Application of a co‑expression network for the analysis of aggressive and non‑aggressive breast cancer cell lines to predict the clinical outcome of patients.
Application of a co‑expression network for the analysis of aggressive and non‑aggressive breast cancer cell lines to predict the clinical outcome of patients.
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共表达网络的应用用于分析侵袭性和非侵略性乳腺癌细胞系，以预测患者的临床结果。
DOI：
10.3892/mmr.2017.7608
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发表时间：
2017-12
影响因子：
3.4
通讯作者：
Bing Z
中科院分区：
文献类型：
--
作者：
Guo L;Zhang K;Bing Z
乳腺癌转移是乳腺癌临床治疗中的一个棘手问题。研究肿瘤转移的机制对于发展治疗方法是必要的。乳腺癌侵袭性的分类是生物学研究和临床决策中的重要问题。虽然侵袭性和非侵袭性乳腺癌细胞可以很容易地在不同的细胞系中区分，但在临床实践中很难区分。本研究的目的是利用乳腺癌细胞系的基因表达分析来预测乳腺癌患者的临床结局。加权基因共表达网络分析（WGCNA）是一种从大规模表达数据集中分析基因间相关性和提取基因共表达模块的有效方法。因此，WGCNA被应用于探索侵袭性和非侵袭性乳腺癌细胞系之间的子网络的差异。两组拓扑重叠网络的最大差异包括理解攻击性机制的潜在信息。结果表明，蓝色和红色模块与攻击性的生物过程显着相关。在蓝色模块中由TMEM 47、GJC 1、ANXA 3、TWIST 1和C19 orf 33组成的子网络与侵袭性表型相关。红色模块中的LOC 100653217、CXCL 12、SULF 1、DOK 5和DKK 3的子网络与非侵袭性表型相关。为了验证这些基因的风险比，构建预后指数以整合它们，并使用来自癌症基因组图谱（TCGA）和基因表达综合数据库（GEO）的数据进行检查。与低危组患者相比，高危组TCGA乳腺癌患者的总生存时间显著缩短（Wald检验，风险比=1.231，95%置信区间=1.058-1.433，P=0.0071）。对地观测数据库也得出了类似的结果。这一发现可能为测量乳腺癌患者的癌症侵袭性提供了一种新的策略。
Breast cancer metastasis is a demanding problem in clinical treatment of patients with breast cancer. It is necessary to examine the mechanisms of metastasis for developing therapies. Classification of the aggressiveness of breast cancer is an important issue in biological study and for clinical decisions. Although aggressive and non-aggressive breast cancer cells can be easily distinguished among different cell lines, it is very difficult to distinguish in clinical practice. The aim of the current study was to use the gene expression analysis from breast cancer cell lines to predict clinical outcomes of patients with breast cancer. Weighted gene co-expression network analysis (WGCNA) is a powerful method to account for correlations between genes and extract co-expressed modules of genes from large expression datasets. Therefore, WGCNA was applied to explore the differences in sub-networks between aggressive and non-aggressive breast cancer cell lines. The greatest difference topological overlap networks in both groups include potential information to understand the mechanisms of aggressiveness. The results show that the blue and red modules were significantly associated with the biological processes of aggressiveness. The sub-network, which consisted of TMEM47, GJC1, ANXA3, TWIST1 and C19orf33 in the blue module, was associated with an aggressive phenotype. The sub-network of LOC100653217, CXCL12, SULF1, DOK5 and DKK3 in the red module was associated with a non-aggressive phenotype. In order to validate the hazard ratio of these genes, the prognostic index was constructed to integrate them and examined using data from the Cancer Genomic Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Patients with breast cancer from TCGA in the high-risk group had a significantly shorter overall survival time compared with patients in the low-risk group (hazard ratio=1.231, 95% confidence interval=1.058–1.433, P=0.0071, by the Wald test). A similar result was produced from the GEO database. The findings may provide a novel strategy for measuring cancer aggressiveness in patients with breast cancer.
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