Prognostic and Predictive Value of Immune-Related Gene Pair Signature in Primary Lower-Grade Glioma Patients.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34123829", "kind": "science-record", "revision": 7, "content_hash": "sha256:b12f656a90962c9fe4fd4b29800725e5e40feeaa8f4cf7a633a061c17cb63506", "created_at": "2026-11-01T15:41:38Z", "updated_at": "2026-11-01T22:03:57Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Prognostic and Predictive Value of Immune-Related Gene Pair Signature in Primary Lower-Grade Glioma Patients.", "doi": "10.3389/fonc.2021.665870", "pmid": "34123829", "authors": ["Kunjian Lei", "Jingying Li", "Zewei Tu", "Feng Liu", "Minhua Ye", "Miaojing Wu", "Yue Zhu", "Min Luo", "Li Lin", "Chuming Tao", "Kai Huang", "Xingen Zhu"], "journal": "Frontiers in oncology", "year": 2021, "abstract": "Immune-related gene pairs (IRGPs) have been associated with prognosis in various cancer types, but few studies have examined their prognostic capabilities in glioma patients. Here, we gathered the gene expression and clinical profile data of primary lower-grade glioma (LGG) patients from The Cancer Genome Atlas (TCGA), the Chinese Glioma Genome Atlas (CGGA, containing CGGAseq1 and CGGAseq2), the Gene Expression Omnibus (GEO: GSE16011), and Rembrandt datasets. In the TCGA dataset, univariate Cox regression was performed to detect overall survival (OS)-related IRGs, Lasso regression, and multivariate Cox regression were used to screen robust prognosis-related IRGs, and 19 IRGs were selected for the construction of an IRGP prognostic signature. All patients were allotted to high- and low-risk subgroups based on the TCGA dataset median value risk score. Validation analysis indicated that the IRGP signature returned a stable prognostic value among all datasets. Univariate and multivariate Cox regression analyses indicated that the IRG -signature could efficiently predict the prognosis of primary LGG patients. The IRGP-signature-based nomogram model was built, revealing the reliable ability of the IRGP signature to predict clinical prognosis. The single-sample gene set enrichment analysis (ssGSEA) suggested that high-risk samples contained higher numbers of immune cells but featured lower tumor purity than low-risk samples. Finally, we verified the prognostic ability of the IRGP signature using experiments performed in LGG cells. These results indicated that the IRGP signature could be regarded as a stable prognostic assessment predictor for identifying high-risk primary LGG patients.", "claims": [{"id": "claim-method", "statement": "We implemented Kaplan-Meier method to contrast the survival outcomes between low and high IRGP score LGG patients by using the two-sided log-rank statistical test.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34123829", "locator": "sections[13] \"Statistical Analysis\" paragraph 2", "quote": "We implemented Kaplan-Meier method to contrast the survival outcomes between low and high IRGP score LGG patients by using the two-sided log-rank statistical test."}], "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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