ABSTRACT
Prostate cancer is one of the most widespread cancers in men and is fundamentally a genetic disease. Identifying regulators in cancer using novel systems biology approaches will potentially lead to new insight into this disease. It was sought to address this by inferring gene regulatory networks [GRNs]. Moreover, dynamical analysis of GRNs can explain how regulators change among different conditions, such as cancer subtypes. In our approach, independent gene regulatory networks from each prostate state were reconstructed using one of the current state-of-art reverse engineering approaches. Next, crucial genes involved in this cancer were highlighted by analyzing each network individually and also in comparison with each other. In this paper, a novel network-based approach was introduced to find critical transcription factors involved in prostate cancer. The results led to detection of 38 essential transcription factors based on hub type variation. Additionally, experimental evidence was found for 29 of them as well as 9 new transcription factors. The results showed that dynamical analysis of biological networks may provide useful information to gain better understanding of the cell
Subject(s)
Gene Regulatory Networks , Transcription FactorsABSTRACT
Prostate cancer, a serious genetic disease, has known as the first widespread cancer in men, but the molecular changes required for the cancer progression has not fully understood. Availability of high-throughput gene expression data has led to the development of various computational methods, for identification of the critical genes, have involved in the cancer. In this paper, we have shown the construction of co-expression networks, which have been using Y-chromosome genes, provided an alternative strategy for detecting of new candidate, might involve in prostate cancer. In our approach, we have constructed independent co-expression networks from normal and cancerous stages have been using a reverse engineering approach. Then we have highlighted crucial Y chromosome genes involved in the prostate cancer, by analyzing networks, based on party and date hubs. Our results have led to the detection of 19 critical genes, related to prostate cancer, which 12 of them have previously shown to be involved in this cancer. Also, essential Y chromosome genes have searched based on reconstruction of sub-networks which have led to the identification of 4 experimentally established as well as 4 new Y chromosome genes might be linked putatively to prostate cancer. Correct inference of master genes, which mediate molecular, has changed during cancer progression would be one of the major challenges in cancer genomics. In this paper, we have shown the role of Y chromosome genes in finding of the prostate cancer susceptibility genes. Application of our approach to the prostate cancer has led to the establishment of the previous knowledge about this cancer as well as prediction of other new genes