Liu Wei, Li Li, Li Weidong
Department of Pathology, Human Centrifuge Medical Training Center, Institute of Aviation Medicine of Chinese PLA Air Force, Beijing, China.
Int J Cancer. 2014 Dec 15;135(12):2795-803. doi: 10.1002/ijc.28935. Epub 2014 May 5.
To discover a common gene co-expression network in cancer cell, we applied weighted gene co-expression network analysis to transcriptional profiles of 917 cancer cell lines. Fourteen biologically meaningful modules were identified, including cytoskeleton, cell cycle, RNA splicing, signaling pathway, transcription, translation and others. These modules were robust in an independent human cancer microarray dataset. Furthermore, we collected 11 independent cancer microarray datasets, and correlated these modules with clinical outcome. Most of these modules could predict patient survival in one or more cancer types. Some modules were predictive of relapse, metastasis and drug resistance. Novel regulatory mechanisms were also implicated. In summary, our findings, for the first time, provide a modular map for cancer cell lines, new targets for therapy and modules for regulatory mechanism of cancer development and drug resistance.
为了在癌细胞中发现一个共同的基因共表达网络，我们将加权基因共表达网络分析应用于917个癌细胞系的转录谱。识别出了14个具有生物学意义的模块，包括细胞骨架、细胞周期、RNA剪接、信号通路、转录、翻译等。这些模块在一个独立的人类癌症微阵列数据集中是稳健的。此外，我们收集了11个独立的癌症微阵列数据集，并将这些模块与临床结果相关联。这些模块中的大多数能够预测一种或多种癌症类型患者的生存情况。一些模块能够预测复发、转移和耐药性。还涉及到了新的调控机制。总之，我们的研究结果首次为癌细胞系提供了一个模块化图谱、新的治疗靶点以及癌症发展和耐药性调控机制的模块。
