title: Integrative analysis of transcriptional regulatory network and copy number variation in intrahepatic cholangiocarcinoma.
abstract: BACKGROUND
  Transcriptional regulatory network (TRN) is used to study conditional regulatory relationships between transcriptional factors and genes. However few studies have tried to integrate genomic variation information such as copy number variation (CNV) with TRN to find causal disturbances in a network. Intrahepatic cholangiocarcinoma (ICC) is the second most common hepatic carcinoma with high malignancy and poor prognosis. Research about ICC is relatively limited comparing to hepatocellular carcinoma, and there are no approved gene therapeutic targets yet.
  
  
  METHODS
  We first constructed TRN of ICC (ICC-TRN) using forward-and-reverse combined engineering method, and then integrated copy number variation information with ICC-TRN to select CNV-related modules and constructed CNV-ICC-TRN. We also integrated CNV-ICC-TRN with KEGG signaling pathways to investigate how CNV genes disturb signaling pathways. At last, unsupervised clustering method was applied to classify samples into distinct classes.
  
  
  RESULTS
  We obtained CNV-ICC-TRN containing 33 modules which were enriched in ICC-related signaling pathways. Integrated analysis of the regulatory network and signaling pathways illustrated that CNV might interrupt signaling through locating on either genomic sites of nodes or regulators of nodes in a signaling pathway. In the end, expression profiles of nodes in CNV-ICC-TRN were used to cluster the ICC patients into two robust groups with distinct biological function features.
  
  
  CONCLUSIONS
  Our work represents a primary effort to construct TRN in ICC, also a primary effort to try to identify key transcriptional modules based on their involvement of genetic variations shown by gene copy number variations (CNV). This kind of approach may bring the traditional studies of TRN based only on expression data one step further to genetic disturbance. Such kind of approach can easily be extended to other disease samples with appropriate data.
authors.affiliation: School of Life Sciences and Technology, Tongji University, Shanghai, P.R.China; Shanghai Center for Bioinformation Technology, Shanghai Academy of Science and Technology, Shanghai, P.R.China.
authors.forename: Ling
authors.initials: L
authors.lastname: Li
authors.affiliation: Shanghai Center for Bioinformation Technology, Shanghai Academy of Science and Technology, Shanghai, P.R.China; School of Life Sciences and Technology, Shanghai Jiaotong University, Shanghai, P.R.China.
authors.forename: Baofeng
authors.initials: B
authors.lastname: Lian
authors.affiliation: Key Lab of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, P.R.China.
authors.forename: Chao
authors.initials: C
authors.lastname: Li
authors.affiliation: Shanghai Center for Bioinformation Technology, Shanghai Academy of Science and Technology, Shanghai, P.R.China.
authors.forename: Wei
authors.initials: W
authors.lastname: Li
authors.affiliation: Shanghai Center for Bioinformation Technology, Shanghai Academy of Science and Technology, Shanghai, P.R.China.
authors.forename: Jing
authors.initials: J
authors.lastname: Li
authors.affiliation: Key Lab of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, P.R.China.
authors.forename: Yuannv
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: Key Lab of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, P.R.China.
authors.forename: Xianghuo
authors.initials: X
authors.lastname: He
authors.affiliation: School of Life Sciences and Technology, Tongji University, Shanghai, P.R.China; School of Life Sciences and Technology, Shanghai Jiaotong University, Shanghai, P.R.China; Key Lab of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, P.R.China.
authors.forename: Yixue
authors.initials: Y
authors.lastname: Li
authors.affiliation: Shanghai Center for Bioinformation Technology, Shanghai Academy of Science and Technology, Shanghai, P.R.China.
authors.forename: Lu
authors.initials: L
authors.lastname: Xie
mesh_terms: D001650:Bile Duct Neoplasms / Q000235:genetics* / Q000378:metabolism; D001653:Bile Ducts, Intrahepatic / Q000378:metabolism; D018281:Cholangiocarcinoma / Q000235:genetics* / Q000378:metabolism; D002869:Chromosome Aberrations; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods; D056915:DNA Copy Number Variations; D030561:Databases, Nucleic Acid; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D053263:Gene Regulatory Networks; D006801:Humans; D020935:MAP Kinase Signaling System; D015398:Signal Transduction; D016212:Transforming Growth Factor beta / Q000378:metabolism; D060449:Wnt Signaling Pathway
chemical_list: D016212:Transforming Growth Factor beta