A macrophage related signature for predicting prognosis and drug sensitivity in ovarian cancer based on integrative machine learning.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37784081", "kind": "science-record", "revision": 3, "content_hash": "sha256:81a47938e756851455beb5c5408b49188230cdd326ee23fe5f9feff589fc1626", "created_at": "2026-11-01T19:02:38Z", "updated_at": "2026-11-01T22:10:23Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A macrophage related signature for predicting prognosis and drug sensitivity in ovarian cancer based on integrative machine learning.", "doi": "10.1186/s12920-023-01671-z", "pmid": "37784081", "authors": ["Bo Zhao", "Lipeng Pei"], "journal": "BMC medical genomics", "year": 2023, "abstract": "BACKGROUND: Ovarian cancer ranks the leading cause of gynecologic cancer-related death in the United States and the fifth most common cause of cancer-related mortality among American women. Increasing evidences have highlighted the vital role of macrophages M2/M1 proportion in tumor progression, prognosis and immunotherapy.\nMETHODS: Weighted gene co-expression network analysis (WGCNA) was performed to identify macrophages related markers. Integrative procedure including 10 machine learning algorithms were performed to develop a prognostic macrophage related signature (MRS) with TCGA, GSE14764, GSE140082 datasets. The role of MRS in tumor microenvironment (TME) and therapy response was evaluated with the data of CIBERSORT, MCPcounter, QUANTISEQ, XCELL, CIBERSORT-ABS, TIMER and EPIC, GSE91061 and IMvigor210 dataset.\nRESULTS: The optimal MRS developed by the combination of CoxBoost and StepCox[forward] algorithm served as an independent risk factor in ovarian cancer. Compared with stage, grade and other established prognostic signatures, the current MRS had a better performance in predicting the overall survival rate of ovarian cancer patients. Low risk score indicated a higher TME score, higher level of immune cells, higher immunophenoscore, higher tumor mutational burden, lower TIDE score and lower IC50 value in ovarian cancer. The survival prediction nomogram had a good potential for clinical application in predicting the 1-, 3-, and 5-year overall survival rate of ovarian cancer patients.\nCONCLUSION: All in all, the current study constructed a powerful prognostic MRS for ovarian cancer patients using 10 machine learning algorithms. This MRS could predict the prognosis and drug sensitivity in ovarian cancer.", "claims": [{"id": "claim-method", "statement": "Weighted gene co-expression network analysis (WGCNA) was performed to identify macrophages related markers.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37784081", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Weighted gene co-expression network analysis (WGCNA) was performed to identify macrophages related markers."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The analyzed data sets generated during the study were sourced from the TCGA database (https://portal.gdc.cancer.gov/repository) and GEO database (https://www.ncbi.nlm.nih.gov/geo).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37784081", "locator": "sections[30] \"notes\" paragraph 1", "quote": "The analyzed data sets generated during the study were sourced from the TCGA database (https://portal.gdc.cancer.gov/repository) and GEO database (https://www.ncbi.nlm.nih.gov/geo)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The optimal prognostic macrophages-related signature combined CoxBoost and StepCox[forward] and achieved the highest average C-index of 0.63.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37784081", "locator": "sections[14] \"Integrative machine learning algorithms constructed a prognostic MRS\" paragraph 2", "quote": "Finally, the model constructed by the combination of CoxBoost and StepCox[forward] was suggested as the optimal model with the highest average C-index of 0.63 (Fig. 3B)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The optimal prognostic macrophages-related signature combined CoxBoost and StepCox[forward] and achieved the highest average C-index of 0.63.", "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:37784081"], "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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