Essaghir et al introduced an integrated approach to construct minimal connected network to TFs in 305 different human cancer cell lines and found several universal cancer biomarkers. These researches suggest the importance and feasibility of integrating TRN with CNVs. Intrahepatic cholangiocarcinoma is the second most common primary hepatic cancer with the highest occurring rate in Thailand and other eastern Asian areas due to chronic inflammation of bile ducts. In 2013, Sia et al performed gene expression and copy number variation integrated analysis in ICC samples and classified these samples into two groups: proliferation and inflammation. Pathogenesis studies based on gene expression profiling have evolved through several stages: single gene expression profiling; network construction and RWJ 52353 functional annotation; causal hub SB 258585 hydrochloride discovery and intervention design. Single gene expression profiling is straightforward and simple, numerous gene list signatures have been reported to either diagnose samples or predict outcome or prognosis. However it is hard to explain the functional categories of single genes. Network analysis allows structured grouping of genes, and functional module discovery can often lead to next-step research focus, which is a big progress compared to single gene profiling. The most popularly studied networks are probably the TRN and PPI. However functional modules in a network may still be dispersed and unconnected among each other, trying to find causal disturbances in a network has been a major goal of many computational biologists. For examples, our group have tried to develop algorithms to identify primary and secondary regulatory effects from a microRNA initiated TRN, have tried to identify possible hepatitis B- or C- virus protein disturbances to PPI network in hepatocellular cancer development and progression, and we have even tried to validate causal TFs in constructed TRN by knocking out gene expression data and posttranslational modification regulation data. However, genetic variation was rarely considered in either our efforts or others�� when trying to identify causal disturbances in a transcriptional regulation network. This probably was due to a lack of genomic sequencing and transcriptomic profiling on the same set of samples. Gene expression data alone largely prevail and bioinformatics PPI background networks are easily available too, these may have brought about some research biases in this field. However it should be readily conceived that if some functional modules in a TRN are already genetically modified, then they very likely may become the weakest points in a network that can divert the network function to adverse pathologic directions. Based on this rationale, and with the quickly increasing new generation genome sequencing data of disease samples, recently people start to investigate the genetic variation disturbance to gene expression networks.