Identification of necroptosis-related diagnostic biomarkers in coronary heart disease.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38726127", "kind": "science-record", "revision": 3, "content_hash": "sha256:39df5ce98fcf86e6dfecd405776af2f023590acbd4b6521c48c68499796ac049", "created_at": "2026-11-01T19:20:41Z", "updated_at": "2026-11-01T22:11:50Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of necroptosis-related diagnostic biomarkers in coronary heart disease.", "doi": "10.1016/j.heliyon.2024.e30269", "pmid": "38726127", "authors": ["Hongjun You", "Wenqi Han"], "journal": "Heliyon", "year": 2024, "abstract": "Background: The implication of necroptosis in cardiovascular disease was already recognized. However, the molecular mechanism of necroptosis has not been extensively studied in coronary heart disease (CHD).\nMethods: The differentially expressed genes (DEGs) between CHD and control samples were acquired in the GSE20681 dataset downloaded from the GEO database. Key necroptosis-related DEGs were captured and ascertained by bioinformatics analysis techniques, including weighted gene co-expression network analysis (WGCNA) and two machine learning algorithms, while single-gene gene set enrichment analysis (GSEA) revealed their molecular mechanisms. The diagnostic biomarkers were selected via receiver operating characteristic (ROC) analysis. Moreover, an analysis of immune elements infiltration degree was carried out. Authentication of pivotal gene expression at the mRNA level was investigated in vitro utilizing quantitative real-time PCR (qRT-PCR).\nResults: A total of 94 DE-NRGs were recognized here, among which, FAM166B, NEFL, POLDIP3, PRSS37, and ZNF594 were authenticated as necroptosis-related biomarkers, and the linear regression model based on them presented an acceptable ability to different sample types. Following regulatory analysis, the ascertained biomarkers were markedly abundant in functions pertinent to blood circulation, calcium ion homeostasis, and the MAPK/cAMP/Ras signaling pathway. Single-sample GSEA exhibited that APC co-stimulation and CCR were more abundant, and aDCs and B cells were relatively scarce in CHD patients. Consistent findings from bioinformatics and qRT-PCR analyses confirmed the upregulation of NEFL and the downregulation of FAM166B, POLDIP3, and PRSS37 in CHD.\nConclusion: Our current investigation identified 5 necroptosis-related genes that could be diagnostic markers for CHD and brought a novel comprehension of the latent molecular mechanisms of necroptosis in CHD.", "claims": [{"id": "claim-method", "statement": "Then, we evaluated the ability of the above five signature genes to distinguish CHD and healthy control samples by ROC curve analysis in the GSE20681 dataset. The results were shown in Fig. 3E, and the 5 signature genes were not satisfactory in identifying sample types with all AUC <0.6. However, the linear regression model based on these 5 signature genes had an AUC of 0.75 in the GSE20681 dataset (Fig. 3F), indicating the satisfactory ability of the 5-gene model in distinguishing CHD samples from healthy control samples.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38726127", "locator": "sections[19] \"Identification and evaluation of necroptosis-related biomarkers\" paragraph 3", "quote": "Then, we evaluated the ability of the above five signature genes to distinguish CHD and healthy control samples by ROC curve analysis in the GSE20681 dataset. The results were shown in Fig. 3E, and the 5 signature genes were not satisfactory in identifying sample types with all AUC <0.6. However, the linear regression model based on these 5 signature genes had an AUC of 0.75 in the GSE20681 dataset (Fig. 3F), indicating the satisfactory ability of the 5-gene model in distinguishing CHD samples from healthy control samples."}], "state": "complete"}, {"id": "claim-data-access", "statement": "GSE20681 dataset analyzed in this study was collected from the Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo/). The necroptosis genes were sourced from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Further inquiries can be directed to the corresponding author/s.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38726127", "locator": "sections[25] \"Data availability statement\" paragraph 2", "quote": "GSE20681 dataset analyzed in this study was collected from the Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo/). The necroptosis genes were sourced from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Further inquiries can be directed to the corresponding author/s."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A five-gene linear regression model comprising FAM166B, NEFL, POLDIP3, PRSS37, and ZNF594 achieved an AUC of 0.75 for distinguishing CHD samples from healthy control samples.", "kind": "quantitative", "target": {"value": 0.75, "unit": "auc", "tolerance": null, "source": "However, the linear regression model based on these 5 signature genes had an AUC of 0.75 in the GSE20681 dataset (Fig. 3F), indicating the satisfactory ability of the 5-gene model in distinguishing CHD samples from healthy control samples."}, "evidence": [{"asset": "source:pmid:38726127", "locator": "sections[19] \"Identification and evaluation of necroptosis-related biomarkers\" paragraph 3", "quote": "However, the linear regression model based on these 5 signature genes had an AUC of 0.75 in the GSE20681 dataset (Fig. 3F), indicating the satisfactory ability of the 5-gene model in distinguishing CHD samples from healthy control samples."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "A five-gene linear regression model comprising FAM166B, NEFL, POLDIP3, PRSS37, and ZNF594 achieved an AUC of 0.75 for distinguishing CHD samples from healthy control samples.", "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:38726127"], "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:GSE20681"], "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:GSE20681"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:38726127", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1016/j.heliyon.2024.e30269", "sha256": "sha256:d2aa2462d6af34f784f9f470d36251381808d33b2c324ce93a81044483871f70", "bytes": 52028, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1016/j.heliyon.2024.e30269", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11079106", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE20681", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE20681", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE20681", "version": null, "note": "GSE20681 dataset analyzed in this study was collected from the Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo/). The necroptosis genes were sourced from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Further inquiries can be directed to the corresponding author/s.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "GSE20681 dataset analyzed in this study was collected from the Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo/). The necroptosis genes were sourced from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. 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Results: A total of 94 DE-NRGs we"], "order_basis": "paper_text_order:974", "id": "assay:1"}, {"role": "data_generation", "definition_id": "microarray", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.93, "evidence": ["comprehensively analyzed the gene expression microarray dataset obtained from the GSE20681 dataset."], "order_basis": "paper_text_order:3135", "id": "assay:2"}, {"role": "analysis", "definition_id": "regression_modelling", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["ols_fit"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["Then, we evaluated the ability of the above five signature genes to distinguish CHD and healthy control samples by ROC curve analysis in the GSE20681 dataset. The results were shown in Fig. 3E, and the 5 signature genes were not satisfactory in identifying sample types with all AUC <0.6. However, the linear regression model based on these 5 signature genes had an AUC of 0.75 in the GSE20681 dataset (Fig. 3F), indicating the satisfactory ability of the 5-gene model in distinguishing CHD samples from healthy control samples."], "order_basis": "consumes_earlier_output", "id": "assay:3"}]}