A ten-genes-based diagnostic signature for atherosclerosis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34688276", "kind": "science-record", "revision": 4, "content_hash": "sha256:23c09131428f0b45a513cfda822938f86b455a39f9960c626b965e1869ab9997", "created_at": "2026-11-01T17:36:21Z", "updated_at": "2026-11-01T22:05:00Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A ten-genes-based diagnostic signature for atherosclerosis.", "doi": "10.1186/s12872-021-02323-9", "pmid": "34688276", "authors": ["Feng Zhu", "Lili Zuo", "Rui Hu", "Jin Wang", "Zhihua Yang", "Xin Qi", "Limin Feng"], "journal": "BMC cardiovascular disorders", "year": 2021, "abstract": "BACKGROUND: Atherosclerosis is the leading cause of cardiovascular disease with a high mortality worldwide. Understanding the atherosclerosis pathogenesis and identification of efficient diagnostic signatures remain major problems of modern medicine. This study aims to screen the potential diagnostic genes for atherosclerosis.\nMETHODS: We downloaded the gene chip data of 135 peripheral blood samples, including 57 samples with atherosclerosis and 78 healthy subjects from GEO database (Accession Number: GSE20129). The weighted gene co-expression network analysis was applied to identify atherosclerosis-related genes. Functional enrichment analysis was conducted by using the clusterProfiler R package. The interaction pairs of proteins encoded by atherosclerosis-related genes were screened using STRING database, and the interaction network was further optimized with the cytoHubba plug-in of Cytoscape software.\nRESULTS: The logistic regression diagnostic model was constructed to predict normal and atherosclerosis samples. A gene module which included 532 genes related to the occurrence of atherosclerosis were screened. Functional enrichment analysis basing on the 532 genes identified 235 significantly enriched GO terms and 44 significantly enriched KEGG pathways. The top 50 hub genes of the protein-protein interaction network were identified. The final logistic regression diagnostic model was established by the optimal 10 key genes, which could distinguish atherosclerosis samples from normal samples.\nCONCLUSIONS: A predictive model based on 10 potential atherosclerosis-related genes was obtained, which should shed light on the diagnostic research of atherosclerosis.", "claims": [{"id": "claim-method", "statement": "The logistic regression diagnostic model was constructed to predict normal and atherosclerosis samples.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34688276", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The logistic regression diagnostic model was constructed to predict normal and atherosclerosis samples."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets generated and analysed during the current study are available in the GEO repository [https://www.ncbi.nlm.nih.gov/geo/, Accession Number: GSE20129].", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34688276", "locator": "sections[21] \"Availability of data and materials\" paragraph 2", "quote": "The datasets generated and analysed during the current study are available in the GEO repository [https://www.ncbi.nlm.nih.gov/geo/, Accession Number: GSE20129]."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A logistic regression diagnostic model based on 10 key genes was established and could distinguish atherosclerosis samples from normal samples.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:34688276", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The final logistic regression diagnostic model was established by the optimal 10 key genes, which could distinguish atherosclerosis samples from normal samples."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "A logistic regression diagnostic model based on 10 key genes was established and could distinguish atherosclerosis samples from normal 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:34688276"], "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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