Tissue-specific gene expression of head and neck squamous cell carcinoma in vivo by complementary DNA microarray analysis.

OBJECTIVES: To identify distinct gene expression profiles of human head and neck squamous cell carcinomas (HNSCCAs) using complementary DNA (cDNA) microarray analysis and to create a preliminary, comprehensive database of HNSCCA gene expression.

PATIENTS AND METHODS: Nine patients with histologically confirmed HNSCCAs, staged according to the American Joint Committee on Cancer, were enrolled. The HNSCCA tumor tissue and normal mucosal tissue were harvested at the time of surgery. A cDNA library was constructed from the paired fresh-frozen human surgical specimens of HNSCCAs and nonmalignant epithelial tissues. Biotinylated RNA was transcribed from the cDNA library and hybridized to high-density microarrays containing approximately 12 000 human genes. Altered gene expression of HNSCCAs was identified by comparison to corresponding normal mucosal tissues after a bayesian statistical analysis of variance. Results were analyzed using the gene database of the National Institutes of Health. Hierarchical clustering of the genomic data sets was determined by similarity metrics based on Pearson correlation.

RESULTS: Hierarchical clustering analysis revealed that the gene expression profiles obtained from the nonselected panel of 12 000 genes could distinguish the tumors from nonmalignant tissues. Gene expression changes were reproducibly observed in 227 genes representing previously identified chemokines, tumor suppressors, differentiation markers, matrix molecules, membrane receptors, and transcription factors that correlated with neoplasia, including 46 previously uncharacterized genes. Moreover, significant expression of the collagen type XI alpha1 gene and a novel gene was reproducibly observed in all 9 tumors, whereas these genes were virtually undetectable in their corresponding, adjacent nonmalignant tissues.

CONCLUSIONS: Complementary DNA microarray analysis of human HNSCCAs has produced a preliminary, comprehensive database of tumor-specific gene expression profiles and provided important insights into modeling gene expression changes implicated in carcinogenesis. A large-scale analysis of gene expression carries the future potential of identifying sensitive molecular markers for early tumor detection, prognosis, and novel targets for interceptive therapeutics.