IRGS: an immune-related gene classifier for lung adenocarcinoma prognosis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:32019546", "kind": "science-record", "revision": 7, "content_hash": "sha256:6a60603ff6bb187d8b296ec235f8a063c347b9f57e881708789afcca0f6b6407", "created_at": "2026-11-01T15:40:55Z", "updated_at": "2026-11-01T22:01:16Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "IRGS: an immune-related gene classifier for lung adenocarcinoma prognosis.", "doi": "10.1186/s12967-020-02233-y", "pmid": "32019546", "authors": ["Xiaoshun Shi", "Ruidong Li", "Xiaoying Dong", "Allen Menglin Chen", "Xiguang Liu", "Di Lu", "Siyang Feng", "He Wang", "Kaican Cai"], "journal": "Journal of translational medicine", "year": 2020, "abstract": "BACKGROUND: Tumour cells interfere with normal immune functions by affecting the expression of some immune-related genes, which play roles in the prognosis of cancer patients. In recent years, immunotherapy for tumours has been widely studied, but a practical prognostic model based on immune-related genes in lung adenocarcinoma comparable to existing model has not been established and reported.\nMETHODS: We first obtained publicly accessible lung adenocarcinoma RNA expression data from The Cancer Genome Atlas (TCGA) for differential gene expression analysis and then filtered immune-related genes based on the ImmPort database. By using the lasso algorithm and multivariate Cox Proportional-Hazards (CoxPH) regression analysis, we identified candidate genes for model development and validation. The robustness of the model was further examined by comparing the model with three established gene models.\nRESULTS: Gene expression data from a total of 524 lung adenocarcinoma patients from TCGA were used for model development. We identified four biomarkers (MAP3K8, CCL20, VEGFC, and ANGPTL4) that could predict overall survival in lung adenocarcinoma (HR = 1.98, 95% CI 1.48 to 2.64, P = 4.19e-06) and this model could be used as a classifier for the evaluation of low-risk and high-risk groups. This model was validated with independent microarray data and was highly comparable with previously reported gene expression signatures for lung adenocarcinoma prognosis.\nCONCLUSIONS: In this study, we identified a practical and robust four-gene prognostic model based on an immune gene dataset with cross-platform compatibility. This model has potential value in improving TNM staging for survival predictions in patients with lung adenocarcinoma.\nIMPACT: The study provides a method of immune relevant gene prognosis model and the identification of immune gene classifier for the prediction of lung adenocarcinoma prognosis with RNA sequencing and microarray compatibility.", "claims": [{"id": "claim-method", "statement": "R (Version 3.4.3) was used for most of the bioinformatics and statistical analyses including RNAseq and microarray data normalization and transformation, differential gene expression analysis, gene coexpression analysis, CoxPH and KM survival analyses, as well as ROC analysis.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:32019546", "locator": "sections[8] \"Statistical analysis\" paragraph 2", "quote": "R (Version 3.4.3) was used for most of the bioinformatics and statistical analyses including RNAseq and microarray data normalization and transformation, differential gene expression analysis, gene coexpression analysis, CoxPH and KM survival analyses, as well as ROC analysis."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All analysed data are accessible online, and the results of this article are included within the article as well as in additional files. 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