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Novel analyses improve identification of cancer-associated genes from microarray data
Date:5/2/2014

Dartmouth Institute for Quantitative Biomedical Sciences (iQBS) researchers developed a new gene expression analysis approach for identifying cancer genes. The paper entitled, "How to get the most from microarray data: advice from reverse genomics," was published online March 21, 2014 in BMC Genomics. The study results challenge the current paradigm of microarray data analysis and suggest that the new method may improve identification of cancer-associated genes.

Typical microarray-based gene expression analyses compare gene expression in adjacent normal and cancerous tissues. In these analyses, genes with strong statistical differences in expression are identified. However, many genes are aberrantly expressed in tumors as a byproduct of tumorigenesis. These "passenger" genes are differentially expressed between normal and tumor tissues, but they are not "drivers" of tumorigenesis. Therefore, better analytical approaches that enrich the list of candidate genes with authentic cancer-associated "driver" genes are needed.

Lead authors of the study, Ivan P. Gorlov, Ph.D., Associate Professor of Community and Family Medicine and Christopher Amos, Ph.D., Professor of Community and Family Medicine and Director of the Center for Genomic Medicine described a new method to analyze microarray data. The research team demonstrated that ranking genes based on inter-tumor variation in gene expression outperforms traditional analytical approaches. The results were consistent across 4 major cancer types: breast, colorectal, lung, and prostate cancer.

The team used text-mining to identify genes known to be associated with breast, colorectal, lung, and prostate cancers. Then, they estimated enrichment factors by determining how frequently those known cancer-associated genes occurred among the top gene candidates identified by different analysis methods. The enrichment factor described how frequently cancer associated genes were identified co
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Contact: Derik Hertel
derik.hertel@dartmouth.edu
603-650-1211
The Geisel School of Medicine at Dartmouth
Source:Eurekalert

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