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

Abstract

Objective: In 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.

Methods: Stromal 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.

Results: We 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.

Conclusions: The 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.

Keywords: biomarker; glioma; immune signature; prognosis; tumor microenvironment.

Figure 1

Flowchart of the study process.

Figure 2

Identification of intersected DEGs based on stromal score (SS) and immune score (IS). (A) Survival analysis between high-SS group and low-SS groups. (B) Survival analysis between high-IS and low-IS groups. (C) Heatmap of the 100 DEGs with the most significant P values between the SS groups. (D) Heatmap of the 100 DEGs with the most significant P values between the IS groups. (E) Venn plots of the feature DEGs identified as unanimously upregulated or downregulated DEGs in both the high-SS and high-IS groups.

Figure 3

Functional Enrichment and PPI Network Analysis. (A) The top 30 significantly enriched GO terms. (B) The top most enriched KEGG pathways. (C) PPI network analysis. (D) Potential hub genes and their numbers of adjacent nodes.

Figure 4

Development of the PROMISE model for TCGA-LGG and validation in TCGA-GBM and TCGA combined set. (A, B) Gene selection by the LASSO regression analysis. (C) Survival analysis between high-risk and low-risk groups in TCGA-LGG. (D) Survival analysis between high-risk and low-risk groups in TCGA-GBM. (E) Survival analysis between high-risk and low-risk groups in TCGA combined set. (F) ROC curve analysis of the PROMISE model in TCGA combined set. (G) Risk plot encompassing distribution of groups based on the PROMISE model in TCGA combined set.

Figure 5

Validation of the PROMISE model in CGGA. (A) Survival analysis between high-risk and low-risk groups in CGGA-LGG. (B) Survival analysis between high-risk and low-risk groups in CGGA-GBM. (C) Survival analysis between high-risk and low-risk groups in CGGA combined set. (D) ROC curve analysis of the PROMISE model in CGGA combined set. (E) Risk plot encompassing distribution of groups based on the PROMISE model in CGGA combined set.

Figure 6

Development and validation of a Nomogram. (A) Univariate and multivariate Cox regression analysis for Glioma with clinicopathological factors in the TCGA data set. (B) The nomogram based on the independent prognostic factors. (C) ROC curve analysis of the all clinicopathological factors in TCGA data set. (D) ROC curve analysis of the all clinicopathological factors in CGGA data set.

Figure 7

Gene Set Enrichment Analysis (GSEA) analysis. (A) GSEA of KEGG pathways in TCGA glioma samples between risk groups based on the PROMISE model. (B) GSEA in KEGG pathways in CGGA Glioma samples between risk groups based on the PROMISE model.

Figure 8

Distribution of immune cells between risk groups. (A) Proportions of immune cells based on risk groups in TCGA glioma samples. (B) Violin plot of the differentiation of immune cells between risk groups in TCGA glioma samples. (C) Proportions of immune cells based on risk groups in CGGA Glioma samples. (D) Violin plot of the differentiation of immune cells between risk groups in CGGA Glioma samples.