Abstract
ObjectiveIn the development of immunotherapies in gliomas, the tumor microenvironment (TME) needs to be investigated. We aimed to construct a prognostic microenvironment-related immune signature via ESTIMATE (PROMISE model) for glioma.MethodsStromal score (SS) and immune score (IS) were calculated via ESTIMATE for each glioma sample in the cancer genome atlas (TCGA), and differentially expressed genes (DEGs) were identified between high-score and low-score groups. Prognostic DEGs were selected via univariate Cox regression analysis. Using the lower-grcade glioma (LGG) data set in TCGA, we performed LASSO regression based on the prognostic DEGs and constructed a PROMISE model for glioma. The model was validated with survival analysis and the receiver operating characteristic (ROC) in TCGA glioma data sets (LGG, glioblastoma multiforme [GBM] and LGG+GBM) and Chinese glioma genome atlas (CGGA). A nomogram was developed to predict individual survival chances. Further, we explored the underlying mechanisms using gene set enrichment analysis (GSEA) and Cibersort analysis of tumor-infiltrating immune cells between risk groups as defined by the PROMISE model.ResultsWe obtained 220 upregulated DEGs and 42 downregulated DEGs in both high-IS and high-SS groups. The Cox regression highlighted 155 prognostic DEGs, out of which we selected 4 genes (CD86, ANXA1, C5AR1, and CD5) to construct a PROMISE model. The model stratifies glioma patients in TCGA as well as in CGGA with distinct survival outcome (P<0.05, Hazard ratio [HR]>1) and acceptable predictive accuracy (AUCs>0.6). With the nomogram, an individualized survival chance could be predicted intuitively with specific age, tumor grade, Isocitrate dehydrogenase (IDH) status, and the PROMISE risk score. ROC showed significant discrimination with the area under curves (AUCs) of 0.917 and 0.817 in TCGA and CGGA, respectively. GSEA between risk groups in both data sets were significantly enriched in multiple immune-related pathways. The Cibersort analysis highlighted four immune cells, i.e., CD 8 T cells, neutrophils, follicular helper T (Tfh) cells, and Natural killer (NK) cells.ConclusionsThe PROMISE model can further stratify both LGG and GBM patients with distinct survival outcomes.These findings may help further our understanding of TME in gliomas and shed light on immunotherapies.
Highlights
Gliomas are the most prevalent type of intracranial malignant neoplasms, accounting for 30% of tumors and 81% of malignancy in the brain [1]
We obtained the RNA-Seq data and clinical information of 529 LGG samples and 169 GBM samples from The Cancer Genome Atlas (TCGA), out of which 604 cases were recorded with positive values of survival time
Likewise, normalized RNA-Seq data and clinical information for 443 LGG samples and 249 GBM samples were collected from the Chinese Glioma Genome Atlas (CGGA)
Summary
Gliomas are the most prevalent type of intracranial malignant neoplasms, accounting for 30% of tumors and 81% of malignancy in the brain [1]. While immunotherapies are actively under investigation in the treatment of glioma, the tumor microenvironment (TME) related to immune response needs to be studied [6, 8]. TME has been extensively reported to alter the gene expression in tumor cells and subsequently prognosis of patients [9,10,11]. An immune-related gene signature for GBM by Cheng W et al indicated the significance of immune milieus in the prognosis of GBM patients; the signature was not evaluated with ROC analysis or compared with established classifications [15]. Deng X et al [16] screened a total of 122 prognostic immune-related genes in LGG, which was only validated using Kaplan-Meier survival analysis without a specific cutoff value. No other prognostic signature related to TME has been reported in glioma
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