Integrating Mendelian Randomization and Machine Learning to Identify Hypoxia-Related Diagnostic Biomarkers and Causal Relationship in COPD

Q: What is the main objective of this study?

To identify hypoxia-related diagnostic biomarkers for COPD using machine learning and to evaluate causal relationships between candidate genes and COPD using Mendelian randomization.

Q: Which gene shows strong diagnostic and causal relevance in the results?

SLC2A1, which demonstrates strong diagnostic value (AUC > 0.8) and a significant causal effect on COPD risk in the MR analysis.

Aug 2, 2026

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Chronic obstructive pulmonary disease (COPD) features progressive lung-function decline in which hypoxia is a key pathogenic driver, yet systematic evaluation of hypoxia-related genes remains limited. This study integrates machine learning to discover hypoxia-related diagnostic biomarkers, validates performance with ROC analysis, and applies Mendelian randomization to test causal effects on COPD risk. A nomogram is built for clinical utility, and a ceRNA network is constructed to infer upstream regulators. Results identify SLC2A1 as both diagnostic and causal, with supporting functional evidence.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages16

Reading Time40 min

AccessFree to download

Introduction

COPD background and clinical burden

Role of hypoxia and hypoxia-inducible factors

Methods

Machine learning for biomarker discovery

ROC evaluation

Mendelian randomization for causal inference

Nomogram modeling and clinical utility

ceRNA network construction

Results

Identified hypoxia-related diagnostic biomarkers

Causal effect of SLC2A1 on COPD risk

Functional implications and upstream regulators

Conclusion

Key findings and implications for future strategies

International Journal of Chronic Obstructive Pulmonary Disease downloaded from [https://www.dovepress.com/](https://www.dovepress.com/) For personal use only. International Journal of Chronic Obstructive Pulmonary Disease Open Access Full Text Article ORIGINAL RESEARCH Integrating Mendelian Randomization and Machine Learning to Identify Hypoxia-Related Diagnostic Biomarkers and Causal Relationship in COPD Wenhui Fu, Yangli Liu, Renjie Li, Haiying Jin Department of Respiratory Medicine, Jinyun People’s Hospital, Lishui, Zhejiang, 321400, People’s Republic of China Correspondence: Haiying Jin, Department of Respiratory Medicine, Jinyun People’s Hospital, No. 299 North Ziwei Road, Wuyun Street, Lishui, Zhejiang, 321400, People’s Republic of China, Tel +86 13754278979, Email [jinhaiying8979@yeah.net](jinhaiying8979@yeah.net) | 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 hypoxiadriven disease mechanisms and supporting future personalized therapeutic strategies.
Keywords: chronic obstructive pulmonary disease, hypoxia-related genes, Mendelian randomization, machine learning | | --- | | Introduction
Chronic obstructive pulmonary disease (COPD) is a chronic inflammatory airway disorder and one of the most prevalent respiratory conditions, currently recognized as the third leading cause of death worldwide.1,2 The key pathological hallmark of COPD is persistent airflow limitation, which progressively impairs lung function and leads to irreversible airway damage. Common symptoms include chronic cough, sputum production, chest tightness, dyspnea, and respiratory distress.3 As the disease progresses, patients experience a gradual decline in their ability to work and perform daily activities, which significantly reduces their quality of life and imposes a growing economic burden. Consequently, COPD has become a critical global public health concern.4 Given these challenges, advancing our understanding of COPD pathogenesis and identifying novel biomarkers are critical for improving therapeutic strategies and enhancing patient outcomes.
Hypoxia-inducible factors (HIFs) play a pivotal role in the pathogenesis of various diseases, including cardiovascular conditions and metabolic disorders.5,6 In particular, hypoxic conditions have been implicated in exacerbating the progression of COPD.7 In COPD patients, chronic hypoxia not only drives persistent inflammation in the airways, lung parenchyma, and pulmonary vasculature, but also induces a systemic inflammatory response.7 This inflammatory state is further amplified by hypoxia-triggered neutrophil elastase, which exacerbates tissue damage. Lodge et al demonstrated that hypoxia intensifies neutrophil-mediated endothelial injury in COPD pati

FAQs

What is the main objective of this study?

To identify hypoxia-related diagnostic biomarkers for COPD using machine learning and to evaluate causal relationships between candidate genes and COPD using Mendelian randomization.

Which gene shows strong diagnostic and causal relevance in the results?

SLC2A1, which demonstrates strong diagnostic value (AUC > 0.8) and a significant causal effect on COPD risk in the MR analysis.

How is clinical utility assessed in the study?

A nomogram model is constructed and its clinical applicability is evaluated, supporting potential use in clinical decision-making.

Aug 2, 2026

Upload by Skyler