Identifying Common Diagnostic Biomarkers and Therapeutic Targets between COPD and Sepsis: A Bioinformatics and Machine Learning Approach
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".