Machine learning constructs a T cell-related signature for predicting prognosis and drug sensitivity in ovarian cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38345575", "kind": "science-record", "revision": 2, "content_hash": "sha256:097844468f992b0c9a7730717ae18ec3055a67ff5a6832cbed146bd5c243ed67", "created_at": "2026-11-01T19:20:16Z", "updated_at": "2026-11-01T21:32:30Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Machine learning constructs a T cell-related signature for predicting prognosis and drug sensitivity in ovarian cancer.", "doi": "10.18632/aging.205536", "pmid": "38345575", "authors": ["Yunzheng Zhang", "Lipeng Pei"], "journal": "Aging", "year": 2024, "abstract": "BACKGROUND: The leading cause of death related to gynecologic cancer is ovarian cancer, which typically has a poor prognosis. T cells are referred to as key mediators of immunosurveillance and tumor eradication, and unbalanced regulation or lack of T cells in tumors result in immunotherapy resistance.\nMETHODS: The identification of T cell related markers depended on single-cell RNA-seq analysis. Using data from multiple datasets, including TCGA, GSE14764, GSE26193, GSE26712, and GSE140082, we constructed a prognostic signature called TRS (T cell-related signature) using 10 different machine learning algorithms. The correlation between TRS and drug sensitivity were analyzed using the data from GSE91061 and IMvigor210 dataset.\nRESULTS: PlsRcox method based TRS was as a risk factor for the clinical outcome of ovarian cancer patients. In comparison with stage, grade and many prognostic signatures, the performance of our TRS in evaluating the clinical outcome was better in ovarian cancer. TRS-based risk score showed distinct association with the level of ESTIMATE score, immune-related function score and immune cells. Moreover, TRS could be used to predict the immunotherapy response and chemotherapy response in ovarian cancer.\nCONCLUSION: In conclusion, we constructed a powerful TRS in ovarian cancer, which could accurately predict the clinical outcome of patients and be used to predict the immunotherapy response and chemotherapy response.", "claims": [{"id": "claim-method", "statement": "The screening of the prognostic markers in ovarian cancer in TCGA dataset relied on univariate cox regression analysis (p < 0.05). The development of prognostic TRS relied on 10 integrative machine learning algorithms in ovarian cancer.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38345575", "locator": "sections[5] \"Integrative machine learning algorithms constructed a prognostic T cells-related signature\" paragraph 2", "quote": "The screening of the prognostic markers in ovarian cancer in TCGA dataset relied on univariate cox regression analysis (p < 0.05). The development of prognostic TRS relied on 10 integrative machine learning algorithms in ovarian cancer."}], "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:38345575", "locator": "sections[9] \"Availability of data and materials\" paragraph 2", "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 plsRcox method-based model was referred as the best model and it had a highest average C-index being 0.60.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38345575", "locator": "sections[12] \"Machine learning algorithms based prognostic TRS\" paragraph 2", "quote": "The plsRcox method-based model was referred as the best model and it had a highest average C-index being 0.60 (Figure 3A)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The plsRcox method-based model was referred as the best model and it had a highest average C-index being 0.60.", "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:38345575"], "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:GSE140082"], "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:GSE140082"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:38345575", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.18632/aging.205536", "sha256": 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