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基于生物信息学分析鉴定三阴性与非三阴性乳腺癌的差异表达基因。

Identification of differentially expressed genes between triple and non-triple-negative breast cancer using bioinformatics analysis.

机构信息

Department of Ultrasound, the First Hospital of China Medical University, Shenyang, 110001, China.

Tumor Etiology and Screening Department of Cancer Institute and General Surgery, the First Hospital of China Medical University, North Nanjing Street 155#, Heping District, Shenyang, 110001, China.

出版信息

Breast Cancer. 2019 Nov;26(6):784-791. doi: 10.1007/s12282-019-00988-x. Epub 2019 Jun 13.


DOI:10.1007/s12282-019-00988-x
PMID:31197620
Abstract

BACKGROUND: Triple-negative breast cancer (TNBC), defined by lack of expression of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2), is characterized by early recurrence of disease and poor survival. OBJECTIVE: Here, we sought to identify genes associated with TNBC that could provide new insight into gene dysregulation in TNBC and, at the same time, provide additional potential therapeutic targets for breast cancer treatment. METHODS: Gene expression profiles from accession series GSE76275 were downloaded from the Gene Expression Omnibus database (GEO). The Cancer Genome Atlas (TCGA) was used to validate potential hub genes in the TCGA database. Protein-protein interaction (PPI) networks were identified using STRING (Search Tool for the Retrieval of Interacting Genes/Proteins). Finally, overall survival (OS) and relapse-free survival (RFS) analysis of hub genes was performed using a Kaplan-Meier plotter online tool. RESULTS: A total of 750 genes were identified after analysis of GSE76275. After validation with the TCGA database, a total of 155 differentially expressed genes (DEGs) were consistent with those identified by GSE76275. Based on the STRING database, we constructed a PPI network using the DEGs obtained from GSE76275 datasets. Furthermore, in the prognostic analysis of the 155 DEGs, we found that there were 10 genes associated with OS and 33 genes associated with RFS. Combined with the degree scores from the PPI network, a total of ten genes with the highest degree scores were selected as hub genes pertaining to TNBC. CONCLUSION: Our research provides new insight into the subnetwork of biomarkers connected with TNBC, which could be useful for prognostication and risk stratification of TNBC patients.

摘要

背景:三阴性乳腺癌(TNBC)定义为雌激素受体(ER)、孕激素受体(PR)和人表皮生长因子受体 2(HER2)表达缺失,其特点是疾病复发早、生存预后差。 目的:本研究旨在鉴定与 TNBC 相关的基因,以期深入了解 TNBC 中的基因失调,并为乳腺癌的治疗提供更多潜在的治疗靶点。 方法:从基因表达综合数据库(GEO)中下载基因表达谱数据集 GSE76275。利用癌症基因组图谱(TCGA)数据库验证 TCGA 数据库中潜在的枢纽基因。使用 STRING(检索相互作用基因/蛋白质的工具)鉴定蛋白质-蛋白质相互作用(PPI)网络。最后,使用 Kaplan-Meier 绘图仪在线工具对枢纽基因的总生存期(OS)和无复发生存期(RFS)进行分析。 结果:经 GSE76275 分析后共鉴定出 750 个基因。经 TCGA 数据库验证后,共筛选出 155 个与 GSE76275 一致的差异表达基因(DEGs)。基于 STRING 数据库,我们使用 GSE76275 数据集获得的 DEGs 构建了一个 PPI 网络。此外,在 155 个 DEGs 的预后分析中,我们发现有 10 个基因与 OS 相关,33 个基因与 RFS 相关。结合 PPI 网络的度评分,我们选择了 10 个具有最高度评分的基因作为与 TNBC 相关的枢纽基因。 结论:本研究为与 TNBC 相关的生物标志物子网络提供了新的见解,可能有助于 TNBC 患者的预后和风险分层。

相似文献

[1]
Identification of differentially expressed genes between triple and non-triple-negative breast cancer using bioinformatics analysis.

Breast Cancer. 2019-6-13

[2]
Estrogen receptor 1 and progesterone receptor are distinct biomarkers and prognostic factors in estrogen receptor-positive breast cancer: Evidence from a bioinformatic analysis.

Biomed Pharmacother. 2019-11-13

[3]
Identification of key genes as potential biomarkers for triple‑negative breast cancer using integrating genomics analysis.

Mol Med Rep. 2019-12-6

[4]
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Ann Diagn Pathol. 2021-12

[5]
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Gene. 2019-1-14

[6]
Bioinformatics Analysis Identifies IL6ST as a Potential Tumor Suppressor Gene for Triple-Negative Breast Cancer.

Reprod Sci. 2021-8

[7]
Identification of key candidate genes, pathways and related prognostic values in ER-negative/HER2-negative breast cancer by bioinformatics analysis.

J BUON. 2018

[8]
Identification of a five genes prognosis signature for triple-negative breast cancer using multi-omics methods and bioinformatics analysis.

Cancer Gene Ther. 2022-11

[9]
KEGG-expressed genes and pathways in triple negative breast cancer: Protocol for a systematic review and data mining.

Medicine (Baltimore). 2020-5

[10]
Identification of Differentially Expressed Genes (DEGs) Relevant to Prognosis of Ovarian Cancer by Use of Integrated Bioinformatics Analysis and Validation by Immunohistochemistry Assay.

Med Sci Monit. 2019-12-24

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[3]
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[4]
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[5]
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[6]
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[7]
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[8]
MYBL2-induced PITPNA-AS1 upregulates SIK2 to exert oncogenic function in triple-negative breast cancer through miR-520d-5p and DDX54.

J Transl Med. 2021-8-5

[9]
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[10]
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