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. This kind of approach may bring the generally constructed TRN one step further to genetic disturbance, which may help greatly in discovering possible intervention targets for ICC. Such kind of approach can easily be extended to other disease samples with TC-MCH 7c appropriate data. On the other hand, we put forward a new method of interpreting impact of genomic variations on signaling pathways. Integrative analysis of regulatory modules and KEGG signaling SMBA 1 pathway illustrated that the disturbance of genomic variation on signaling pathway can happen on components of pathway which was the focus of previous studies, such as variation of MAP3K7, MAP2K7 and FGFR2 in MAPK signaling, and FZD10 in Wnt signaling; but may also happen more effectively on regulators, such as variation of ZSCAN1, RFX1 which regulate SMAD proteins, the key joints of TGF-b signaling. Previous studies mostly focused on mutations in genes of signaling pathway, our studies extended to mutations in genes outside signaling pathway by integrating regulatory network. This approach broadens the way of exploring the potential impact of gene mutations. At last, using the expression profiles of genes in CNV-ICCTRN, we classified 125 ICC samples into two robust molecular clusters with distinct biological function features.