{"title":"整合孟德尔随机化和机器学习来识别COPD中与缺氧相关的诊断生物标志物和因果关系。","authors":"Wenhui Fu, Yangli Liu, Renjie Li, Haiying Jin","doi":"10.2147/COPD.S524381","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Chronic obstructive pulmonary disease (COPD) involves progressive lung function decline, with hypoxia playing a key pathogenic role. However, systematic investigations focusing on hypoxia-related genes (HRGs) in COPD remain limited.</p><p><strong>Methods: </strong>We applied machine learning to identify HRG-associated diagnostic biomarkers and evaluated their performance via Receiver Operating Characteristic (ROC) analysis. Mendelian randomization (MR) was conducted to assess causal relationships between candidate genes and COPD. A nomogram model was constructed to evaluate clinical utility, and a ceRNA network was developed using ENCORI database.</p><p><strong>Results: </strong>Six HRG-based diagnostic biomarkers were identified, including <i>SLC2A1</i>, which demonstrated strong diagnostic value (AUC > 0.8). MR analysis revealed a significant causal effect of <i>SLC2A1</i> expression on COPD risk (OR = 1.32, 95% CI: 1.02-1.71, P < 0.05). Functional evidence suggests <i>SLC2A1</i> promotes hypoxia-induced metabolic reprogramming in airway epithelial cells. The constructed nomogram showed good clinical applicability. ceRNA analysis highlighted <i>MALAT1</i>, <i>NEAT1</i>, and <i>XIST</i> as potential upstream regulators.</p><p><strong>Conclusion: </strong>Our findings identify <i>SLC2A1</i> as a causal and diagnostically relevant gene in COPD, offering novel insight into hypoxia-driven disease mechanisms and supporting future personalized therapeutic strategies.</p>","PeriodicalId":48818,"journal":{"name":"International Journal of Chronic Obstructive Pulmonary Disease","volume":"20 ","pages":"3187-3202"},"PeriodicalIF":3.1000,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12439713/pdf/","citationCount":"0","resultStr":"{\"title\":\"Integrating Mendelian Randomization and Machine Learning to Identify Hypoxia-Related Diagnostic Biomarkers and Causal Relationship in COPD.\",\"authors\":\"Wenhui Fu, Yangli Liu, Renjie Li, Haiying Jin\",\"doi\":\"10.2147/COPD.S524381\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Chronic obstructive pulmonary disease (COPD) involves progressive lung function decline, with hypoxia playing a key pathogenic role. However, systematic investigations focusing on hypoxia-related genes (HRGs) in COPD remain limited.</p><p><strong>Methods: </strong>We applied machine learning to identify HRG-associated diagnostic biomarkers and evaluated their performance via Receiver Operating Characteristic (ROC) analysis. Mendelian randomization (MR) was conducted to assess causal relationships between candidate genes and COPD. A nomogram model was constructed to evaluate clinical utility, and a ceRNA network was developed using ENCORI database.</p><p><strong>Results: </strong>Six HRG-based diagnostic biomarkers were identified, including <i>SLC2A1</i>, which demonstrated strong diagnostic value (AUC > 0.8). MR analysis revealed a significant causal effect of <i>SLC2A1</i> expression on COPD risk (OR = 1.32, 95% CI: 1.02-1.71, P < 0.05). Functional evidence suggests <i>SLC2A1</i> promotes hypoxia-induced metabolic reprogramming in airway epithelial cells. The constructed nomogram showed good clinical applicability. ceRNA analysis highlighted <i>MALAT1</i>, <i>NEAT1</i>, and <i>XIST</i> as potential upstream regulators.</p><p><strong>Conclusion: </strong>Our findings identify <i>SLC2A1</i> as a causal and diagnostically relevant gene in COPD, offering novel insight into hypoxia-driven disease mechanisms and supporting future personalized therapeutic strategies.</p>\",\"PeriodicalId\":48818,\"journal\":{\"name\":\"International Journal of Chronic Obstructive Pulmonary Disease\",\"volume\":\"20 \",\"pages\":\"3187-3202\"},\"PeriodicalIF\":3.1000,\"publicationDate\":\"2025-09-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12439713/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.S524381\",\"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.S524381","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}
Integrating Mendelian Randomization and Machine Learning to Identify Hypoxia-Related Diagnostic Biomarkers and Causal Relationship in COPD.
Background: Chronic obstructive pulmonary disease (COPD) involves progressive lung function decline, with hypoxia playing a key pathogenic role. However, systematic investigations focusing on hypoxia-related genes (HRGs) in COPD remain limited.
Methods: We applied machine learning to identify HRG-associated diagnostic biomarkers and evaluated their performance via Receiver Operating Characteristic (ROC) analysis. Mendelian randomization (MR) was conducted to assess causal relationships between candidate genes and COPD. A nomogram model was constructed to evaluate clinical utility, and a ceRNA network was developed using ENCORI database.
Results: Six HRG-based diagnostic biomarkers were identified, including SLC2A1, which demonstrated strong diagnostic value (AUC > 0.8). MR analysis revealed a significant causal effect of SLC2A1 expression on COPD risk (OR = 1.32, 95% CI: 1.02-1.71, P < 0.05). Functional evidence suggests SLC2A1 promotes hypoxia-induced metabolic reprogramming in airway epithelial cells. The constructed nomogram showed good clinical applicability. ceRNA analysis highlighted MALAT1, NEAT1, and XIST as potential upstream regulators.
Conclusion: Our findings identify SLC2A1 as a causal and diagnostically relevant gene in COPD, offering novel insight into hypoxia-driven disease mechanisms and supporting future personalized therapeutic strategies.
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