Subbannayya Yashwanth, Pinto Sneha M, Gowda Harsha, Prasad T S Keshava
a YU-IOB Center for Systems Biology and Molecular Medicine , Yenepoya University , Mangalore, India.
b Institute of Bioinformatics , Bangalore , India.
Expert Rev Proteomics. 2016;13(3):297-308. doi: 10.1586/14789450.2016.1136217. Epub 2016 Jan 25.
The concept of proteogenomics has emerged rapidly as a valuable approach to integrate mass spectrometry-derived proteomic data with genomic and transcriptomic data. It is used to harness the full potential of the former dataset in the discovery of potential biomarkers, therapeutic targets and novel proteins associated with various biological processes including diseases. Proteogenomic strategies have been successfully utilized to identify novel genes and redefine annotation of existing gene models in various genomes. In recent years, this approach has been extended to the field of cancer biology to unravel complexities in the tumor genomes and proteomes. Standard proteomics workflows employing translated cancer genomes and transcriptomes can potentially identify peptides from mutant proteins, splice variants and fusion proteins in the tumor proteome, which in addition to the currently available biomarker panels can serve as potential diagnostic and prognostic biomarkers, besides having therapeutic utility. This review focuses on the role of proteogenomics to understand cancer biology.
蛋白质基因组学的概念迅速兴起,成为一种将质谱衍生的蛋白质组学数据与基因组和转录组数据整合的重要方法。它被用于挖掘前一个数据集的全部潜力,以发现与包括疾病在内的各种生物过程相关的潜在生物标志物、治疗靶点和新蛋白质。蛋白质基因组学策略已成功用于识别新基因,并重新定义各种基因组中现有基因模型的注释。近年来,这种方法已扩展到癌症生物学领域,以揭示肿瘤基因组和蛋白质组的复杂性。采用翻译后的癌症基因组和转录组的标准蛋白质组学工作流程有可能识别肿瘤蛋白质组中突变蛋白、剪接变体和融合蛋白的肽段,除了目前可用的生物标志物面板外,这些肽段还可作为潜在的诊断和预后生物标志物,同时具有治疗用途。本综述重点关注蛋白质基因组学在理解癌症生物学方面的作用。