Identification of genes differentially expressed in benign versus malignant thyroid tumors

Nijaguna B. Prasad, Helina Somervell, Ralph P. Tufano, Alan P.B. Dackiw, Michael R. Marohn, Joseph A. Califano, Yongchun Wang, William H. Westra, Douglas P. Clark, Christopher B. Umbricht, Steven K. Libutti, Martha A. Zeiger

Research output: Contribution to journalArticlepeer-review

76 Scopus citations


Purpose: Although fine-needle aspiration biopsy is the most useful diagnostic tool in evaluating a thyroid nodule, preoperative diagnosis of thyroid nodules is frequently imprecise, with up to 30% of fine-needle aspiration biopsy cytology samples reported as "suspicious" or "indeterminate." Therefore, other adjuncts, such as molecular-based diagnostic approaches are needed in the preoperative distinction of these lesions. Experimental Design: In an attempt to identify diagnostic markers for the preoperative distinction of these lesions, we chose to study by microarray analysis the eight different thyroid tumor subtypes that can present a diagnostic challenge to the clinician. Results: Our microarray-based analysis of 94 thyroid tumors identified 75 genes that are differentially expressed between benign and malignant tumor subtypes. Of these, 33 were overexpressed and 42 were underexpressed in malignant compared with benign thyroid tumors. Statistical analysis of these genes, using nearest-neighbor classification, showed a 73% sensitivity and 82% specificity in predicting malignancy. Real-time reverse transcription-PCR validation for 12 of these genes was confirmatory. Western blot and immunohistochemical analyses of one of the genes, high mobility group AT-hook 2, further validated the microarray and real-time reverse transcription - PCR data. Conclusions: Our results suggest that these 12 genes could be useful in the development of a panel of markers to differentiate benign from malignant tumors and thus serve as an important first step in solving the clinical problem associated with suspicious thyroid lesions.

Original languageEnglish (US)
Pages (from-to)3327-3337
Number of pages11
JournalClinical Cancer Research
Issue number11
StatePublished - Jun 1 2008
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • General Medicine


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