Clinical significance of miRNA-21, -103, -129, -150 in serous ovarian cancer

Strategies for data normalization and missing data imputation and consequences for potential diagnostic microRNA biomarkers in epithelial ovarian cancer

MicroRNAs (miRNAs) are small non-coding RNA molecules regulating gene expression with diagnostic potential in different diseases, including epithelial ovarian carcinomas (EOC). As only a few studies have been published on the identification of stable endogenous miRNA in EOC, there is no consensus which miRNAs should be used aiming standardization. Currently, U6-snRNA is widely adopted as a normalization control in RT-qPCR when investigating miRNAs in EOC; despite its variable expression across cancers being reported. Therefore, our goal was to compare different missing data and normalization approaches to investigate their impact on the choice of stable endogenous controls and subsequent survival analysis while performing expression analysis of miRNAs by RT-qPCR in most frequent subtype of EOC: high-grade serous carcinoma (HGSC). 40 miRNAs were included based on their potential as stable endogenous controls or as biomarkers in EOC. Following RNA extraction from formalin-fixed paraffin embedded tissues from 63 HGSC patients, RT-qPCR was performed with a custom panel covering 40 target miRNAs and 8 controls. The raw data was analyzed by applying various strategies regarding choosing stable endogenous controls (geNorm, BestKeeper, NormFinder, the comparative ΔCt method and RefFinder), missing data (single/multiple imputation), and normalization (endogenous miRNA controls, U6-snRNA or global mean). Based on our study, we propose hsa-miR-23a-3p and hsa-miR-193a-5p, but not U6-snRNA as endogenous controls in HGSC patients. Our findings are validated in two external cohorts retrieved from the NCBI Gene Expression Omnibus database. We present that the outcome of stability analysis depends on the histological composition of the cohort, and it might suggest unique pattern of miRNA stability profiles for each subtype of EOC. Moreover, our data demonstrates the challenge of miRNA data analysis by presenting various outcomes from normalization and missing data imputation strategies on survival analysis.

Noncoding RNAs Interplay in Ovarian Cancer Therapy and Drug Resistance

Noncoding RNAs (ncRNAs) are several types of RNA that do not encode proteins, but are essential for cell regulation. Ovarian cancer (OC) is a type of gynecological cancer with a high mortality rate and a 5-year prognosis. OC is becoming more common with each passing year, and the symptoms of early-stage OC are sometimes undetectable. Meanwhile, early-stage OC has no symptoms and is difficult to diagnose. Because ncRNA has been shown to affect the development of OC and is widely distributed, it could be employed as a new biomarker for early OC. Furthermore, ncRNA has the potential to promote or inhibit drug resistance in OC, potentially giving a solution to multiple drug resistance. Various prior studies have found that different ncRNAs perform differently in OC. This article examines how mainstream ncRNAs have been expressed in OC in recent years, as well as their function in tumor growth.

Expression, Clinical Significance, and Prospective Pathway Signaling of miR-501-3p in Ovarian Cancer Based on Database and Informatics Analysis

Background

The present study aims to explore the expression, clinical significance, and prospective pathway signaling of miR-501-3p in ovarian cancer (OC) based on database and informatics analysis.

Methods

Kruskal–Wallis test, Wilcoxon sign-rank test, and logistic regression were used to evaluate the relationship between clinical features and miR-501-3p expression. Kaplan–Meier survival curve analysis was used to explore the relationship between miR-501-3p expression and the prognosis of OC patients. The miRNA targets were obtained from databases TargetScan, miRanda, TarBase, miRTarBase, miR2Disease, miRecords, and miRWalk. GO and KEGG analyses were used to analyze the significant involvement of miR-501-3p target genes in function.

Results

The low miR-501-3p expression in OC was significantly associated with histologic grade (P=0.015). Low miR-501-3p expression predicted a poorer overall survival (HR: 0.77; 95% CI: 0.61–0.96; P=0.02) and disease-specific survival (HR: 0.77; 95% CI: 0.61–0.99; P=0.038). GO and KEGG analyses demonstrated that miR-501-3p might participate in the development of OC by pathways including one carbon pool by folate, protein digestion and absorption, cell cycle, kaposi sarcoma-associated herpesvirus infection, and viral carcinogenesis.

