通过整合生物信息学分析检测与乳腺癌不良预后相关的关键基因。

Detection of critical genes associated with poor prognosis in breast cancer via integrated bioinformatics analyses.

机构信息

Department of Medical Oncology 3, The Meizhou People's Hospital, Meizhou, China.

出版信息

J BUON. 2020 Nov-Dec;25(6):2537-2545.

Abstract

PURPOSE

To explore the potential prognostic differentially expressed genes (DEGs) in breast cancer (BC) via bioinformatic analysis and elucidate possible mechanisms underlying the effects on BC progression.

METHODS

Three datasets (GSE21422, GSE31192 and GSE42568) were extracted from Gene Expression Omnibus (GEO) information bank. The GEO2R tool and Venn diagram softwares were used for data filtration, GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) analysis method were used to functionally annotate the selected DEGs. Protein-protein interaction (PPI) network of the selected DEGs was visualized by Cytoscape. Lastly, Kaplan-Meier (KM) plotter and Profiling Interactive Analysis (GEPIA) were employed to validate the values of the DEGs.

RESULTS

A total of 46 up-regulated and 65 down-regulated DEGs were identified. Of these, up-regulated DEGs were enriched in pathways related to cancer, p53 signaling pathway, ECM-receptor interaction, PI3K-Akt signaling pathway, while down-regulated DEGs were enriched in pathways involved in PPAR signaling pathway, proteoglycans in cancer, focal adhesion. 24 genes were selected from the PPI network analysis by Molecular Complex Detection (MCODE), and 20 vital genes were found to be correlated to poorer overall survival (OS) rates in BC. The prognostic values of these genes were validated by both KM and GEPIA. Finally, the CCNE2, CCNB1 and RRM2 genes were found to be markedly enriched in the p53 signaling pathway through the DAVID analysis.

CONCLUSION

This study revealed that the p53 signaling pathway could be an important pathway in BC progression. The three p53-related genes CCNE2, CCNB1 and RRM2 may represent candidate therapeutic gene targets for the treatment of BC.

摘要

目的

通过生物信息学分析探讨乳腺癌（BC）中潜在的预后差异表达基因（DEGs），并阐明其对 BC 进展影响的可能机制。

方法

从基因表达综合数据库（GEO）中提取了三个数据集（GSE21422、GSE31192 和 GSE42568）。使用 GEO2R 工具和 Venn 图软件进行数据筛选，使用 GO（基因本体论）和 KEGG（京都基因与基因组百科全书）分析方法对选定的 DEGs 进行功能注释。使用 Cytoscape 可视化选定的 DEGs 的蛋白质-蛋白质相互作用（PPI）网络。最后，使用 Kaplan-Meier（KM）绘图器和 Profiling Interactive Analysis（GEPIA）验证 DEGs 的值。

结果

共鉴定出 46 个上调和 65 个下调的 DEGs。其中，上调的 DEGs 富集在与癌症、p53 信号通路、ECM-受体相互作用、PI3K-Akt 信号通路相关的途径中，而下调的 DEGs 富集在涉及 PPAR 信号通路、癌症中的蛋白聚糖、焦点黏附的途径中。通过分子复合物检测（MCODE）从 PPI 网络分析中选择了 24 个基因，发现其中 20 个关键基因与 BC 较差的总生存率（OS）相关。通过 KM 和 GEPIA 验证了这些基因的预后价值。最后，通过 DAVID 分析发现，CCNE2、CCNB1 和 RRM2 基因在 p53 信号通路中明显富集。