Tsz-Lun Yeung

Dr. Tsz-Lun Yeung


Research Focus & Metrics

Academic Career Length

7Years (2020-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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2 January 2020 in Cancers
Verified
Journal Article
Open access
PDF file
SKSuet Ying Kwan
Suet Ying Kwan
CYChi Lam Au Yeung
Chi Lam Au Yeung
TYTsz-Lun Yeung
Tsz-Lun Yeung
ARAngela Rynne-Vidal
Angela Rynne-Vidal
KWKwong-Kwok Wong
Kwong-Kwok Wong
KLKaren Lu
Karen Lu
Uterine serous carcinoma (USC) is the most aggressive form of endometrial cancer, with poor survival rates and high recurrence risk. Therefore, the purpose of this study was to identify therapeutic targets that could aid in the management of USC. By analyzing endometrial cancer samples from The Cancer Genome Atlas (TCGA), we found Ubiquitin Carboxyl-Terminal Hydrolase L1 (UCHL1) to be highly expressed in USC and to correlate with poorer overall survival. UCHL1 silencing reduced cell proliferation in vitro and in vivo, cyclin B1 protein levels and cell cycle progression. Further studies showed that UCHL1 interacts with cyclin B1 and increases cyclin B1 protein stability by deubiquitination. Treatment of USC-bearing mice with the UCHL1-specific inhibitor reduced tumor growth and improved overall survival. Our findings suggest that cyclin B1 is a novel target of UCHL1 and targeting UCHL1 is a potential therapeutic strategy for USC.

8 April 2021 in Cancers
Verified
Journal Article
Open access
PDF file
YZYing Zhu
Ying Zhu
SBSammy Ferri Borgogno
Sammy Ferri Borgogno
JSJianting Sheng
Jianting Sheng
TYTsz-Lun Yeung
Tsz-Lun Yeung
JBJared Burks
Jared Burks
Kelvin WongKelvin Wong
Kelvin Wong
Stromal and immune cells in the tumor microenvironment (TME) have been shown to directly affect high-grade serous ovarian cancer (HGSC) malignant phenotypes, however, how these cells interact to influence HGSC patients’ survival remains largely unknown. To investigate the cell-cell communication in such a complex TME, we developed a SpatioImageOmics (SIO) pipeline that combines imaging mass cytometry (IMC), location-specific transcriptomics, and deep learning to identify the distribution of various stromal, tumor and immune cells as well as their spatial relationship in TME. The SIO pipeline automatically and accurately segments cells and extracts salient cellular features to identify biomarkers, and multiple nearest-neighbor interactions among tumor, immune, and stromal cells that coordinate to influence overall survival rates in HGSC patients. In addition, SIO integrates IMC data with microdissected tumor and stromal transcriptomes from the same patients to identify novel signaling networks, which would lead to the discovery of novel survival rate-modulating mechanisms in HGSC patients.