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Submitted on July 20, 2005
Accepted on January 3, 2006
III. Medical Department, University of Leipzig; Ph.-Rosenthal-Str. 27, D-04103 Leipzig, Germany; Department of Nuclear Medicine and Endocrine Oncology, Maria Sklodowska-Curie Memorial Cancer Center and Institute of Oncology, Gliwice Branch, Poland; Interdisciplinary Center for Clinical Research Leipzig; Interdisciplinary Center for Bioinformatics Leipzig; Institute of Biometrics and Medical Informatics, University of Magdeburg; Institute of Automatic Control, Silesian University of Technology, Gliwice, Poland
* To whom correspondence should be addressed. E-mail: pasr{at}medizin.uni-leipzig.de.
Context: There is an increasing number of studies analyzing gene expression profiles in various benign and malignant thyroid pathologies. This opens the opportunity to validate results obtained in one microarray study on other data sets but requires careful methods for comparison.
Objective: Therefore, the ability to compare data sets derived from different Affymetrix GeneChip generations and the influence of intra and inter-individual comparisons of gene expression data were evaluated to build multigene classifiers of benign thyroid nodules, to verify a previously proposed papillary thyroid cancer (PTC) classifier and to look for molecular pathways essential for PTC.
Methods: The data sets of autonomously functioning thyroid nodules (AFTNs) and cold thyroid nodules (CTNs) and of PTC were analyzed by Support Vector Machines. GenMAPP analysis was used for PTC data analysis to examine the expression patterns of biologically relevant gene sets.
Results: Only intra-individual reference samples allowed to identify subtle changes in the expression patterns of signaling cascades, as we were able to detect in case of the MAPK pathway in PTC. By machine learning approach the AFTN and CTN multigene classifiers were obtained and evaluated by cross-comparisons.
Conclusion: We recommend to build classifiers only within one generation of gene chips and to subsequently check them across the different array generations analyzed. By this approach we demonstrate the good specificity of the previously published molecular PTC classifier on an independent collection of benign tumors. Moreover, we propose multigene classifiers for different types of benign thyroid nodules.
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