Abstract

Bladder cancer (BLCA) is a common malignancy and has a poor prognosis. Single nucleotide polymorphisms (SNPs) in genes are closely associated with the tumorigenesis and tumor development and yet are not well illustrated in BLCA. In this study, we downloaded SNP‐related data and transcriptome profiling of BLCA from the Cancer Genome Atlas (TCGA) database and identified high frequency mutation genes using Maftools package and differentially expressed genes (DEGs) between BLCA and normal bladder tissues by the Limma package. Then, the overlapping genes between high frequency mutation genes and DEGs were obtained by the Venn diagram tool. These overlapping genes were analyzed by Gene Ontology (GO) and Kyoto Gene and Genome Encyclopedia (KEGG) pathway enrichment analysis, protein–protein interaction (PPI) network construction, survival analysis and drug–gene interaction analysis. As a result, 33 overlapping mutant genes were obtained, which were enriched in multiple KEGG pathways (e.g. cellular senescence) and GO items (e.g. muscle organ development, costamere and actin binding). A significant gene module was constructed by PPI network analysis and included 12 hub genes (SYNE1, DMD, ATM, EP300, ANK2, LAMA2, FAT1, SRRM2, MACF1, TSC1, VCAN and RXRA). Moreover, ATM, RXRA, TSC1, DMD, EP300, LAMA2 and VCAN were drug targets. These findings provide important bioinformatics and theoretical basis for understanding the pathogenesis of BLCA and exploring the detailed mechanisms of mutant genes in the disease.

摘要

膀胱癌（BLCA）是一种常见的恶性肿瘤，预后较差。基因中的单核苷酸多态性（SNP）与肿瘤发生和肿瘤发展密切相关，但在BLCA中并未得到很好的说明。在这项研究中，我们从癌症基因组图谱（TCGA）数据库下载了BLCA的SNP相关数据和转录组图谱，并使用Maftools软件包鉴定了高频突变基因，并通过Limma软件包鉴定了BLCA和正常膀胱组织之间的差异表达基因（DEG）。 。然后，通过Venn图工具获得高频突变基因和DEG之间的重叠基因。通过基因本体论（GO）和《京都议定书》和《基因组百科全书》（KEGG）途径富集分析，蛋白质间相互作用（PPI）网络构建，对这些重叠的基因进行了分析，生存分析和药物基因相互作用分析。结果，获得了33个重叠的突变基因，其在多个KEGG途径（例如细胞衰老）和GO项目（例如肌肉器官发育，costamere和肌动蛋白结合）中富集。通过PPI网络分析构建了一个重要的基因模块，其中包括12个集线器基因（SYNE1，DMD，ATM，EP300，ANK2，LAMA2，FAT1，SRRM2，MACF1，TSC1，VCAN和RXRA）。此外，ATM，RXRA，TSC1，DMD，EP300，LAMA2和VCAN是药物靶标。这些发现为了解BLCA的发病机理和探索该疾病中突变基因的详细机制提供了重要的生物信息学和理论基础。