B.S.S. was a predoctoral fellow from FAPESP.
Class distinction between follicular adenomas and follicular carcinomas of the thyroid gland on the basis of their signature expression
Article first published online: 24 MAR 2006
Copyright © 2006 American Cancer Society
Volume 106, Issue 9, pages 1891–1900, 1 May 2006
How to Cite
Stolf, B. S., Santos, M. M. S., Simao, D. F., Diaz, J. P., Cristo, E. B., Hirata, R., Curado, M. P., Neves, E. J., Kowalski, L. P. and Carvalho, A. F. (2006), Class distinction between follicular adenomas and follicular carcinomas of the thyroid gland on the basis of their signature expression. Cancer, 106: 1891–1900. doi: 10.1002/cncr.21826
- Issue published online: 18 APR 2006
- Article first published online: 24 MAR 2006
- Manuscript Accepted: 23 NOV 2005
- Manuscript Revised: 20 OCT 2005
- Manuscript Received: 9 SEP 2005
- National Council for Scientific and Technological Development
- Foundation to Support Research in the State of Sao Paulo (FAPESP). Grant Number: 98/14335-2
- Centers of Research, Innovation, and Diffusion. Grant Numbers: 99/11962-9, 99/07390-0
- follicular carcinoma;
- molecular classifiers;
Nodules of the thyroid gland are observed frequently in patients who undergo ultrasound studies. The majority of these nodules are benign, corresponding to goiters or adenomas, and only a small fraction corresponds to carcinomas. Among thyroid tumors, the diagnosis of follicular adenocarcinomas by preoperative fine-needle aspiration biopsy is a major challenge, because it requires inspection of the entire capsule to differentiate it from adenoma. Consequently, large numbers of patients undergo unnecessary thyroidectomy.
Using data from gene expression analysis, the authors applied Fisher linear discriminant analysis and searched for expression signatures of individual samples of adenomas and follicular carcinomas that could be used as molecular classifiers for the precise classification of malignant and nonmalignant lesions.
Fourteen trios of genes were described that fulfilled the criteria for the correct classification of 100% of samples. The robustness of these trios was verified by using leave-1-out cross-validation and bootstrap analyses. The results demonstrated that, by combining trios, better classifiers could be generated that correctly classified >92% of samples.
The strategy of classifiers based on individual signatures was a useful strategy for distinguishing between samples with very similar expression profiles. Cancer 2006. © 2006 American Cancer Society.