Construction of a predictive model for immunotherapy efficacy in lung squamous cell carcinoma based on the degree of tumor-infiltrating immune cells and molecular typing.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35962453", "kind": "science-record", "revision": 4, "content_hash": "sha256:407ebc76f3bfe6cb2479ee772f072b75612774bab2d8b32b7ed8b664789e7576", "created_at": "2026-11-01T18:15:04Z", "updated_at": "2026-11-01T22:07:15Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Construction of a predictive model for immunotherapy efficacy in lung squamous cell carcinoma based on the degree of tumor-infiltrating immune cells and molecular typing.", "doi": "10.1186/s12967-022-03565-7", "pmid": "35962453", "authors": ["Lingge Yang", "Shuli Wei", "Jingnan Zhang", "Qiongjie Hu", "Wansong Hu", "Mengqing Cao", "Long Zhang", "Yongfang Wang", "Pingli Wang", "Kai Wang"], "journal": "Journal of translational medicine", "year": 2022, "abstract": "BACKGROUND: To construct a predictive model of immunotherapy efficacy for patients with lung squamous cell carcinoma (LUSC) based on the degree of tumor-infiltrating immune cells (TIIC) in the tumor microenvironment (TME).\nMETHODS: The data of 501 patients with LUSC in the TCGA database were used as a training set, and grouped using non-negative matrix factorization (NMF) based on the degree of TIIC assessed by single-sample gene set enrichment analysis (GSEA). Two data sets (GSE126044 and GSE135222) were used as validation sets. Genes screened for modeling by least absolute shrinkage and selection operator (LASSO) regression and used to construct a model based on immunophenotyping score (IPTS). RNA extraction and qPCR were performed to validate the prognostic value of IPTS in our independent LUSC cohort. The receiver operating characteristic (ROC) curve was constructed to determine the predictive value of the immune efficacy. Kaplan-Meier survival curve analysis was performed to evaluate the prognostic predictive ability. Correlation analysis and enrichment analysis were used to explore the potential mechanism of IPTS molecular typing involved in predicting the immunotherapy efficacy for patients with LUSC.\nRESULTS: The training set was divided into a low immune cell infiltration type (C1) and a high immune cell infiltration type (C2) by NMF typing, and the IPTS molecular typing based on the 17-gene model could replace the results of the NMF typing. The area under the ROC curve (AUC) was 0.82. In both validation sets, the IPTS of patients who responded to immunotherapy were significantly higher than those who did not respond to immunotherapy (P = 0.0032 and P = 0.0451), whereas the AUC was 0.95 (95% CI = 1.00-0.84) and 0.77 (95% CI = 0.58-0.96), respectively. In our independent cohort, we validated its ability to predict the response to cancer immunotherapy, for the AUC was 0.88 (95% CI = 1.00-0.66). GSEA suggested that the high IPTS group was mainly involved in immune-related signaling pathways.\nCONCLUSIONS: IPTS molecular typing based on the degree of TIIC in the TME could well predict the efficacy of immunotherapy in patients with LUSC with a certain prognostic value.", "claims": [{"id": "claim-method", "statement": "The receiver operating characteristic (ROC) curve was constructed to determine the predictive value of the immune efficacy.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35962453", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "The receiver operating characteristic (ROC) curve was constructed to determine the predictive value of the immune efficacy."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets in the current study are open to the public at the TCGA (https://portal.gdc.cancer.gov) and GEO (https://www.ncbi.nlm.nih.gov/geo) databases. The original contributions presented in the study are included in the article/Additional files. Further inquiries can be directed to the corresponding author.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35962453", "locator": "sections[34] \"notes\" paragraph 1", "quote": "The datasets in the current study are open to the public at the TCGA (https://portal.gdc.cancer.gov) and GEO (https://www.ncbi.nlm.nih.gov/geo) databases. The original contributions presented in the study are included in the article/Additional files. 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