Related Experiment Video
Updated: May 29, 2025
In Silico Clinical Trials for Cardiovascular Disease
Published on: May 27, 2022
Decoding the cytokine code for heart failure based on bioinformatics, machine learning and Bayesian networks
Yiding Yu1, Xiujuan Liu2, Wenwen Liu3
1Shandong University of Traditional Chinese Medicine, Jinan 250014, China.
Insights
This study identifies key cytokine genes involved in heart failure (HF) progression, revealing potential therapeutic targets and drugs. Findings offer new insights into HF pathophysiology and immunomodulatory strategies for future treatments.
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.
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.
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