Exploration of Prognostic Biomarkers of Muscle-Invasive Bladder Cancer (MIBC) by Bioinformatics.

IF 1.7 4区 生物学 Q4 EVOLUTIONARY BIOLOGY Evolutionary Bioinformatics Pub Date : 2021-10-28 eCollection Date: 2021-01-01 DOI:10.1177/11769343211049270
Xianglai Xu, Yelin Wang, Sihong Zhang, Yanjun Zhu, Jiajun Wang
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引用次数: 3

Abstract

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 (P < .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 (P < .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.

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肌肉浸润性膀胱癌(MIBC)预后生物标志物的生物信息学探索。
我们的目的是发现肌肉浸润性膀胱癌(MIBC)的预后因素,并探讨它们与免疫治疗的关系。MIBC的在线数据来源于The Cancer Genome Atlas (TCGA)和Gene Expression Omnibus database (GEO)数据库。采用加权基因共表达网络分析(WGCNA)和单变量Cox分析对基因进行分组。采用维恩图寻找基因交集,Kaplan-Meier分析证实预后疗效。采用热图进行差异分析。采用多变量Cox分析计算风险评分(RS),采用受试者工作特征曲线(ROC)评价。TCGA和GEO的MIBC样本通过WGCNA和单变量Cox分析进行分析,并在CLK4、DEDD2、ENO1和SYTL1 4个基因上相交。高SYTL1和DEDD2表达与高肿瘤分级显著相关。基于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)和无进展间期(PFI)方面显示出很高的预后效率
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来源期刊
Evolutionary Bioinformatics
Evolutionary Bioinformatics 生物-进化生物学
CiteScore
4.20
自引率
0.00%
发文量
25
审稿时长
12 months
期刊介绍: Evolutionary Bioinformatics is an open access, peer reviewed international journal focusing on evolutionary bioinformatics. The journal aims to support understanding of organismal form and function through use of molecular, genetic, genomic and proteomic data by giving due consideration to its evolutionary context.
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