通过生物信息学分析鉴定参与膀胱癌进展的 ECM 和 EMT 相关基因。

IF 1.5 Q3 UROLOGY & NEPHROLOGY
American journal of clinical and experimental urology Pub Date : 2024-08-25 eCollection Date: 2024-01-01 DOI:10.62347/XNTC7030
Kai Cao, Honglei Shi, Bin Wu, Zhong Lv, Rong Yang
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引用次数: 0

摘要

背景:膀胱癌(BC)是泌尿系统癌症中非常常见的一种:膀胱癌(BC)是泌尿系统癌症中非常常见的一种。它通常分为两种类型:非肌浸润性膀胱癌(NMIBC)和肌浸润性膀胱癌(MIBC)。非肌层浸润性膀胱癌(NMIBC)和肌层浸润性膀胱癌(MIBC)的分组具有异质性和不同的特征:本研究旨在发现可能参与膀胱癌进展的一些枢纽基因和相关信号通路,并探讨其与临床分期的关系及其预后意义:方法:从基因表达总库(Gene Expression Omnibus,GEO)数据库中获取GSE37317数据集。方法:从基因表达总库(GEO)中获取 GSE37317 数据集,选择 GEO2R 在线工具筛选两种不同类型 BC 的差异表达基因(DEGs)。然后,对这些DEGs进行了基因本体(GO)富集和KOBAS-京都基因和基因组百科全书(KEGG)通路分析。蛋白-蛋白相互作用(PPI)网络被用来帮助我们筛选枢纽基因并找到重要的模块。最后,我们利用 GEPIA 和 Kaplan-Meier plotter 数据库对基因表达和生存曲线进行了分析。GO和KEGG通路富集分析表明,DEGs主要参与胶原纤维组织、细胞外基质(ECM)结构成分、bHLH转录因子结合、AGE-RAGE信号通路和TGF-beta信号通路。与健康对照组相比,只有 3 个中枢基因(DCN、JUN 和 THBS1)的表达量明显较高。这些中枢基因还与临床分期以及BC患者的总生存期(OS)密切相关:综上所述,大多数参与BC进展的枢纽基因与ECM和EMT有关。结论:综上所述,大多数参与BC进展的中枢基因与ECM和EMT有关,此外,3个中枢基因(DCN、JUN和THBS1)与BC患者的临床分期和OS密切相关。这项研究可加深我们对 NMIBC 进展的理解,并为 MIBC 找出新的潜在靶点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Identification of ECM and EMT relevant genes involved in the progression of bladder cancer through bioinformatics analysis.

Background: Bladder cancer (BC) is very common among cancers of urinary system. It was usually categorized into two types: non-muscle invasive bladder cancer (NMIBC) and muscle invasive bladder cancer (MIBC). NMIBC and MIBC groupings are heterogeneous and have different characteristics.

Objectives: The study was aimed to find some hub genes and related signal pathways which might be engaged in the progression of BC and to investigate the relationship with clinical stages and its prognostic significance.

Methods: GSE37317 datasets were acquired from Gene Expression Omnibus (GEO) database. GEO2R on-line tool was selected to screen the differentially expressed genes (DEGs) of the two different types of BC. Then, Gene Ontology (GO) enrichment and KOBAS-Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of these DEGs were conducted. A protein-protein interaction (PPI) network was employed to help us screen hub genes and find significant modules. Finally, we made analysis of gene expression and survival curve by GEPIA and Kaplan-Meier plotter database.

Results: 224 DEGs were screened in total, with 110 showing increased expression and 114 demonstrating decreased expression. GO and KEGG pathway enrichment analysis showed that DEGs were mostly involved in collagen fibril organization, extracellular matrix (ECM) structural constituent, bHLH transcription factor binding, AGE-RAGE signaling pathway and TGF-beta signaling pathway. Only 3 hub genes (DCN, JUN, THBS1) displayed significantly higher expression compared to those in the healthy controls. These hub genes were also strongly related to clinical stages as well as overall survival (OS) of BC patients.

Conclusions: Taken together, most of hub genes involved in the progression of BC were related to ECM and EMT. In addition, 3 hub genes (DCN, JUN, THBS1) were strongly related with clinical stages and OS of BC patients. This study can enhance our comprehension of the progression of NMIBC and identify novel potential targets for MIBC.

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