Journal
GENOMICS
Volume 107, Issue 6, Pages 223-230Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.ygeno.2016.04.005
Keywords
Multidimensional omics data; Melanoma prognosis; Integration; The Cancer Genome Atlas (TCGA)
Funding
- National Institutes of Health [CA182984, CA142774,, P50CA121974, P30CA016359]
- National Social Science Foundation of China [13CTJ001, 13ZD148]
- VA Cooperative Studies Program of the Department of VA, Office of Research and Development
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Multiple types of genetic, epigenetic, and genomic changes have been implicated in cutaneous melanoma prognosis. Many of the existing studies are limited in analyzing a single type of omics measurement and cannot comprehensively describe the biological processes underlying prognosis. As a result, the obtained prognostic models may be less satisfactory, and the identified prognostic markers may be less informative. The recently collected TCGA (The Cancer Genome Atlas) data have a high quality and comprehensive omics measurements, making it possible to more comprehensively and more accurately model prognosis. In this study, we first describe the statistical approaches that can integrate multiple types of omics measurements with the assistance of variable selection and dimension reduction techniques. Data analysis suggests that, for cutaneous melanoma, integrating multiple types of measurements leads to prognostic models with an improved prediction performance. Informative individual markers and pathways are identified, which can provide valuable in sights into melanoma prognosis. (C) 2016 Elsevier Inc. All rights reserved.
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