A clinical prognostic model related to T cells based on machine learning for predicting the prognosis and immune response of ovarian cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39296051", "kind": "science-record", "revision": 3, "content_hash": "sha256:605eedb70772916f802a17130c32b6424e6c28acd859aee57da875b6e5361cd8", "created_at": "2026-11-01T20:29:28Z", "updated_at": "2026-11-01T22:12:43Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A clinical prognostic model related to T cells based on machine learning for predicting the prognosis and immune response of ovarian cancer.", "doi": "10.1016/j.heliyon.2024.e36898", "pmid": "39296051", "authors": ["Qiwang Lin", "Weixu Ma", "Mengchang Xu", "Zijin Xu", "Jing Wang", "Zhu Liang", "Lin Zhu", "Menglu Wu", "Jiejun Luo", "Haiying Liu", "Jianqiao Liu", "Yunfeng Jin"], "journal": "Heliyon", "year": 2024, "abstract": "Background: Ovarian cancer (OV) is regarded as one of the most lethal malignancies affecting the female reproductive system, with individuals diagnosed with OV often facing a dismal prognosis due to resistance to chemotherapy and the presence of an immunosuppressive environment. T cells serve as a crucial mediator for immune surveillance and cancer elimination. This study aims to analyze the mechanism of T cell-associated markers in OV and create a prognostic model for clinical use in enhancing outcomes for OV patients.\nMethods: Based on the single-cell dataset GSE184880, this study used single-cell data analysis to identify characteristic T cell subsets. Analysis of high dimensional weighted gene co-expression network analysis (hdWGCNA) is utilized to identify crucial gene modules along with their corresponding hub genes. A grand total of 113 predictive models were formed utilizing ten distinct machine learning algorithms along with the combination of the cancer genome atlas (TCGA)-OV dataset and the GSE140082 dataset. The most dependable clinical prognostic model was created utilizing the leave one out cross validation (LOOCV) framework. The validation process for the models was achieved by conducting survival curve analysis and receiver operating characteristic (ROC) analysis. The relationship between risk scores and immune cells was explored through the utilization of the Cibersort algorithm. Additionally, an analysis of drug sensitivity was carried out to anticipate chemotherapy responses across various risk groups. The genes implicated in the model were authenticated utilizing qRT-PCR, cell viability experiments, and EdU assay.\nResults: This study developed a clinical prognostic model that includes ten risk genes. The results obtained from the training set of the study indicate that patients classified in the low-risk group experience a significant survival advantage compared to those in the high-risk group. The ROC analysis demonstrates that the model holds significant clinical utility. These results were verified using an independent dataset, strengthening the model's precision and dependability. The risk assessment provided by the model also serves as an independent prognostic factor for OV patients. The study also unveiled a noteworthy relationship between the risk scores calculated by the model and various immune cells, suggesting that the model may potentially serve as a valuable tool in forecasting responses to both immune therapy and chemotherapy in ovarian cancer patients. Notably, experimental evidence suggests that PFN1, one of the genes included in the model, is upregulated in human OV cell lines and has the capacity to promote cancer progression in in vitro models.\nConclusion: We have created an accurate and dependable clinical prognostic model for OV capable of predicting clinical outcomes and categorizing patients. This model effectively forecasts responses to both immune therapy and chemotherapy. By regulating the immune microenvironment and targeting the key gene PFN1, it may improve the prognosis for high-risk patients.", "claims": [{"id": "claim-method", "statement": "Through the application of the LOOCV framework on the TCGA-OV dataset, we tested 113 different predictive models and computed the C-index for each. An important observation was the RSF model, which exhibited the highest C-index of 0.748 (Fig. 4C).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39296051", "locator": "sections[20] \"Construction and evaluation of a clinical prognosis model based on machine learning algorithms\" paragraph 2", "quote": "Through the application of the LOOCV framework on the TCGA-OV dataset, we tested 113 different predictive models and computed the C-index for each. An important observation was the RSF model, which exhibited the highest C-index of 0.748 (Fig. 4C)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All public data comes from the TCGA database (https://portal.gdc.cancer.gov) and GEO database (https://www.ncbi.nlm.nih.gov/geo/).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39296051", "locator": "sections[27] \"Data availability statement\" paragraph 2", "quote": "All public data comes from the TCGA database (https://portal.gdc.cancer.gov) and GEO database (https://www.ncbi.nlm.nih.gov/geo/)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "An important observation was the RSF model, which exhibited the highest C-index of 0.748 (Fig. 4C).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39296051", "locator": "sections[20] \"Construction and evaluation of a clinical prognosis model based on machine learning algorithms\" paragraph 2", "quote": "An important observation was the RSF model, which exhibited the highest C-index of 0.748 (Fig. 4C)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "An important observation was the RSF model, which exhibited the highest C-index of 0.748 (Fig. 4C).", "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:39296051"], "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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