Screening of core genes and pathways in breast cancer development via comprehensive analysis of multi gene expression datasets
Screening of core genes and pathways in breast cancer development via comprehensive analysis of multi gene expression datasets
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DOI：
10.3892/ol.2019.10979
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发表时间：
2019-12-01
期刊：
影响因子：
2.9
通讯作者：
Wang, Zunyi
中科院分区：
文献类型：
--
作者：
Bai, Jie;Zhang, Xiaoyu;Wang, Zunyi
乳腺癌一直是全球女性癌症相关死亡的主要原因。癌基因和抑癌基因表达的紊乱通常被认为是癌症发生和发展的根本原因。在本研究中，下载了用传统基因芯片检测的包含乳腺癌和邻近正常组织信息的三个基因表达数据集，并用R编程软件去除了批量效应。乳腺癌和正常组织之间的差异表达基因与肿瘤的发展途径密切相关。有趣的是，“细胞外基质-受体相互作用”、“过氧化物酶体增殖物激活受体信号通路”、“丙酸代谢”、“丙酮酸代谢”和“脂肪细胞中脂肪分解的调节”等五条通路被10个基因彻底连接。其中6个Hub基因(乙酰辅酶A羧基酶β、乙酰辅酶A脱氢酶中链、脂联素、C1q和胶原域、乙酰辅酶A合成酶短链家族成员2、磷酸烯醇式丙酮酸羧酸激酶1和Perilipin 1)上调的患者显示乳腺癌预后改善。此外，乳腺癌特异性网络分析确定了几个基因-基因相互作用模块。根据整个网络的评分，这些基因簇具有很强的相互作用，这可能在乳腺癌的发生发展中起重要作用。综上所述，本研究可能提高对乳腺癌发病机制的理解，并提供一些有价值的预后和治疗标志。
Breast cancer has been the leading cause of cancer-associated mortality in women worldwide. Perturbation of oncogene and tumor suppressor gene expression is generally considered as the fundamental cause of cancer initiation and progression. In the present study, three gene expression datasets containing information of breast cancer and adjacent normal tissues that were detected using traditional gene microarrays were downloaded and batch effects were removed with R programming software. The differentially expressed genes between breast cancer and normal tissue groups were closely associated with cancer development pathways. Interestingly, five pathways, including 'extracellular matrix-receptor interaction', 'peroxisome proliferator-activated receptors signaling pathway', 'propanoate metabolism', 'pyruvate metabolism' and 'regulation of lipolysis in adipocytes', were thoroughly connected by 10 genes. Patients with upregulation of six of these hub genes (acetyl-CoA carboxylase beta, acyl-CoA dehydrogenase medium chain, adiponectin, C1Q and collagen domain containing, acyl-CoA synthetase short chain family member 2, phosphoenolpyruvate carboxykinase 1 and perilipin 1) exhibited improved breast cancer prognosis. Additionally, breast cancer-specific network analysis identified several gene-gene interaction modules. These gene clusters had strong interactions according to the scoring in the whole network, which may be important to the development of breast cancer. In conclusion, the present study may improve the understanding of the mechanisms of breast cancer and provide several valuable prognosis and treatment signatures.