Loss and gain of N-linked glycosylation sequons due to single-nucleotide variation in cancer

被引:0
作者
Yu Fan
Yu Hu
Cheng Yan
Radoslav Goldman
Yang Pan
Raja Mazumder
Hayley M. Dingerdissen
机构
[1] The George Washington University Medical Center,The Department of Biochemistry & Molecular Medicine
[2] Georgetown University,Department of Oncology
[3] The George Washington University,McCormick Genomic and Proteomic Center
来源
Scientific Reports | / 8卷
关键词
D O I
暂无
中图分类号
学科分类号
摘要
Despite availability of sequence site-specific information resulting from years of sequencing and sequence feature curation, there have been few efforts to integrate and annotate this information. In this study, we update the number of human N-linked glycosylation sequons (NLGs), and we investigate cancer-relatedness of glycosylation-impacting somatic nonsynonymous single-nucleotide variation (nsSNV) by mapping human NLGs to cancer variation data and reporting the expected loss or gain of glycosylation sequon. We find 75.8% of all human proteins have at least one NLG for a total of 59,341 unique NLGs (includes predicted and experimentally validated). Only 27.4% of all NLGs are experimentally validated sites on 4,412 glycoproteins. With respect to cancer, 8,895 somatic-only nsSNVs abolish NLGs in 5,204 proteins and 12,939 somatic-only nsSNVs create NLGs in 7,356 proteins in cancer samples. nsSNVs causing loss of 24 NLGs on 23 glycoproteins and nsSNVs creating 41 NLGs on 40 glycoproteins are identified in three or more cancers. Of all identified cancer somatic variants causing potential loss or gain of glycosylation, only 36 have previously known disease associations. Although this work is computational, it builds on existing genomics and glycobiology research to promote identification and rank potential cancer nsSNV biomarkers for experimental validation.
引用
收藏
相关论文
共 167 条
[21]  
Brenner S(2014)A framework for organizing cancer-related variations from existing databases, publications and NGS data using a High-performance Integrated Virtual Environment (HIVE) Database: the journal of biological databases and curation 44 D862-233
[22]  
Bentley DR(2016)ClinVar: public archive of interpretations of clinically relevant variants Nucleic Acids Res 1 229-935
[23]  
Mortazavi A(2004)Update on genome completion and annotations: Protein Information Resource Hum Genomics 35 927-4958
[24]  
Williams BA(2014)Genetic variations and diseases in UniProtKB/Swiss-Prot: the ins and outs of expert manual curation Hum Mutat 7 4945-8
[25]  
McCue K(2014)Prognostic significance of mucin expression in urothelial bladder cancer International journal of clinical and experimental pathology 2011 bar009-1743
[26]  
Schaeffer L(2011)UniProt Knowledgebase: a hub of integrated protein data Database : the journal of biological databases and curation 1473 4-555
[27]  
Wold B(1999)On the frequency of protein glycosylation, as deduced from analysis of the SWISS-PROT database Biochimica et biophysica acta 9 e86088-329
[28]  
Shendure J(2014)Encoding asymmetry of the N-glycosylation motif facilitates glycoprotein evolution PloS one 113 1737-5318
[29]  
Marino K(2013)Congenital disorders of glycosylation Handbook of clinical neurology 15 540-3671
[30]  
Bones J(2016)Hallmarks of glycosylation in cancer Oncotarget 63 322-270