| Summary: | 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 <.0001) in the cancer versus normal and adenoma versus normal comparisons, but not in the cancer versus adenoma comparison. We performed a review of some of the high ranking candidates and determined that some have previously been shown to have diagnostic and/or prognostic utility in colorectal cancer. More interestingly, the meta-analysis method also identified genes that had yet to be tested and validated as biomarkers. Thus, these candidates are currently being validated at the protein level on colorectal tissue microarrays.
CONCLUSION: Our meta-analysis method identified genes that were consistently reported as differentially expressed. Besides identifying new biomarker candidates, our meta-analysis method also provides another filter to remove false positive genes from further consideration. In conclusion, the genes presented here will aid in the identification of highly sensitive and specific biomarkers in colorectal cancer.
|
|---|
