Abstract 1984: A novel logistic regression model for microRNA microarray normalization

Abstract

Abstract MicroRNA (miRNA) is a set of newly discovered non-coding small RNA molecules. It regulates gene expression at the translational level through binding to the 3′-UTR of the target gene. Its significant effects have contributed to a number of critical biological events including cell proliferation, apoptosis, development as well as tumorigenesis. High throughput genomic discovery platforms (e.g. microarray) have been employed to evaluate the important roles of miRNAs by analyzing their expression profiling. However, because of the small total number of miRNAs and the absence of well known endorse controls, the traditional normalization methods for messenger RNA (mRNA) profiling analysis could not offer a suitable normalization solution for miRNA analysis. The establishment of new adaptive normalization methods has come to the forefront. In this study, Locked Nucleic Acid (LNA) based miRNA array was employed to profile miRNAs using colorectal cancer cell lines HCT116 under different drug treatments (5FU, CPT-11, and oxaliplatin). We randomly selected approximate 10% of total microRNAs from the microarray (37 miRNAs) and determined their expression patterns using quantitive real-time PCR (qRT-PCR), which has become the gold standard for relative gene expression analysis and one of the major validation methods after high throughput discovery. Then, a logistic regression model was built to normalize the miRNA expression data from miRNA array, by utilizing the results from qRT-PCR and some auxiliary variables. Twenty additional miRNAs were selected to validate the predicted results by the novel model. The result shows that the consistency between the qRT-PCR and array results was significantly improved with the new normalization method. Compared to other popularly used normalization methods, the logistic regression model efficiently calibrates the variance across arrays and improves miRNA microarray discovery accuracy. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 1984.