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Gene expression analysis of autonomously functioning thyroid nodules (AFTNs) and cold thyroid nodules (CTNs)


Markus Eszlinger, Ralf Paschke
Clinic III, Medical Faculty
University of Leipzig

Knut Krohn
IZKF
Medical Faculty
University of Leipzig

Cooperation:

Dirk Hasenclever, Ingo Röder, Ernst Schuster
Institute for Medical Informatics, Statistics and Epidemiology
University of Leipzig

Friedemann Horn
Institute of Clinical Immunology and Transfusion Medicine
University Hospital Leipzig

Jürgen Läuter, Toralf Kirsten, Jörg Lange
Interdisciplinary Centre for Bioinformatics
University of Leipzig


Background: Although prevalently defined by constitutively activating TSHR mutations (>60% of all cases) autonomously functioning thyroid nodules (AFTNs) are clinically very heterogeneous. No genotype phenotype correlation has been established for clinical characteristics such as the time span from subclinical to overt hyperthyroidism, the extent of nodular growth or the severity of hyperthyroidism. It is therefore very likely that unknown signaling events contribute to the etiology of AFTNs and the clinical phenotype. Such events could be the coupling of the TSHR to further downstream cascades in addition to cAMP and IP or a shift of the coupling specificity or other TSHR independent alterations in the signal transduction of the thyroid.
In contrast to the molecular etiology of AFTNs the molecular cause of cold thyroid nodules (CTNs), their benign, functional inactive counterparts is so far largely unknown. Because of the partially dedifferentiated phenotype of cold thyroid nodules, alterations in signaling cascades that favor proliferation but not differentiation are likely candidates for tumor induction and progression.
However, data about altered signal transduction or changed expression of oncogenes, tumor suppressors, or signaling molecules in thyroid nodules are rare. Nevertheless, differentially expressed genes in nodular versus surrounding thyroid tissue could present a molecular signature of the development of both, AFTNs and CTNs.

Results: Our data show distinct differences of gene expression in AFTNs with and without a TSHR mutation and in their corresponding normal surrounding tissue (ST). Particularly the TGF-β signaling cascade is characterized by strong changes in its pattern of gene expression between AFTNs and their ST which might be due to the constitutively activated cAMP cascade (Figure 1).

Figure 1. Diagram of the TGF-ß signaling cascade from GenMAPP. Red colored boxes indicate significantly increased expression, whereas green colored boxes indicate a significantly decreased expression in AFTNs in comparison to STs. Besides the gene boxes the P value is given.


Moreover, based on the gene expression data of the AFTNs, which showed an increased expression of the sialyltransferase (SIAT)1 in AFTNs, we could demonstrate that the transfer of sialic acid improves and prolongs the cell-surface expression of the TSHR. This might be the scaffold of a new regulatory loop for TSH receptor activity.
In CTNs we found significant differences in the expression pattern of several cell cycle associated genes (figure 2) which are clearly in line with our previous findings demonstrating an increased proliferation in CTNs. Moreover, the analysis of gene sets revealed significant differences in the G-protein signaling that is characterized by an increased PKC and Gqα expression in CTNs. These findings are of special interest since it is known, that a long term PKC stimulation of thyroid cells in culture causes a general loss of thyroid-specific functions (e.g. loss of iodide transport and thyroglobulin iodination). However, further experiments have to evaluate the initiating event for the increased PKC signaling in CTNs and the relevance of increased Gq- and PKC mRNA expression for the development of CTNs.

Figure 2. Diagram of the cell cycle depicting the genes with the most significant differential expression between CTNs and their normal surrounding tissue according to the Westfall-Young procedure applied to gene sets. Red lines indicate an increased gene expression in CTNs, whereas green lines indicate a decreased gene expression in CTNs. Arrows indicate genes involved in cell cycle progression and circles indicate genes involved in cell cycle retardation.


Summary and Outlook: During the last years the gene expression profiles of both, malignant thyroid tumors (i.e. follicular and papillary thyroid carcinoma), and benign thyroid tumors (i.e. AFTNs and CTNs) were analyzed using microarrays. Some investigations tried to answer (patho)physiologic questions or aimed to elucidate the tumor’s molecular etiology, whereas other investigations looked primarily for genetic markers that could improve the differential diagnosis of thyroid tumors. In addition, there are important methodological differences between the reports (e.g. different GeneChip generations, different reference tissues). Moreover, different studies use different data analysis methods, which vary from simple empiric filters to sophisticated statistic algorithms. Therefore, the topic of further studies will comprise data integration and metaanalysis of available microarray data.

Publications:
Eszlinger M, Wiench M, Jarzab B, Krohn K, Beck M, Läuter J, Gubala E, Fujarewicz K, Swierniak A & Paschke R. 2006
Meta- and reanalysis of gene expression profiles of hot and cold thyroid nodules and papillary thyroid carcinoma for gene groups.
J Clin Endocrinol Metab In press.
Eszlinger M, Krohn K, Beck M, Kipling D, Forbes-Robertson S, Läuter J, Tonjes A, Wynford-Thomas D & Paschke R. 2006
Comparison of differential gene expression of hot and cold thyroid nodules with primary epithelial cell culture models by investigation of co-regulated gene sets. Biochim Biophys Acta In press.
Eszlinger M, Krohn K, Berger K, Läuter J, Kropf S, Beck M, Fuhrer D & Paschke R. 2005
Gene expression analysis reveals evidence for increased expression of cell cycle-associated genes and Gq-protein-protein kinase C signaling in cold thyroid nodules.
J Clin Endocrinol Metab 90 1163-1170.
Krohn K, Eszlinger M, Paschke R, Roeder I & Schuster E. 2005
Increased power of microarray analysis by use of an algorithm based on a multivariate procedure.
Bioinformatics 21 3530-3534.
Läuter J, Glimm E & Eszlinger M. 2005
Search for relevant sets of variables in a high-dimensional setup keeping the familywise error rate.
Statistica Neerlandica 59 298-312.
Eszlinger M, Krohn K, Frenzel R, Kropf S, Tonjes A & Paschke R. 2004
Gene expression analysis reveals evidence for inactivation of the TGF-beta signaling cascade in autonomously functioning thyroid nodules.
Oncogene 23 795-804.
Kropf S, Läuter J, Eszlinger M, Krohn K & Paschke R. 2004
Nonparametric multiple test procedures with data-driven order of hypotheses and with weigthed hypotheses.
Journal of Statistical Planning and Interference 125 31-47.

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