Conclusions

Low miR-501-3p expression is significantly associated with poor survival in OC patients. It may be a promising prognostic biomarker for OC patients.

MicroRNA as Biomarker in Ovarian Cancer Management: Advantages and Challenges

Ovarian cancer is the most prevalent gynecological malignancy affecting women throughout the globe. Ovarian cancer has several subtypes, including epithelial ovarian cancer (EOC) with a whopping incidence rate of 239,000 per year, making it the sixth most common gynecological malignancy worldwide. Despite advancement of detection and therapeutics, death rate accounts for 152,000 per annum. Several protein-based biomarkers such as CA125 and HE4 are currently being used for diagnosis, but their sensitivity and specificity for early detection of ovarian cancer are under question. MicroRNA (a small noncoding RNA molecule that participates in post-transcription regulation of gene expression) and its functional deregulation in most cancers have been discovered in the previous two decades. Studies support that miRNA deregulation has an epigenetic component as well. Aberrant miRNA expression is often correlated with the form of EOC tumor, histological grade, prognosis, and FIGO stage. In this review, we addressed epigenetic regulation of miRNAs, the latest research on miRs as a biomarker in the detection of EOC, and tailored assays to use miRNAs as a biomarker in ovarian cancer diagnosis.

MiRNA-103/107 in Primary High-Grade Serous Ovarian Cancer and Its Clinical Significance

High levels of miRNA-103/107 are associated with poor outcomes in the case of breast cancer patients. MiRNA-103/107-DICER axis may be one of the key regulators of cancer aggressiveness. MiRNA-103/107 expression levels have never been related to patients' clinicopathological data in epithelial ovarian cancer. We aimed to assess miRNA-103/107 expression levels in high grade serous ovarian cancer tissues. Expression levels of both miRNAs were related to the clinicopathological features and survival. We also evaluated expression levels of miRNA-103/107 and DICER in selected ovarian cancer cell lines (A2780, A2780cis, SK-OV-3, OVCAR3). We assessed the relative expression of miRNA-103/107 (quantitative reverse transcription-polymerase chain reaction) in fifty archival formalin-fixed paraffin-embedded tissue samples of primary high grade serous ovarian cancer. Then, miRNA-103/107 and DICER expression levels were evaluated in selected ovarian cancer cell lines. Additionally, DICER, N-/E-cadherin protein levels were assessed with the use of western blot. We identified miRNA-107 up-regulation in ovarian cancer in comparison to healthy tissues (p = 0.0005). In the case of miRNA-103, we did not observe statistically significant differences between cancerous and healthy tissues (p = 0.07). We did not find any correlations between miRNA-103/107 expression levels and clinicopathological features. Kaplan-Meier survival (disease-free and overall survival) analysis revealed that both miRNAs could not be considered as prognostic factors. SK-OV-3 cancer cell lines were characterized by high expression of miRNA-103/107, relatively low expression of DICER (western-blot), and relatively high N-cadherin levels in comparison to other ovarian cancer cell lines. Clinical and prognostic significance of miRNA-103/107 was not confirmed in our study.

