Liao Zhuangyao, Yao Haohua, Wei Jinhuan, Feng Zihao, Chen Wei, Luo Junhang, Chen Xu
Department of Urology, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China.
Transl Androl Urol. 2021 Apr;10(4):1607-1619. doi: 10.21037/tau-20-1348.
Clear cell renal cell carcinoma (ccRCC) is a highly heterogeneous tumor, resulting a challenge of developing target therapeutics. Not long ago, immune checkpoint blockade regimens combine with tyrosin kinase inhibitors have evolved frontline options in metastatic RCC, which implies arrival of the era of tumor immunotherapy. Studies have demonstrated immune-related genes (IRGs) could characterize tumor milieu and related to patient survival. Nevertheless, the clinical significance of classifier depending on IRGs in ccRCC has not been well established.
The R package limma, univariate and LASSO cox regression analysis were used to screen the prognostic related IRGs from TCGA database. Multivariate cox regression was utilized to establish a risk prediction model for candidate genes. Quantitative real-time PCR was used to confirm the expression of candidates in clinical samples from our institution. CIBERSORT algorithm and correlation analysis were applied to explore tumor-infiltrating immune cells signature between different risk groups. A clinical nomogram was also developed to predict OS by using the rms R package based on the risk prediction model and other independent risk factors. The ICGC data was used for external validation of either gene risk model or nomogram.
We identified 382 differentially expressed immune related genes. Four unique prognostic IRGs (CRABP2, LTB4R, PTGER1 and TEK) were finally affirmed to associate with tumor survival independently and utilized to establish the risk score model. All candidates' expression was successfully laboratory confirmed by q-PCR. CIBERSORT analysis implied patients in unfavorable-risk group with high CD8 T cell, regulatory T cell and NK cell infiltration, as well as high expression of PD-1, CTLA4, TNFRSF9, TIGIT and LAG3. A nomogram combined IRGs risk score with age, gender, TNM stage, Fuhrman grade, necrosis was further generated to predict of 3- and 5-year OS, which exhibited superior discriminative power (AUCs were 0.811 and 0.795).
Our study established and validated a survival prognostic model system based on 4 unique immune related genes in ccRCC, which expands knowledge in tumor immune status and provide a potent prediction tool in future.
透明细胞肾细胞癌（ccRCC）是一种高度异质性肿瘤，这给开发靶向治疗带来了挑战。不久前，免疫检查点阻断方案联合酪氨酸激酶抑制剂已成为转移性肾细胞癌的一线治疗选择，这意味着肿瘤免疫治疗时代的到来。研究表明，免疫相关基因（IRGs）可表征肿瘤微环境并与患者生存相关。然而，基于IRGs的ccRCC分类器的临床意义尚未得到充分确立。
使用R包limma、单变量和LASSO Cox回归分析从TCGA数据库中筛选与预后相关的IRGs。利用多变量Cox回归为候选基因建立风险预测模型。采用定量实时PCR法在本机构的临床样本中验证候选基因的表达。应用CIBERSORT算法和相关分析探索不同风险组之间的肿瘤浸润免疫细胞特征。还基于风险预测模型和其他独立风险因素，使用rms R包开发了一个临床列线图来预测总生存期（OS）。ICGC数据用于对基因风险模型或列线图进行外部验证。
我们鉴定出382个差异表达的免疫相关基因。最终确定了四个独特的预后IRGs（CRABP2、LTB4R、PTGER1和TEK）与肿瘤生存独立相关，并用于建立风险评分模型。所有候选基因的表达均通过q-PCR成功在实验室得到证实。CIBERSORT分析表明，处于不良风险组的患者有较高的CD8 T细胞、调节性T细胞和NK细胞浸润，以及较高的PD-1、CTLA4、TNFRSF9、TIGIT和LAG3表达。进一步生成了一个将IRGs风险评分与年龄、性别、TNM分期、Fuhrman分级、坏死情况相结合的列线图，以预测3年和5年总生存期，其显示出卓越的判别能力（AUC分别为0.811和0.795）。
我们的研究建立并验证了一个基于ccRCC中4个独特免疫相关基因的生存预后模型系统，这扩展了对肿瘤免疫状态的认识，并为未来提供了一个有效的预测工具。
