Machine Learning Developed a Programmed Cell Death Signature for Predicting Prognosis, Ecosystem, and Drug Sensitivity in Ovarian Cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37868825", "kind": "science-record", "revision": 4, "content_hash": "sha256:63edaf50ed7643e9aedcb0d4a52c33dbc730ef0463ebdb789de3249cf880e7c9", "created_at": "2026-11-01T19:02:43Z", "updated_at": "2026-11-01T22:52:55Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Machine Learning Developed a Programmed Cell Death Signature for Predicting Prognosis, Ecosystem, and Drug Sensitivity in Ovarian Cancer.", "doi": "10.1155/2023/7365503", "pmid": "37868825", "authors": ["Le Wang", "Xi Chen", "Lei Song", "Hua Zou"], "journal": "Analytical cellular pathology (Amsterdam)", "year": 2023, "abstract": "Background: Ovarian cancer (OC) is the leading cause of gynecological cancer death and the fifth most common cause of cancer-related death in women in America. Programmed cell death played a vital role in tumor progression and immunotherapy response in cancer.\nMethods: The prognostic cell death signature (CDS) was constructed with an integrative machine learning procedure, including 10 methods, using TCGA, GSE14764, GSE26193, GSE26712, GSE63885, and GSE140082 datasets. Several methods and single-cell analysis were used to explore the correlation between CDS and the ecosystem and therapy response of OC patients.\nResults: The prognostic CDS constructed by the combination of StepCox (n = both) + Enet (alpha = 0.2) acted as an independent risk factor for the overall survival (OS) of OC patients and showed stable and powerful performance in predicting the OS rate of OC patients. Compared with tumor grade, clinical stage, and many developed signatures, the CDS had a higher C-index. OC patients with low CDS score had a higher level of CD8+ cytotoxic T, B cell, and M1-like macrophage, representing a related immunoactivated ecosystem. A low CDS score indicated a higher PD1 and CTLA4 immunophenoscore, higher tumor mutation burden score, lower tumor immune dysfunction and exclusion score, and lower tumor escape score in OC, demonstrating a better immunotherapy response. OC patients with high CDS score had a higher gene set score of cancer-related hallmarks, including angiogenesis, epithelial-mesenchymal transition, hypoxia, glycolysis, and notch signaling.\nConclusion: The current study constructed a novel CDS for OC, which could serve as an indicator for predicting the prognosis, ecosystem, and immunotherapy benefits of OC patients.", "claims": [{"id": "claim-method", "statement": "After obtaining potential prognostic biomarkers with univariate Cox analysis, we then summited these prognostic biomarkers to integrative analysis procedure with 10 machine-learning algorithms, including random survival forest, elastic network (Enet), Lasso, Ridge, stepwise Cox, CoxBoost, partial least squares regression for Cox (plsRcox), supervised principal components (SuperPC), generalized boosted regression modeling, and survival support vector machine, with which we could develop an accurate and stable prognostic CDS.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37868825", "locator": "sections[4] \"2.2. Machine-Learning Algorithms Developed a Prognostic CDS\" paragraph 2", "quote": "After obtaining potential prognostic biomarkers with univariate Cox analysis, we then summited these prognostic biomarkers to integrative analysis procedure with 10 machine-learning algorithms, including random survival forest, elastic network (Enet), Lasso, Ridge, stepwise Cox, CoxBoost, partial least squares regression for Cox (plsRcox), supervised principal components (SuperPC), generalized boosted regression modeling, and survival support vector machine, with which we could develop an accurate and stable prognostic CDS."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The analyzed datasets generated during the study were sourced from the TCGA database (https://portal.gdc.cancer.gov/repository) and the GEO database (https://www.ncbi.nlm.nih.gov/geo).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37868825", "locator": "sections[21] \"Data Availability\" paragraph 2", "quote": "The analyzed datasets generated during the study were sourced from the TCGA database (https://portal.gdc.cancer.gov/repository) and the GEO database (https://www.ncbi.nlm.nih.gov/geo)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The model constructed by StepCox (n = both) + Enet (alpha = 0.2) method was considered the optimal prognostic model as they had the highest average C-index of 0.59 (Figure 2(a)).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37868825", "locator": "sections[12] \"3.2. Integrative Machine-Learning Algorithms Developed a Prognostic CDS\" paragraph 2", "quote": "The model constructed by StepCox (n = both) + Enet (alpha = 0.2) method was considered the optimal prognostic model as they had the highest average C-index of 0.59 (Figure 2(a))."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The model constructed by StepCox (n = both) + Enet (alpha = 0.2) method was considered the optimal prognostic model as they had the highest average C-index of 0.59 (Figure 2(a)).", "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:37868825"], "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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