Epigenetic Biomarkers in the Management of Ovarian Cancer: Current Prospectives

Ovarian cancer (OC) causes significant morbidity and mortality as neither detection nor screening of OC is currently feasible at an early stage. Difficulty to promptly diagnose OC in its early stage remains challenging due to non-specific symptoms in the early-stage of the disease, their presentation at an advanced stage and poor survival. Therefore, improved detection methods are urgently needed. In this article, we summarize the potential clinical utility of epigenetic signatures like DNA methylation, histone modifications, and microRNA dysregulation, which play important role in ovarian carcinogenesis and discuss its application in development of diagnostic, prognostic, and predictive biomarkers. Molecular characterization of epigenetic modification (methylation) in circulating cell free tumor DNA in body fluids offers novel, non-invasive approach for identification of potential promising cancer biomarkers, which can be performed at multiple time points and probably better reflects the prevailing molecular profile of cancer. Current status of epigenetic research in diagnosis of early OC and its management are discussed here with main focus on potential diagnostic biomarkers in tissue and body fluids. Rapid and point of care diagnostic applications of DNA methylation in liquid biopsy has been precluded as a result of cumbersome sample preparation with complicated conventional methods of isolation. New technologies which allow rapid identification of methylation signatures directly from blood will facilitate sample-to answer solutions thereby enabling next-generation point of care molecular diagnostics. To date, not a single epigenetic biomarker which could accurately detect ovarian cancer at an early stage in either tissue or body fluid has been reported. Taken together, the methodological drawbacks, heterogeneity associated with ovarian cancer and non-validation of the clinical utility of reported potential biomarkers in larger ovarian cancer populations has impeded the transition of epigenetic biomarkers from lab to clinical settings. Until addressed, clinical implementation as a diagnostic measure is a far way to go.

lncRNA mortal obligate RNA transcript was downregulated in ovarian carcinoma and inhibits cancer cell proliferation by downregulating miRNA-21

microRNA-21 (miRNA-21) is a well-characterized oncogenic miRNA in human cancers. In the present study, we found that miRNA-21 was upregulated, while long noncoding RNA Mortal Obligate RNA Transcript (lncRNA MORT), which has been reported to be silenced in 16 types of cancers, was downregulated in tumor tissues than in adjacent healthy tissues of patients with ovarian carcinoma. Expression of lncRNA MORT in tumor tissues was found to be significantly affected by tumor size but not by tumor metastasis. Expression levels of lncRNA MORT and miRNA-21 were significantly and inversely correlated in both tumor tissues and adjacent healthy tissues. Overexpression of lncRNA MORT inhibited miRNA-21, while miRNA-21 overexpression failed to significantly affect lncRNA MORT expression. Overexpression of lncRNA MORT inhibited, while miRNA-21 overexpression promoted the proliferation of cells of ovarian cancer cell lines. In addition, miRNA-21 overexpression partially reversed the inhibitory effects of lncRNA MORT overexpression on cancer cell proliferation. However, overexpression of lncRNA MORT showed no significant effects on cancer cell migration and invasion. Therefore, lncRNA MORT was downregulated in ovarian carcinoma and lncRNA MORT overexpression inhibited cancer cell proliferation, possibly by downregulating miRNA-21.

A 19‑miRNA Support Vector Machine classifier and a 6‑miRNA risk score system designed for ovarian cancer patients

Ovarian cancer (OC) is the most common gynecologic malignancy with high incidence and mortality. The present study aimed to develop approaches for determining the recurrence type and identify potential miRNA markers for OC prognosis. The miRNA expression profile of OC (the training set, including 390 samples with recurrence information) was downloaded from The Cancer Genome Atlas database. The validation sets GSE25204 and GSE27290 were obtained from the Gene Expression Omnibus database. Prescreening of clinical factors was conducted using the survival package, and the differentially expressed miRNAs (DE-miRNAs) were identified using the limma package. Using the Caret package, the optimal miRNA set was selected to build a Support Vector Machine (SVM) classifier. The miRNAs and clinical factors independently related to prognosis were analyzed using the survival package, and the risk score system was constructed. Finally, the miRNA-target regulatory network was built by Cytoscape software, and enrichment analysis was performed. There were 46 DE-miRNAs between the recurrent and non-recurrent samples. After the optimal 19-miRNA set was selected for constructing the SVM classifier, 6 DE-miRNAs (miR-193b, miR-211, miR-218, miR-505, miR-508 and miR-514) independently related to prognosis were further extracted to build the risk score system. The neoplasm cancer status was independently correlated with the prognosis and conducted with stratified analysis. Additionally, the target genes in the regulatory network were enriched in the regulation of actin cytoskeleton and the TGF-β signaling pathway. The 6-miRNA signature may serve as a potential biomarker for OC prognosis, particularlyfor recurrence.