Downregulation of Ubiquitin-conjugating Enzyme UBE2D3 Promotes Telomere Maintenance and Radioresistance of Eca-109 Human Esophageal Carcinoma Cells

Yang, Hui; Wu, Lin; Shi, Ke; Wang, Wenbo; Liu, Yang; Gao, Xiang; Fang, Hongyan; Yu, Haijun; Ye, Zhong; Xie, Conghua; Zhou, Fuxiang; Zhou, Youwen

doi:10.7150/jca.14745

Cited by 26 publications

(22 citation statements)

References 36 publications

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“…While UBE2D3 has been described to be an in vitro tumor suppressor for its role in modulating radiosensitivity [71, 72] and proliferation [73], the in vivo data of TCGA suggested that higher UBE2D3 expression predicts poorer clinical outcomes. One study has shown that the targeting of RMND5A significantly attenuates HeLa cell migration.…”

Section: Discussionmentioning

confidence: 99%

Identification of MicroRNAs as Breast Cancer Prognosis Markers through the Cancer Genome Atlas

et al. 2016

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Breast cancer is the second-most common cancer and second-leading cause of cancer mortality in American women. The dysregulation of microRNAs (miRNAs) plays a key role in almost all cancers, including breast cancer. We comprehensively analyzed miRNA expression, global gene expression, and patient survival from the Cancer Genomes Atlas (TCGA) to identify clinically relevant miRNAs and their potential gene targets in breast tumors. In our analysis, we found that increased expression of 12 mature miRNAs—hsa-miR-320a, hsa-miR-361-5p, hsa-miR-103a-3p, hsa-miR-21-5p, hsa-miR-374b-5p, hsa-miR-140-3p, hsa-miR-25-3p, hsa-miR-651-5p, hsa-miR-200c-3p, hsa-miR-30a-5p, hsa-miR-30c-5p, and hsa-let-7i-5p —each predicted improved breast cancer survival. Of the 12 miRNAs, miR-320a, miR-361-5p, miR-21-5p, miR-103a-3p were selected for further analysis. By correlating global gene expression with miRNA expression and then employing miRNA target prediction analysis, we suggest that the four miRNAs may exert protective phenotypes by targeting breast oncogenes that contribute to patient survival. We propose that miR-320a targets the survival-associated genes RAD51, RRP1B, and TDG; miR-361-5p targets ARCN1; and miR-21-5p targets MSH2, RMND5A, STAG2, and UBE2D3. The results of our stringent bioinformatics approach for identifying clinically relevant miRNAs and their targets indicate that miR-320a, miR-361-5p, and miR-21-5p may contribute to breast cancer survival.

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Section: Discussionmentioning

confidence: 99%

Identification of MicroRNAs as Breast Cancer Prognosis Markers through the Cancer Genome Atlas

et al. 2016

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“…Ube2d3 is an important member of the ubiquitin‐proteasome pathways that has attracted increasing attention from biologists due to its role as the initial ubiquitinating enzyme of key regulatory molecule of p53 . Ube2d3 affects cancer progression, cell apoptosis, and DNA damage repair, for example, Ube2d3 knockdown‐induced apoptosis . In this study, the RNA‐seq and qRT‐PCR results show that the expression of Ube2d3 in Exp‐ERRγ cells was significantly reduced compared with the control group.…”

Section: Discussionmentioning

confidence: 61%

“…In this study, the RNA‐seq and qRT‐PCR results show that the expression of TFPT in Exp‐ERRγ cells was significantly increased compared with the control group. Ube2d3 is an important member of the ubiquitin‐proteasome pathways that has attracted increasing attention from biologists due to its role as the initial ubiquitinating enzyme of key regulatory molecule of p53 . Ube2d3 affects cancer progression, cell apoptosis, and DNA damage repair, for example, Ube2d3 knockdown‐induced apoptosis .…”

