Exploration of dilated cardiomyopathy for biomarkers and immune microenvironment: evidence from RNA-seq.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35850644", "kind": "science-record", "revision": 4, "content_hash": "sha256:16365c01cb0a6dc554bd71239ac432dd266e6e3c324d47746e5ac8b25e5f34cc", "created_at": "2026-11-01T18:14:56Z", "updated_at": "2026-11-01T22:07:04Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Exploration of dilated cardiomyopathy for biomarkers and immune microenvironment: evidence from RNA-seq.", "doi": "10.1186/s12872-022-02759-7", "pmid": "35850644", "authors": ["Chenggang Fang", "Zhan Lv", "Zhimin Yu", "Kexin Wang", "Chengkai Xu", "Yixuan Li", "Yanggan Wang"], "journal": "BMC cardiovascular disorders", "year": 2022, "abstract": "BACKGROUND: The pathogenic mechanism of dilated cardiomyopathy (DCM) remains to be defined. This study aimed to identify hub genes and immune cells that could serve as potential therapeutic targets for DCM.\nMETHODS: We downloaded four datasets from the Gene Expression Omnibus (GEO) database: GSE141910, GSE3585, GSE42955 and GSE79962. Weighted gene coexpression network analysis (WGCNA) and differential expression analysis were performed to identify gene panels related to DCM. Meanwhile, the CIBERSORT algorithm was used to estimate the immune cells in DCM tissues. Multiple machine learning approaches were used to screen the hub genes and immune cells. Finally, the diagnostic value of the hub genes was assessed by receiver operating characteristic (ROC) analysis. An experimental mouse model of dilated cardiomyopathy was used to validate the bioinformatics results.\nRESULTS: FRZB and EXT1 were identified as hub biomarkers, and the ROC curves suggested an excellent diagnostic ability of the above genes for DCM. In addition, naive B cells were upregulated in DCM tissues, while eosinophils, M2 macrophages, and memory CD4 T cells were downregulated in DCM tissues. The increase in two hub genes and naive B cells was validated in animal experiments.\nCONCLUSION: These results indicated that FRZB and EXT1 could be used as promising biomarkers, and eosinophils, M2 macrophages, resting memory CD4 T cells and naive B cells may also affect the occurrence of DCM.", "claims": [{"id": "claim-method", "statement": "Finally, the diagnostic value of the hub genes was assessed by receiver operating characteristic (ROC) analysis.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35850644", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Finally, the diagnostic value of the hub genes was assessed by receiver operating characteristic (ROC) analysis."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The gene expression profiles of GSE141910, GSE3585, GSE42955 and GSE79962 were downloaded from Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo/).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35850644", "locator": "sections[31] \"Availability of data and materials\" paragraph 2", "quote": "The gene expression profiles of GSE141910, GSE3585, GSE42955 and GSE79962 were downloaded from Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo/)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "FRZB and EXT1 were identified as hub biomarkers, and the ROC curves suggested an excellent diagnostic ability of the above genes for DCM.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35850644", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "FRZB and EXT1 were identified as hub biomarkers, and the ROC curves suggested an excellent diagnostic ability of the above genes for DCM."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "FRZB and EXT1 were identified as hub biomarkers, and the ROC curves suggested an excellent diagnostic ability of the above genes for DCM.", "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:35850644"], "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:GSE141910"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "blocked", "blocked_on": "method:group_comparison_wilcoxon", "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:GSE141910"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:run_analysis blocked on method:group_comparison_wilcoxon"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:35850644", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": 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