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Zhu, Y.-H.

Publications and source records attributed to Zhu, Y.-H..

2 recordsLinked to original sources

A CLIC1 network coordinates matrix stiffness and the Warburg effect to promote tumor growth in pancreatic cancer

BACKGROUND & AIMSPDAC is characterized by significant matrix stiffening and reprogrammed glucose metabolism, particularly the Warburg effect. However, it is not clear the connection between matrix stiffness and the Warburg effect and the mechanisms of action in tumor progression. METHODSThe relationship between matrix stiffness and the Warburg effect was investigated from clinical, cellular, and bioinformatical perspectives. The ChIP and luciferase reporter gene assays were used to clarify the regulation mechanism of matrix stiffness on the expression of CLIC1. The expression profile and clinical significance of CLIC1 were determined in GEO datasets and a TMA. Loss-of-function and gain-of-function technics were used to determine the in vitro and in vivo functions of CLIC1. GSEA and western blotting revealed the underlying molecular mechanisms. RESULTSPDAC matrix stiffness is closely associated with the Warburg effect, and CLIC1 is a key molecule connecting tumor matrix stiffness and the Warburg effect. Increased CLIC1 expression induced by matrix stiffness correlates with poor prognosis in PDAC. CLIC1 acts as a promoter of glycolytic metabolism and facilitates tumor growth in a glycolysis-dependent manner. Mechanistically, CLIC1 inhibits the hydroxylation of HIF1 via ROS, which then increases the stability of HIF1. Collectively, PDAC cells can sense extracellular matrix stiffness and upregulate the expression of CLIC1, which facilitates the Warburg effect through ROS/HIF1 signaling, thereby supporting tumor growth. CONCLUSIONSIn the context of tumor therapy, targeted approaches can be considered from the perspectives of both extracellular matrix stiffness and tumor metabolism, of which CLIC1 is one of the targets.

cancer biology↗

TripletGO: Integrating Transcript Expression Profiles with Protein Homology Inferences for High-Accuracy Gene Function Annotations

Gene Ontology (GO) has been widely used to annotate functions of genes and gene products. We proposed a new method (TripletGO) to deduce GO terms of protein-coding and non-coding genes, through the integration of four complementary pipelines built on transcript expression profiling, genetic sequence alignment, protein sequence alignment and naive probability, respectively. TripletGO was tested on a large set of 5,754 genes from 8 species (human, mouse, arabidopsis, rat, fly, budding yeast, fission yeast, and nematoda) and 2,433 proteins with available expression data from the CAFA3 experiment and achieved function annotation accuracy significantly beyond the current state-of-the-art approaches. Detailed analyses show that the major advantage of TripletGO lies in the coupling of a new triplet-network based profiling method with the feature space mapping technique which can accurately recognize function patterns from transcript expressions. Meanwhile, the combination of multiple complementary models, especially those from transcript expression and protein-level alignments, improves the coverage and accuracy of the final GO annotation results. The standalone package and an online server of TripletGO are freely available at https://zhanglab.ccmb.med.umich.edu/TripletGO/.

bioinformatics↗