Integrating Clinical and Multiple Omics Data for Prognostic Assessment across Human Cancers.

ABSTRACT: Multiple omic profiles have been generated for many cancer types; however, comprehensive assessment of their prognostic values across cancers is limited. We conducted a pan-cancer prognostic assessment and presented a multi-omic kernel machine learning method to systematically quantify the prognostic values of high-throughput genomic, epigenomic, and transcriptomic profiles individually, integratively, and in combination with clinical factors for 3,382 samples across 14 cancer types. We found that the prognostic performance varied substantially across cancer types. mRNA and miRNA expression profile frequently performed the best, followed by DNA methylation profile. Germline susceptibility variants displayed low prognostic performance consistently across cancer types. The integration of omi

SUBMITTER: Zhu B 

PROVIDER: S-EPMC5717223 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature