Class distinction between follicular adenomas and follicular carcinomas of the thyroid gland on the basis of their signature expression
| Autor: | Elier B. Cristo, Maria P. Curado, Roberto Hirata, Luiz P. Kowalski, Juan P. Diaz, Daniel F. Simao, Beatriz S. Stolf, Eduardo J. Neves, Mariana M. S. Santos, Alex F. Carvalho |
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| Rok vydání: | 2006 |
| Předmět: |
Adenoma
Cancer Research Pathology medicine.medical_specialty business.industry medicine.medical_treatment Gene Expression Profiling Thyroid Thyroidectomy Cancer ANÁLISE DISCRIMINANTE medicine.disease Gene expression profiling Thyroid carcinoma medicine.anatomical_structure Oncology Follicular phase Adenocarcinoma Follicular Medicine Humans Thyroid Neoplasms business Thyroid cancer |
| Zdroj: | Repositório Institucional da USP (Biblioteca Digital da Produção Intelectual) Universidade de São Paulo (USP) instacron:USP |
| Popis: | BACKGROUND 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. METHODS 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. RESULTS 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. CONCLUSIONS 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. |
| Databáze: | OpenAIRE |
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