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基于综合生物信息学分析鉴定乳腺癌枢纽基因并分析其预后价值。

Identification of breast cancer hub genes and analysis of prognostic values using integrated bioinformatics analysis.

出版信息

Cancer Biomark. 2017 Dec 12;21(1):373-381. doi: 10.3233/CBM-170550.

Abstract

BACKGROUND

Breast cancer (BC) is the second most common cause of death from cancer in women in the United States. As the molecular mechanism of BC has not yet been completely discovered, identification of hub genes and pathways of this disease is of importance for revealing molecular mechanism of breast cancer initiation and progression.

OBJECTIVE

This study aimed to identify potential biomarkers and survival analysis of hub genes for BC treatment.

METHODS

The differentially expressed genes (DEGs) between breast cancer and normal cells were screened using microarray data obtained from the Gene Expression Omnibus (GEO) database. Gene ontology (GO) and KEGG pathway enrichment analyses were performed for DEGs using DAVID database, the protein-protein interaction (PPI) network was constructed using the Cytoscape software, and module analysis was performed using MCODE. Then, overall survival (OS) analysis of hub genes was performed by the Kaplan-Meier plotter online tool. Finally, the potential molecular agents were identified with Connectivity Map (cMap) database.

RESULTS

A total of 585 DEGs were obtained, which were significantly enriched in the terms related to positive regulation of cell migration, regulation of cell proliferation and focal adhesion. KEGG pathway analysis showed that the significant pathways included Focal adhesion, Pathways in cancer, ECM-receptor interaction, Ribosome, Transcriptional misregulation in cancer and other signaling pathways about cancer. The PPI network was established with 576 nodes and 1943 edges. A significant module was found from the PPI network, the enriched functions and pathways included ECM-receptor interaction and Focal adhesion.

CONCLUSIONS

Fifteen genes were selected as hub genes because of high degrees, among which, low expression of four genes was associated with worse OS of patients with BC, including RPS9, RPL11, RPS14 and RPL10A. Additionally, the small molecular agent emetine may be a potential drug for BC.

摘要

背景

乳腺癌(BC)是美国女性癌症死亡的第二大常见原因。由于乳腺癌的分子机制尚未完全发现,因此鉴定该疾病的枢纽基因和途径对于揭示乳腺癌发生和发展的分子机制非常重要。

目的

本研究旨在鉴定乳腺癌治疗的潜在生物标志物和枢纽基因的生存分析。

方法

使用从基因表达综合(GEO)数据库中获得的微阵列数据筛选乳腺癌和正常细胞之间的差异表达基因(DEGs)。使用 DAVID 数据库对 DEGs 进行基因本体论(GO)和 KEGG 通路富集分析,使用 Cytoscape 软件构建蛋白质-蛋白质相互作用(PPI)网络,并使用 MCODE 进行模块分析。然后,通过在线 Kaplan-Meier 绘图器工具对枢纽基因进行总体生存(OS)分析。最后,使用 Connectivity Map(cMap)数据库鉴定潜在的分子药物。

结果

共获得 585 个 DEGs,这些基因在与细胞迁移的正调控、细胞增殖和焦点附着的调节相关的术语中显著富集。KEGG 通路分析表明,显著途径包括焦点附着、癌症途径、ECM-受体相互作用、核糖体、癌症转录失调和其他与癌症相关的信号通路。使用 576 个节点和 1943 个边建立了 PPI 网络。从 PPI 网络中发现了一个显著的模块,富集的功能和途径包括 ECM-受体相互作用和焦点附着。

结论

由于高程度,选择了 15 个基因作为枢纽基因,其中 4 个低表达基因与 BC 患者的 OS 较差相关,包括 RPS9、RPL11、RPS14 和 RPL10A。此外,小分子药物依米丁可能是一种潜在的乳腺癌药物。

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