Nonnegative matrix factorization analysis and multiple machine learning methods identified IL17C and ACOXL as novel diagnostic biomarkers for atherosclerosis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37173646", "kind": "science-record", "revision": 4, "content_hash": "sha256:a27433b503a6c73755cc1cb298dae9a1b6c8ae4e30d9b861995f62ae59cc8b65", "created_at": "2026-11-01T19:01:50Z", "updated_at": "2026-11-01T22:51:52Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Nonnegative matrix factorization analysis and multiple machine learning methods identified IL17C and ACOXL as novel diagnostic biomarkers for atherosclerosis.", "doi": "10.1186/s12859-023-05244-w", "pmid": "37173646", "authors": ["Li Rao", "Bo Peng", "Tao Li"], "journal": "BMC bioinformatics", "year": 2023, "abstract": "BACKGROUND: Atherosclerosis is the common pathological basis for many cardiovascular and cerebrovascular diseases. The purpose of this study is to identify the diagnostic biomarkers related to atherosclerosis through machine learning algorithm.\nMETHODS: Clinicopathological parameters and transcriptomics data were obtained from 4 datasets (GSE21545, GSE20129, GSE43292, GSE100927). A nonnegative matrix factorization algorithm was used to classify arteriosclerosis patients in GSE21545 dataset. Then, we identified prognosis-related differentially expressed genes (DEGs) between the subtypes. Multiple machine learning methods to detect pivotal markers. Discrimination, calibration and clinical usefulness of the predicting model were assessed using area under curve, calibration plot and decision curve analysis respectively. The expression level of the feature genes was validated in GSE20129, GSE43292, GSE100927.\nRESULTS: 2 molecular subtypes of atherosclerosis was identified, and 223 prognosis-related DEGs between the 2 subtypes were identified. These genes are not only related to epithelial cell proliferation, mitochondrial dysfunction, but also to immune related pathways. Least absolute shrinkage and selection operator, random forest, support vector machine- recursive feature elimination show that IL17C and ACOXL were identified as diagnostic markers of atherosclerosis. The prediction model displayed good discrimination and good calibration. Decision curve analysis showed that this model was clinically useful. Moreover, IL17C and ACOXL were verified in other 3 GEO datasets, and also have good predictive performance.\nCONCLUSION: IL17C and ACOXL were diagnostic genes of atherosclerosis and associated with higher incidence of ischemic events.", "claims": [{"id": "claim-method", "statement": "After that, we identified 2 potential diagnosis genes by using LASSO, RF, SVM-RFE methods and developed a novel prediction model for AS diagnosis.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37173646", "locator": "sections[1] \"Background\" paragraph 4", "quote": "After that, we identified 2 potential diagnosis genes by using LASSO, RF, SVM-RFE methods and developed a novel prediction model for AS diagnosis."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All data used in the study can be downloaded from the GEO database (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE21545; https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE20129;https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE43292;https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE100927; accessed 26 May 2022).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37173646", "locator": "sections[27] \"notes\" paragraph 1", "quote": "All data used in the study can be downloaded from the GEO database (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE21545; https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE20129;https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE43292;https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE100927; accessed 26 May 2022)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "IL17C and ACOXL were diagnostic genes of atherosclerosis and associated with higher incidence of ischemic events.", "kind": "quantitative", "target": {"value": 95.0, "unit": "per cent", "tolerance": null, "source": "The IL17C achieved a AUC of 0.917 (95% CI 0.865–0.968) (Fig. 4C) and the ACOXL achieved a AUC of 0.899 (95% CI 0.842–0.955) (Fig. 4F), which indicated good discrimination of the diagnostic biomarkers."}, "evidence": [{"asset": "source:pmid:37173646", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "IL17C and ACOXL were diagnostic genes of atherosclerosis and associated with higher incidence of ischemic events."}, {"asset": "source:pmid:37173646", "locator": "sections[15] \"IL17C and ACOXL were identified as diagnostic biomarkers for AS\" paragraph 2", "quote": "The IL17C achieved a AUC of 0.917 (95% CI 0.865–0.968) (Fig. 4C) and the ACOXL achieved a AUC of 0.899 (95% CI 0.842–0.955) (Fig. 4F), which indicated good discrimination of the diagnostic biomarkers."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "IL17C and ACOXL were diagnostic genes of atherosclerosis and associated with higher incidence of ischemic events.", "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:37173646"], "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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