Using Gene Expression Profiling to Differentiate Benign versus Malignant Thyroid Tumors

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Abstract
DNA microarrays allow quick and complete evaluation of a cell’s transcriptional activity. Expression genomics is very powerful in that it can generate expression data for a large number of genes simultaneously across multiple samples. In cancer research, an intriguing application of expression arrays includes assessing the molecular components of the neoplastic process and utilizing the data for cancer classification (Miller LD, et al. Cancer Cell 2002;2:353–61). Classification of human cancers into distinct groups based on their molecular profile rather than their histological appearance may prove to be more relevant to specific cancer diagnoses and cancer treatment regimes. Several attempts to formulate a consensus about classification and treatment of thyroid carcinoma based on standard histopathological analysis have resulted in published guidelines for diagnosis and initial disease management (Sherman SI. Lancet 2003;361:501–11). In the past few decades, no improvement has been made in the differential diagnosis of thyroid tumors by fine needle aspiration biopsy, specifically suspicious or indeterminate thyroid lesions, suggesting that a new approach to this should be explored. Therefore, in this study, we developed a gene expression approach to diagnose benign versus malignant thyroid lesions in 73 patients with thyroid tumors. We successfully built a 10 and 6 gene model able to differentiate benign versus malignant thyroid tumors. Our results support the premise that a molecular classification system for thyroid tumors is possible, and this in turn may provide a more accurate diagnostic tool for the clinician managing patients with suspicious thyroid lesions.