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Open AccessArticle

Integrative Analysis of Cancer Omics Data for Prognosis Modeling

1
School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
2
School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai 200433, China
3
Department of Biostatistics, Yale University, New Haven, CT 06520, USA
*
Authors to whom correspondence should be addressed.
Genes 2019, 10(8), 604; https://doi.org/10.3390/genes10080604
Received: 13 July 2019 / Revised: 30 July 2019 / Accepted: 7 August 2019 / Published: 9 August 2019
(This article belongs to the Special Issue Statistical Methods for the Analysis of Genomic Data)
Prognosis modeling plays an important role in cancer studies. With the development of omics profiling, extensive research has been conducted to search for prognostic markers for various cancer types. However, many of the existing studies share a common limitation by only focusing on a single cancer type and suffering from a lack of sufficient information. With potential molecular similarity across cancer types, one cancer type may contain information useful for the analysis of other types. The integration of multiple cancer types may facilitate information borrowing so as to more comprehensively and more accurately describe prognosis. In this study, we conduct marginal and joint integrative analysis of multiple cancer types, effectively introducing integration in the discovery process. For accommodating high dimensionality and identifying relevant markers, we adopt the advanced penalization technique which has a solid statistical ground. Gene expression data on nine cancer types from The Cancer Genome Atlas (TCGA) are analyzed, leading to biologically sensible findings that are different from the alternatives. Overall, this study provides a novel venue for cancer prognosis modeling by integrating multiple cancer types. View Full-Text
Keywords: multiple cancer types; integrative analysis; omics data; prognosis modeling multiple cancer types; integrative analysis; omics data; prognosis modeling
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Wang, S.; Wu, M.; Ma, S. Integrative Analysis of Cancer Omics Data for Prognosis Modeling. Genes 2019, 10, 604.

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