基于整合生物信息学分析鉴定去势抵抗性前列腺癌的关键基因和通路。

Identification of key genes and pathways in castrate-resistant prostate cancer by integrated bioinformatics analysis.

机构信息

Department of Urology, the First Affiliated Hospital of Fujian Medical University, Fuzhou 350005, China.

出版信息

Pathol Res Pract. 2020 Oct;216(10):153109. doi: 10.1016/j.prp.2020.153109. Epub 2020 Jul 13.

DOI:10.1016/j.prp.2020.153109

PMID:32853947

Abstract

OBJECTIVE

To identify hub genes and pathways involved in castrate-resistant prostate cancer (CRPC).

METHODS

The gene expression profiles of GSE70768 were downloaded from Gene Expression Omnibus (GEO) datasets. A total of 13 CRPC samples and 110 tumor samples were identified. The differentially expressed genes (DEGs) were identified, and the gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analysis was performed. Protein-protein interaction (PPI) network module analysis was constructed and performed in Cytoscape software. Weighted correlation network analysis (WGCNA) was conducted to determine hub genes involved in the development and progression of CRPC. The gene expression profiles of GSE80609 were used for validation.

RESULTS

A total of 1738 DEGs were identified, consisting of 962 significantly down-regulated DEGs and 776 significantly upregulated DEGs for the subsequent analysis. GO term enrichment analysis suggested that DEGs were mainly enriched in the extracellular matrix organization, extracellular exosome, extracellular matrix, and extracellular space. KEGG pathway analysis found DEGs significantly enriched in the focal adhesion pathway. PPI network demonstrated that the top 10 hub genes were ALB, ACACB, KLK3, CDH1, IL10, ALDH1A3, KLK2, ALDH3B2, HBA1, COL1A1. Also, WGCNA identified the top 5 hub genes in the turquoise module, including MBD4, BLZF1, PIP5K2B, ZNF486, LRRC37B2. Plus, the Venn diagram demonstrated that HBA1 was the key gene in both GSE70768 and GSE80609 datasets.

CONCLUSIONS

These newly identified genes and pathways could help urologists understand the differences in the mechanism between CRPC and PCa. Besides, it might be promising targets for the treatment of CRPC.

摘要

目的

鉴定去势抵抗性前列腺癌（CRPC）相关的枢纽基因和通路。

方法

从基因表达综合数据库（GEO）数据集下载 GSE70768 的基因表达谱。鉴定出 13 例 CRPC 样本和 110 例肿瘤样本。识别差异表达基因（DEGs），并进行基因本体论（GO）和京都基因与基因组百科全书（KEGG）通路富集分析。在 Cytoscape 软件中构建和执行蛋白质-蛋白质相互作用（PPI）网络模块分析。进行加权相关网络分析（WGCNA）以确定参与 CRPC 发生和进展的枢纽基因。使用 GSE80609 的基因表达谱进行验证。

结果

共鉴定出 1738 个 DEGs，其中包括 962 个显著下调的 DEGs 和 776 个显著上调的 DEGs 用于后续分析。GO 术语富集分析表明，DEGs 主要富集在细胞外基质组织、细胞外小泡、细胞外基质和细胞外空间。KEGG 通路分析发现 DEGs 显著富集在粘着斑通路中。PPI 网络表明，前 10 个枢纽基因是 ALB、ACACB、KLK3、CDH1、IL10、ALDH1A3、KLK2、ALDH3B2、HBA1、COL1A1。此外，WGCNA 鉴定出 turquoise 模块中的前 5 个枢纽基因，包括 MBD4、BLZF1、PIP5K2B、ZNF486、LRRC37B2。此外，Venn 图表明 HBA1 是 GSE70768 和 GSE80609 数据集的关键基因。