Identifying Common Diagnostic Biomarkers and Therapeutic Targets between COPD and Sepsis: A Bioinformatics and Machine Learning Approach

Xinyi Li,1,* Yuyang Xiao,1,* Meng Yang,1 Xupeng Zhang,1 Zhangchi Yuan,1 Zaiqiu Zhang,1 Hanyong Zhang,2 Lin Liu,1 Mingyi Zhao1 1Department of Pediatrics, The Third Xiangya Hospital of Central South University, Changsha, Hunan, 410013, People’s Republic of China; 2Development of Novel Pharmaceutical Preparations, Changsha Medical University, Changsha, 410219, People’s Republic of China*These authors contributed equally to this workCorrespondence: Mingyi Zhao, Department of Pediatrics, Third Xiangya Hospital of Central South University, Changsha, Hunan, 410013, People’s Republic of China, Email zhao_mingyi@csu.edu.cn Lin Liu, Department of Pediatrics, Third Xiangya Hospital of Central South University, Changsha, Hunan, 410013, People’s Republic of China, Email liulin602243@csu.edu.cnBackground: 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.Keywords: Mendelian randomization, comprehensive bioinformatics analysis, machine learning, molecular docking, chronic obstructive pulmonary disease, sepsis, immune cells, co-diagnostic genes, predictive drugs

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