NEDD9 promotes oncogenic signaling, a stem/mesenchymal gene signature, and aggressive ovarian cancer growth in mice

Conventional/targeted chemotherapies and ionizing radiation (IR) are being used both as monotherapies and in combination for the treatment of epithelial ovarian cancer (EOC). Several studies show that these therapies might favor oncogenic signaling and impede anti-tumor responses. MiR-200c is considered a master regulator of EOC-related oncogenes. In this study, we sought to investigate if chemotherapy and IR could influence the expression of miR-200c-3p and its target genes, like the immune checkpoint PD-L1 and other oncogenes in a cohort of EOC patients’ biopsies. Indeed, PD-L1 expression was induced, while miR-200c-3p was significantly reduced in these biopsies post-therapy. The effect of miR-200c-3p target genes was assessed in miR-200c transfected SKOV3 cells untreated and treated with olaparib and IR alone. Under all experimental conditions, miR-200c-3p concomitantly reduced PD-L1, c-Myc and β-catenin expression and sensitized ovarian cancer cells to olaparib and irradiation. In silico analyses further confirmed the anti-correlation between miR-200c-3p with c-Myc and β-catenin in 46 OC cell lines and showed that a higher miR-200c-3p expression associates with a less tumorigenic microenvironment. These findings provide new insights into how miR-200c-3p could be used to hold in check the adverse effects of conventional chemotherapy, targeted therapy and radiation therapy, and offer a novel therapeutic strategy for EOC.

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Tumor derived UBR5 promotes ovarian cancer growth and metastasis through inducing immunosuppressive macrophages

Nature Communications ◽

10.1038/s41467-020-20140-0 ◽

2020 ◽

Vol 11 (1) ◽

Author(s):

Mei Song ◽

Oladapo O. Yeku ◽

Sarwish Rafiq ◽

Terence Purdon ◽

Xue Dong ◽

...

Keyword(s):

Ovarian Cancer ◽

P53 Protein ◽

Cellular Adhesion ◽

Cancer Growth ◽

The Novel ◽

Conventional Chemotherapy ◽

Potential Therapeutic Target ◽

Cancer Stemness ◽

Tumor Associated Macrophage ◽

Immunosuppressive Tumor Microenvironment

AbstractImmunosuppressive tumor microenvironment (TME) and ascites-derived spheroids in ovarian cancer (OC) facilitate tumor growth and progression, and also pose major obstacles for cancer therapy. The molecular pathways involved in the OC-TME interactions, how the crosstalk impinges on OC aggression and chemoresistance are not well-characterized. Here, we demonstrate that tumor-derived UBR5, an E3 ligase overexpressed in human OC associated with poor prognosis, is essential for OC progression principally by promoting tumor-associated macrophage recruitment and activation via key chemokines and cytokines. UBR5 is also required to sustain cell-intrinsic β-catenin-mediated signaling to promote cellular adhesion/colonization and organoid formation by controlling the p53 protein level. OC-specific targeting of UBR5 strongly augments the survival benefit of conventional chemotherapy and immunotherapies. This work provides mechanistic insights into the novel oncogene-like functions of UBR5 in regulating the OC-TME crosstalk and suggests that UBR5 is a potential therapeutic target in OC treatment for modulating the TME and cancer stemness.

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The DEK Oncoprotein Functions in Ovarian Cancer Growth and Survival

Neoplasia ◽

10.1016/j.neo.2018.10.005 ◽

2018 ◽

Vol 20 (12) ◽

pp. 1209-1218 ◽

Cited By ~ 5

Author(s):

Kari E Hacker ◽

Danielle E Bolland ◽

Lijun Tan ◽

Anjan K Saha ◽

Yashar S Niknafs ◽

...

Keyword(s):

Ovarian Cancer ◽

Cancer Growth ◽

Growth And Survival

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Tissue-Specific Pathways for Estrogen Regulation of Ovarian Cancer Growth and Metastasis

Cancer Research ◽

10.1158/0008-5472.can-10-1238 ◽

2010 ◽

Vol 70 (21) ◽

pp. 8927-8936 ◽

Cited By ~ 52

Author(s):

Monique A. Spillman ◽

Nicole G. Manning ◽

Wendy W. Dye ◽

Carol A. Sartorius ◽

Miriam D. Post ◽

...

Keyword(s):

Ovarian Cancer ◽

Cancer Growth ◽

Estrogen Regulation ◽

Tissue Specific

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A Novel Defined Risk Signature of the Ferroptosis-Related Genes for Predicting the Prognosis of Ovarian Cancer

Frontiers in Molecular Biosciences ◽

10.3389/fmolb.2021.645845 ◽

2021 ◽

Vol 8 ◽

Author(s):

Ying Ye ◽

Qinjin Dai ◽

Shuhong Li ◽

Jie He ◽

Hongbo Qi

Keyword(s):

