Machine learning developed a CD8+ exhausted T cells signature for predicting prognosis, immune infiltration and drug sensitivity in ovarian cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38461331", "kind": "science-record", "revision": 4, "content_hash": "sha256:94ccd167ac6b1eae1147cd8c65f81a96c73875fa3f485475cb5124de25ed377a", "created_at": "2026-11-01T19:20:24Z", "updated_at": "2026-11-01T22:11:24Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Machine learning developed a CD8+ exhausted T cells signature for predicting prognosis, immune infiltration and drug sensitivity in ovarian cancer.", "doi": "10.1038/s41598-024-55919-4", "pmid": "38461331", "authors": ["Rujun Chen", "Yicai Zheng", "Chen Fei", "Jun Ye", "He Fei"], "journal": "Scientific reports", "year": 2024, "abstract": "CD8+ exhausted T cells (CD8+ Tex) played a vital role in the progression and therapeutic response of cancer. However, few studies have fully clarified the characters of CD8+ Tex related genes in ovarian cancer (OC). The CD8+ Tex related prognostic signature (TRPS) was constructed with integrative machine learning procedure including 10 methods using TCGA, GSE14764, GSE26193, GSE26712, GSE63885 and GSE140082 dataset. Several immunotherapy benefits indicators, including Tumor Immune Dysfunction and Exclusion (TIDE) score, immunophenoscore (IPS), TMB score and tumor escape score, were used to explore performance of TRPS in predicting immunotherapy benefits of OC. The TRPS constructed by Enet (alpha = 0.3) method acted as an independent risk factor for OC and showed stable and powerful performance in predicting clinical outcome of patients. The C-index of the TRPS was higher than that of tumor grade, clinical stage, and many developed signatures. Low TRPS score indicated a higher level of CD8+ T cell, B cell, macrophage M1, and NK cells, representing a relative immunoactivated ecosystem in OC. OC patients with low risk score had a higher PD1&CTLA4 immunophenoscore, higher TMB score, lower TIDE score and lower tumor escape score, suggesting a better immunotherapy response. Moreover, higher TRPS score indicated a higher score of cancer-related hallmarks, including angiogenesis, EMT, hypoxia, glycolysis, and notch signaling. Vitro experiment showed that ARL6IP5 was downregulated in OC tissues and inhibited tumor cell proliferation. The current study constructed a novel TRPS for OC, which could serve as an indicator for predicting the prognosis, immune infiltration and immunotherapy benefits for OC patients.", "claims": [{"id": "claim-method", "statement": "The data suggested that the prognostic signature constructed by Enet (alpha = 0.3) method was considered as the optimal TRPS with a highest average C-index of 0.58 (Fig. 3A).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38461331", "locator": "sections[14] \"Integrative machine learning algorithms developed a TRPS\" paragraph 2", "quote": "The data suggested that the prognostic signature constructed by Enet (alpha = 0.3) method was considered as the optimal TRPS with a highest average C-index of 0.58 (Fig. 3A)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets utilized in this study are available in TCGA (TCGA-BLCA, https://portal.gdc.cancer.gov/) and GEO repository (GSE14764, GSE26193, GSE26712, GSE63885 and GSE140082, https://www.ncbi.nlm.nih.gov/geo/).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38461331", "locator": "sections[28] \"notes\" paragraph 1", "quote": "The datasets utilized in this study are available in TCGA (TCGA-BLCA, https://portal.gdc.cancer.gov/) and GEO repository (GSE14764, GSE26193, GSE26712, GSE63885 and GSE140082, https://www.ncbi.nlm.nih.gov/geo/)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The CD8+ Tex related prognostic signature (TRPS) was constructed with integrative machine learning procedure including 10 methods using TCGA, GSE14764, GSE26193, GSE26712, GSE63885 and GSE140082 dataset.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38461331", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "The CD8+ Tex related prognostic signature (TRPS) was constructed with integrative machine learning procedure including 10 methods using TCGA, GSE14764, GSE26193, GSE26712, GSE63885 and GSE140082 dataset."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "The CD8+ Tex related prognostic signature (TRPS) was constructed with integrative machine learning procedure including 10 methods using TCGA, GSE14764, GSE26193, GSE26712, GSE63885 and GSE140082 dataset.", "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:38461331"], "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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