A Prognostic Microenvironment-Related Immune Signature via ESTIMATE (PROMISE Model) Predicts Overall Survival of Patients With Glioma

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.

Immunotherapy for Glioblastoma: Current Progress and Challenges

“…As CAR T cell therapy continues to advance, CAR-NK cell therapy has also gained attention as a potential tool for cancer immunotherapy. In glioblastoma, NK cells can mediate tumor cell killing and are associated with good prognosis ( 135 ). A notable advantage of CAR-NK cell therapy is the ability to be administered to an HLA-mismatched patient, thus allowing the possibility of an off-the-shelf therapy ( 136 ).…”

Integrated Analysis Reveals Prognostic Value and Immune Correlates of CD86 Expression in Lower Grade Glioma

“…Although CD86 has been reported to be associated with poor prognosis in chronic lymphocytic leukemia (9), myeloma (29), and overall glioma (30), there was no report of its prognostic value in LGG and melanoma. As shown in the present study, CD86 expression level was significantly correlated with worse survival and it was upregulated as the tumor grade increases in LGG.…”

CD80 and CD86: expression and prognostic value in newly diagnosed glioblastoma

“…A recent study constructed a more robust model, using GBM and LGG data from the TCGA and CGGA (Chinese Glioma Genomic Atlas), and identi ed that low expression of CD86 molecules is a good prognostic indicator for OS. PFS analysis was not applied in this study [24].…”