Towards a comprehensive view on underlying regulatory mechanisms of cancer induction by Integration of transcriptomic data with genomic data

Recent advances in sequencing technologies has provided a great resources of genomic andtranscriptomic data. In this study, we predicted potential risk regions in different cancers byintegration of transcriptomic data into genomic data. To this end, the number of altered genes andmicroRNAs in each chromosomal region were calculated using transcriptomic data. Fisher exact testdetermined the regions with higher density of altered genes and microRNAs in cancer. Interestingly,regions harboring new cancer-associated variants were detected. Based on altered mRNA andmicroRNAs in each cancer associated region, regulatory networks were also constructed. The resultshighlighted a crosstalk between GAPDH, LIFR, ZEB2, mir-21, mir-30a, mir-141 and mir-200c, alllocated on important genomic risk regions (e.g. 12p13.31), as common underpinning mechanism ofcancer induction. Large scale determination of cancer-associated regions by integration oftranscriptomic data with genomic data opens a new avenue in comprehensive study of cancerpathology.