Decoding the cytokine code for heart failure based on bioinformatics, machine learning and Bayesian networks
- PMID: 39909085
- DOI: 10.1016/j.bbadis.2025.167701
Decoding the cytokine code for heart failure based on bioinformatics, machine learning and Bayesian networks
Abstract
Background: Despite maximal pharmacological treatment guided by clinical guidelines, the prognosis of heart failure (HF) remains poor, posing a significant public health burden. This necessitates uncovering novel pathological and cardioprotective pathways. Targeting cytokines presents a promising therapeutic strategy for HF, yet their intricate mechanisms in HF progression remain obscure.
Methods: HF datasets were obtained from the GEO database. Cytokine-related genes were identified through WGCNA and the CytReg database. GO and KEGG enrichment analyses were conducted using the clusterProfiler package. Reactome pathway enrichment analysis and Bayesian regulatory network construction were performed using the CBNplot package. Key genes were identified via LASSO regression and RF algorithms, with diagnostic accuracy evaluated by ROC curves. Potential therapeutic drugs were predicted using the DSigDB database, and immune cell infiltration was assessed with the CIBERSORT package.
Results: We identified 13 cytokine-related genes associated with HF. Enrichment analyses indicated these genes mediate inflammatory responses and immune cell recruitment. Bayesian network analysis revealed two cytokine regulatory chains: IL34-CCL5-CCL4 and IL34-CCL5-CXCL12. Machine learning algorithms identified five key cytokine genes: CCL4, CCL5, CXCL12, CXCL14, and IL34. The DSigDB database predicted 47 potential therapeutic drugs, including Proscillaridin. Immune infiltration analysis showed significant differences in seven immune cell types between HF and healthy samples.
Conclusion: Our study provides insights into cytokines' molecular mechanisms in HF pathophysiology and highlights potential immunomodulatory strategies, gene therapies, and candidate drugs. Future research should validate these findings in clinical settings to develop effective HF therapies.
Keywords: Bayesian network; Cytokine; Heart failure; Immune infiltration analysis; Machine learning.
Copyright © 2025 Elsevier B.V. All rights reserved.
Conflict of interest statement
Declaration of competing interest The authors have no conflict of interest to disclose.
Similar articles
-
Identification and validation of aging-related genes in heart failure based on multiple machine learning algorithms.Front Immunol. 2024 Apr 15;15:1367235. doi: 10.3389/fimmu.2024.1367235. eCollection 2024. Front Immunol. 2024. PMID: 38686376 Free PMC article.
-
Exploration of the shared diagnostic genes and mechanisms between periodontitis and primary Sjögren's syndrome by integrated comprehensive bioinformatics analysis and machine learning.Int Immunopharmacol. 2024 Nov 15;141:112899. doi: 10.1016/j.intimp.2024.112899. Epub 2024 Aug 13. Int Immunopharmacol. 2024. PMID: 39142001
-
Identification of biomarkers and immune microenvironment associated with heart failure through bioinformatics and machine learning.Front Mol Biosci. 2025 May 8;12:1580880. doi: 10.3389/fmolb.2025.1580880. eCollection 2025. Front Mol Biosci. 2025. PMID: 40406620 Free PMC article.
-
Uncovering the molecular mechanisms between heart failure and end-stage renal disease via a bioinformatics study.Front Genet. 2023 Jan 10;13:1037520. doi: 10.3389/fgene.2022.1037520. eCollection 2022. Front Genet. 2023. PMID: 36704339 Free PMC article.
-
STAT4 and COL1A2 are potential diagnostic biomarkers and therapeutic targets for heart failure comorbided with depression.Brain Res Bull. 2022 Jun 15;184:68-75. doi: 10.1016/j.brainresbull.2022.03.014. Epub 2022 Mar 31. Brain Res Bull. 2022. PMID: 35367598 Review.
References
Publication types
MeSH terms
Substances
LinkOut - more resources
Full Text Sources
Medical
Research Materials
Miscellaneous