BioMed Central Genomics
Geisel School of Medicine
Whole-genome profiling of gene expression is a powerful tool for identifying cancer-associated genes. Genes differentially expressed between normal and tumorous tissues are usually considered to be cancer associated. We recently demonstrated that the analysis of interindividual variation in gene expression can be useful for identifying cancer associated genes. The goal of this study was to identify the best microarray data–derived predictor of known cancer associated genes. We found that the traditional approach of identifying cancer genes—identifying differentially expressed genes—is not very efficient. The analysis of interindividual variation of gene expression in tumor samples identifies cancer-associated genes more effectively. The results were consistent across 4 major types of cancer: breast, colorectal, lung, and prostate. We used recently reported cancer-associated genes (2011–2012) for validation and found that novel cancer-associated genes can be best identified by elevated variance of the gene expression in tumor samples.
Dartmouth Digital Commons Citation
Gorlov, Ivan P.; Yang, Ji-Yeon; Byun, Jinyoung; Logothetis, Christopher; Gorlova, Olga Y.; Do, Kim-Anh; and Amos, Christopher, "How to Get the Most from Microarray Data: Advice from Reverse Genomics" (2014). Dartmouth Scholarship. 601.