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. 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. 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 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 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. This result at one hand helps to get insight into ICC molecular classification which is still ambiguous, on the other hand proves the application value of our innovation. There are limitations to this early work of integrating genetic variation and TRN. We did not analyze single nucleotide polymorphisms which may affect genes more specifically. We could not obtain clinic information to validate our subtype classification of patient samples. With the development of technology, more and more genetic variation information, such as SNP, chromosomal translocations, CNV, and so on, could be used to investigate their disturbance to TRN. On the other hand, more annotation to TRN construction itself, such as referencing protein-protein interaction relationship, kinase-substrate relationship, other post-translational modification relationship, should be carried out. Progresses in both these two directions will help in finding causal network modules and modulators. With the increment of drug-target database volume, or increase of novel drug development strategy, such kind of bioinformatics analyses which integrate genetic variation with network construction will bring experimental data closer to possible clinical intervention. The recognition that dysfunction in the cellular biology of the ubiquitous RNA/DNA-binding protein FUS contributes to fALS, as well as frontotemporal lobar dementia has led to the development of cell and animal models aiming to evaluate FUS function and its role in mechanisms of cell pathology and neurodegeneration. Several in vitro studies have shown that fALS FUS mutations clustered at the C-terminal nuclear localization signal region prevent nuclear import, cause relative FDI-6 mislocalization of FUS to the cytosol and the generation of transient stress granules under applied conditions in cell lines. SGs have been proposed as an early precursor to pathological cytosolic FUS inclusions observed in ALS. Linkage between SGs and pathological FUS inclusions in fALS is suggested in post-mortem tissue where inclusions in part label positive for SG markers. These inclusions usually reside in specific neurons in afflicted parts of the motor or cognitive system, indicating vulnerability and sensitivity of certain cell populations, although the basis for selective susceptibility is unclear given that FUS is ubiquitously expressed.