Epigenetic Pathways Offer Targets for Ovarian Cancer Treatment

LncRNA-MSC-AS1 inhibits the ovarian cancer progression by targeting miR-425-5p

BACKGROUND

Long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) were reported to be aberrantly expressed and related to the pathogenesis of ovarian cancer. However, the role and regulatory mechanism of MSC-AS1 in ovarian cancer has yet to be fully elucidated.

METHODS

Expression of lncRNA MSC-AS1 (MSC-AS1) and microRNA-425-5p (miR-425-5p) in the ovarian cancer tissue samples and cell lines was examined by quantitative real-time polymerase chain reaction (qRT-PCR). The functions of MSC-AS1 on ovarian cancer cell proliferation, cell cycle and apoptosis were determined using MTT, colony formation and flow cytometry analyses. The protein expression levels were evaluated using western blot assay. The targeting relationship MSC-AS1 and miR-425-5p was verified via dual-luciferase reporter assay.

RESULTS

MSC-AS1 expression level was lowly expressed, while miR-425-5p level was highly in ovarian cancer tissues and cells. Elevation of MSC-AS1 has the ability to significantly inhibit cell proliferation and facilitate cell apoptosis in SKOV3 and A2780 cells. Moreover, MSC-AS1 targeted and negatively modulated miR-425-5p. MiR-425-5p up-regulation has been proved to partially reverse the tumor suppressive function of MSC-AS1 overexpression CONCLUSION: MSC-AS1 sponged miR-425-5p to inhibit the ovarian cancer progression. These findings may provide a promising therapeutic target for the treatment of ovarian cancer.

Creation and validation of models to predict response to primary treatment in serous ovarian cancer

Nearly a third of patients with high-grade serous ovarian cancer (HGSC) do not respond to initial therapy and have an overall poor prognosis. However, there are no validated tools that accurately predict which patients will not respond. Our objective is to create and validate accurate models of prediction for treatment response in HGSC. This is a retrospective case–control study that integrates comprehensive clinical and genomic data from 88 patients with HGSC from a single institution. Responders were those patients with a progression-free survival of at least 6 months after treatment. Only patients with complete clinical information and frozen specimen at surgery were included. Gene, miRNA, exon, and long non-coding RNA (lncRNA) expression, gene copy number, genomic variation, and fusion-gene determination were extracted from RNA-sequencing data. DNA methylation analysis was performed. Initial selection of informative variables was performed with univariate ANOVA with cross-validation. Significant variables (p < 0.05) were included in multivariate lasso regression prediction models. Initial models included only one variable. Variables were then combined to create complex models. Model performance was measured with area under the curve (AUC). Validation of all models was performed using TCGA HGSC database. By integrating clinical and genomic variables, we achieved prediction performances of over 95% in AUC. Most performances in the validation set did not differ from the training set. Models with DNA methylation or lncRNA underperformed in the validation set. Integrating comprehensive clinical and genomic data from patients with HGSC results in accurate and robust prediction models of treatment response.

RETRACTED: MicroRNA-34 suppresses proliferation of human ovarian cancer cells by triggering autophagy and apoptosis and inhibits cell invasion by targeting Notch 1

This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been retracted at the request of the Editors. The substance of the retraction is as follows: Attention has been drawn by Dr Elisabeth Bik to the fact that similar images representing the results of different experiments are shown in Figures 1G and 3F. She also points to the remarkable similarity between the legend text and line graphs of Figure 1 of the Biochimie article, Jia et al (2019), and Figure 1 of a publication in Eur Rev Med Pharmacol Sci (2019), Xu et al (2019). The latter article has since been retracted. An anomymous reader has observed that Figures 1G and 3F show three rows of images shared with Figure 4 of the first-published version of a manuscript in the Journal of the Balkan Union of Oncology (J BUON), Tan et al (2019), submitted and accepted for publication before the submission of the Biochimie article, though it has since publication undergone unexplained modification to Figure 4. Furthermore, the panels shared between the Biochimie and J BUON articles are similar to panels in Figure 7 of a much earlier manuscript, published in October 2017 in Science Reports (Dai et al), as well as to a figure in a later article, Li et al (2020). In addition, the top panel of Figure 3G in Jia et al (2019) as well as the two lower panels is also similar to a part of Figure 7, Dia et al (2017). Further unexplained anomalies concern Figure 3C of Jia et al (2019). The editors would like to thank Dr Bik and the anonymous reader for their valuable insight in this matter. The above observations cast serious doubt on the scientific integrity of the Biochimie article. Confronted with the observation that panels in Figures 1G and 3F were similar though representing different experiments, the authors replied to the editors that the bottom panel in Figure 3F was a mistake and would be corrected. They were not able to produce the raw data when requested, and proposed to the editors to repeat the experiments. However, no new data have been received by the editors at the time of writing, and would not change the serious doubts that they harbour concerning the integrity of the work published in Biochimie. Therefore, the editorial team has opted for retraction irrespective of any new results. This decision has been accepted by the authors.

Molecular mechanisms of drug resistance in ovarian cancer

Ovarian cancer is the most lethal malignancy among the gynecological cancers, with a 5-year survival rate, mainly due to being diagnosed at advanced stages, recurrence and resistance to the current chemotherapeutic agents. Drug resistance is a complex phenomenon and the number of known involved genes and cross-talks between signaling pathways in this process is growing rapidly. Thus, discovering and understanding the underlying molecular mechanisms involved in chemo-resistance are crucial for management of treatment and identifying novel and effective drug targets as well as drug discovery to improve therapeutic outcomes. In this review, the major and recently identified molecular mechanisms of drug resistance in ovarian cancer from relevant literature have been investigated. In the final section of the paper, new approaches for studying detailed mechanisms of chemo-resistance have been briefly discussed.