ArticleComputational and mathematical methods in medicine2022
A Risk Score Signature Consisting of Six Immune Genes Predicts Overall Survival in Patients with Lower-Grade Gliomas.
Article in Computational and mathematical methods in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed, 5 citations in OpenAlex.
- Evaluating a Glioma Transcriptomic Signature Against a Clinical Reference Model and a Random-Signature Null Distribution: A Leakage-Controlled Internal Audit and a Survey of the Field.Diagnostics (Basel, Switzerland) · 2026Article
- Article
- Computational analysis of the structural and functional impact of the deleterious nsSNPs in the humanBiochemistry and biophysics reports · 2026Article
- Transcriptomic Signatures in TP53 Positive and Negative Tumor Samples in NSCLC.Current gene therapy · 2025Article
- Exploring the relationship between abnormally high expression ofFrontiers in oncology · 2023Article
- Identification of prognosis-related gene features in low-grade glioma based on ssGSEA.Frontiers in oncology · 2022Article
- A Novel CRISPR/Cas9 Screening Potential Index for Prognostic and Immunological Prediction in Low-Grade Glioma.Frontiers in genetics · 2022Article
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5 authors at 1 institution in 1 country.
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Abstract
backgroundLower-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.
methodsDifferential 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.
resultsA 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.
conclusionIRSS may serve as a novel survival prediction tool in the classification of LGG patients.
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