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A bioinformatics meta-analysis of differentially expressed genes in colorectal cancer Chan, Simon Kit

Abstract

BACKGROUND: Elucidation of candidate colorectal cancer biomarkers often begins by comparing the expression profiles of cancerous and normal tissue by performing high throughput gene expression profiling. While many such studies have been performed, the resulting lists of differentially expressed genes tend to be inconsistent with each other, suggesting that there are some false positives and negatives. One logical solution to this problem is to determine the intersection of the lists of differentially expressed genes from independent studies. It is expected that genes that are biologically relevant to cancer tumorigenesis will be reported most often, while sporadically reported genes are due to the inherent biases and limitations of each of the profiling platforms used. However, the statistical significance of the observed intersection among many independent studies is usually not considered. PURPOSE: To address these issues, we developed a computational meta-analysis method that ranked differentially expressed genes based on the following criteria, which are presented in order of importance: the amount of intersection among studies, total tissue sample sizes, and average fold change in expression. We applied this meta-analysis method to 25 independent colorectal cancer profiling studies that compared cancer versus normal, adenoma versus normal, and cancer versus adenoma tissues. RESULTS: We observed that some genes were consistently reported as differentially expressed with a statistically significant frequency (P

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