Biology subjects

Chan, S. L.

Publications and source records attributed to Chan, S. L..

4 recordsLinked to original sources

bioRxiv2020

Applications of genetic-epigenetic tissue mapping for plasma DNA in prenatal testing, transplantation and oncology

We developed Genetic-Epigenetic Tissue Mapping (GETMap) to determine the tissue composition of plasma DNA carrying genetic variants not present in the constitutional genome through comparing their methylation profiles with relevant tissues. We validated this approach by showing that, in pregnant women, circulating DNA carrying fetal-specific alleles was entirely placenta-derived. In lung-transplant recipients, we showed that, at 72 hours after transplantation, the lung contributed only a median of 17% to the plasma DNA carrying donor-specific alleles and hematopoietic cells contributed a median of 78%. In hepatocellular cancer patients, the liver was identified as the predominant source of plasma DNA carrying tumor-specific mutations. In a pregnant woman with lymphoma, plasma DNA molecules carrying cancer mutations and fetal-specific alleles were accurately shown to be derived from the lymphocytes and placenta, respectively. Analysis of tissue origin for plasma DNA carrying genetic variants is potentially useful for noninvasive prenatal testing, transplantation monitoring and cancer screening.

physiology↗

bioRxiv2019

Ectopic HOTTIP Expression Induces Non-canonical Transactivation Pathways to Promote Growth and Invasiveness in Pancreatic Ductal Adenocarcinoma

LncRNA HOTTIP (HOXA transcript at the distal tip) is upregulated in pancreatic ductal adenocarcinoma (PDAC). However, oncogenic pathway mediated by HOTTIP is not fully understood. We identified canonical HOTTIP-HOXA13 targets: CYP26B1, CLIC5, CHI3L1 and UCP2 which were responsible for cell growth and cell invasion. Importantly, genome-wide analysis revealed that 38% of the genes regulated by HOTTIP contained H3K4me3 and HOTTIP enrichment at their promoters, without HOXA13 binding. HOTTIP complexed with WDR5-MLL1 to trans-activate oncogenic proteins CYB5R2, SULT1A1, KIF26A, SLC1A4 and TSC22D1 through directly triggering H3K4me3 at their promoters. The WDR5, MLL1 and H3K4me3 levels at their promoters and their expression levels were sensitive to HOTTIP overexpression and knockdown. These suggested the importance of non-canonical trans-acting HOTTIP-WDR5-MLL1 pathway in HOTTIP-regulatory mechanism by promoting the expression of oncogenic proteins. Furthermore, we dissected the mechanism by which HOTTIP was regulated by miR-497 in PDAC. Analysis of PDAC human tissues also revealed that HOTTIP was negatively correlated with miR-497 level. Our findings demonstrated that HOTTIP, upregulated in PDAC due to the loss of inhibitory miR-497, promoted PDAC progression through canonical HOTTIP-HOXA13 axis and a novel non-canonical trans-acting HOTTIP-WDR5-MLL1-H3K4me3 pathway.

cancer biology↗

bioRxiv2019

CircFOXK2 Promotes Tumor Growth and Metastasis of Pancreatic Ductal Adenocarcinoma via Complexing with RNA Binding Proteins and Sponging MiR-942

ObjectiveCircular RNA (circRNA) is a novel class of non-coding RNAs that regulate gene expression. However, the role of circRNAs in pancreatic ductal adenocarcinoma (PDAC) is largely unknown.\n\nDesignWe performed circRNA sequencing of non-tumor HPDE and PDAC cells. We investigated the functions of circFOXK2 in PDAC by gain-of-function and loss-of-function assays. Bioinformatics analysis, luciferase assay and microRNA pulldown assays were performed to identify circFOXK2 interacting-miRNAs. To further investigate the mechanism, we performed circRNA-pulldown and mass spectrometry to identify circFOXK2-interacting proteins in PDAC.\n\nResultsWe identified 169 differentially expressed circRNAs in PDAC cells. We validated that one of the circRNAs circFOXK2 was significantly up-regulated in PDAC cells and in 63 % of primary tumor (53 out of 84). Gain-of-function and loss-of-function assays demonstrated that circFOXK2 promoted PDAC cell growth, migration and invasion. CircFOXK2 was also involved in cell cycle progression and apoptosis. circFOXK2 functioned as sponge for miR-942, and in turn promoted the expression of miR-942 targets ANK1, GDNF and PAX6. Furthermore, circFOXK2 interacted with 94 proteins, which were involved in cell adhesion and mRNA splicing. Among these circFOXK2-interacting proteins, YBX1 and hnRNPK were validated by RNA immunoprecipitation. Importantly, circFOKX2 interacted with YBX1 and hnRNPK targets NUF2 and PDXK in PDAC cells. Knockdown of circFOXK2 reduced the binding of YBX1 and hnRNPK to NUF2 and PDXK, and in turn decreased their expressions in PDAC cells.\n\nConclusionWe identified that circFOXK2 promoted PDAC cells growth and metastasis. Also, circFOXK2 complexed with YBX1 and hnRNPK to promote the expressions of oncogenic proteins.\n\nSignificance of this studyWhat is already known on this subject?\n\nO_LIDifferentially expressed circRNAs are involved in carcinogenesis of many cancers.\nC_LIO_LICircRNAs function as microRNA sponges to regulate gene expression.\nC_LIO_LIThe roles of circRNAs in PDAC progression is largely unknown.\nC_LI\n\nWhat are the new findings?\n\nO_LIcircFOXK2 is upregulated in PDAC primary tumors.\nC_LIO_LIcircFOXK2 promotes PDAC tumor growth and liver metastasis.\nC_LIO_LIcircFOXK2 functions as sponges for miR-942 to promote the expressions of oncogenic ANK1, GDNF and PAX6.\nC_LIO_LIcircFOXK2 complexes with YBX1 and hnRNPK to promote the expressions of oncogenic proteins in PDAC.\nC_LI\n\nHow might it impact on clinical practice in the foreseeable future?\n\nO_LIcircFOXK2 upregulation in PDAC may function as a novel biomarker for diagnosis.\nC_LIO_LIcircFOXK2 may be a novel therapeutic target in treating PDAC.\nC_LI

cancer biology↗

bioRxiv2018

Infer related genes from large gene expression dataset with embedding

Artificial neural networks (ANNs) have been utilized for classification and prediction task with remarkable accuracy. However, its implications for unsupervised data mining using molecular data is under-explored. We adopted a method of unsupervised ANN, namely word embedding, to extract biologically relevant information from TCGA gene expression dataset. Ground truth relationship, such as cancer types of the input sample and semantic meaning of genes, were showed to retain in the resulting entity matrices. We also demonstrated the interpretability and usage of these matrices in shortlisting candidates from a long gene list. This method is feasible to mine big volume of biological data, and would be a valuable tool to discover novel knowledge from omics data. The resulting embedding matrices mined from TCGA gene expression data are interactively explorable online (http://bit.ly/tcga-embedding-cancer) and could serve as an informative reference.

cancer biology↗