Research Paper Volume 11, Issue 12 pp 4198—4215
Integrated multi-omics analysis of genomics, epigenomics, and transcriptomics in ovarian carcinoma
- 1 Department of Obstetrics and Gynecology, Shengjing Hospital Affiliated to China Medical University, Shenyang, Liaoning 110004, China
- 2 Key Laboratory of Maternal-Fetal Medicine of Liaoning Province, Key Laboratory of Obstetrics and Gynecology of Higher Education of Liaoning Province, Liaoning, China
received: April 19, 2019 ; accepted: June 17, 2019 ; published: June 29, 2019 ;https://doi.org/10.18632/aging.102047
How to Cite
Copyright: Zheng et al. This is an open‐access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
In this study, we identified prognostic biomarkers in ovarian carcinoma by integrating multi-omics DNA copy number variation (CNV) and methylation variation (MET) data. CNV, MET, and messenger RNA (mRNA) expression were examined in 351 ovarian carcinoma patients. Genes for which expression was correlated with DNA copy-number or DNA methylation were identified; three ovarian carcinoma gene subtypes were defined based on these correlations. Overall survival and B cell scores were lower, while the macrophage cell score was higher, in the DNA imprinting centre 1 (iC1) subtype than in the iC2 and iC3 subtypes. Comparison of CNV, MET, and mRNA expression among the subtypes identified two genes, ubiquitin B (UBB) and interleukin 18 binding protein (IL18BP), that were associated with prognosis. Mutation spectrum results based on subtype indicated that UBB and IL18BP expression may be influenced by mutation loci. Mutation levels were higher in iC1 samples than in iC2 or iC3 samples, indicating that the iC1 subtype is associated with disease progression. This integrated multi-omics analysis of genomics, epigenomics, and transcriptomics provides new insight into the molecular mechanisms of ovarian carcinoma and may help identify biomolecular markers for early disease diagnosis.