Abstract
ObjectiveDespite several clinicopathological factors being integrated as prognostic biomarkers, the individual variants and risk stratification have not been fully elucidated in lower grade glioma (LGG). With the prevalence of gene expression profiling in LGG, and based on the critical role of the immune microenvironment, the aim of our study was to develop an immune-related signature for risk stratification and prognosis prediction in LGG.MethodsRNA-sequencing data from The Cancer Genome Atlas (TCGA), Genome Tissue Expression (GTEx), and Chinese Glioma Genome Atlas (CGGA) were used. Immune-related genes were obtained from the Immunology Database and Analysis Portal (ImmPort). Univariate, multivariate cox regression, and Lasso regression were employed to identify differentially expressed immune-related genes (DEGs) and establish the signature. A nomogram was constructed, and its performance was evaluated by Harrell’s concordance index (C-index), receiver operating characteristic (ROC), and calibration curves. Relationships between the risk score and tumor-infiltrating immune cell abundances were evaluated using CIBERSORTx and TIMER.ResultsNoted, 277 immune-related DEGs were identified. Consecutively, 6 immune genes (CANX, HSPA1B, KLRC2, PSMC6, RFXAP, and TAP1) were identified as risk signature and Kaplan–Meier curve, ROC curve, and risk plot verified its performance in TCGA and CGGA datasets. Univariate and multivariate Cox regression indicated that the risk group was an independent predictor in primary LGG. The prognostic signature showed fair accuracy for 3- and 5-year overall survival in both internal (TCGA) and external (CGGA) validation cohorts. However, predictive performance was poor in the recurrent LGG cohort. The CIBERSORTx algorithm revealed that naïve CD4+ T cells were significant higher in low-risk group. Conversely, the infiltration levels of M1-type macrophages, M2-type macrophages, and CD8+T cells were significant higher in high-risk group in both TCGA and CGGA cohorts.ConclusionThe present study constructed a robust six immune-related gene signature and established a prognostic nomogram effective in risk stratification and prediction of overall survival in primary LGG.
Highlights
Lower-grade gliomas (LGG) constitute the prevalent primary malignances of the central nervous system, demonstrating great intrinsic heterogeneity in terms of their biological behavior (Ostrom et al, 2013; Zeng et al, 2018)
A total of 916 patients who met the inclusion criteria, including 432 patients with primary LGG from the The Cancer Genome Atlas (TCGA) database, 353 patients with primary LGG from the Chinese Glioma Genome Atlas (CGGA) database, and 131 patients with recurrent LGG from the CGGA database were obtained for further analysis
The Principal component analysis (PCA) plot found that TCGA and Genome Tissue Expression (GTEx) datasets separated obviously (Supplementary Figures S2B,D)
Summary
Lower-grade gliomas (LGG) constitute the prevalent primary malignances of the central nervous system, demonstrating great intrinsic heterogeneity in terms of their biological behavior (Ostrom et al, 2013; Zeng et al, 2018). Several biomarkers, including the isocitrate dehydrogenase (IDH) mutation, co-deletion of chromosome arms 1p and 19q (1p/19q codeletion), and O-6-methylguanine-DNA methyltransferase (MGMT) methylation have been integrated to the 2016 WHO classification, to illustrate the histological features and guide the therapeutic strategy (Hartmann et al, 2010; Wick et al, 2013; Hainfellner et al, 2014; Louis et al, 2016). These widely utilized biomarkers do not fully elucidate the individual variants and properly address risk stratification in LGG. It would only be reasonable to attempt to integrate various methods, including gene expression profiles that have gathered enormous attention, to further improve stratification of LGG
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