Integrating bioinformatics and machine learning to investigate the mechanisms by which three major respiratory infectious diseases exacerbate heart failure.

Heart failure (HF) is a severe cardiovascular disease often worsened by respiratory infections like influenza, COVID-19, and community-acquired pneumonia (CAP). This study aims to uncover the molecular commonalities among these respiratory diseases and their impact on HF, identifying key mediating genes. By performing differential expression analysis on GEO database data, we found 51 common molecules of three respiratory diseases. The gene module of HF was identified by weighted gene co-expression network analysis, and 10 characteristic genes of respiratory diseases that aggravate HF were obtained. GO and KEGG enrichment analysis showed that these genes were mainly involved in innate immune response, inflammation and coagulation pathways. By using three machine learning algorithms, LASSO, RF and SVM-RFE, we identified RSAD2 and IFI44L as key genes, and the Receiver Operating Characteristic (ROC) curve verification results showed high accuracy (Area Under the Curve, AUC > 0.7). ssGSEA showed that RSAD2 was involved in complement and coagulation cascade reactions, while IFI44L was related to myocardial contraction in the progression of heart failure. DSigDB prediction results showed that 6 drugs such as acetohexamide may have potential therapeutic effects on HF aggravated by respiratory diseases. Immune infiltration analysis revealed significant differences in eight immune cell types between HF patients and healthy controls. Our findings enhance the understanding of molecular interactions between respiratory diseases and heart failure, paving the way for future research and therapeutic strategies.