Department of Urology & Andrology, Minimally Invasive Surgery Center, Guangdong Provincial Key Laboratory of Urology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou 510230, China.
Int J Mol Sci. 2023 Mar 10;24(6):5332. doi: 10.3390/ijms24065332.
Immunotherapy has greatly improved the survival time and quality of life of patients with renal cell carcinoma, but the benefits are limited to a small portion of patients. There are too few new biomarkers that can be used to identify molecular subtypes of renal clear cell carcinoma and predict survival time with anti-PD-1 treatment. Single-cell RNA data of clear cell renal cell carcinoma (ccRCC) treated with anti-PD-1 were obtained from public databases, then 27,707 high-quality CD4 + T and CD8 + T cells were obtained for subsequent analysis. Firstly, genes set variation analysis and CellChat algorithm were used to explore potential molecular pathway differences and intercellular communication between the responder and non-responder groups. Additionally, differentially expressed genes (DEGs) between the responder and non-responder groups were obtained using the "edgeR" package, and ccRCC samples from TCGA-KIRC ( = 533) and ICGA-KIRC ( = 91) were analyzed by the unsupervised clustering algorithm to recognize molecular subtypes with different immune characteristics. Finally, using univariate Cox analysis, least absolute shrinkage and selection operator (Lasso) regression, and multivariate Cox regression, the prognosis model of immunotherapy was established and verified to predict the progression-free survival of ccRCC patients treated with anti-PD-1. At the single cell level, there are different signal pathways and cell communication between the immunotherapy responder and non-responder groups. In addition, our research also confirms that the expression level of PDCD1/PD-1 is not an effective marker for predicting the response to immune checkpoint inhibitors (ICIs). The new prognostic immune signature (PIS) enabled the classification of ccRCC patients with anti-PD-1 therapy into high- and low-risk groups, and the progression-free survival times (PFS) and immunotherapy responses were significantly different between these two groups. In the training group, the area under the ROC curve (AUC) for predicting 1-, 2- and 3-year progression-free survival was 0.940 (95% CI: 0.894-0.985), 0.981 (95% CI: 0.960-1.000), and 0.969 (95% CI: 0.937-1.000), respectively. Validation sets confirm the robustness of the signature. This study revealed the heterogeneity between the anti-PD-1 responder and non-responder groups from different angles and established a robust PIS to predict the progression-free survival of ccRCC patients receiving immune checkpoint inhibitors.
免疫疗法大大提高了肾细胞癌患者的生存时间和生活质量，但获益仅限于一小部分患者。目前，能够用于识别肾透明细胞癌的分子亚型并预测抗 PD-1 治疗的生存时间的新型生物标志物仍然很少。从公共数据库中获得接受抗 PD-1 治疗的肾透明细胞癌 (ccRCC) 的单细胞 RNA 数据，然后获得 27707 个高质量的 CD4+T 和 CD8+T 细胞进行后续分析。首先，使用基因集变异分析和 CellChat 算法来探索应答者和无应答者之间潜在的分子通路差异和细胞间通讯。此外，使用“edgeR”包获得应答者和无应答者之间的差异表达基因 (DEGs)，并使用无监督聚类算法对 TCGA-KIRC(=533)和 ICGA-KIRC(=91)中的 ccRCC 样本进行分析，以识别具有不同免疫特征的分子亚型。最后，使用单变量 Cox 分析、最小绝对收缩和选择算子 (Lasso) 回归和多变量 Cox 回归建立免疫治疗的预后模型，并验证其预测抗 PD-1 治疗的 ccRCC 患者无进展生存期的能力。在单细胞水平上，免疫治疗应答者和无应答者之间存在不同的信号通路和细胞通讯。此外，我们的研究还证实，PDCD1/PD-1 的表达水平不是预测免疫检查点抑制剂 (ICI) 反应的有效标志物。新的预后免疫特征 (PIS) 使接受抗 PD-1 治疗的 ccRCC 患者能够分为高风险和低风险组，这两组之间的无进展生存期 (PFS) 和免疫治疗反应有显著差异。在训练组中，预测 1、2 和 3 年无进展生存率的 ROC 曲线下面积 (AUC) 分别为 0.940(95%CI:0.894-0.985)、0.981(95%CI:0.960-1.000)和 0.969(95%CI:0.937-1.000)。验证集证实了该特征的稳健性。本研究从不同角度揭示了抗 PD-1 应答者和无应答者之间的异质性，并建立了一个稳健的 PIS 来预测接受免疫检查点抑制剂治疗的 ccRCC 患者的无进展生存率。
