Integration of genome scale data for identifying newplayers in colorectal cancer

作     者：Viktorija Sokolova Elisabetta Crippa Manuela Gariboldi 

作者机构：Department of Experimental Oncology and Molecular MedicineFondazione IRCCS Istituto Nazionale dei Tumori20133 MilanoItaly Molecular Genetics of CancerFondazione Istituto FIRC di Oncologia Molecolare20139 MilanoItaly 

出 版 物：《World Journal of Gastroenterology》 (世界胃肠病学杂志（英文版）)

年 卷 期：2016年第22卷第2期

页      面：534-545页

核心收录：

学科分类：1002[医学-临床医学] 100214[医学-肿瘤学] 10[医学] 

基　　金：Supported by Associazione Italiana per la Ricerca sul Cancro Grants No.10529 and No.12162 funds obtained throughan Italian law that allows taxpayers to allocate 0.5%share of theirincome tax contribution to a research institution of their choice 

主　　题：Colorectal cancer Copy number variations Gene expression miRNA expression Methylome Dataintegration 

摘      要：Colorectal cancers(CRCs) display a wide variety of genomic aberrations that may be either causally linked to their development and progression, or might serve as biomarkers for their presence. Recent advances in rapid high-throughput genetic and genomic analysis have helped to identify a plethora of alterations that can potentially serve as new cancer biomarkers, and thus help to improve CRC diagnosis, prognosis, and treatment. Each distinct data type(copy number variations, gene and micro RNAs expression, Cp G island methylation) provides an investigator with a different, partially independent, and complementary view of the entire genome. However, elucidation of gene function will require more information than can be provided by analyzing a single type of data. The integration of knowledge obtained from different sources is becoming increasingly essential for obtaining an interdisciplinary view of large amounts of information, and also for cross-validating experimental results. The integration of numerous types of genetic and genomic data derived from public sources, and via the use of ad-hoc bioinformatics tools and statistical methods facilitates the discovery and validation of novel, informative biomarkers. This combinatory approach will also enable researchers to more accurately and comprehensively understand the associations between different biologic pathways, mechanisms, and phenomena, and gain new insights into the etiology of CRC.