Genomic analysis using regularized regression in high-grade serous ovarian cancer
Loading...
Citations
Altmetric:
Altmetric::
Download
Associated Records
Authors
File Licence
Editors
Date
2018-01-01
Source Title
Cancer Informatics
Journal Title
Journal ISSN
Volume Title
Publisher
SAGE PUBLICATIONS LTD
Melbourne Authors
Affiliation
Sir Peter MacCallum Department of Oncology
Document Type
Journal Article
Subject Code
Citation
Natanzon, Y., Earp, M., Cunningham, J. M., Kalli, K. R., Wang, C., Armasu, S. M., Larson, M. C., Bowtell, D. D. L., Garsed, D. W., Fridley, B. L., Winham, S. J. & Goode, E. L. (2018). Genomic analysis using regularized regression in high-grade serous ovarian cancer. Cancer Informatics, 17, https://doi.org/10.1177/1176935118755341.
Access Status
Open Access
Access URL
Open Access URL
Open Access at PMC
Linked Resource URL
NHMRC Grant Code
ARC Grant Code
Description
Abstract
High-grade serous ovarian cancer (HGSOC) is a complex disease in which initiation and progression have been associated with copy number alterations, epigenetic processes, and, to a lesser extent, germline variation. We hypothesized that, when summarized at the gene level, tumor methylation and germline genetic variation, alone or in combination, influence tumor gene expression in HGSOC. We used Elastic Net (ENET) penalized regression method to evaluate these associations and adjust for somatic copy number in 3 independent data sets comprising tumors from more than 470 patients. Penalized regression models of germline variation, with or without methylation, did not reveal a role in HGSOC gene expression. However, we observed significant association between regional methylation and expression of 5 genes (WDPCP, KRT6C, BRCA2, EFCAB13, and ZNF283). CpGs retained in ENET model for BRCA2 and ZNF283 appeared enriched in several regulatory elements, suggesting that regularized regression may provide a novel utility for integrative genomic analysis.