Identifying immune cells-related phenotype to predict immunotherapy and clinical outcome in gastric cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36032097", "kind": "science-record", "revision": 3, "content_hash": "sha256:6487870448a880fdc34af91001cc1d4e98c648ca2e8adf83de54a520e80c6057", "created_at": "2026-11-01T18:15:11Z", "updated_at": "2026-11-01T21:28:44Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identifying immune cells-related phenotype to predict immunotherapy and clinical outcome in gastric cancer.", "doi": "10.3389/fimmu.2022.980986", "pmid": "36032097", "authors": ["Sutian Jiang", "Xuzhong Ding", "Qianqian Wu", "Tong Cheng", "Manyu Xu", "Jianfei Huang"], "journal": "Frontiers in immunology", "year": 2022, "abstract": "Background: The tumor microenvironment is mainly composed of tumor-infiltrating immune cells (TIICs), fibroblast, extracellular matrix, and secreted factors. TIICs are often associated with sensitivity to immunotherapy and the prognosis of multiple cancers, yet the predictive role of individual cells on tumor prognosis is limited.\nMethods: Based on single-sample gene set enrichment analysis, we combined three Gene Expression Omnibus (GEO) cohorts to build a TIIC model for risk stratification and prognosis prediction. The performance of the TIIC model was validated using our clinical cohort and the TCGA cohort. To assess the predictive power of the TIIC model for immunotherapy, we plotted the receiver operating characteristic curve with the IMvigor210 and GSE135222 cohorts.\nResults: Chemokines, tumor-infiltrating immune cells, and immunomodulators differed between the two TIIC groups. The TIIC model was vital for predicting the outcome of immunotherapy. In our clinical samples, we verified that the expression levels of PD-1 and PD-L1 were higher in the low TIIC score group than in the high TIIC score group, both in the tumor and stroma.\nConclusions: Collectively, the TIIC model could provide a novel idea for immune cell targeting strategies in gastric cancer and predict the survival outcome of patients.", "claims": [{"id": "claim-method", "statement": "We used single-sample gene set enrichment analysis (ssGSEA) by the R package GSVA to quantify the infiltration levels of 24 immune cell signatures for each GC sample with mRNA expression data (12).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36032097", "locator": "sections[5] \"Evaluation of immune cell infiltration, GO, and KEGG pathway analyses\" paragraph 2", "quote": "We used single-sample gene set enrichment analysis (ssGSEA) by the R package GSVA to quantify the infiltration levels of 24 immune cell signatures for each GC sample with mRNA expression data (12)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/ Supplementary Material . 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