It is estimated that there will be about 12,340 new cases of cervical cancer and those 4,030 women will die of this disease this year in the United States (American Cancer Society). Worldwide, invasive cervical cancer is an even greater problem and is the second most commonly diagnosed cancer in women. A method to accurately predict cervical cancer outcome prior to standard therapy would be critical for the early identification of patients with high risk of treatment failures. For these high-risk patients modified therapy could potentially be applied for improved patient survival. However, traditional clinic pathologic features, such as tumor grade and stage, have limited prognostic values in cervical cancer. Thus, new methods need to be developed for improved prognostic performance. In this proposed research, we will test the hypothesis that the expression signature of multiple selected microRNAs can reliably predict the outcome of cervical cancer. MicroRNAs (miRNAs) are a newly discovered family of small non- coding RNA molecules that collectively control the expression of thousands of protein-coding genes. Recent studies indicate that miRNAs are promising biomarkers to predict cancer therapy outcomes. However, the prognostic value of miRNAs in cervical cancer has not been investigated. The major goal of this research is to build a miRNA-based model to robustly predict cervical cancer outcome. Our preliminary analysis has identified two miRNAs that are predictive of cervical cancer outcome. Here, we propose to significantly expand our preliminary study to identify new miRNA biomarkers by analyzing all cervix-related miRNAs in a large number of cervical tumors. These miRNA biomarkers will then be combined to build a robust model for significantly improved prognosis of cervical cancer.
Cervical cancer is the second most common cancer in women worldwide, leading to about 300,000 deaths each year. The long-term goal of this research is to develop clinical prognostic assays for individualized cervical cancer treatment by focusing on microRNAs, which are a class of small non- coding RNAs that play important regulatory roles in tumorigenesis.
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