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Meta- and Reanalysis of Gene Expression Profiles of Hot and Cold Thyroid Nodules and Papillary Thyroid Carcinoma for Gene Groups

Markus Eszlinger¹, Magorzata Wiench¹, Barbara Jarzb, Knut Krohn, Martin Beck, Jürgen Läuter, Elbieta Gubaa, Krzysztof Fujarewicz, Andrzej wierniak and Ralf Paschke

III. Medical Department (M.E., K.K., R.P.), University of Leipzig, 04103 Leipzig, Germany; Department of Nuclear Medicine and Endocrine Oncology (M.W., B.J., E.G.), Maria Sklodowska-Curie Memorial Cancer Center and Institute of Oncology, Gliwice Branch, 44-100 Gliwice, Poland; Interdisciplinary Center for Clinical Research Leipzig (K.K.), 04103 Leipzig, Germany; Interdisciplinary Center for Bioinformatics Leipzig (M.B.), 04107 Leipzig, Germany; Institute of Biometrics and Medical Informatics (J.L.), University of Magdeburg, 39114 Magdeburg, Germany; and Institute of Automatic Control (K.F., A.S.), Silesian University of Technology, 44-100 Gliwice, Poland

Address all correspondence and requests for reprints to: Ralf Paschke, M.D., III. Medical Department, University of Leipzig, Philipp-Rosenthal-Strasse 27, D-04103 Leipzig, Germany. E-mail: pasr{at}medizin.uni-leipzig.de.

Context: There are an increasing number of studies analyzing gene expression profiles in various benign and malignant thyroid tumors. This creates the opportunity to validate results obtained from one microarray study with those from other data sets. This process requires rigorous methods for accurate comparison.

Objective: The ability to compare data sets derived from different Affymetrix GeneChip generations and the influence of intra- and interindividual comparisons of gene expression data were evaluated to build multigene classifiers of benign thyroid nodules to verify a previously proposed papillary thyroid carcinoma (PTC) classifier and to look for molecular pathways essential for PTC oncogenesis.

Methods: Gene expression profile data sets from autonomously functioning and cold thyroid nodules and from 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 intraindividual reference samples allowed the identification of subtle changes in the expression patterns of relevant signaling cascades, such as the MAPK pathway in PTC. Using an artificial intelligence approach, the autonomously functioning and cold thyroid nodule multigene classifiers were derived and evaluated by cross-comparisons.

Conclusion: We recommend defining classifiers within one generation of gene chips and subsequently checking them across different array generations. Using this approach, we have demonstrated the specificity of a previously reported 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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