Identification of biomarkers associated with energy metabolism in hypertrophic cardiomyopathy and exploration of potential mechanisms of roles.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40271123", "kind": "science-record", "revision": 4, "content_hash": "sha256:be972091ff13ef5223f821cdb2aad46442f2a6f5987fd90ebbbf0ecbe816b7cc", "created_at": "2026-11-01T20:30:25Z", "updated_at": "2026-11-01T22:56:24Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of biomarkers associated with energy metabolism in hypertrophic cardiomyopathy and exploration of potential mechanisms of roles.", "doi": "10.3389/fcvm.2025.1546865", "pmid": "40271123", "authors": ["Songyan Cai", "Tianying Jin", "Mintong Liu", "Qingyuan Dai"], "journal": "Frontiers in cardiovascular medicine", "year": 2025, "abstract": "Background: In hypertrophic cardiomyopathy (HCM), limited reports exist regarding its association with energy metabolism. Here, biomarkers related to energy metabolism in HCM were identified through bioinformatics analysis.\nMethods: HCM transcriptome data were acquired from the GEO (GSE36961) database for comparative analysis in order to identify differentially expressed genes (DEGs). Subsequently, the identified DEGs were intersected with key module genes in Weighted gene co-expression network analysis (WGCNA) and energy metabolism related genes (EMRGs) to identify DE-EMRGs. Then, feature biomarkers were screened using the least absolute shrinkage and selection operator (LASSO) regression and support vector machine-recursive feature elimination (SVM-RFE) methods, and the intersection of the feature biomarkers obtained from both methods was used for subsequent analysis. Furthermore, biomarkers defined as biomarkers with consistent expression trends across both GSE36961 and GSE89714 datasets and significant inter-cohort differences were selected for subsequent analysis. Subsequently, an immune analysis was conducted. Additionally, the transcription factors (TFs), and drugs regulating the biomarkers were predicted based on online databases.\nResults: The co-selection of seven potential biomarkers based on machine learning identified IGFBP3 and JAK2 as biomarkers in HCM. Upregulation of IGFBP3 and JAK2 in the HCM cohort was observed in the GSE36961 and GSE89714 datasets. Utilizing ssGSEA, it was unveiled that the HCM cohort exhibited elevated ratings of effector memory CD4T cells while displaying diminished scores across 22 other immune cell categories. Notably, JAK2 expression exhibited a strong negative correlation with myeloid-derived suppressor cells (MDSCs) infiltration, while IGFBP3 showed no significant associations with immune cell infiltration. Utilizing NetworkAnalyst, miRNAs and TFs regulating biomarkers expression in HCM were predicted, with hsa-mir-16-5p, hsa-mir-147a, hsa-mir-210b-3p, hsa-let-7b-5p, and hsa-mir-34a-5p identified as regulators of both IGFBP3 and JAK2. GATA2 was also found to be a TF regulating the expression of both biomarkers. Furthermore, the potential therapeutic targets of JAK2 and IGFBP3 in HCM were ruxolitinib and celecoxib, respectively.\nConclusion: In conclusion, the identification of IGFBP3 and JAK2 as biomarkers in HCM, highlight promising avenues for further research and treatment development in HCM.", "claims": [{"id": "claim-method", "statement": "In the GSE36961 dataset, the DEGs were identified through the application of the “limma” R package (version 3.46.0) (19) (p-value < 0.05).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40271123", "locator": "sections[5] \"Identification of DE-EMRGs\" paragraph 2", "quote": "In the GSE36961 dataset, the DEGs were identified through the application of the “limma” R package (version 3.46.0) (19) (p-value < 0.05)."}], "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:40271123", "locator": "sections[23] \"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": "Among them, 2 biomarkers (IGFBP3 and JAK2) exhibited consistent expression trends in both the GSE36961 and GSE89714 datasets, showing significantly higher expression in the HCM cohort (Figure 6A,B).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:40271123", "locator": "sections[16] \"IGFBP3 and JAK2 were identified biomarkers\" paragraph 2", "quote": "Among them, 2 biomarkers (IGFBP3 and JAK2) exhibited consistent expression trends in both the GSE36961 and GSE89714 datasets, showing significantly higher expression in the HCM cohort (Figure 6A,B)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Among them, 2 biomarkers (IGFBP3 and JAK2) exhibited consistent expression trends in both the GSE36961 and GSE89714 datasets, showing significantly higher expression in the HCM cohort (Figure 6A,B).", "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:40271123"], "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. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE36961"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE36961"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:40271123", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fcvm.2025.1546865", "sha256": 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