, offering insights into potential therapeutic targets.</p>\",\"PeriodicalId\":48818,\"journal\":{\"name\":\"International Journal of Chronic Obstructive Pulmonary Disease\",\"volume\":\"20 \",\"pages\":\"1761-1786\"},\"PeriodicalIF\":2.7000,\"publicationDate\":\"2025-05-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12126980/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Chronic Obstructive Pulmonary Disease\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.2147/COPD.S510846\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q2\",\"JCRName\":\"RESPIRATORY SYSTEM\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Chronic Obstructive Pulmonary Disease","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.2147/COPD.S510846","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"RESPIRATORY SYSTEM","Score":null,"Total":0}
Identifying Common Diagnostic Biomarkers and Therapeutic Targets between COPD and Sepsis: A Bioinformatics and Machine Learning Approach.
Background: Evidence suggests a bidirectional association between chronic obstructive pulmonary disease (COPD) and sepsis, but the underlying mechanisms remain unclear. This study aimed to explore shared diagnostic genes, potential mechanisms, and the role of immune cells in the COPD-sepsis relationship using Mendelian randomization (MR) and bioinformatics approaches, while also identifying potential therapeutic drugs.
Methods: Two-sample MR analysis was performed using genome-wide association data to assess genetically predicted COPD and sepsis. Immune cell-mediated effects were quantified using a two-way two-sample MR analysis. Differential expression gene (DEG) analysis and weighted gene co-expression network analysis (WGCNA) were used to identify common genes. Functional enrichment analyses were conducted to explore the biological roles of these genes. LASSO and SVM-RFE algorithms identified shared diagnostic genes, which were evaluated using receiver operating characteristic (ROC) curves. Immune cell infiltration was analyzed with CIBERSORT, while transcription factor (TF) and miRNA networks were constructed using NetworkAnalyst. Drug predictions were made using DSigDB, and molecular docking validated potential drugs.
Results: Three immune cell types were identified as mediators between COPD and sepsis, with genetically predicted effects mediated by these cells at rates of 6.5%, 12.8%, and 3.9%. A total of 33 overlapping genes were identified, and AIM2 and RNF125 were highlighted as key diagnostic genes. Immune infiltration analysis revealed dysregulated monocyte, macrophage, plasma, and dendritic cells. Regulatory network analysis identified nine key co-regulators. Ten potential drug targets were identified, with seven validated via molecular docking.
Conclusion: AIM2 and RNF125 may serve as diagnostic biomarkers, and identified immune cell subsets could mediate the COPD-sepsis connection, offering insights into potential therapeutic targets.
期刊介绍:
An international, peer-reviewed journal of therapeutics and pharmacology focusing on concise rapid reporting of clinical studies and reviews in COPD. Special focus will be given to the pathophysiological processes underlying the disease, intervention programs, patient focused education, and self management protocols. This journal is directed at specialists and healthcare professionals