Immune-related diagnostic indicators and targeted therapies for COPD combined with NASH were identified and verified via WGCNA and LASSO.

Introduction: The incidence of chronic obstructive pulmonary disease (COPD) and non-alcoholic fatty liver disease (NAFLD) has increased significantly in past decades, posing a significant public health burden. An increasing amount of research points to a connection between COPD and NAFLD. This study aimed to identify the key genes of these two diseases, construct a diagnostic model, and predict potential therapeutic agents based on critical genes. Methods: NAFLD and COPD datasets were obtained from the GEO database, differential genes were identified by differential analysis and WGCNA, PPI networks were constructed and enriched for differential genes and COPD-associated secreted proteins, small molecule compounds were screened, and immune cell infiltration was assessed. Meanwhile, LASSO and RF further screened the essential genes, and finally, two key genes were obtained. Subsequently, the nomogram diagnostic model and lncRNA-miRNA-mRNA network were constructed based on these two core genes, subjected to drug prediction and GSEA enrichment analysis, and validated in an external cohort using qRT-PCR. Results: KEGG enrichment analysis indicated that the NF-kappa B and TNF signaling pathways may be associated with COPD and NASH co-morbidities. Ten small-molecule drugs associated with COPD and NASH were identified through cMAP analysis, including ansoprazole and atovaquone. In addition, we further identified the hub genes S100A9 and MYH2 for NAFLD and COPD by machine learning methods. The immune infiltration indicated that these two core genes might be involved in the immunomodulatory process of NASH by regulating the function or recruitment of specific immune cell types. A nomogram diagnostic model was constructed based on these two core genes. The AUC value for S100A9 was 0.887, for MYH2 was 0.877, and for the nomogram was 0.889, demonstrating excellent diagnostic efficacy. Two hundred fifty-four potential drugs targeting S100A9 and 67 MYH2 were searched in the DGIdb database. Meanwhile, the lncRNA-miRNA-mRNA network was constructed by predicting target miRNAs of biomarkers and further predicting lncRNAs targeting miRNAs. qRT-PCR analysis revealed that S100A9 was upregulated in both COPD and NAFLD, consistent with bioinformatic predictions, while MYH2 showed increased expression in COPD but decreased expression in NAFLD, diverging from the predicted downregulation in both diseases. These findings suggest that S100A9 serves as a common inflammatory marker for both diseases, whereas MYH2 may be regulated by disease-specific mechanisms, highlighting its potential for distinguishing COPD from NAFLD. Conclusion: The hub genes S100A9 and MYH2 in COPD and NASH were identified by various bioinformatics methods and a diagnostic model was constructed to improve the diagnostic efficiency. We also revealed some potential biological mechanisms of COPD and NASH and potential drugs for COPD-related NASH. Our findings provide potential new diagnostic and therapeutic options for COPD-associated NASH and may help reduce its prevalence.