Selection and validation of differentially expressed genes in head and neck cancer

Abstract

We applied a robust combinatorial (multi-test) approach to microarray data to identify genes consistently up- or down-regulated in head and neck squamous cell carcinoma (HNSCC). RNA was extracted from 22 paired samples of HNSCC and normal tissue from the same donors and hybridized to the Affymetrix U95A chip. Forty-two differentially expressed probe sets (representing 38 genes and one expressed sequence tag) satisfied all statistical tests of significance and were selected for further validation. Selected probe sets were validated by hierarchical clustering, multiple probe set concordance, and target-subunit agreement. In addition, real-time PCR analysis of 8 representative (randomly selected from 38) genes performed on both microarray-tested and independently obtained samples correlated well with the microarray data. The genes identified and validated by this method were in comparatively good agreement with other rigorous HNSCC microarray studies. From this study, we conclude that combinatorial analysis of microarray data is a promising technique for identifying differentially expressed genes with few false positives.