Multiomics kaleidoscope to visualize cancer hallmarks
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EDITORIAL
Open Access
Multiomics kaleidoscope to visualize cancer hallmarks Shengtao Zhou Correspondence: taotaovip2005@ 163.com; [email protected] Department of Obstetrics and Gynecology, Key Laboratory of Birth Defects and Related Diseases of Women and Children of MOE and State Key Laboratory of Biotherapy, West China Second Hospital, Sichuan University and Collaborative Innovation Center, Chengdu 610041, People’s Republic of China
Today, we have entered a data-explosive realm, which requires us to have a rational and clear viewpoint to visualize the underlying contour of a spectrum of events, especially life sciences. Cancer biology, as an important branch of life sciences, also experiences an explosion of data and related molecular characterization. Our evolving understanding of cancer hallmarks derives from rapid progress in the development of multiomics technologies. According to PubMed, omics-based investigations, either single omics or multiomics, of a variety of cancer types have been expanding each year. Correspondingly, our conception of this deadly disease no longer stays at a mono-gene level, but becomes more multidimensional and network-based. In this context, Genome Biology has launched this special issue entitled “Cancer Evolution and Metastasis” incorporating articles that give us additional explanation for the molecular mechanisms driving cancer evolution, heterogeneity, and metastasis. These resources will give us a bird’s-eye view of how intra-tumor and inter-tumor heterogeneity formed and how we can rationally design optimized treatment strategies based upon these discoveries.
The central dogma-based profiling The central dogma for molecular inheritance states that DNA makes RNA, which makes protein. This anatomy of the living phenomenon has led to subsequent development of a series of omics-based technologies, for instance, genome-wide association studies (GWAS), transcriptome-wide association studies (TWAS), and proteome-wide association studies (PWAS). Nearly a decade ago, GWAS-based sequencing technologies have been prevalent to identify associations between genetic variations and phenotypic traits, including cancers. However, while indeed thousands of novel cancer susceptibility loci have been unraveled, seldom have been translated into clinical use or show any direct biological relevance to tumorigenesis [1]. With these controversies, much attention has turned to transcriptome-level (transcriptomics) and proteome-level (proteomics) studies, especially when the scientific community calls now “a postgenomics era.” Indeed, global mapping of transcriptome or proteome profiles, which are direct effectors of the living information, more straightly links cancer phenotypes with molecular mechanisms. Furthermore, TWAS integrates GWAS and gene expression datasets to identify gene–trait associations, which could accurately prioritize the © The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, ada
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