A microRNA expression signature for cervical cancer prognosis

Abstract

Invasive cervical cancer is a leading cause of cancer death in women worldwide, resulting in about 300,000 deaths each year. The clinical outcomes of cervical cancer vary significantly and are difficult to predict. Thus, a method to reliably predict disease outcome would be important for individualized therapy by identifying patients with high risk of treatment failures before therapy. In this study, we have identified a microRNA (miRNA)-based signature for the prediction of cervical cancer survival. miRNAs are a newly identified family of small noncoding RNAs that are extensively involved in human cancers. Using an established PCR-based miRNA assay to analyze 102 cervical cancer samples, we identified miR-200a and miR-9 as two miRNAs that could predict patient survival. A logistic regression model was developed based on these two miRNAs and the prognostic value of the model was subsequently validated with independent cervical cancers. Furthermore, functional studies were done to characterize the effect of miRNAs in cervical cancer cells. Our results suggest that both miR-200a and miR-9 could play important regulatory roles in cervical cancer control. In particular, miR-200a is likely to affect the metastatic potential of cervical cancer cells by coordinate suppression of multiple genes controlling cell motility.

Figure 1

Kaplan-Meier analysis to evaluate the statistical power of a miRNA-based model on predicting cervical cancer survival. A risk score was assigned to each patient as calculated by the prediction model. Based on the risk score, the patients were classified into either the low risk group (score <0) or the high risk group (score >0). (A) Application of the model to 59 training tumor samples. The risk scores generated by the model were significantly predictive of overall patient survival (p=0.0003 with the log-rank test). (B) Application of the model to 42 independent testing tumor samples. The risk scores generated by the model were significantly predictive of overall patient survival (p=0.002 with the log-rank test).

Figure 2

Functional characterization of the prognostic miRNAs by target analysis. (A) Identification of miRNA gene targets by combining computational target prediction with microarray expression profiling. Genome-wide miRNA target prediction was performed with the MirTarget2 program. In parallel, microarrays were performed to identify genes downregulated by miRNAs. Fifty-eight genes were both predicted miR-200a targets and downregulated by miR-200a as revealed by microarrays; fifty-two genes were both predicted miR-9 targets and downregulated by miR-9 as revealed by microarrays. (B) Real-time RT-PCR validation of miR-200a gene targets. Cervical cancer HeLa cells were transfected with miR-200a, and the expression levels of 13 candidate miR-200 targets were determined. Nine of the 13 genes were downregulated by at least 40% (<60% remaining expression). (C) The effect of miR-200a overexpression on cervical cancer cell motility. miR-200a was transfected into cervical HeLa cells, and the effect of miRNA overexpression on cell motility was evaluated by Transwell migration assays. The percentage of migrated cells with overexpressed miR-200a was normalized to that of control cells transfected with a negative control RNA.