Identification and validation of a T cell marker gene-based signature to predict prognosis and immunotherapy response in gastric cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38049463", "kind": "science-record", "revision": 4, "content_hash": "sha256:7ee3d17df4c2d28ce5dc8e48e2c140b658461fc78577a8da9f701bc6a08c2fe7", "created_at": "2026-11-01T19:02:59Z", "updated_at": "2026-11-01T22:10:49Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification and validation of a T cell marker gene-based signature to predict prognosis and immunotherapy response in gastric cancer.", "doi": "10.1038/s41598-023-48930-8", "pmid": "38049463", "authors": ["Jinlin Zhong", "Rongling Pan", "Miao Gao", "Yuqian Mo", "Xin Peng", "Guoxiao Liang", "Zixuan Chen", "Jinlin Du", "Zhigang Huang"], "journal": "Scientific reports", "year": 2023, "abstract": "Although the role of T cells in tumor immunity and modulation of the tumor microenvironment (TME) has been extensively studied, their precise involvement in gastric adenocarcinoma remains inadequately explored. In this work, we analyzed the single-cell RNA sequencing data set in GSE183904 and identified 322 T cell marker genes using the \"FindAllMarkers\" method of the R package \"Seurat\". STAD patients in the TCGA database were divided into high-risk and low-risk categories based on risk scores. The five-gene prediction signature based on T cell marker genes can predict the prognosis of gastric cancer patients with high accuracy. In the training cohort, the areas under the receiver operating characteristic (ROC) curve were 0.667, 0.73, and 0.818 at 1, 3, and 5 years. External validation of the predictive signature was also performed using multiple clinical subgroups and GEO cohorts. To help with practical application, a diagnostic model was created that shows values of 0.732, 0.752, and 0.816 for the relevant areas under the ROC curve at 1, 3, and 5 years. The T cell marker genes identified in this study may serve as potential therapeutic targets, and the developed predictive signatures and nomograms may aid in the clinical management of gastric cancer.", "claims": [{"id": "claim-method", "statement": "To reduce overfitting and choose the best prognostic genes, we used the LASSO approach with Cox proportional hazards regression.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38049463", "locator": "sections[6] \"Construction and verification of a prognostic model\" paragraph 2", "quote": "To reduce overfitting and choose the best prognostic genes, we used the LASSO approach with Cox proportional hazards regression."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The entire set of findings presented here is based on information from TCGA, available at https://www.cancer.gov/tcga, and the GEO database, accessible at https://www.ncbi.nlm.nih.gov/geo/, with accession numbers GSE183904, GSE62254, GSE84437, and GSE84433.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38049463", "locator": "sections[30] \"notes\" paragraph 1", "quote": "The entire set of findings presented here is based on information from TCGA, available at https://www.cancer.gov/tcga, and the GEO database, accessible at https://www.ncbi.nlm.nih.gov/geo/, with accession numbers GSE183904, GSE62254, GSE84437, and GSE84433."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Using time-dependent ROC curves, we evaluated how well the signature predicts OS at 1, 3, 5 years. The corresponding AUC values for these intervals were 0.667, 0.73, and 0.818 (Fig. 3F).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38049463", "locator": "sections[16] \"Constructing and evaluating a prognostic model\" paragraph 2", "quote": "Using time-dependent ROC curves, we evaluated how well the signature predicts OS at 1, 3, 5 years. The corresponding AUC values for these intervals were 0.667, 0.73, and 0.818 (Fig. 3F)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Using time-dependent ROC curves, we evaluated how well the signature predicts OS at 1, 3, 5 years. The corresponding AUC values for these intervals were 0.667, 0.73, and 0.818 (Fig. 3F).", "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:38049463"], "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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In this work, we analyzed the single-cell RNA sequencing data set in GSE183904 and identified 322 T c", "Materials and methods Method Analysis of scRNA-seq data scRNA-seq data was processed using the", "nd methods Method Analysis of scRNA-seq data scRNA-seq data was processed using the R packages “Seu", "e R packages “Seurat” and “SingleR”9,10. The scRNA-seq data quality control method started by exclu"], "order_basis": "paper_text_order:333", "id": "assay:1"}, {"role": "data_generation", "definition_id": "rna_seq", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.93, "evidence": ["d. 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