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Updated: Aug 4, 2025
Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
Published on: September 20, 2024
WGCNA combined with machine learning algorithms for analyzing key genes and immune cell infiltration in heart failure
XiangJin Kong1,2, HouRong Sun1,2, KaiMing Wei1,2
1Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.
Insights
This study identifies seven key genes, including CHCHD4 and CASP3, as potential biomarkers for ischemic cardiomyopathy-induced heart failure (ICM-HF). These findings highlight the roles of mitochondrial damage and lipid metabolism in ICM-HF progression.
Area of Science:
- Cardiovascular Research
- Genomics
- Biomarker Discovery
Background:
- Ischemic cardiomyopathy (ICM) leading to heart failure (HF) is a major global cause of mortality.
- Identifying genetic factors and reliable biomarkers for ICM-induced HF is crucial for improving patient outcomes.
Purpose of the Study:
- To identify candidate genes associated with ICM-induced heart failure (ICM-HF).
- To discover novel biomarkers for ICM-HF using machine learning (ML) approaches.
Main Methods:
- Analysis of gene expression data from ICM-HF and normal samples.
- Identification of differentially expressed genes (DEGs) and pathway enrichment analysis (KEGG, GO, GSEA).
- Application of Weighted Gene Co-expression Network Analysis (WGCNA) and four ML algorithms for biomarker discovery.
Main Results:
- 313 DEGs were identified, enriched in cell cycle, lipid metabolism, and immune response pathways.
- WGCNA and ML algorithms identified 11 candidate genes, with 7 validated using independent datasets.
- Significant differences in immune cell infiltration (mast cells, plasma cells, naive B cells, NK cells) were observed.
Conclusions:
- Seven genes, including CHCHD4, TMEM53, ACPP, AASDH, P2RY1, CASP3, and AQP7, are identified as potential biomarkers for ICM-HF.
- Mitochondrial damage and lipid metabolism disorders are implicated in ICM-HF pathogenesis.
- Immune cell infiltration plays a critical role in the progression of ICM-HF.
Background:
Ischemic cardiomyopathy (ICM) induced heart failure (HF) is one of the most common causes of death worldwide. This study aimed to find candidate genes for ICM-HF and to identify relevant biomarkers by machine learning (ML).
Methods:
The expression data of ICM-HF and normal samples were downloaded from Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between ICM-HF and normal group were identified. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment and gene ontology (GO) annotation analysis, protein-protein interaction (PPI) network, gene pathway enrichment analysis (GSEA), and single-sample gene set enrichment analysis (ssGSEA) were performed. Weighted gene co-expression network analysis (WGCNA) was applied to screen for disease-associated modules, and relevant genes were derived using four ML algorithms. The diagnostic values of candidate genes were assessed using receiver operating characteristic (ROC) curves. The immune cell infiltration analysis was performed between the ICM-HF and normal group. Validation was performed using another gene set.
Results:
A total of 313 DEGs were identified between ICM-HF and normal group of GSE57345, which were mainly enriched in biological processes and pathways related to cell cycle regulation, lipid metabolism pathways, immune response pathways, and intrinsic organelle damage regulation. GSEA results showed positive correlations with pathways such as cholesterol metabolism in the ICM-HF group compared to normal group and lipid metabolism in adipocytes. GSEA results also showed a positive correlation with pathways such as cholesterol metabolism and a negative correlation with pathways such as lipolytic presentation in adipocytes compared to normal group. Combining multiple ML and cytohubba algorithms yielded 11 relevant genes. After validation using the GSE42955 validation sets, the 7 genes obtained by the machine learning algorithm were well verified. The immune cell infiltration analysis showed significant differences in mast cells, plasma cells, naive B cells, and NK cells.
Conclusion:
Combined analysis using WGCNA and ML identified coiled-coil-helix-coiled-coil-helix domain containing 4 (CHCHD4), transmembrane protein 53 (TMEM53), acid phosphatase 3 (ACPP), aminoadipate-semialdehyde dehydrogenase (AASDH), purinergic receptor P2Y1 (P2RY1), caspase 3 (CASP3) and aquaporin 7 (AQP7) as potential biomarkers of ICM-HF. ICM-HF may be closely related to pathways such as mitochondrial damage and disorders of lipid metabolism, while the infiltration of multiple immune cells was identified to play a critical role in the progression of the disease.
