Zhao Hao, Zhang Xuening, Shi Zhan, Guo Bingxin, Zhang Wenli, He Kun, Hu Xueqi, Shi Songhe
Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou 450001, China.
Department of Medicine, Zhengzhou First People's Hospital, Zhengzhou 450004, China.
J Cancer. 2021 Feb 22;12(8):2371-2384. doi: 10.7150/jca.51173. eCollection 2021.
The tumor microenvironment (TME) and immune checkpoint inhibitors have been shown to promote active immune responses through different mechanisms. We attempted to identify the important prognostic genes and prognostic characteristics related to TME in prostate cancer (PCa). The gene transcriptome profiles and clinical information of PCa patients were obtained from The Cancer Genome Atlas (TCGA) database, and the immune and stromal scores were calculated by the ESTIMATE algorithm. We evaluated the prognostic value of the risk score (RS) model based on univariate Cox analysis and least absolute shrinkage and selection operation (LASSO) Cox regression analysis and established a nomogram to predict disease-free survival (DFS) in PCa patients. The GSE70768 dataset was utilized for external validation. Twenty-two subsets of tumor-infiltrating immune cells were analyzed using the CIBERSORT algorithm. In this study, the patients with higher immune/stromal scores were associated with a worse DFS, higher Gleason score, and higher pathological T stage. Based on the immune and stromal scores, 515 differentially expressed genes (DEGs) were identified. The univariate Cox and LASSO Cox regression models were employed to select 18 DEGs from 515 DEGs and construct an RS model. The DFS of the high-RS group was significantly lower than that of the low-RS group (<0.001). The AUCs for the 1-year, 3-year and 5-year DFS rates in the RS model were 0.890, 0.877 and 0.841, respectively. A nomogram of DFS was established based on the RS and Gleason score, and the AUCs for the 1-year, 3-year and 5-year DFS rates in the nomogram were 0.907, 0.893, and 0.872, respectively. These results were further validated in the GSE70768 dataset. In addition, the proportion of Tregs was determined to be higher in high-RS patients (<0.05), and the expression levels of five immune checkpoints (CTLA-4, PD-1, LAG-3, TIM-3 and TIGIT) were observed to be higher in high-RS patients (<0.05). Our study established and validated an 18-gene prognostic signature model associated with TME, which might serve as a prognosis stratification tool to predict DFS in PCa patients after radical prostatectomy.
肿瘤微环境（TME）和免疫检查点抑制剂已被证明可通过不同机制促进活跃的免疫反应。我们试图确定前列腺癌（PCa）中与TME相关的重要预后基因和预后特征。从癌症基因组图谱（TCGA）数据库中获取PCa患者的基因转录组谱和临床信息，并通过ESTIMATE算法计算免疫和基质评分。我们基于单变量Cox分析和最小绝对收缩和选择运算（LASSO）Cox回归分析评估了风险评分（RS）模型的预后价值，并建立了列线图以预测PCa患者的无病生存期（DFS）。利用GSE70768数据集进行外部验证。使用CIBERSORT算法分析了22个肿瘤浸润免疫细胞亚群。在本研究中，免疫/基质评分较高的患者与较差的DFS、较高的Gleason评分和较高的病理T分期相关。基于免疫和基质评分，鉴定出515个差异表达基因（DEG）。采用单变量Cox和LASSO Cox回归模型从515个DEG中选择18个DEG并构建RS模型。高RS组的DFS显著低于低RS组（<0.001）。RS模型中1年、3年和5年DFS率的AUC分别为0.890、0.877和0.841。基于RS和Gleason评分建立了DFS列线图，列线图中1年、3年和5年DFS率的AUC分别为0.907、0.893和0.872。这些结果在GSE70768数据集中得到进一步验证。此外，确定高RS患者中调节性T细胞（Tregs）的比例较高（<0.05），并且观察到高RS患者中五个免疫检查点（CTLA-4、PD-1、LAG-3、TIM-3和TIGIT）的表达水平较高（<0.05）。我们的研究建立并验证了一个与TME相关的18基因预后特征模型，该模型可作为一种预后分层工具，用于预测根治性前列腺切除术后PCa患者的DFS。
