Inhibition of RUNX1 promotes cisplatin-induced apoptosis in ovarian cancer cells

Yao

et al. 2021

Front. Oncol.

AimsDysregulated long noncoding RNAs (lncRNAs) contributing to ovarian cancer (OC) development may serve as prognostic biomarker. We aimed to explore a lncRNA signature to serve as prognostic biomarker of OC.MethodsUnivariate Cox regression was conducted on the lncRNA expression dataset from the TCGA cohort, and 246 genes significantly associated with survival were retained for building a model. A random forest survival model was carried out, and a model was developed using 6 genes with the highest frequency. The selected genes were applied in a Cox multivariate regression model for prognostic prediction by calculating the risk score. We also used CCK-8, EdU, and colony formation assays to validate the function of these lncRNAs in OC cells.ResultsThis study confirmed that the 6-lncRNA combined signature was related to OC prognosis. Systematic analysis demonstrated that lncRNA-associated genes were enriched in oncogenic signalling pathways. Five out of the 6 lncRNAs participated in OC proliferation.ConclusionWe established a 6-lncRNA combined signature for OC prognosis, which may serve as powerful prognostic biomarker for OC after further validation.

Section: Discussionmentioning

confidence: 85%

A Combined Long Noncoding RNA Signature as a Candidate Prognostic Biomarker for Ovarian Cancer

Yao

et al. 2021

Front. Oncol.

“…It exerted cytotoxic effects in cells and had chemotherapy effects. The speci c mechanism preliminarily con rmed a possible association with apoptosis process (15,16). Cisplatin is the rst-line chemotherapeutic drug for OC, and there is a close association of platinum resistance with postoperative survival of OC patients (17,18).…”

Section: Discussionmentioning

confidence: 94%

PTPRZ1 Inhibits the Cisplatin Resistance of Ovarian Cancer by Regulating PI3K/AKT/mTOR Signal Pathway

et al. 2020

Preprint

Background: Cisplatin resistance (DDP resistance) is a major cause for poor prognosis of ovarian cancer patients. PTPRZ1 has been proven to participate in the occurrence and development of multiple tumors, including tumor resistance. This study was designed to investigate the roles of PTPRZ1 in DDP-resistant ovarian cancer cells and their possible mechanism.Methods: PTPRZ1 expression in ovarian cancer tissues and normal tissues was analyzed by GEPIA database and verified by qRT-PCR. PTPRZ1 expression in normal ovarian cancer cells and DDP-resistant ovarian cancer cells was also analyzed. Subsequently, qRT-PCR, western blot, MTT experiment and flow cytometry were used to assess the effects of PTPRZ1-PI3K/AKT/mTOR regulating axis on cisplatin resistance of ovarian cancer.Results: PTPRZ1 expression was abnormally low in ovarian cancer tissues, and notably reduced in DDP-resistant ovarian cancer cells. MTT experiment and flow cytometer indicated that overexpression of PTPRZ1 enhanced the cisplatin sensitivity of ovarian cancer cells and promoted the cell apoptosis. The results of mechanism research showed that PTPRZ1 exerted its biological effects possibly through blocking PI3K/AKT/mTOR pathway.Conclusion: PTPRZ1 suppresses the cisplatin resistance of ovarian cancer and induces the cytotoxicity by blocking PI3K/AKT/mTOR pathway.

“…However, no study has focused on the relationship between Dlk1 methylation and ovarian cancer. Inhibition of RUNX1 promotes cisplatin-induced apoptosis in ovarian cancer cells (Xiao et al, 2020). MMP11 was found to be related with bowel metastases in ovarian cancer (Mariani et al, 2019).…”

Section: Discussionmentioning

confidence: 96%

Comprehensive genomic analysis of microenvironment phenotypes in ovarian cancer

Zhang

Yang

et al. 2020

PeerJ

Background Ovarian cancer is one of the leading causes of cancer-related death in women. The incidence of ovarian cancer is insidious, and the recurrence rate is high. The survival rate of ovarian cancer has not significantly improved over the past decade. Recently, immune checkpoint inhibitors such as those targeting CTLA-4, PD-1, or PD-L1 have been used to treat ovarian cancer. Therefore, a full analysis of the immune biomarkers associated with this malignancy is necessary. Methods In this study, we used data from The Cancer Genome Atlas (TCGA) database to analyze the infiltration patterns of specific immune cell types in tumor samples. Data from the Gene Expression Omnibus (GEO) database was used for external validation. According to the invasion patterns of immune cells, we divided the ovarian cancer microenvironment into two clusters: A and B. These tumor microenvironment (TME) subtypes were associated with genomic and clinicopathological characteristics. Subsequently, a random forest classification model was established. Differential genomic features, functional enrichment, and DNA methylation were analyzed between the two clusters. The characteristics of immune cell infiltration and the expression of immune-related cytokines or markers were analyzed. Somatic mutation analysis was also performed between clusters A and B. Finally, multivariate Cox analysis was used to analyze independent prognostic factors. Results The ovarian cancer TME cluster A was characterized by less infiltration of immune cells and sparse distribution and low expression of immunomodulators. In contrast, cytotoxic T cells and immunosuppressive cells were significantly increased in the ovarian cancer TME cluster B. Additionally, immune-related cytokines or markers, including IFN-γ and TNF-β, were also expressed in large quantities. In total, 35 differentially methylated and expressed genes (DMEGs) were identified. Functional enrichment analyses revealed that the DMEGs in cluster B participated in important biological processes and immune-related pathways. The mutation load in cluster B was insignificantly higher than that of cluster A (p = 0.076). Multivariate Cox analysis showed that TME was an independent prognostic factor for ovarian cancer (hazard ratio: 1.33, 95% confidence interval: 1.01–1.75, p = 0.041). Conclusion This study described and classified basic information about the immune invasion pattern of ovarian cancer and integrated biomarkers related to different immunophenotypes to reveal interactions between ovarian cancer and the immune system.