Integrated Proteomic, Transcriptomic, and Biological Network Analysis of Breast Carcinoma Reveals Molecular Features of Tumorigenesis and Clinical Relapse
Integrated Proteomic, Transcriptomic, and Biological Network Analysis of Breast Carcinoma Reveals Molecular Features of Tumorigenesis and Clinical Relapse
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DOI：
10.1074/mcp.m111.014910
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发表时间：
2012-06-01
影响因子：
7
通讯作者：
Sgroi, Dennis C.
中科院分区：
文献类型：
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作者：
Imielinski, Marcin;Cha, Sangwon;Sgroi, Dennis C.
在肿瘤发生过程中观察到的基因和蛋白质表达变化通常被相互独立地解释，并且脱离了生物网络的背景。为了解决这些局限性，这项研究考察了在雌激素受体阳性(ER+)乳腺癌肿瘤中，将转录和蛋白质组数据与已知的蛋白质-蛋白质和信号相互作用整合的几种方法。一种从差异表达的蛋白质构建网络并在其中识别丰富差异表达基因的网络的方法取得了最大的成功。这种方法确定了一组连接细胞应激反应、癌症新陈代谢和肿瘤微环境的基因和蛋白质。这个建议的网络强调了几个以前在ER+乳腺癌背景下没有研究过的生物学有趣的事件，包括p38丝裂原活化蛋白激酶的过度表达和聚(ADP-核糖)聚合酶1的过度表达。从这个网络构建的基于基因的表达签名生物标记物可以显著预测多个独立的ER+乳腺癌患者的临床复发，即使在校正了标准的临床病理变量之后也是如此。这项研究的结果证明了一种集成的定量蛋白质组、转录和网络分析方法在发现肿瘤中强大的和有临床意义的分子变化方面的实用性和威力。分子与细胞蛋白质组学11：10.1074/mcp.M111.014910，1-15,2012年。
Gene and protein expression changes observed with tumorigenesis are often interpreted independently of each other and out of context of biological networks. To address these limitations, this study examined several approaches to integrate transcriptomic and proteomic data with known protein-protein and signaling interactions in estrogen receptor positive (ER+) breast cancer tumors. An approach that built networks from differentially expressed proteins and identified among them networks enriched in differentially expressed genes yielded the greatest success. This method identified a set of genes and proteins linking pathways of cellular stress response, cancer metabolism, and tumor microenvironment. The proposed network underscores several biologically intriguing events not previously studied in the context of ER+ breast cancer, including the overexpression of p38 mitogen-activated protein kinase and the overexpression of poly(ADP-ribose) polymerase 1. A gene-based expression signature biomarker built from this network was significantly predictive of clinical relapse in multiple independent cohorts of ER+ breast cancer patients, even after correcting for standard clinicopathological variables. The results of this study demonstrate the utility and power of an integrated quantitative proteomic, transcriptomic, and network analysis approach to discover robust and clinically meaningful molecular changes in tumors. Molecular & Cellular Proteomics 11: 10.1074/mcp.M111.014910, 1-15, 2012.