A Risk Score Signature Consisting of Six Immune Genes Predicts Overall Survival in Patients with Lower-Grade Gliomas.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35186111", "kind": "science-record", "revision": 4, "content_hash": "sha256:e42903021e04afded6d7ec83a80ebcf2452f388122f9a252170779a9daffd670", "created_at": "2026-11-01T17:36:57Z", "updated_at": "2026-11-01T22:05:56Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A Risk Score Signature Consisting of Six Immune Genes Predicts Overall Survival in Patients with Lower-Grade Gliomas.", "doi": "10.1155/2022/2558548", "pmid": "35186111", "authors": ["Yuxi Wu", "Zesheng Peng", "Sujie Gu", "Haofei Wang", "Wei Xiang"], "journal": "Computational and mathematical methods in medicine", "year": 2022, "abstract": "BACKGROUND: Lower-grade gliomas (LGGs) are less aggressive with a long overall survival (OS) time span. Because of individualized genomic features, a prognostic system incorporating molecular signatures can more accurately predict OS.\nMETHODS: Differential expression analysis between LGGs and normal tissues was performed using the Gene Expression Omnibus (GEO) datasets (GSE4290 and GSE12657). Immune-related differentially expressed genes (ImmPort-DEGs) were analyzed for functional enrichment. The least absolute shrinkage and selection operator (LASSO) analysis was performed to develop an immune risk score signature (IRSS). We extracted information from the Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) to establish and validate the model. The relationship of model gene sets with immune infiltration was analyzed based on gene set variation analysis (GSVA) scores. Patients were divided into low- and high-risk groups based on the median score. The time-dependent receiver-operating characteristic (ROC) curve and the Kaplan-Meier curve were used to evaluate the model. Then, a precise prognostic nomogram was established, and its efficacy was verified.\nRESULTS: A total of 18 related immune genes were identified, building a 6-gene IRSS (BMP2, F2R, FGF13, PCSK1, PRKCB, and PTGER3). DEGs were enriched in T cell and NK cell regulatory pathways. Immune infiltration analysis confirmed that the gene signature correlated with a decrease in innate immune cells. In terms of model evaluation, ROC curves at 1, 3, and 5 years showed moderate predictive ability of IRSS (AUC = 0.930, 0.797, and 0.728). The Cox regression analysis revealed that IRSS was an independent prognostic factor, and the nomogram model had good predictive ability (C - index = 0.828). Meanwhile, the predictive power of IRSS was also confirmed in the training cohort. The Kaplan-Meier results showed that the prognosis of the high-risk group was significantly worse in all cohorts.\nCONCLUSION: IRSS may serve as a novel survival prediction tool in the classification of LGG patients.", "claims": [{"id": "claim-method", "statement": "Subsequently, LASSO regression analysis obtained 6 model genes.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35186111", "locator": "sections[14] \"3.3. Development and Evaluation of IRSS\" paragraph 2", "quote": "Subsequently, LASSO regression analysis obtained 6 model genes."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets supporting the conclusions of this article are available in the public databases The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/), the Chinese glioma Genome Atlas (CGGA, http://www.cgga.org.cn/), the Gene Set Caner Analysis (GSCA) (http://bioinfo.life.hust.edu.cn/GSCA), and the Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/) with the accession numbers: GSE4290 and GSE12657.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35186111", "locator": "sections[22] \"Data Availability\" paragraph 2", "quote": "The datasets supporting the conclusions of this article are available in the public databases The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/), the Chinese glioma Genome Atlas (CGGA, http://www.cgga.org.cn/), the Gene Set Caner Analysis (GSCA) (http://bioinfo.life.hust.edu.cn/GSCA), and the Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/) with the accession numbers: GSE4290 and GSE12657."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The six-gene immune risk score signature showed moderate prognostic predictive ability, with AUC values of 0.930, 0.797, and 0.728 at 1, 3, and 5 years, respectively.", "kind": "quantitative", "target": {"value": 0.93, "unit": "auc", "tolerance": null, "source": "In terms of model evaluation, ROC curves at 1, 3, and 5 years showed moderate predictive ability of IRSS (AUC = 0.930, 0.797, and 0.728)."}, "evidence": [{"asset": "source:pmid:35186111", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "In terms of model evaluation, ROC curves at 1, 3, and 5 years showed moderate predictive ability of IRSS (AUC = 0.930, 0.797, and 0.728)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The six-gene immune risk score signature showed moderate prognostic predictive ability, with AUC values of 0.930, 0.797, and 0.728 at 1, 3, and 5 years, respectively.", "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:35186111"], "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. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE12657"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE12657"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:35186111", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1155/2022/2558548", "sha256": 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