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Gene Expression Profile of Papillary Thyroid Cancer: Sources of Variability and Diagnostic Implications

Barbara Jarzb¹, Magorzata Wiench¹, Krzysztof Fujarewicz⁵, Krzysztof Simek⁵, Micha Jarzb², Magorzata Oczko-Wojciechowska¹, Jan Woch³, Agnieszka Czarniecka³, Ewa Chmielik⁴, Dariusz Lange⁴, Agnieszka Pawlaczek¹, Sylwia Szpak¹, Elbieta Gubaa¹ and Andrzej wierniak⁵

Departments of ¹ Nuclear Medicine and Endocrine Oncology, ² Tumor Biology, ³ Oncological Surgery, and ⁴ Tumor Pathology, Maria Skodowska-Curie Memorial Cancer Center and Institute of Oncology, Gliwice Branch; and ⁵ Automatic Control, Silesian University of Technology, Gliwice, Poland

Requests for reprints: Barbara Jarzb, Department of Nuclear Medicine and Endocrine Oncology, Maria Skodowska-Curie Memorial Cancer Center and Institute of Oncology, Gliwice Branch, Wybrzee Armii Krajowej 15, 44-100 Gliwice, Poland. Phone: 48-32-2789301; Fax: 48-32-2789325; E-mail: bjarzab{at}io.gliwice.pl.

The study looked for an optimal set of genes differentiating between papillary thyroid cancer (PTC) and normal thyroid tissue and assessed the sources of variability in gene expression profiles. The analysis was done by oligonucleotide microarrays (GeneChip HG-U133A) in 50 tissue samples taken intraoperatively from 33 patients (23 PTC patients and 10 patients with other thyroid disease). In the initial group of 16 PTC and 16 normal samples, we assessed the sources of variability in the gene expression profile by singular value decomposition which specified three major patterns of variability. The first and the most distinct mode grouped transcripts differentiating between tumor and normal tissues. Two consecutive modes contained a large proportion of immunity-related genes. To generate a multigene classifier for tumor-normal difference, we used support vector machines-based technique (recursive feature replacement). It included the following 19 genes: DPP4, GJB3, ST14, SERPINA1, LRP4, MET, EVA1, SPUVE, LGALS3, HBB, MKRN2, MRC2, IGSF1, KIAA0830, RXRG, P4HA2, CDH3, IL13RA1, and MTMR4, and correctly discriminated 17 of 18 additional PTC/normal thyroid samples and all 16 samples published in a previous microarray study. Selected novel genes (LRP4, EVA1, TMPRSS4, QPCT, and SLC34A2) were confirmed by Q-PCR.Our results prove that the gene expression signal of PTC is easily detectable even when cancer cells do not prevail over tumor stroma. We indicate and separate the confounding variability related to the immune response. Finally, we propose a potent molecular classifier able to discriminate between PTC and nonmalignant thyroid in more than 90% of investigated samples.

Key Words: papillary thyroid cancer • gene expression profile • oligonucleotide microarrays • Support Vector Machines • Singular Value Decomposition

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