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. METHOD: 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. RESULT: 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. CONCLUSION: 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.