期刊
METHODS
卷 179, 期 -, 页码 89-100出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.ymeth.2020.05.008
关键词
Breast cancer; Biological pathway; Gene expression; Pathway logic; Transcription factor
资金
- National Research Foundation of Korea (NRF) - Ministry of Science, ICT [NRF2017M3C4A7065887]
- Collaborative Genome Program for Fostering New Post-Genome Industry of the National Research Foundation (NRF) - Ministry of Science and ICT (MSIT) [NRF-2014M3C9A3063541]
- Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI) - Ministry of Health & Welfare, Republic of Korea [HI15C3224]
- Bio & Medical Technology Development Program of the National Research Foundation (NRF) - Ministry of Science ICT [NRF-2019M3E5D307337511, NRF-2019M3E5D4065965]
- National Research Foundation of Korea (NRF) - Korea government (MSIT) [NRF-2020R1G1A1003558]
For breast cancer, clinically important subtypes are well characterized at the molecular level in terms of gene expression profiles. In addition, signaling pathways in breast cancer have been extensively studied as therapeutic targets due to their roles in tumor growth and metastasis. However, it is challenging to put signaling pathways and gene expression profiles together to characterize biological mechanisms of breast cancer subtypes since many signaling events result from post-translational modifications, rather than gene expression differences. We designed a logic-based computational framework to explain the differences in gene expression profiles among breast cancer subtypes using Pathway Logic and transcriptional network information. Pathway Logic is a rewriting-logic-based formal system for modeling biological pathways including post-translational modifications. Our method demonstrated its utility by constructing subtype-specific path from key receptors (TNFR, TGFBR1 and EGFR) to key transcription factor (TF) regulators (RELA, ATF2, SMAD3 and ELK1) and identifying potential association between pathways via TFs in basal-specific paths, which could provide a novel insight on aggressive breast cancer subtypes. Codes and results are available at http://epigenomics.snu.ac.kr/PL/.
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