WGCNA combined with machine learning algorithms for analyzing key genes and immune cell infiltration in heart failure due to ischemic cardiomyopathy.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37008314", "kind": "science-record", "revision": 4, "content_hash": "sha256:b70a1058156d00439e01ea8baffabe521206304b460c8e3ae61d678253719ecf", "created_at": "2026-11-01T19:01:40Z", "updated_at": "2026-11-01T22:51:38Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "WGCNA combined with machine learning algorithms for analyzing key genes and immune cell infiltration in heart failure due to ischemic cardiomyopathy.", "doi": "10.3389/fcvm.2023.1058834", "pmid": "37008314", "authors": ["XiangJin Kong", "HouRong Sun", "KaiMing Wei", "LingWei Meng", "Xin Lv", "ChuanZhen Liu", "FuShun Lin", "XingHua Gu"], "journal": "Frontiers in cardiovascular medicine", "year": 2023, "abstract": "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).\nMethods: 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.\nResults: 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.\nConclusion: 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.", "claims": [{"id": "claim-method", "statement": "We used the limma package in R to distinguish between differentially expressed genes (DEGs) and then set |log2 (fold change)| ≥ 0.5 and adjusted p < 0.05 as the threshold for DEGs, followed by WGCNA and identification of modules.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37008314", "locator": "sections[4] \"Identification of differentially expressed genes\" paragraph 2", "quote": "We used the limma package in R to distinguish between differentially expressed genes (DEGs) and then set |log2 (fold change)| ≥ 0.5 and adjusted p < 0.05 as the threshold for DEGs, followed by WGCNA and identification of modules."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37008314", "locator": "sections[21] \"Data availability statement\" paragraph 2", "quote": "The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Combined analysis using WGCNA and machine learning identified CHCHD4, TMEM53, ACPP, AASDH, P2RY1, CASP3, and AQP7 as potential biomarkers of ICM-HF.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37008314", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "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."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Combined analysis using WGCNA and machine learning identified CHCHD4, TMEM53, ACPP, AASDH, P2RY1, CASP3, and AQP7 as potential biomarkers of ICM-HF.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:37008314"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. 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