Department of Urology, Zhongshan Hospital, Fudan University, Shanghai, China.
Department of Urology, Xuhui Hospital, Fudan University, Shanghai, China.
Comput Biol Med. 2022 Dec;151(Pt A):106186. doi: 10.1016/j.compbiomed.2022.106186. Epub 2022 Oct 12.
The innovation of immunotherapy was a milestone in the treatment of bladder cancer (BLCA). However, the treatment benefits varied by individual thus promoting the investigation of the biomarker of the patients. Unfortunately, there were not many effective predictive models, which were desired by clinicians, for BLCA that can predict the prognosis and benefit of immunotherapy. We constructed a three genes prognosis prediction model termed RiskScore based on the result of weighted correlation network analysis (WGCNA) from The Cancer Genome Atlas (TCGA) cohort (n = 406). We then validated the prediction accuracy with three validation cohort(GSE13507 (n = 165), GSE48075(n = 73), GSE32894(n = 224)). We compared the differences in gene expression, immune relate function, and immune infiltration between two groups divided by RiskScore. We further discovered the potential drug target and suitable compounds for high-risk groups. Our results suggested that the low-risk group may be more potential for immunotherapy for they have higher B cell infiltration, higher expression of immune checkpoints(PDCD1, CTLA4), and much more active immune-related pathways(B cell and T cell receptor signaling pathway). The RiskScore showed a well predictive accuracy for the prognosis of BLCA. After Spearman analysis, we found the suitable drug target and compounds for the patients in the high-risk group. The model we constructed is able to predict the prognosis of BLCA patients with ease and accuracy. PLK1 and gefitinib may be utilized for further treatment of BLCA patients.
免疫疗法的创新是膀胱癌 (BLCA) 治疗的一个里程碑。然而，治疗效果因个体而异，因此促进了对患者生物标志物的研究。不幸的是，临床医生所期望的能够预测 BLCA 免疫治疗预后和获益的有效预测模型并不多。我们基于癌症基因组图谱 (TCGA) 队列 (n=406) 的加权相关网络分析 (WGCNA) 结果构建了一个称为 RiskScore 的三个基因预后预测模型。然后，我们使用三个验证队列 (GSE13507(n=165)、GSE48075(n=73)、GSE32894(n=224)) 验证了预测的准确性。我们比较了 RiskScore 分组后两组之间的基因表达、免疫相关功能和免疫浸润的差异。我们进一步发现了高风险组的潜在药物靶点和合适的化合物。我们的结果表明，低风险组可能更适合免疫治疗，因为它们具有更高的 B 细胞浸润、更高的免疫检查点 (PDCD1、CTLA4) 表达和更活跃的免疫相关途径 (B 细胞和 T 细胞受体信号通路)。RiskScore 对 BLCA 的预后具有良好的预测准确性。通过 Spearman 分析，我们发现了高风险组患者的合适药物靶点和化合物。我们构建的模型能够轻松准确地预测 BLCA 患者的预后。PLK1 和吉非替尼可能用于进一步治疗 BLCA 患者。
