Comparative profiling of immune genes improves the prognoses of lower grade gliomas.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34623790", "kind": "science-record", "revision": 5, "content_hash": "sha256:1b6c2067cb2182ced188546c0183f0d5efa174f423a54a3e32304163e5136fd9", "created_at": "2026-11-01T17:36:16Z", "updated_at": "2026-11-01T22:47:40Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Comparative profiling of immune genes improves the prognoses of lower grade gliomas.", "doi": "10.20892/j.issn.2095-3941.2021.0173", "pmid": "34623790", "authors": ["Zhiliang Wang", "Wen Cheng", "Zheng Zhao", "Zheng Wang", "Chuanbao Zhang", "Guanzhang Li", "Anhua Wu", "Tao Jiang"], "journal": "Cancer biology & medicine", "year": 2021, "abstract": "OBJECTIVE: Lower grade gliomas (LGGs), classified as World Health Organization (WHO) grade II and grade III gliomas, comprise a heterogeneous group with a median survival time ranging from 4-13 years. Accurate prediction of the survival times of LGGs remains a major challenge in clinical practice.\nMETHODS: We reviewed the expression data of 865 LGG patients from 5 transcriptomics cohorts. The comparative profile of immune genes was analyzed for signature identification and validation. In-house RNAseq and microarray data from the Chinese Glioma Genome Atlas (CGGA) dataset were used as training and internal validation cohorts, respectively. The samples from The Cancer Genome Atlas (TCGA) and GSE16011 cohorts were used as external validation cohorts, and the real-time PCR of frozen LGG tissue samples (n = 36) were used for clinical validation.\nRESULTS: A total of 2,214 immune genes were subjected to pairwise comparison to generate 2,449,791 immune-related gene pairs (IGPs). A total of 402 IGPs were identified with prognostic values for LGGs. The HOXA9-related and CRH-related scores facilitated identification of patients with different prognoses. An immune signature based on 10 IGPs was constructed to stratify patients into low and high risk groups, exhibiting different clinical outcomes. A nomogram, combining immune signature, 1p/19q status, and tumor grade, was able to predict the overall survival (OS) with c-indices of 0.85, 0.80, 0.80, 0.79, and 0.75 in the training, internal validation, external validation, and tissue sample cohorts, respectively.\nCONCLUSIONS: This study was the first to report a comparative profiling of immune genes in large LGG cohorts. A promising individualized immune signature was developed to estimate the survival time for LGG patients.", "claims": [{"id": "claim-method", "statement": "The risk-score formula for predicting survival was developed based on the 10 IGPs and the regression coefficient derived from Lasso regression analysis.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34623790", "locator": "sections[10] \"Statistical analysis\" paragraph 2", "quote": "The risk-score formula for predicting survival was developed based on the 10 IGPs and the regression coefficient derived from Lasso regression analysis."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The expression data were collected from the Chinese Glioma Genome Atlas (CGGA, http://www.cgga.org.cn/index.jsp), The Cancer Genome Atlas (TCGA, (http://cancergenome.nih.gov/) database, and the Gene Expression Omnibus database (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=gse16011).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34623790", "locator": "sections[24] \"Availability of data and materials\" paragraph 2", "quote": "The expression data were collected from the Chinese Glioma Genome Atlas (CGGA, http://www.cgga.org.cn/index.jsp), The Cancer Genome Atlas (TCGA, (http://cancergenome.nih.gov/) database, and the Gene Expression Omnibus database (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=gse16011)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "An immune-based prognostic signature and clinical nomogram predicted overall survival in lower-grade glioma patients across transcriptomic and tissue-sample validation cohorts.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:34623790", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "A nomogram, combining immune signature, 1p/19q status, and tumor grade, was able to predict the overall survival (OS) with c-indices of 0.85, 0.80, 0.80, 0.79, and 0.75 in the training, internal validation, external validation, and tissue sample cohorts, respectively."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "An immune-based prognostic signature and clinical nomogram predicted overall survival in lower-grade glioma patients across transcriptomic and tissue-sample validation cohorts.", "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:34623790"], "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-house RNAseq and microarray data from the Chinese Glioma", "were analyzed (Figure 1B). The in-house CGGA RNAseq and microarray cohorts were used for trainin", "h a Gaussian window smoothing algorithm from RNA sequencing data, and exhibited a good concordance with", "e expression data of 865 LGG patients from 5 transcriptomics cohorts. The comparative profile of immune g"], "order_basis": "paper_text_order:407", "id": "assay:1"}, {"role": "data_generation", "definition_id": "microarray", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["fication and validation. In-house RNAseq and microarray data from the Chinese Glioma Genome Atlas (C", "ed (Figure 1B). 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