Proteogenomic characterization and integrative analysis of glioblastoma multiforme
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Ying-Chun Song1,*, Gai-Xia Lu1,*, Hong-Wei Zhang2,*, Xiao-Ming Zhong3,*, Xian-Ling Cong4,*, Shao-Bo Xue1,*, Rui Kong1,*, Dan Li1, Zheng-Yan Chang5, Xiao-Feng Wang6, Yun-Jie Zhang6, Ran Sun4, Li Chai1, Ru-Ting Xie5, Ming-Xiang Cai1, Ming Sun1, Wei-Qing Mao1, Hui-Qiong Yang5, Yun-Chao Shao6, Su-Yun Fan1, Ting-Miao Wu1, Qing Xia6, Zhong-Wei Lv1, David A. Fu6 and Yu-Shui Ma1,7
1Department of Nuclear Medicine, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai 200072, China
2Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200025, China
3Department of Radiology, Jiangxi Provincial Tumor Hospital/Ganzhou City People’s Hospital, Nanchang 330029, China
4Department of Biobank, China-Japan Unoin Hospital, Jilin University, Changchun 130033, China
5Department of Pathology, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai 200072, China
6Department of Orthopedic Surgery, Zhongshan Hospital, Fudan University, Shanghai 200032, China
7Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, College of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200062, China
*These authors have contributed equally to this work
Zhong-Wei Lv, email: [email protected]
David A. Fu, email: [email protected]
Yu-Shui Ma, email: [email protected]
Keywords: GBM, glioma, proteomics analysis, gene expression analysis, Bioinformatics Analysis
Received: May 24, 2017 Accepted: August 26, 2017 Published: October 19, 2017
Glioblastoma multiforme (GBM), the most aggressive and lethal primary brain tumor, is characterized by very low life expectancy. Understanding the genomic and proteogenomic characteristics of GBM is essential for devising better therapeutic approaches.Here, we performed proteomic profiling of 8 GBM and paired normal brain tissues. In parallel, comprehensive integrative genomic analysis of GBM was performed in silico using mRNA microarray and sequencing data. Two whole transcript expression profiling cohorts were used - a set of 3 normal brain tissues and 22 glioma tissue samples and a cohort of 5 normal brain tissues and 49 glioma tissue samples. A validation cohort included 529 GBM patients from The Cancer Genome Atlas datasets. We identified 36 molecules commonly changed at the level of the gene and protein, including up-regulated TGFBI and NES and down-regulated SNCA and HSPA12A. Single amino acid variant analysis identified 200 proteins with high mutation rates in GBM samples. We further identified 14 differentially expressed genes with high-level protein modification, among which NES and TNC showed differential expression at the protein level. Moreover, higher expression of NES and TNC mRNAs correlated with shorter overall survival, suggesting that these genes constitute potential biomarkers for GBM.
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