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  • miR TF TF br gene TF mRNA miR

    2019-10-08

    miR- TF- TF-
    gene TF mRNA miR
    The ACTB gene was reported highly expressed in prostate cancer tissue than matched normal tissue and might promote prostate cancer [38,39].
    12 DE miRNAs detected from the 22 miRNAs in these FFLs. Moustafa et al. [40] revealed that hsa-let-7b-5p was down-regulated, as a key regulatory factor. It may be the utility biomarker, prognostic indicator and therapeutic target for early detection of prostate cancer. Lin et al. [41] found the hsa-miR-204–5p was screened as a novel biomarker for metastasis. Additionally, many studies reported that microRNA-320a (miR-320a) was significantly reduced in prostate cancer tissue, and exhibited a promising anticancer miRNA [21,22]. These results sug-gested that most of the miRNAs in the selected FFLs are associated with prostate cancer.
    Thirteen TFs are included in the sub-network, eight of which were demonstrated related to the prostate cancer by the literature research, including NRF1, E2F4, RXRA, POU2F2, CTCFL, SETDB1, MXI1 and SIRT6. It is surprising that only one of them is differentially expressed. However, we found they indeed be involved in prostate cancer biological processes. NRF1 is a master regulator of oxidative stress-induced gene expression. Schultz et al. [42] showed that NRF1 can physically interact with the androgen receptor (AR) and enhance the AR's DNA-binding activity, which implied the NRF1 is a potential AR co-activator. E2F4 is a transcription factor that is critical for cell proliferation, whose activity is controlled by Rb-related Veratridine [43,44]. Therefore, p53 and Rb related pathways may play important roles in prostate cancer through the E2F4 participated [45]. RXRA, POU2F2 and CTCFL were proposed to play an important role in prostate tumori-genesis [46–48]. SETDB1 is a 
    histone methyltransferase. It has been established as an oncogene in a number of human carcinomas, including human lung cancer, melanoma and kidney tumors [49–51]. Sun et al. [52] demonstrated that SETDB1 would be up-regulated in human prostate cancer and silence SETDB1 inhibited prostate cancer cell proliferation, migration and invasion. Some reported the MXI1 protein negatively regulates MYC oncoprotein activity and thus potentially serves a tumor suppressor function. Eagle et al. [53] detected mutations in the MXI1 may contribute to the pathogenesis or neoplastic evolution of prostate tumor. The SIRT6 gene, as a member of the mammalian sirtuins family, would be overexpressed in human prostate cancer and exploited for the prostate cancer therapy [54].
    4. Discussion
    Traditionally, the expression alterations are focused on identifying the key genes as biomarkers. However, the previous study also reported that even non-significantly DE genes, by interacting with other genes, could contribute to distinguishing normal and disease samples [18]. By emphasizing the gene functional context, Jiang et al. proposed the gene essentiality by incorporating the expression information of its direct neighbors in the PPI network [19]. Here, we postulated the dysfunction gene would accompany with its essentiality alteration in the network. We then developed a strategy to identify prostate cancer related genes ac-cording to their essentiality alterations in the regulatory network. For this purpose, a miRNA-TF-mRNA co-regulatory network was constructed and Jaccard similarity coefficient was adopted to weight gene associa-tions. The Jaccard similarity represents the proportion of common reg-ulatory elements of two genes in the whole co-regulatory network. Our method was firstly compared with NEST. On a dataset of RNA-Seq data and miRNA-Seq data of PRAD from TCGA data portal (more details in Materials and Methods). The top-ranked 3596 mRNA sets by these two methods showed >60% consistency and both enriched on cancer-related pathways, such as Wnt signaling pathway, ECM-receptor interaction, Focal adhesion and Pathways in cancer. These results suggest the Jaccard similarity coefficient should be reliable to weight the mRNA association. Moreover, our method could be also applied to the miRNAs.