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Genomics. 2016 Jun;107(6):223-30. doi: 10.1016/j.ygeno.2016.04.005. Epub 2016 Apr 30.

Integrated analysis of multidimensional omics data on cutaneous melanoma prognosis.

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Division of Epidemiology, Biostatistics, and Environmental Health, School of Public Health, University of Memphis, Memphis, TN 38152, USA; VA Cooperative Studies Program Coordinating Center, West Haven, CT 06516, USA.
Department of Statistics, Nanjing University of Finance and Economics, Nanjing, China.
Merck Research Laboratories, 126 East Lincoln Avenue, RY34, Rahway, NJ 07065, USA.
Department of Pathology, Yale University, New Haven, CT 06520, USA.
Cancer Center, Department of Internal Medicine, Pathology, Chronic Disease Epidemiology, Yale University, New Haven, CT 06520, USA.
VA Cooperative Studies Program Coordinating Center, West Haven, CT 06516, USA; Department of Biostatistics, Yale University, New Haven, CT 06520, USA. Electronic address:


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 insights into melanoma prognosis.


Integration; Melanoma prognosis; Multidimensional omics data; The Cancer Genome Atlas (TCGA)

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