The diagnostic value investigation of programmed cell death genes in heart failure.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39574022", "kind": "science-record", "revision": 4, "content_hash": "sha256:378811f2fdcbc9db02e87410b8b79d4a393c2a49d111e73419936f4aaf12ebf0", "created_at": "2026-11-01T20:29:50Z", "updated_at": "2026-11-01T22:55:38Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "The diagnostic value investigation of programmed cell death genes in heart failure.", "doi": "10.1186/s12872-024-04343-7", "pmid": "39574022", "authors": ["Qiuyue Chen", "Su Tu"], "journal": "BMC cardiovascular disorders", "year": 2024, "abstract": "BACKGROUND: We aimed to identify the potential diagnostic markers and associated molecular mechanisms based on programmed cell death (PCD)-related genes in patients with heart failure (HF).\nMETHODS: Three HF gene expression data were extracted from the GEO database, including GSE57345 (training data), GSE141910 and GSE76701 (validation data), followed by differentially PCD related genes (DPCDs) was shown between HF and control samples. Enrichment and protein-protein interaction (PPI) network analyses were performed based on the DPCDs. Subsequently, a diagnostic model was constructed and validated after exploring the diagnostic markers using machine learning. A nomogram was used to determine the clinical diagnostic value. Diagnostic marker-based immune, transcription network, and gene set enrichment (GSE) analyses were performed. Finally, the drug-target network was investigated.\nRESULTS: Twenty DPCDs were revealed between the two groups. These genes, such as Serpin Family E Member 1 (SERPINE1), are mainly enriched in pathways such as the regulation of the inflammatory response. A PPI network was constructed using 14 DPCDs. Eight diagnostic markers, such as SERPINE1, CD38 molecule (CD38), and S100 calcium-binding protein A9 (S100A9), were explored using machine learning algorithms, followed by diagnostic model construction. A nomogram and immune-associated analysis was used to validate the diagnostic value of these genes and the model. Moreover, the transcription regulation network and drug-target interactions were further investigated. Finally, qRT-PCR confirmed that the expression levels of eight signature genes (CD14, CD38, CTSK, LAPTM5, S100A9, SERPINE1, SLC11A1, and STAT3) were significantly elevated in the observation group, consistent with the results of bioinformatics analysis.\nCONCLUSIONS: This study constructed a valuable diagnostic model for HF using the eight identified DPCDs as diagnostic markers.", "claims": [{"id": "claim-method", "statement": "Based on the LASSO Cox regression in R software (version: 4.1–6; Parameters: family = binomial, type. Measure = class) [23], related signature genes analyzed with 10 fold (nfold = 10s) cross-validation using the glment package (version: 4.0–2) were screened.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39574022", "locator": "sections[6] \"Diagnostic model construction\" paragraph 2", "quote": "Based on the LASSO Cox regression in R software (version: 4.1–6; Parameters: family = binomial, type. Measure = class) [23], related signature genes analyzed with 10 fold (nfold = 10s) cross-validation using the glment package (version: 4.0–2) were screened."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data used to support the findings of this study are available from the corresponding author upon request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39574022", "locator": "sections[35] \"Data availability\" paragraph 2", "quote": "The data used to support the findings of this study are available from the corresponding author upon request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "This study constructed a valuable diagnostic model for HF using the eight identified DPCDs as diagnostic markers.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39574022", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "This study constructed a valuable diagnostic model for HF using the eight identified DPCDs as diagnostic markers."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "This study constructed a valuable diagnostic model for HF using the eight identified DPCDs as diagnostic markers.", "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:39574022"], "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": "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:GSE141910"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39574022", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s12872-024-04343-7", "sha256": 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