Elsevier

Gynecologic Oncology

Volume 114, Issue 3, September 2009, Pages 457-464
Gynecologic Oncology

A miR-200 microRNA cluster as prognostic marker in advanced ovarian cancer

https://doi.org/10.1016/j.ygyno.2009.05.022Get rights and content

Abstract

Objective

Ovarian cancer is one of the most deadly human cancers, resulting in over 15,000 deaths in the US each year. A reliable method that could predict disease outcome would improve care of patients with this disease. The main aim of this study is to identify novel prognostic biomarkers for advanced ovarian cancer.

Methods

We hypothesized that microRNAs (miRNAs) may predict outcome and have examined the prognostic value of these small RNA molecules on disease outcome prediction. miRNAs are a newly identified family of non-coding RNA genes, and recent studies have shown that miRNAs are extensively involved in the tumor development process. We have profiled the expression of miRNAs in advanced ovarian cancer using a novel PCR-based platform and correlated miRNA expression profiles with disease outcome.

Results

By performing miRNA expression profiling analysis of 55 advanced ovarian tumors, we have shown that three miR-200 miRNAs (miR-200a, miR-200b and miR-429) in the miR-200b–429 cluster are significantly associated with cancer recurrence and overall survival. Further target analysis indicates that these miR-200 miRNAs target multiple genes that are involved in cancer development. In addition, we have also shown that overexpression of this miR-200 cluster inhibits ovarian cancer cell migration.

Conclusions

miR-200b–429 may be used as a prognostic marker for ovarian cancer outcome, and low-level expression of miR-200 miRNAs in this cluster predicts poor survival. In addition, our study suggests that miR-200 miRNAs could play an important regulatory role in ovarian cancer.

Introduction

Ovarian cancer is one of the most deadly cancers among women. It is estimated that 21,650 new cases of ovarian cancer will be diagnosed in the US in 2008 [1]. In general less than half (45%) of ovarian cancer patients can survive more than five years after the initial diagnosis [1]. Given the poor survival of ovarian cancer patients, it is important to accurately identify those patients who would fail standard treatment.

At present, clinicopathologic features are principally used to make treatment decisions for ovarian cancer. However, it has been well known that ovarian tumors with similar clinicopathologic features may have a broad range of clinical outcomes. Despite intense study on this topic, it is still very challenging to reliably predict tumor recurrence to date. In recent years, molecular biomarkers have been increasingly investigated for cancer diagnosis and prognosis. The emergence of molecular diagnostic techniques brings new tools to individualized cancer patient care: therapeutic protocols will be designed for each patient based on the prognostic result from the same individual [2].

One major category of molecular cancer markers that holds promise is based on gene expression studies. The expression of protein-coding messenger RNAs (mRNAs) has been extensively studied for cancer diagnosis and prognosis; and laboratory tests, such as Oncotype DX from Genomic Health for breast cancer prognosis, have been developed based on gene expression profiles [3]. In contrast to breast cancer studies, there has been only limited success in using gene expression signatures as prognostic markers for ovarian cancer. Several recent studies have demonstrated that the expression signatures of protein-coding genes identified by microarrays have significant prognostic value in ovarian cancer, independent of other widely used clinicopathologic features [4], [5], [6].

Here, instead of focusing on protein-coding genes, we have explored the possibility of using a newly identified family of genes, microRNAs for the prediction of ovarian cancer outcome. MicroRNAs (miRNAs) are non-coding RNAs that regulate many important biological processes including cell growth, tissue differentiation, apoptosis and viral infection [7], [8]. Over 600 human miRNAs have been identified in recent years. miRNAs are only about 22 nucleotides long, which is much shorter than the length of most other genes in humans. Despite the short sequence length and the relatively small number of microRNAs, both computational and experimental studies have shown that thousands of human protein-coding genes are regulated by miRNAs, and miRNAs may act as master regulators for many important biological pathways. Thus, deregulation of miRNAs may have a profound effect on miRNA-mediated pathways. Many recent studies have shown that miRNAs are extensively involved in tumorigenesis and they often have deregulated expression in tumors. Thus, they are considered as potential cancer biomarkers [9]. To date, significant microRNA expression changes have been observed in every type of tumor analyzed by profiling experiments. Multiple recent profiling studies also indicate that miRNA expression is significantly changed in ovarian cancer [10], [11], [12], [13], [14]. However, these profiling studies were performed with microarrays, which tend to give relatively high false positive readings when applied to miRNA studies. To address this issue, we have recently developed a novel PCR-based profiling platform for miRNA expression analysis [15]. We have shown that our new method has much higher detection specificity and sensitivity than existing microarray platforms, and it can accurately differentiate miRNAs with a single-base difference [15]. This new profiling method has been applied in this study to identify prognostic miRNA markers in advanced ovarian cancer.

In this study, we have demonstrated that the expression of a miR-200 miRNA cluster is significantly associated with ovarian cancer survival as revealed by expression profiling analysis. This miR-200 cluster suppresses the expression of many gene targets that are involved in cancer initiation and progression, and miR-200 overexpression leads to reduced motility for ovarian cancer cells. Combined together, our study indicates that the miR-200 cluster may play an important role in ovarian cancer control and its expression signature has prognostic value for the prediction of ovarian cancer outcome.

Section snippets

Patients and tumor samples

Fifty-five patients with advanced ovarian cancer were included in this study, representing 48 epithelial ovarian carcinomas and 7 primary peritoneal carcinomas treated at Washington University School of Medicine in St. Louis (Table 1). These patients were enrolled in a prospective multi-institutional clinical trial evaluating treatment of patients with advanced ovarian cancer (GOG 182). All the patients underwent surgery between March 2001 and May 2003, and subsequently received platinum-based

miR-200a expression signature was prognostic of ovarian cancer survival

miRNA expression profiles of 55 advanced ovarian tumors (FIGO stages III and IV) were analyzed to evaluate whether miRNA expression signature is associated with cancer outcome (Table 1). The expression levels of 96 cancer-related miRNAs were determined by real-time RT-PCR. These miRNAs were selected based on literature survey for their involvement in cancer development. Expression changes of these selected miRNAs have been associated with various human cancers based on previous studies, and the

Discussion

Little is known about the involvement of miRNAs in ovarian cancer development. Previously, researchers had focused primarily on the deregulated expression of protein-coding genes in ovarian cancer. For example, EPHA2 overexpression has been demonstrated by multiple groups to be a predictor of poor prognosis. With the discovery of miRNAs in recent years, it is now possible to expand our view to better understand ovarian cancer by analyzing miRNA-mediated pathways. Several recent studies indicate

Conflict of interest statement

The authors declare that there are no conflicts of interest.

Acknowledgments

This research was supported by a start-up fund from Washington University School of Medicine in St. Louis.

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    1

    Current address: People's Hospital of Guangxi Province, People's Republic of China.

    2

    Current address: Walter Reed Army Medical Center, Washington, DC 20307, USA.

    3

    Current address: Memorial Sloan-Kettering Cancer Center, New York, NY 10065, USA.

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