ORCID Profile
0000-0002-2422-0548
Current Organisations
University of Toronto
,
Beijing Institute of Technology
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Publisher: Elsevier BV
Date: 03-2014
DOI: 10.1016/J.MOLCEL.2014.01.020
Abstract: Impaired phosphatase activity contributes to the persistent activation of STAT3 in tumors. Given that STAT family members with various or even opposite functions are often phosphorylated or dephosphorylated by the same enzymes, the mechanism for STAT3-specific dephosphorylation in cells remains largely unknown. Here, we report that GdX (UBL4A) promotes STAT3 dephosphorylation via mediating the interaction between TC45 (the nuclear isoform of TC-PTP) and STAT3 specifically. GdX stabilizes the TC45-STAT3 complex to bestow upon STAT3 an efficient dephosphorylation by TC45. Inasmuch, GdX suppresses tumorigenesis and tumor development by reducing the level of phospho-STAT3 (p-STAT3), whereas deletion of GdX results in a high level of p-STAT3 and accelerated colorectal tumorigenesis induced by AOM/DSS. Thus, GdX converts TC45, a nonspecific phosphatase, into a STAT3-specific phosphatase by bridging an association between TC45 and STAT3.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2021
Publisher: Elsevier BV
Date: 2012
DOI: 10.1016/J.CCR.2011.12.016
Abstract: Tumorigenesis is caused by an uncontrolled cell cycle and the altered expression of many genes. Here, we report a gene CREPT that is preferentially expressed in erse human tumors. Overexpression of CREPT accelerates tumor growth, whereas depletion of CREPT demonstrates a reversed effect. CREPT regulates cyclin D1 expression by binding to its promoter, enhancing its transcription both in vivo and in vitro, and interacting with RNA polymerase II (RNAPII). Interestingly, CREPT promotes the formation of a chromatin loop and prevents RNAPII from reading through the 3' end termination site of the gene. Our findings reveal a mechanism where CREPT increases cyclin D1 transcription during tumorigenesis, through enhancing the recruitment of RNAPII to the promoter region, possibly, as well as chromatin looping.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2020
Publisher: Association for Computing Machinery (ACM)
Date: 21-08-2017
DOI: 10.1145/3094786
Abstract: Topic modelling methods such as Latent Dirichlet Allocation (LDA) have been successfully applied to various fields, since these methods can effectively characterize document collections by using a mixture of semantically rich topics. So far, many models have been proposed. However, the existing models typically outperform on full analysis on the whole collection to find all topics but difficult to capture coherent and specifically meaningful topic representations. Furthermore, it is very challenging to incorporate user preferences into existing topic modelling methods to extract relevant topics. To address these problems, we develop a novel personalized Association-based Topic Selection (ATS) model, which can identify semantically valid and relevant topics from a set of raw topics based on the semantical relatedness between users’ preferences and the structured patterns captured in topics. The advantage of the proposed ATS model is that it enables an interactive topic modelling process driven by users’ specific interests. Based on three benchmark datasets, namely, RCV1, R8, and WT10G under the context of information filtering (IF) and information retrieval (IR), our rigorous experiments show that the proposed ATS model can effectively identify relevant topics with respect to users’ specific interests, and hence to improve the performance of IF and IR.
No related grants have been discovered for Yang Gao.