Section: Discussionmentioning

confidence: 99%

Candidate genes mediated by estrogen‐related receptor γ in pancreatic β cells

Zhou

JunHui

2019

J Biochem & Molecular Tox

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Recent studies have established the importance of estrogen‐related receptor γ (ERRγ) as a required participant for insulin secretion in pancreatic β cells. Key downstream genes of ERRγ remain unclear in the pancreatic β cell. To understand the molecular role of ERRγ and elucidate potential key candidate genes involved in pancreatic β cells, the eukaryotic expression plasmid containing mouse ERRγ was constructed and transfected into NIT‐1 pancreatic β cells. Overexpression of ERRγ was confirmed by quantitative real‐time reverse‐transcription polymerase chain reaction and Western blot analyses. RNA‐seq was conducted to get the gene expression profiling between the overexpression group cells and control cells. Differentially expressed genes (DEGs) were identified by edgR and subsequently analyzed by gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. We found that overexpression of ERRγ in pancreatic β cells enables regulation of the expression of certain genes involved in cell apoptosis and mitochondrial function, such as TFPT, Bcl7c, Dap, Thoc6, Ube2d3, ATP5H, MPV17, and NDUFA6. GO analysis revealed that the DEGs were mainly enriched in apoptotic process, cytoplasm, and protein binding. KEGG pathway analysis demonstrated that downregulated DEGs were mainly enriched in protein processing in endoplasmic reticulum, estrogen signaling pathway, and metabolic pathways. This study helps to further understand and reposition the molecular mechanisms of ERRγ in β cells.

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“…Cluster 8, which has the highest genomic instability, is enriched for a gain on 7q (including SMURF1; also increased in expression) and a loss on 4q, and high expression of 35 genes including PIK3CA and the transcriptional regulator YEATS2, as well as low expression of the ubiquitin-conjugating enzyme UBE2D3, a phenotype linked to cell cycle progression, reduced apoptosis, and telomere stability 28 . In addition, these tumors show reduced expression of genes for protein secretion, unfolded protein response, and RNA degradation.…”

Section: Liver Hepatocellular Carcinoma Hepatocellular Carcinoma Ismentioning

confidence: 99%

Multi-omic tumor data reveal diversity of molecular mechanisms that correlate with survival

Ramazzotti

Wang

et al. 2018

Preprint

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Outcomes for cancer patients vary greatly even within the same tumor type, and characterization of molecular subtypes of cancer holds important promise for improving prognosis and personalized treatment. This promise has motivated recent efforts to produce large amounts of multidimensional genomic ('multi-omic') data, but current algorithms still face challenges in the integrated analysis of such data . Here we present Cancer Integration via Multikernel Learning (CIMLR; based on an algorithm originally developed for analysis of single-cell RNA-Seq data), a new cancer subtyping method that integrates multi-omic data to reveal molecular subtypes of cancer. We apply CIMLR to multi-omic data from 32 cancer types and show significant improvements in both computational efficiency and ability to extract biologically meaningful cancer subtypes. The discovered subtypes exhibit significant differences in patient survival for 21 of the 32 studied cancer types. Our analysis reveals integrated patterns of gene expression, methylation, point mutations and copy number peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission.The copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/267245 doi: bioRxiv preprint first posted online Feb. 16, 2018; changes in multiple cancers and highlights patterns specifically associated with poor patient outcomes. IntroductionCancer is a heterogeneous disease that evolves through many pathways, involving changes in the activity of multiple oncogenes and tumor suppressor genes. The basis for such changes is the vast number and diversity of somatic alterations that produce complex molecular and cellular phenotypes, ultimately influencing each individual tumor's behavior and response to treatment. Due to the diversity of mutations and molecular mechanisms, outcomes vary greatly and it is therefore important to identify cancer subtypes based on common molecular features, and then correlate those with outcomes. This will lead to an improved understanding of the pathways by which cancer commonly evolves, as well as better prognosis and personalized treatment.Efforts to distinguish subtypes are complicated by the many kinds of genomic changes that contribute to cancer -for example, point mutations, DNA copy number aberrations, DNA methylation, gene expression, protein levels, and post-translational modifications. While gene expression clustering has often been used to discover subtypes (e.g., the PAM50 subtypes 1 of breast cancer), analysis of a single data type does not typically capture the full complexity of a tumor genome and its molecular phenotypes.For example, a copy number change may be biologically relevant only if it causes a gene expression change; gene expression data alone ignores point mutations that may alter the function of the gene product; and point mutations in two different genes may have the same downstream effect, which may become apparent only when also considering methylation or gene expression. Therefore, comprehen...

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Downregulation of Ubiquitin-conjugating Enzyme UBE2D3 Promotes Telomere Maintenance and Radioresistance of Eca-109 Human Esophageal Carcinoma Cells

Cited by 26 publications

References 36 publications

Identification of MicroRNAs as Breast Cancer Prognosis Markers through the Cancer Genome Atlas

Identification of MicroRNAs as Breast Cancer Prognosis Markers through the Cancer Genome Atlas

Candidate genes mediated by estrogen‐related receptor γ in pancreatic β cells

Multi-omic tumor data reveal diversity of molecular mechanisms that correlate with survival

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