Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

TRAF4 promotes the malignancy of ovarian high-grade serous carcinoma by activating YAP pathway

ABSTRACT: Ovarian high-grade serous carcinoma (HGSC) accounts for the majority of deaths caused by epithelial ovarian cancer. The precise molecular changes attributable for the pathogenesis of HGSC are still largely unknown. TRAF4 has been identified to be up-regulated in some cancers. However, the association between TRAF4 and the malignancy of HGSC has not been elucidated before. In this study, we aim to explore the prognostic value and function of TRAF4 in HGSC. The results of immunohistochemistry in 174 cases of HGSC showed that high expression of TRAF4 was significantly associated with a shorter overall survival (p=0.005) and recurrence-free survival (p=0.003) in HGSC. Knock-down of TRAF4 inhibited the malignancy of ovarian cancer cells, and over-expression of TRAF4 increased cell malignancy both in vitro and in vivo. Moreover, mechanism studies demonstrated that TRAF4 promoted cell malignancy by activating YAP pathway in HGSC. In conclusion, TRAF4 could be a prognostic biomarker for HGSC and increase HGSC cell malignancy by activating YAP pathway, which may serve as a potential therapeutic target for HGSC.

ORGANISM(S): Homo sapiens

PROVIDER: GSE202840 | GEO | 2022/05/15

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

TRAF4 promotes the malignancy of ovarian high-grade serous carcinoma by activating YAP pathway

Project description:TRAF4 promotes the malignancy of ovarian high-grade serous carcinoma by activating YAP pathway

| PRJNA837527 | ENA

USP19 and RPL23 as candidate prognostic markers in advanced-stage high-grade serous ovarian carcinoma

Project description:Ovarian cancer is one of the leading causes of death among patients with gynecological malignancies worldwide. To identify prognostic markers for ovarian cancer, we performed RNA-sequencing and analyzed the transcriptome data from 51 patients who received conventional therapies for high-grade serous ovarian carcinoma (HGSC). Patients with early-stage (I or II) HGSC exhibited higher immune gene expression than patients with advanced stage (III or IV) HGSC. To predict the prognosis of HGSC patients, we created machine learning-based models and identified RPL23 and USP19 as candidate prognostic markers. Specifically, patients with higher RPL23 mRNA levels had a worse prognosis and patients with higher USP19 mRNA levels had a better prognosis. This model was then used to analyze HGSC patient data hosted on The Cancer Genome Atlas; this exercise validated the prognostic abilities of these two genes with respect to patient survival. Taken together, the transcriptome profiles of RPL23 and USP19 determined using a machine-learning model could serve as prognostic markers in HGSC patients receiving conventional therapy.

2021-08-09 | GSE165808 | GEO

Human BMSCs protein analysis that interact with TRAF4 during adipogenic differentiation

Project description:To explore the mechanism underlying the negative regulation of adipogenesis by TRAF4, we performed the LC-MS/MS experiments to identify the proteins that interact with TRAF4 during adipogenic differentiation.

2020-05-27 | PXD017524 | Pride

Total RNA-Seq in control and TRAF4-overexpressing LNCaP cells

Project description:We reported the total gene expression in control and TRAF4-overexpressing LNCaP cells

2023-02-17 | GSE225509 | GEO

Evaluation of mRNA expression changes upon TRAF4 mis-expression in HT1376 and T24 bladder cancer cell lines

Project description:TRAF4 is highly expressed in the epithelial bladder cancer cell line, HT1376 and poorly expressed in the mesenchymal cell line, T24. We evaluated the genetic changes in HT1376 upon TRAF4 knockdown using two independent shRNAs targetting TRAF4. The changes were documented with respect to the control cell line transduced with empty vector shRNA plasmid. Also, we over-expressed (myc-tagged) TRAF4 in T24 and evaluated mRNA changes with respect to cell line over-expressing empty vector with a myc-tag.

2020-11-16 | GSE161509 | GEO

Genome-wide AR binding under androgen deprivation condition upon TRAF4 overexpression

Project description:We report that TRAF4-mediated AR ubiquitination alters AR genomic binding profile under androgen deprivation condition.

2023-05-24 | GSE154001 | GEO

Biomarkers in older ovarian cancer patients

Project description:Identification and validation of potential prognostic biomarkers in older ovarian cancer patients with high-grade serous adenocarcinoma (HGSC)

2020-03-01 | GSE126308 | GEO

CTCFL regulates the PI3K-Akt pathway and it is a target for personalized ovarian cancer therapy

Project description:High-grade serous ovarian carcinoma (HGSC) is the most lethal gynecologic malignancy due to the lack of reliable biomarkers, effective treatment and chemoresistance. Improving the diagnosis and the development of targeted therapies is still needed. The molecular pathomechanisms driving HGSC progression are not fully understood though crucial for effective diagnosis and identification of novel targeted therapy options. The oncogene CTCFL (BORIS), the paralog of CTCF, is a transcriptional factor highly expressed in ovarian cancer (but in rarely any other tissue in females) with cancer-specific characteristics and therapeutic potential. In this work, we seek to understand the regulatory functions of CTCFL to unravel new target genes with clinical relevance. We used in vitro models to evaluate the transcriptional changes due to the presence of CTCFL, followed by a selection of gene candidates using de novo network enrichment analysis. The resulting mechanistic candidates were further assessed regarding their prognostic potential and druggability. We show that CTCFL-driven genes are involved in cytoplasmic membrane functions; in particular, the Pi3K-AKT initiators EGFR1 and VEGFA, as well as ITGB3 and ITGB6 are potential drug targets. Finally, we identified the CTCFL targets ACTBL2, MALT1 and PCDH7 as mechanistic biomarkers to predict survival in HGSC. Finally, we elucidated the value of CTCFL in combination with its targets as a prognostic marker profile for HGSC progression and as putative drug targets.

2021-12-15 | GSE166767 | GEO

Total RNA-Seq in TRAF4-overexpressing castration-resistant xenograft tumors and the control castration-responsive tumors

Project description:We reported the tumor total gene expression TRAF4-overexpressing castration-resistant xenograft tumors and the control castration-responsive tumors

2020-07-15 | GSE154029 | GEO

Single-cell dissection of multiomics changes and epigenetic regulations in human high-grade serous ovarian cancer

Project description:To systematically characterize the underlying pathological mechanisms and intratumor heterogeneity in human high-grade serous ovarian cancer (HGSC), we profiled normal fallopian tubes (FTs), primary tumors, and matched metastases to unveil the molecular characteristics associated with tumor development. After stringent quality controls, we obtained single-cell transcriptomes for a total of 5,329 cells from 61 specimens of 16 ovarian cancer patients and 6 individuals without fallopian tube malignancy as controls. We found that the genes associated with interferon (IFN) signaling, metallothioneins, and metabolism were commonly upregulated in ovarian cancer cells. In summary, our study provides valuable resources for identifying new molecular mechanisms and potential therapeutic targets for HGSC.

2022-08-17 | GSE189955 | GEO

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data