Gene expression profiling of differentiated thyroid neoplasms: diagnostic and clinical implications.

The purpose of this research was to identify novel genes that can be targeted as diagnostic and clinical markers of differentiated thyroid tumors. Gene expression analysis using microarray platform was performed on 6 pathologically normal thyroid samples and 12 primary follicular and papillary thyroid neoplasms. Microarrays containing probes for 5,760 human full-length cDNAs were used for hybridization with total RNA from normal and tumor thyroid samples labeled with Cy3-dUTP and Cy5-dUTP, respectively. Scanned array images were recorded, and data analysis was performed. Selected sets of differentially expressed genes were analyzed using quantitative real-time reverse transcription-PCR for verification. We identified 155 genes that differentiate histologically normal thyroid tissues from benign and malignant thyroid neoplasms. Of these 75 genes were differentiated between follicular neoplasms (adenoma and carcinoma) and the follicular variant of papillary carcinoma. Purely follicular neoplasms (adenomas and carcinomas) shared many genetic profiles, and only 43 genes were distinctly different between these tumors. Hierarchical cluster analysis also differentiated conventional papillary carcinoma from its follicular variant and follicular tumors. The differentially expressed genes were composed of members of cell differentiation, adhesion, immune response, and proliferation associated pathways. Quantitative real-time reverse transcription-PCR analysis of selected genes corroborated the microarray expression results. Our study show the following: (1) differences in gene expression between tumor and nontumor bearing normal thyroid tissue can be identified, (2) a set of genes differentiate follicular neoplasm from follicular variant of papillary carcinoma, (3) follicular adenoma and carcinoma share many of the differentiated genes, and (4) gene expression differences identify conventional papillary carcinoma from the follicular variant.