Decoding the cytokine code for heart failure based on bioinformatics, machine learning and Bayesian networks

Area of Science:

• Cardiovascular Research
• Immunology
• Genetics

Background:

• Heart failure (HF) remains a significant health burden with poor prognosis despite current treatments.
• Novel pathological and cardioprotective pathways are needed for effective HF management.
• The role of cytokines in HF progression requires further elucidation.

Purpose of the Study:

• To identify cytokine-related genes and pathways implicated in heart failure.
• To explore the potential of cytokines as therapeutic targets for HF.
• To discover novel diagnostic biomarkers and therapeutic drugs for HF.

Main Methods:

• Utilized GEO database for HF datasets and WGCNA for gene identification.
• Performed GO, KEGG, and Reactome pathway enrichment analyses.
• Employed LASSO regression, RF algorithms, and Bayesian networks for gene and pathway analysis.

Main Results:

• Identified 13 cytokine-related genes associated with HF, mediating inflammatory responses.
• Discovered two key cytokine regulatory chains: IL34-CCL5-CCL4 and IL34-CCL5-CXCL12.
• Identified five key genes (CCL4, CCL5, CXCL12, CXCL14, IL34) and predicted 47 potential drugs, including Proscillaridin.

Conclusions:

• Provides novel insights into cytokine mechanisms in HF pathophysiology.
• Highlights potential immunomodulatory strategies and gene therapies for HF.
• Suggests candidate drugs and emphasizes the need for clinical validation of findings.