TY - JOUR
T1 - Genomic analysis using regularized regression in high-grade serous ovarian cancer
AU - Natanzon, Yanina
AU - Earp, Madalene
AU - Cunningham, Julie M.
AU - Kalli, Kimberly R.
AU - Wang, Chen
AU - Armasu, Sebastian M.
AU - Larson, Melissa C.
AU - Bowtell, David D.L.
AU - Garsed, Dale W.
AU - Fridley, Brooke L.
AU - Winham, Stacey J.
AU - Goode, Ellen L.
N1 - Publisher Copyright:
© The Author(s) 2018.
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
KW - Elastic net penalized regression
KW - High-grade serous ovarian cancer
KW - Tumor DNA methylation
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U2 - 10.1177/1176935118755341
DO - 10.1177/1176935118755341
M3 - Article
AN - SCOPUS:85045102248
SN - 1176-9351
VL - 17
JO - Cancer Informatics
JF - Cancer Informatics
ER -