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BMC Med Genomics. 2017 May 24;10(Suppl 1):28. doi: 10.1186/s12920-017-0268-z.

Identifying subtype-specific associations between gene expression and DNA methylation profiles in breast cancer.

Author information

1
Department of Software and Computer Engineering, Ajou University, Suwon, 16499, South Korea.
2
Biomedical & Translational Informatics Institute, Geisinger Health System, Danville, PA, USA.
3
Biomedical & Translational Informatics Institute, Geisinger Health System, Danville, PA, USA. dkim@geisinger.edu.
4
The Huck Institute of the Life Sciences, Pennsylvania State University, University Park, PA, USA. dkim@geisinger.edu.
5
Department of Software and Computer Engineering, Ajou University, Suwon, 16499, South Korea. kasohn@ajou.ac.kr.

Abstract

BACKGROUND:

Breast cancer is a complex disease in which different genomic patterns exists depending on different subtypes. Recent researches present that multiple subtypes of breast cancer occur at different rates, and play a crucial role in planning treatment. To better understand underlying biological mechanisms on breast cancer subtypes, investigating the specific gene regulatory system via different subtypes is desirable.

METHODS:

Gene expression, as an intermediate phenotype, is estimated based on methylation profiles to identify the impact of epigenomic features on transcriptomic changes in breast cancer. We propose a kernel weighted l1-regularized regression model to incorporate tumor subtype information and further reveal gene regulations affected by different breast cancer subtypes. For the proper control of subtype-specific estimation, samples from different breast cancer subtype are learned at different rate based on target estimates. Kolmogorov Smirnov test is conducted to determine learning rate of each sample from different subtype.

RESULTS:

It is observed that genes that might be sensitive to breast cancer subtype show prediction improvement when estimated using our proposed method. Comparing to a standard method, overall performance is also enhanced by incorporating tumor subtypes. In addition, we identified subtype-specific network structures based on the associations between gene expression and DNA methylation.

CONCLUSIONS:

In this study, kernel weighted lasso model is proposed for identifying subtype-specific associations between gene expressions and DNA methylation profiles. Identification of subtype-specific gene expression associated with epigenomic changes might be helpful for better planning treatment and developing new therapies.

PMID:
28589855
PMCID:
PMC5461552
DOI:
10.1186/s12920-017-0268-z
[Indexed for MEDLINE]
Free PMC Article

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