Xu Xianglai, Wang Yelin, Zhang Sihong, Zhu Yanjun, Wang Jiajun
Department of Urology, Zhongshan Hospital, Fudan University, Shanghai, China.
Department of Anesthesiology, Huashan Hospital, Fudan University, Shanghai, China.
Evol Bioinform Online. 2021 Oct 28;17:11769343211049270. doi: 10.1177/11769343211049270. eCollection 2021.
We aimed to discover prognostic factors of muscle-invasive bladder cancer (MIBC) and investigate their relationship with immune therapies. Online data of MIBC were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus database (GEO) database. Weighted gene co-expression network analysis (WGCNA) and univariate Cox analysis were applied to classify genes into different groups. Venn diagram was used to find the intersection of genes, and prognostic efficacy was proved by Kaplan-Meier analysis. Heatmap was utilized for differential analysis. Riskscore (RS) was calculated according to multivariate Cox analysis and evaluated by receiver operating characteristic curve (ROC). MIBC samples from TCGA and GEO were analyzed by WGCNA and univariate Cox analysis and intersected at 4 genes, CLK4, DEDD2, ENO1, and SYTL1. Higher SYTL1 and DEDD2 expressions were significantly correlated with high tumor grades. Riskscore based on genes showed great prognostic efficiency in predicting overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI) in TCGA dataset ( < .001). The area under the ROC curve (AUC) of RS reached 0.671 in predicting 1-year survival and 0.653 in 3-year survival. KEGG pathways enrichment filtered 5 enriched pathways. xCell analysis showed increased T cell CD4+ Th2 cell, macrophage, macrophage M1, and macrophage M2 infiltration in high RS samples ( < .001). In immune checkpoints analysis, PD-L1 expression was significantly higher in patients with high RS. We have, therefore, constructed RS as a convincing prognostic index for MIBC patients and found potential targeted pathways.
我们旨在发现肌层浸润性膀胱癌（MIBC）的预后因素，并研究它们与免疫治疗的关系。MIBC的在线数据来自癌症基因组图谱（TCGA）和基因表达综合数据库（GEO）。应用加权基因共表达网络分析（WGCNA）和单变量Cox分析将基因分类为不同组。使用维恩图查找基因的交集，并通过Kaplan-Meier分析证明预后效果。利用热图进行差异分析。根据多变量Cox分析计算风险评分（RS），并通过受试者工作特征曲线（ROC）进行评估。通过WGCNA和单变量Cox分析对来自TCGA和GEO的MIBC样本进行分析，发现4个基因CLK4、DEDD2、ENO1和SYTL1存在交集。较高的SYTL1和DEDD2表达与高肿瘤分级显著相关。基于这些基因的风险评分在预测TCGA数据集中的总生存期（OS）、疾病特异性生存期（DSS）和无进展生存期（PFI）方面显示出良好的预后效率（<0.001）。RS的ROC曲线下面积（AUC）在预测1年生存期时达到0.671，在预测3年生存期时达到0.653。KEGG通路富集筛选出5条富集通路。xCell分析显示，高RS样本中T细胞CD4 + Th2细胞、巨噬细胞、巨噬细胞M1和巨噬细胞M2浸润增加（<0.001）。在免疫检查点分析中，高RS患者的PD-L1表达显著更高。因此，我们构建了RS作为MIBC患者令人信服的预后指标，并发现了潜在的靶向通路。
