title: Information theoretic sub-network mining characterizes breast cancer subtypes in terms of cancer core mechanisms.
abstract: A breast cancer subtype classification scheme, PAM50, based on genetic information is widely accepted for clinical applications. On the other hands, experimental cancer biology studies have been successful in revealing the mechanisms of breast cancer and now the hallmarks of cancer have been determined to explain the core mechanisms of tumorigenesis. Thus, it is important to understand how the breast cancer subtypes are related to the cancer core mechanisms, but multiple studies are yet to address the hallmarks of breast cancer subtypes. Therefore, a new approach that can explain the differences among breast cancer subtypes in terms of cancer hallmarks is needed. We developed an information theoretic sub-network mining algorithm, differentially expressed sub-network and pathway analysis (DeSPA), that retrieves tumor-related genes by mining a gene regulatory network (GRN) of transcription factors and miRNAs. With extensive experiments of the cancer genome atlas (TCGA) breast cancer sequencing data, we showed that our approach was able to select genes that belong to cancer core pathways such as DNA replication, cell cycle, p53 pathways while keeping the accuracy of breast cancer subtype classification comparable to that of PAM50. In addition, our method produces a regulatory network of TF, miRNA, and their target genes that distinguish breast cancer subtypes, which is confirmed by experimental studies in the literature.
authors.affiliation: * Department of Computer Science and Engineering, Seoul National University, Seoul, Korea.
authors.forename: Jinwoo
authors.initials: J
authors.lastname: Park
authors.affiliation: † Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Korea.
authors.forename: Benjamin
authors.initials: B
authors.lastname: Hur
authors.affiliation: * Department of Computer Science and Engineering, Seoul National University, Seoul, Korea.
authors.forename: Sungmin
authors.initials: S
authors.lastname: Rhee
authors.affiliation: † Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Korea.
authors.forename: Sangsoo
authors.initials: S
authors.lastname: Lim
authors.affiliation: † Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Korea.
authors.forename: Min-Su
authors.initials: MS
authors.lastname: Kim
authors.affiliation: § Division of Clinical Bioinformatics, Seoul National University Hospital, Seoul, Korea.
authors.forename: Kwangsoo
authors.initials: K
authors.lastname: Kim
authors.affiliation: ¶ Department of Surgery, Seoul National University College of Medicine, Seoul, Korea.|∥ Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea.
authors.forename: Wonshik
authors.initials: W
authors.lastname: Han
authors.affiliation: * Department of Computer Science and Engineering, Seoul National University, Seoul, Korea.|† Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Korea.|‡ Bioinformatics Institute, Seoul National University, Seoul, Korea.
authors.forename: Sun
authors.initials: S
authors.lastname: Kim
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000235:genetics* / Q000473:pathology; D057225:Data Mining / Q000379:methods*; D019277:Entropy; D005260:Female; D015972:Gene Expression Regulation, Neoplastic; D053263:Gene Regulatory Networks; D006801:Humans; D035683:MicroRNAs; D015203:Reproducibility of Results; D014157:Transcription Factors / Q000235:genetics
chemical_list: D035683:MicroRNAs; D014157:Transcription Factors
keywords: Breast cancer subtype; DNA replication; cancer core mechanisms; cell cycle; information theory; regulatory network; sub-network mining