Survival differences of CIMP subtypes integrated with CNA information in human breast cancer
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Huihan Wang1,*, Weili Yan2,*, Shumei Zhang1,*, Yue Gu1, Yihan Wang1, Yanjun Wei1, Hongbo Liu1, Fang Wang1, Qiong Wu2 and Yan Zhang1
1College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
2School of Life Science and Technology, State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin, 150001, China
*Joint first authors
Yan Zhang, email: firstname.lastname@example.org
Keywords: CpG island methylator phenotype (CIMP), breast cancer, copy number alteration (CNA), prognosis, biomarker
Received: June 29, 2016 Accepted: March 01, 2017 Published: March 14, 2017
CpG island methylator phenotype of breast cancer is associated with widespread aberrant methylation at specified CpG islands and distinct patient outcomes. However, the influence of copy number contributing to the prognosis of tumors with different CpG island methylator phenotypes is still unclear. We analyzed both genetic (copy number) and epigenetic alterations in 765 breast cancers from The Cancer Genome Atlas data portal and got a panel of 15 biomarkers for copy number and methylation status evaluation. The gene panel identified two groups corresponding to distinct copy number profiles. In status of mere-loss copy number, patients were faced with a greater risk if they presented a higher CpG islands methylation pattern in biomarker panels. But for samples presenting merely-gained copy number, higher methylation level of CpG islands was associated with improved viability. In all, the integration of copy number alteration and methylation information enhanced the classification power on prognosis. Moreover, we found the molecular subtypes of breast cancer presented different distributions in two CpG island methylation phenotypes. Generated by the same set of human methylation 450K data, additional copy number information could provide insights into survival prediction of cancers with less heterogeneity and might help to determine the biomarkers for diagnosis and treatment for breast cancer patients in a more personalized approach.
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