三阴性乳腺癌的雌激素受体、孕激素受体、HER2表达均为阴性，其特征为疾病早期复发和生存结局不佳。
2019年6月13日，日本乳腺癌学会《乳腺癌》在线发表中国医科大学附属第一医院袁媛、王学梅等学者的研究报告，比较了三阴性与非三阴性乳腺癌的基因表达，探讨了三阴性乳腺癌相关基因失调，同时为三阴性乳腺癌治疗提供了其他可能的治疗靶点。
该研究从美国国家图书馆生物技术信息中心（NCBI）基因表达公共数据库（GEO）下载贝勒医学院提交的三阴性乳腺癌基因表达谱GSE76275，利用美国国家癌症研究所（NCI）癌症基因组图谱（TCGA）数据库对可能的关键基因进行验证，利用基因/蛋白质相互作用分析检索工具（STRING）确定蛋白质相互作用关系，最后利用生存曲线在线工具分析关键基因的总生存和无复发生存。
结果，GSE76275分析确定三阴性乳腺癌表达不同基因共计750个。通过TCGA数据库验证，共计155个三阴性乳腺癌表达不同基因与GSE76275确定的基因一致。利用从GSE76275数据集获得的三阴性乳腺癌表达不同基因，通过STRING，构建了蛋白质相互作用关系网络：
此外，根据155个三阴性乳腺癌表达不同基因的预后分析，发现总生存相关基因10个和无复发生存相关基因33个。结合蛋白质相互作用关系级别评分，选出10个级别评分最高的基因作为三阴性乳腺癌关键基因：
低表达7个：RET、PDZK1、XBP1、TFF3、PTGER3、NME5、IL6ST
高表达3个：EGFR、KRT16、SOX10
因此，该研究为三阴性乳腺癌相关生物标志及其相互作用提供了新的认识，可能有助于三阴性乳腺癌患者的预后和风险分层。
Breast Cancer. 2019 Jun 13. [Epub ahead of print]
Identification of differentially expressed genes between triple and non-triple-negative breast cancer using bioinformatics analysis.
Zhai Q, Li H, Sun L, Yuan Y, Wang X.
First Hospital of China Medical University, Shenyang, China.
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.
KEYWORDS: Triple-negative breast cancer Differentially expressed genes Protein-protein Interaction Kaplan-Meier plotter
PMID: 31197620
DOI: 10.1007/s12282-019-00988-x
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