Exploring Hypertrophic Cardiomyopathy Biomarkers through Integrated Bioinformatics Analysis: Uncovering Novel Diagnostic Candidates.

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This study aims to identify biomarkers that have a positive impact on the treatment, diagnosis, and prediction of HCM through bioinformatics analysis. We selected the GSE36961 and GSE180313 datasets from the Gene Expression Omnibus (GEO) database for differential analysis. GSE36961 generated 6 modules through weighted gene co-expression network analysis (WGCNA), with the green and grey modules showing the highest positive correlation with HCM (green module: cor = 0.88, p = 2e - 48; grey module: cor = 0.78, p = 4e - 31). GSE180313 generated 17 modules through WGCNA, with the turquoise module exhibiting the highest positive correlation with HCM (turquoise module: cor = 0.92, p = 6e - 09). We conducted GO and KEGG pathway analysis on the intersection genes of the selected modules from GSE36961 and GSE180313 and intersected their GO enriched pathways with the GO enriched pathways of endothelial cell subtypes calculated after clustering single-cell data GSE181764, resulting in 383 genes on the enriched pathways. Subsequently, we used LASSO prediction on these 383 genes and identified RTN4, COL4A1, and IER3 as key genes involved in the occurrence and development of HCM. The expression levels of these genes were validated in the GSE68316 and GSE32453 datasets. In conclusion, RTN4, COL4A1, and IER3 are potential biomarkers of HCM, and protein degradation, mechanical stress, and hypoxia may be associated with the occurrence and development of HCM.", "claims": [{"id": "claim-method", "statement": "We used weighted gene co-expression network analysis (WGCNA) to select module genes and machine learning (LASSO) to predict potential biomarkers affecting HCM (Figure 1).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38994496", "locator": "sections[1] \"1. Background\" paragraph 4", "quote": "We used weighted gene co-expression network analysis (WGCNA) to select module genes and machine learning (LASSO) to predict potential biomarkers affecting HCM (Figure 1)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The materials and methods dataset provided by Hebl et al. for GSE36961, Li et al. for GSE89714, Ranjbarvaziri et al. for GSE180313, Wu et al. for GSE68316, and Gennebäck et al. for GSE32453 was obtained from the GEO database (https://www.ncbi.nlm.nih.gov/geo/).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38994496", "locator": "sections[14] \"Data Availability\" paragraph 2", "quote": "The materials and methods dataset provided by Hebl et al. for GSE36961, Li et al. for GSE89714, Ranjbarvaziri et al. for GSE180313, Wu et al. for GSE68316, and Gennebäck et al. for GSE32453 was obtained from the GEO database (https://www.ncbi.nlm.nih.gov/geo/)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The study identified RTN4, COL4A1, and IER3 as potential biomarkers associated with the occurrence and development of hypertrophic cardiomyopathy.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38994496", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "Subsequently, we used LASSO prediction on these 383 genes and identified RTN4, COL4A1, and IER3 as key genes involved in the occurrence and development of HCM."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The study identified RTN4, COL4A1, and IER3 as potential biomarkers associated with the occurrence and development of hypertrophic cardiomyopathy.", "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:38994496"], "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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