Research Papers:

Quantitative proteomics reveals the novel co-expression signatures in early brain development for prognosis of glioblastoma multiforme

Xuexin Yu, Lin Feng, Dianming Liu, Lianfeng Zhang, Bo Wu, Wei Jiang, Zujing Han and Shujun Cheng _

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Oncotarget. 2016; 7:14161-14171. https://doi.org/10.18632/oncotarget.7416

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Xuexin Yu1, Lin Feng2, Dianming Liu1, Lianfeng Zhang4, Bo Wu5, Wei Jiang1, Zujing Han3, Shujun Cheng1,2

1College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China

2State Key Laboratory of Molecular Oncology, Department of Etiology and Carcinogenesis, Cancer Institute and Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100021, China

3BGI Tech Solutions Co. Ltd., Yantian District, Shenzhen 518083, China

4Institute of Laboratory Animal Science, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China

5Department of Histology and Embryology, School of Basic Medical Sciences, Capital Medical University, Beijing 100069, China

Correspondence to:

Shujun Cheng, e-mail: [email protected]

Zujing Han, e-mail: [email protected]

Wei Jiang, e-mail: [email protected]

Keywords: brain development, co-expression, glioblastoma multiforme, prognosis, chemoresponse

Received: October 23, 2015     Accepted: January 29, 2016     Published: February 15, 2016


Although several researches have explored the similarity across development and tumorigenesis in cellular behavior and underlying molecular mechanisms, not many have investigated the developmental characteristics at proteomic level and further extended to cancer clinical outcome. In this study, we used iTRAQ to quantify the protein expression changes during macaque rhesus brain development from fetuses at gestation 70 days to after born 5 years. Then, we performed weighted gene co-expression network analysis (WGCNA) on protein expression data of brain development to identify co-expressed modules that highly expressed on distinct development stages, including early stage, middle stage and late stage. Moreover, we used the univariate cox regression model to evaluate the prognostic potentials of these genes in two independent glioblastoma multiforme (GBM) datasets. The results showed that the modules highly expressed on early stage contained more reproducible prognostic genes, including ILF2, CCT7, CCT4, RPL10A, MSN, PRPS1, TFRC and APEX1. These genes were not only associated with clinical outcome, but also tended to influence chemoresponse. These signatures identified from embryonic brain development might contribute to precise prediction of GBM prognosis and identification of novel drug targets in GBM therapies. Thus, the development could become a viable reference model for researching cancers, including identifying novel prognostic markers and promoting new therapies.

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