Ovarian Cancer ◽

High Risk ◽

Risk Model ◽

Cox Regression ◽

Ovarian Tissue ◽

Gene Signature ◽

High Risk Group ◽

Cancer Prognosis ◽

Consensus Clustering ◽

Gynecologic Tumor

Ferroptosis is an iron-dependent, regulated form of cell death, and the process is complex, consisting of a variety of metabolites and biological molecules. Ovarian cancer (OC) is a highly malignant gynecologic tumor with a poor survival rate. However, the predictive role of ferroptosis-related genes in ovarian cancer prognosis remains unknown. In this study, we demonstrated that the 57 ferroptosis-related genes were expressed differently between ovarian cancer and normal ovarian tissue, and based on these genes, all OC cases can be well divided into 2 subgroups by applying consensus clustering. We utilized the least absolute shrinkage and selection operator (LASSO) cox regression model to develop a multigene risk signature from the TCGA cohort and then validated it in an OC cohort from the GEO database. A 5-gene signature was built and reveals a favorable predictive efficacy in both TCGA and GEO cohort (P < 0.001 and P = 0.03). The GO and KEGG analysis revealed that the differentially expressed genes (DEGs) between the low- and high-risk subgroup divided by our risk model were associated with tumor immunity, and lower immune status in the high-risk group was discovered. In conclusion, ferroptosis-related genes are vital factors predicting the prognosis of OC and could be a novel potential treatment target.

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Identification of a Glycolysis-Related Gene Signature for Survival Prediction of Ovarian Cancer Patients

10.21203/rs.3.rs-76061/v1 ◽

2020 ◽

Author(s):

Dai Zhang ◽

Si Yang ◽

Yiche Li ◽

Meng Wang ◽

Jia Yao ◽

...

Keyword(s):

Ovarian Cancer ◽

Risk Model ◽

Cox Regression ◽

Expression Profiles ◽

Gynecological Cancer ◽

Predictive Ability ◽

Gene Signature ◽

The Cancer Genome Atlas ◽

Survival Prediction ◽

Test Sets

Abstract Background: Ovarian cancer (OV) is deemed as the most lethal gynecological cancer in women. The aim of this study was construct an effective gene prognostic model for OV patients.Methods: The expression profiles of glycolysis-related genes (GRGs) and clinical data of patients with OV were extracted from The Cancer Genome Atlas (TCGA) database. Univariate, multivariate, and least absolute shrinkage and selection operator Cox regression analyses were conducted, and a prognostic signature based on GRGs was constructed. The predictive ability of the signature was analyzed in training and test sets.Results: Based on nine GRGs (ISG20, CITED2, PYGB, IRS2, ANGPTL4, TGFBI, LHX9, PC, and DDIT4), a gene risk signature was identified to predict the outcome of patients with OV. The signature showed a good prognostic ability for OV, particularly high-grade OV, in the TCGA dataset, with areas under the curve of 0.709, 0.762, and 0.808 for 3-, 5- and 10-year survival, respectively. Similar results were found in the test sets, and the signature was also an independent prognostic factor. Moreover, a nomogram combining the prediction model and clinical factors was constructed.Conclusion: Our study established a nine-GRG risk model and a nomogram to better perform on OV patients’ survival prediction. The risk model represents a promising and independent prognostic predictor for OV patients. Moreover, our study of GRGs could offer guidances for underlying mechanisms explorations in the future.

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A Liquid-Liquid Phase Separation-Related Gene Signature as Prognostic Biomarker for Epithelial Ovarian Cancer

Frontiers in Oncology ◽

10.3389/fonc.2021.671892 ◽

2021 ◽

Vol 11 ◽

Author(s):

Yan Qiu ◽

Min Pan ◽

Xuemei Chen

Keyword(s):

Ovarian Cancer ◽

Phase Separation ◽

Prognostic Biomarker ◽

Risk Group ◽

Risk Index ◽

Gene Signature ◽

Related Gene ◽

Data Set ◽

Test Set ◽

Liquid Liquid Phase Separation

ObjectiveThe aim of the present study was to construct and test a liquid-liquid phase separation (LLPS)-related gene signature as a prognostic tool for epithelial ovarian cancer (EOC).Materials and MethodsThe data set GSE26712 was used to screen the differentially expressed LLPS-related genes. Functional enrichment analysis was performed to reveal the potential biological functions. GSE17260 and GSE32062 were combined as the discovery to construct an LLPS-related gene signature through a three-step analysis (univariate Cox, least absolute shrinkage and selection operator, and multivariate Cox analyses). The EOC data set from The Cancer Genome Atlas as the test set was used to test the LLPS-related gene signature.ResultsThe differentially expressed LLPS-related genes involved in several cancer-related pathways, such as MAPK signaling pathway, cell cycle, and DNA replication. Eleven genes were selected to construct the LLPS-related gene signature risk index as prognostic biomarker for EOC. The risk index could successfully divide patients with EOC into high- and low-risk groups. The patients in high-risk group had significantly shorter overall survival than those with in low-risk group. The LLPS-related gene signature was validated in the test set and may be an independent prognostic factor compared to routine clinical features.ConclusionWe constructed and validated an LLPS-related gene signature as a prognosis tool in EOC through integrated analysis of multiple data sets.

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