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

Publication date: Jul 02, 2025

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.

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Concepts Keywords
Drugs Bioinformatics
Genes Community-Acquired Infections
Myocardial Computational Biology
Performing COVID-19
Pneumonia Gene Regulatory Networks
Heart Failure
Heart failure
Humans
Immune infiltration analysis
Influenza, Human
Machine Learning
Machine learning
Respiratory infectious diseases
ROC Curve
SARS-CoV-2

Semantics

Type Source Name
disease MESH infectious diseases
disease MESH heart failure
disease MESH cardiovascular disease
disease MESH respiratory infections
disease MESH influenza
disease MESH COVID-19
disease MESH pneumonia
disease MESH respiratory diseases
disease IDO innate immune response
disease MESH inflammation
drug DRUGBANK Saquinavir
drug DRUGBANK Acetohexamide
disease IDO cell
disease MESH death
disease MESH co infection
pathway REACTOME Immune System
drug DRUGBANK Coenzyme M
disease MESH infections
disease IDO blood
drug DRUGBANK Serine Vanadate
drug DRUGBANK Ademetionine
disease MESH viral infections
disease IDO replication
disease MESH mitochondrial dysfunction
disease IDO infection
drug DRUGBANK Gadodiamide
drug DRUGBANK Deoxythymidine
drug DRUGBANK Testosterone enanthate
drug DRUGBANK Tamoxifen
disease MESH hypogonadism
drug DRUGBANK Testosterone
disease MESH insulin sensitivity
disease MESH fibrosis
disease MESH cytokine storms
disease MESH respiratory failure
disease MESH bronchopneumonia
disease MESH aids
drug DRUGBANK Troleandomycin
disease MESH coronavirus infections
disease MESH acute respiratory distress syndrome
disease MESH atherosclerosis
disease MESH polycystic ovary syndrome
disease MESH ulcerative colitis
disease MESH complications
disease MESH causes
disease MESH chronic obstructive pulmonary disease
pathway REACTOME Autophagy
disease MESH myocarditis
disease IDO host
disease MESH iron deficiency
disease MESH atrial fibrillation
disease MESH multiple organ failure
disease MESH syndromes
disease MESH cardiomyopathy
drug DRUGBANK (S)-Des-Me-Ampa
pathway REACTOME Reproduction
disease MESH Community-Acquired Infections

Original Article

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