The most popularly studied networks are probably the TRN and PPI. However functional modules in a RHPS 4 methosulfate 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 Ro 51 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. Xu et al. constructed CNV genes�� co-expression network of breast cancer to study genomic variations�� effect through co-expressed genes�� function. Zaman et al predicted breast cancer subtype-specific drug targets through signaling network assessment of mutations and copy number variations. ICC is the secondly occurring liver cancer which involves a large human population, and yet it was much understudied comparing to hepatocellular carcinoma. Sia et al work represents the first comprehensive multi-level profiling of ICC samples, including RNA and SNP microarray data. Our work, based on their data, represents a primary effort to construct TRN in ICC, using our earlier developed forward-and-reverse combined engineering algorithms. Furthermore, we made another primary effort to try to identify key transcriptional modules based on their involvement of genetic variations shown by gene copy number variations.