Subtype specific biomarkers associated with chemoresistance in epithelial ovarian cancer

Predicting Prognosis and Platinum Resistance in Ovarian Cancer: Role of Immunohistochemistry Biomarkers

Ovarian cancer is a lethal reproductive tumour affecting women worldwide. The advancement in presentation and occurrence of chemoresistance are the key factors for poor survival among ovarian cancer women. Surgical debulking was the mainstay of systemic treatment for ovarian cancer, which was followed by a successful start to platinum-based chemotherapy. However, most women develop platinum resistance and relapse within six months of receiving first-line treatment. Thus, there is a great need to identify biomarkers to predict platinum resistance before enrolment into chemotherapy, which would facilitate individualized targeted therapy for these subgroups of patients to ensure better survival and an improved quality of life and overall outcome. Harnessing the immune response through immunotherapy approaches has changed the treatment way for patients with cancer. The immune outline has emerged as a beneficial tool for recognizing predictive and prognostic biomarkers clinically. Studying the tumour microenvironment (TME) of ovarian cancer tissue may provide awareness of actionable targets for enhancing chemotherapy outcomes and quality of life. This review analyses the relevance of immunohistochemistry biomarkers as prognostic biomarkers in predicting chemotherapy resistance and improving the quality of life in ovarian cancer.

TAZ Regulates the Cisplatin Resistance of Epithelial Ovarian Cancer Cells via the ANGPTL4/SOX2 Axis

Objective

Epithelial ovarian cancer (EOC) is a fatal gynecological malignancy. This study explored the mechanism of TAZ in regulating drug sensitivity of cisplatin (DDP-)-resistant EOC cells through the ANGPTL4/SOX2 axis.

Methods

The A2780/DDP cells were prepared by stepwise progressive concentration method. The drug resistance and TAZ expression in EOC cells were determined. Drug sensitivity was measured after TAZ overexpression in A2780 cells and TAZ downregulation in A2780/DDP cells, respectively. The effects of TAZ knockdown on apoptosis rate, stemness, and cancer stem cell (CSC) marker (CD44, OCT4, and ALDH1A) levels in A2780/DDP and DDP-treated A2780/DDP cells were assessed. The binding of TAZ and ANGPTL4 was verified using ChIP-qPCR, and ANGPTL4 and SOX2 levels were determined. The effects of different combined treatments of TAZ, ANGPTL4, and SOX2 on drug sensitivity of A2780/DDP cells and DDP-treated A2780/DDP cells were evaluated.

Results

TAZ was upregulated in drug-resistant EOC cells. TAZ knockdown significantly increased the drug sensitivity of A2780/DDP cells, while TAZ overexpression markedly decreased the drug sensitivity of A2780 cells. TAZ silencing promoted apoptosis of drug-resistant EOC cells and inhibited cell stemness. TAZ targeted ANGPTL4 and TAZ silencing enhanced drug sensitivity of A2780/DDP cells by inhibiting ANGPTL4. ANGPTL4 overexpression elevated SOX2 expression, and SOX2 downregulation reduced the drug resistance and promoted the apoptosis of A2780/DDP cells.

Conclusion

TAZ regulates DDP sensitivity of drug-resistant EOC cells via the ANGPTL4/SOX2 axis.

Integrated Bioinformatics Analysis for Identification of the Hub Genes Linked with Prognosis of Ovarian Cancer Patients

BACKGROUND

One of the most usual gynecological state of tumor is ovarian cancer and is a major reason of gynecological tumor-related global mortality rate. There have been multiple risk elements related to ovarian cancer like the background of past cases associated with breast cancer or ovarian cancer, or excessive body weight issues, case history of smoking, and untimely menstruation or menopause. Because of unclear expressions, more than 70% of the ovarian cancer patient cases are determined during the early stage. Material and Methods. GSE38666, GSE40595, and GSE66957 were the three microarray datasets which were analyzed using GEO2R for screening the differentially expressed genes. GO, Kyoto Encyclopedia of Genes, and protein expression studies were performed for analysis of hub genes. Then, survival analysis was performed for all the hub genes.

RESULTS

From the dataset, a total of 199 differentially expressed genes (DEGs) were identified. Through the KEGG pathway study, it was noted that the DEGs are mainly linked with the AGE-RAGE signaling pathway, central carbon metabolism, and human papillomavirus infection. The survival analysis showed 4 highly expressed hub genes COL4A1, SDC1, CDKN2A, and TOP2A which correlated with overall survival in ovarian cancer patients. Moreover, the expression of the 4 hub genes was validated by the GEPIA database and the Human Protein Atlas.

CONCLUSION

The results have shown that all 4 hub genes were found to be upregulated in ovarian cancer tissues which predict poor prognosis in patients with ovarian cancer.

Identification of the Hub Genes Associated with the Prognosis of Ovarian Cancer Patients via Integrated Bioinformatics Analysis and Experimental Validation

Background

This study aimed to identify the hub genes associated with prognosis of patients with ovarian cancer by using integrated bioinformatics analysis and experimental validation.

Methods

Four microarray datasets (GSE12470, GSE14407, GSE18521 and GSE46169) were analyzed by the GEO2R tool to screen common differentially expressed genes (DEGs). Gene Ontology, the Kyoto Encyclopedia of Genes and Genomes, the (KEGG) pathway and Reactome pathway enrichment analysis, protein–protein interaction (PPI) construction, and the identification of hub genes were performed. Furthermore, we performed the survival and expression analysis of the hub genes. In vitro functional assays were performed to assess the effects of hub genes on ovarian cancer cell proliferation, caspase-3/7 activity and invasion.

Results

A total of 89 common DEGs were identified among these four datasets. The KEGG and Reactome pathway results showed that the DEGs were mainly associated with cell cycle, mitotic and p53 signaling pathway. A total of 20 hub genes were identified from the PPI network by using sub-module analysis. The survival analysis revealed that high expression of six hub genes (AURKA, BUB1B, CENPF, KIF11, KIF23 and TOP2A) were significantly correlated with shorter overall survival and progression-free survival of patients with ovarian cancer. Furthermore, the expression of the six hub genes were validated by the GEPIA database and Human Protein Atlas, and functional studies revealed that knockdown of KIF11 and KIF23 suppressed the SKOV3 cell proliferation, increased caspase-3/7 activity and attenuated invasive potentials of SKOV3 cells. In addition, knockdown of KIF11 and KIF23 up-regulated E-cadherin mRNA expression but down-regulated N-cadherin and vimentin mRNA expression in SKOV3 cells.

Conclusion

Our results showed that six hub genes were up-regulated in ovarian cancer tissues and may predict poor prognosis of patients with ovarian cancer. KIF11 and KIF23 may play oncogenic roles in ovarian cancer cell progression via promoting ovarian cancer cell proliferation and invasion.