Concurrent overexpression of RET/PTC1 and TTF1 confers tumorigenicity to thyrocytes

Validation of Reference Genes for Normalization of Relative qRT-PCR Studies in Papillary Thyroid Carcinoma

Quantitative reverse transcription polymerase chain reaction (qRT-PCR) in thyroid tumors require accurate data normalization, however, there are no sufficient studies addressing the suitable reference genes for gene expression analysis in malignant and normal thyroid tissue specimens. The purpose of this study was to identify valid internal control genes for normalization of relative qRT-PCR studies in human papillary thyroid carcinoma tissue samples. The expression characteristics of 12 candidate reference genes (GAPDH, ACTB, HPRT1, TBP, B2M, PPIA, 18SrRNA, HMBS, GUSB, PGK1, RPLP0, and PGM1) were assessed by qRT-PCR in 45 thyroid tissue samples (15 papillary thyroid carcinoma, 15 paired normal tissues and 15 multinodular goiters). These twelve candidate reference genes were selected by a systematic literature search. GeNorm, NormFinder, and BestKeeper statistical algorithms were applied to determine the most stable reference genes. The three algorithms were in agreement in identifying GUSB and HPRT1 as the most stably expressed genes in all thyroid tumors investigated. According to the NormFinder software, the pair of genes including ‘GUSB and HPRT1’ or ‘GUSB and HMBS’ or ‘GUSB and PGM1’ were the best combinations for selection of pair reference genes. The optimal number of genes required for reliable normalization of qPCR data in thyroid tissues would be three according to calculations made by GeNorm algorithm. These results suggest that GUSB and HPRT1 are promising reference genes for normalization of relative qRT-PCR studies in papillary thyroid carcinoma.

Adipogenic Differentiation of Thyroid Cancer Cells Through the Pax8-PPARγ Fusion Protein Is Regulated by Thyroid Transcription Factor 1 (TTF-1)

A subset of thyroid carcinomas contains a t(2;3)(q13;p25) chromosomal translocation that fuses paired box gene 8 (PAX8) with the peroxisome proliferator-activated receptor γ gene (PPARG), resulting in expression of a PAX8-PPARγ fusion protein, PPFP. We previously generated a transgenic mouse model of PPFP thyroid carcinoma and showed that feeding the PPARγ agonist pioglitazone greatly decreased the size of the primary tumor and prevented metastatic disease in vivo The antitumor effect correlates with the fact that pioglitazone turns PPFP into a strongly PPARγ-like molecule, resulting in trans-differentiation of the thyroid cancer cells into adipocyte-like cells that lose malignant character as they become more differentiated. To further study this process, we performed cell culture experiments with thyrocytes from the PPFP mouse thyroid cancers. Our data show that pioglitazone induced cellular lipid accumulation and the expression of adipocyte marker genes in the cultured cells, and shRNA knockdown of PPFP eliminated this pioglitazone effect. In addition, we found that PPFP and thyroid transcription factor 1 (TTF-1) physically interact, and that these transcription factors bind near each other on numerous target genes. TTF-1 knockdown and overexpression studies showed that TTF-1 inhibits PPFP target gene expression and impairs adipogenic trans-differentiation. Surprisingly, pioglitazone repressed TTF-1 expression in PPFP-expressing thyrocytes. Our data indicate that TTF-1 interacts with PPFP to inhibit the pro-adipogenic response to pioglitazone, and that the ability of pioglitazone to decrease TTF-1 expression contributes to its pro-adipogenic action.

Consensus reference gene(s) for gene expression studies in human cancers: end of the tunnel visible?

BACKGROUND

Gene expression studies are increasingly used to provide valuable information on the diagnosis and prognosis of human cancers. Also, for in vitro and in vivo experimental cancer models gene expression studies are widely used. The complex algorithms of differential gene expression analyses require normalization of data against a reference or normalizer gene, or a set of such genes. For this purpose, mostly invariant housekeeping genes are used. Unfortunately, however, there are no consensus (housekeeping) genes that serve as reference or normalizer for different human cancers. In fact, scientists have employed a wide range of reference genes across different types of cancer for normalization of gene expression data. As a consequence, comparisons of these data and/or data harmonizations are difficult to perform and challenging. In addition, an inadequate choice for a reference gene may obscure genuine changes and/or result in erroneous gene expression data comparisons.

METHODS

In our effort to highlight the importance of selecting the most appropriate reference gene(s), we have screened the literature for gene expression studies published since the turn of the century on thirteen of the most prevalent human cancers worldwide.

CONCLUSIONS

Based on the analysis of the data at hand, we firstly recommend that in each study the suitability of candidate reference gene(s) should carefully be evaluated in order to yield reliable differential gene expression data. Secondly, we recommend that a combination of PPIA and either GAPDH, ACTB, HPRT and TBP, or appropriate combinations of two or three of these genes, should be employed in future studies, to ensure that results from different studies on different human cancers can be harmonized. This approach will ultimately increase the depth of our understanding of gene expression signatures across human cancers.