Table_2_A Prognostic Microenvironment-Related Immune Signature via ESTIMATE (PROMISE Model) Predicts Overall Survival of Patients With Glioma.xlsx

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.

### 目的
在胶质瘤免疫治疗的研发进程中，亟需对肿瘤微环境（tumor microenvironment, TME）展开探究。本研究旨在依托ESTIMATE算法构建胶质瘤预后相关微环境免疫特征模型（PROMISE模型）。
### 方法
本研究针对癌症基因组图谱（Cancer Genome Atlas, TCGA）中的所有胶质瘤样本，通过ESTIMATE算法计算其基质评分（stromal score, SS）与免疫评分（immune score, IS），并在高评分组与低评分组之间筛选差异表达基因（differentially expressed genes, DEGs）。通过单因素Cox回归分析筛选预后相关差异表达基因。依托TCGA数据库中的低级别胶质瘤（lower-grade glioma, LGG）数据集，基于预后相关差异表达基因开展LASSO回归分析，进而构建胶质瘤PROMISE模型。随后在TCGA胶质瘤数据集（包括LGG、多形性胶质母细胞瘤（glioblastoma multiforme, GBM）以及LGG+GBM合并数据集）及中国胶质瘤基因组图谱（Chinese Glioma Genome Atlas, CGGA）中，通过生存分析与受试者工作特征曲线（receiver operating characteristic, ROC）对该模型进行验证。此外，本研究构建了列线图以预测个体生存概率。进一步地，本研究基于PROMISE模型划分的风险组，通过基因集富集分析（gene set enrichment analysis, GSEA）与肿瘤浸润免疫细胞CIBERSORT分析，探究模型背后的潜在机制。
### 结果
本研究在高免疫评分组与高基质评分组中，共筛选得到220个上调差异表达基因与42个下调差异表达基因。通过Cox回归分析共得到155个预后相关差异表达基因，最终从中筛选出4个基因（CD86、ANXA1、C5AR1及CD5）以构建PROMISE模型。该模型可在TCGA及CGGA数据集的胶质瘤患者中区分出具有显著生存差异的亚组（P<0.05，风险比（Hazard Ratio, HR）>1），且具备良好的预测效能（曲线下面积（AUC）>0.6）。基于列线图，可结合患者具体年龄、肿瘤分级、异柠檬酸脱氢酶（isocitrate dehydrogenase, IDH）状态及PROMISE风险评分，直观预测个体生存概率。受试者工作特征曲线分析显示，TCGA与CGGA数据集的曲线下面积分别为0.917与0.817，表明模型具备显著的区分效能。两个数据集的风险组间基因集富集分析结果显示，模型显著富集于多条免疫相关通路。CIBERSORT分析结果显示，4种免疫细胞的浸润水平在风险组间存在显著差异，分别为CD8+T细胞、中性粒细胞、滤泡辅助性T细胞（follicular helper T cell, Tfh）及自然杀伤细胞（natural killer cell, NK）。
### 结论
PROMISE模型可对低级别胶质瘤与多形性胶质母细胞瘤患者进行有效分层，区分出具有不同生存结局的亚组。本研究结果有助于进一步阐明胶质瘤的肿瘤微环境机制，并为胶质瘤免疫治疗的研发提供新的思路。