contribution of multiple miRNAs rather than affecting gene expression by limiting assessments to specific chromosomal region

Here, we developed a pipeline which was comprised of PCSRs prediction using calculating the transcript-expression changes under cancer for each chromosomal region. We also extracted common altered mRNAs and microRNAs using microarray and expressed sequence tags data following by network analysis to achieve more insights about the predicted PCSRs. Using this pipeline, we predicted potential risk regions interacting with cluster of targets unravelling potential-candidates for further genome association studies. An effective pipeline was developed to predict PCSRs using microarray datasets of different cancer studies. Two different thresholds were applied to predict PCSRs including probsets with at least 2-fold changes and first 200 probsets with the highest fold changes. Most of the predicted PCSRs on each chromosome were similar in both applied thresholds, which confirm the reliability of these PCSRs. In addition to this confirmation, based on literature review we found the presence of several important cancer-associated variants on our predicted PCSRs. Our findings in agreement with these studies identified region 8q24 as a risk region in variety of HCs, which shows involvement of some of risk regions in several types of cancers rather than a specific cancer. Moreover, some of the predicted PCSRs in this study were reported in other types of human diseases including herpes simplex virus type 1, polycystic ovary syndrome, Type 1 diabetes and Rheumatoid arthritis. This similarity might indicate the efficiency of our approach in prediction the risk regions associated with different human diseases besides cancer. We also found that eight chromosomes harbor the most altered genes in different types of cancer including chromosomes. Interestingly, chromosomes 1, 4 and 13 were also recorded as the chromosomes with the highest percentage of predicted PCSRs, which suggests the important role of these chromosomes in cancer biology. Based on these results and those previously reported on chromosomes abnormality, it can be concluded that our pipeline is able to predict risk regions as well as risk chromosomes in a variety of diseases including cancer. This pipeline can also be applied to the fast growing RNA-seq datasets in future studies. Network analysis indicates that DDX5, LIFR, ZEB2, mir-21, mir-27b, mir-30a, mir-141, mir-182 and mir-200c were shared across different constructed networks, indicting their crucial role in cancer biology and progression, which has been reported previously. For example, the potential clinical utility of DDX5 and its associated miRNAs are suggested as therapeutic target in breast cancer. In addition, clinical application of different miRNAs in cancer such as let-7, mir-21and mir-122 are discussed in recent study of NanaSinkam and Croce. Because miRNAs do not function in isolation, we analyzed the cluster of miRNAs on same regions to understand the relative.

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