"Computational analysis of microRNA regulation in ovarian cancer."
MicroRNAs (miRNAs) are small, non-protein-encoding RNAs that function to repress target messenger RNAs (mRNAs) through base-pairing interactions with 3'-untranslated regions (3'-UTRs). Recent studies indicate that miRNAs play important roles in the pathogenesis of human cancer, as well as in many fundamental biological processes. Two major unanswered questions about miRNAs are: (i) How is the expression of miRNAs regulated? and (ii) What are the messenger RNA targets regulated by miRNAs? Answering these questions will ultimately enable a deeper understanding of miRNA regulatory networks both in normal physiology and disease.
We believe that with the availability of genome sequence information and high-throughput miRNA sequence and expression data makes both of the questions above computationally accessible. Dr. Tewari's laboratory has used a massively parallel sequencing approach (i.e., '454 sequencing') to generate several hundred thousand sequence reads derived from miRNA cDNA libraries generated from normal ovarian epithelial cells and three different histologic subtypes of ovarian cancer (endometroid ovarian cancer, clear cell ovarian cancer and serous ovarian cancer). We will use these datasets, along with novel computational approaches being developed in Dr. Ruzzo's research group to address mechanisms of miRNA regulation and target mRNA selection in the context of ovarian cancer.
Specific Aim 1: To identify candidate regulatory elements involved in transcriptional regulation of known and novel microRNAs identified in normal and malignant ovarian epithelial tissues.
Specific Aim 2: To develop an improved microRNA target prediction method by incorporating messenger RNA secondary structure into the predictive model.
Specific Aim 3: To utilize the target prediction method described in Aim 2 to predict targets of known and novel microRNAs expressed in normal and malignant ovarian cancer tissue.
We envision that this interdisciplinary research project will provide insight into mechanisms of miRNA regulation in ovarian cancer, as well as provide an improved approach for prediction of mRNA targets of miRNAs. Some of the target genes predicted from this study are likely to be tumour suppressors and oncogenes relevant to ovarian cancer.