Khosravi Pegah, Zahiri Javad, Gazestani Vahid H, Mirkhalaf Samira, Akbarzadeh Mohammad, Sadeghi Mehdi, Goliaei Bahram
Dept. of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran ; School of Biological Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran.
Faculty of Mathematics, K. N. Toosi University of Technology, Tehran, Iran ; Dept. of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran.
Iran J Cancer Prev. 2014 Fall;7(4):204-11.
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.
前列腺癌是一种严重的遗传性疾病,被认为是男性中第一种广泛传播的癌症,但癌症进展所需的分子变化尚未完全了解。高通量基因表达数据的可用性导致了各种计算方法的发展,用于识别参与癌症的关键基因。
在本文中,我们展示了共表达网络的构建,该网络使用Y染色体基因,为检测可能参与前列腺癌的新候选基因提供了一种替代策略。在我们的方法中,我们使用逆向工程方法从正常和癌性阶段构建了独立的共表达网络。然后,我们通过基于派对和日期枢纽分析网络,突出了参与前列腺癌的关键Y染色体基因。
我们的结果导致检测到19个与前列腺癌相关的关键基因,其中12个先前已被证明参与这种癌症。此外,基于子网重建搜索了重要的Y染色体基因,这导致鉴定出4个实验确定的以及4个可能与前列腺癌推定相关的新Y染色体基因。
正确推断在癌症进展过程中发生分子变化的主控基因将是癌症基因组学的主要挑战之一。在本文中,我们展示了Y染色体基因在寻找前列腺癌易感基因中的作用。我们的方法应用于前列腺癌导致了对该癌症先前知识的建立以及对其他新基因的预测。