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Distinguishing between follicular thyroid cancer (FTC) and follicular thyroid adenoma (FTA) constitutes a long-standing diagnostic problem resulting in equivocal histopathological diagnoses. There is therefore a need for additional molecular markers. To identify molecular differences between FTC and FTA, we analyzed the gene expression microarray data of 52 follicular neoplasms. We also performed a meta-analysis involving 14 studies employing high throughput methods (365 follicular neoplasms analyzed). Based on these two analyses, we selected 18 genes differentially expressed between FTA and FTC. We validated them by quantitative real-time polymerase chain reaction (qRT-PCR) in an independent set of 71 follicular neoplasms from formaldehyde-fixed paraffin embedded (FFPE) tissue material. We confirmed differential expression for 7 genes ( CPQ , PLVAP , TFF3 , ACVRL1 , ZFYVE21 , FAM189A2 , and CLEC3B ). Finally, we created a classifier that distinguished between FTC and FTA with an accuracy of 78%, sensitivity of 76%, and specificity of 80%, based on the expression of 4 genes ( CPQ , PLVAP , TFF3 , ACVRL1 ). In our study, we have demonstrated that meta-analysis is a valuable method for selecting possible molecular markers. Based on our results, we conclude that there might exist a plausible limit of gene classifier accuracy of approximately 80%, when follicular tumors are discriminated based on formalin-fixed postoperative material., Competing Interests: The authors declare no conflict of interest.

Medullary thyroid cancer (MTC) can be caused by germline mutations of the RET proto-oncogene or occurs as a sporadic form. It is well known that RET mutations affecting the cysteine-rich region of the protein (MEN2A-like mutations) are correlated with different phenotypes than those in the kinase domain (MEN2B-like mutations). Our aim was to analyse the whole-gene expression profile of MTC with regard to the type of RET gene mutation and the cancer genetic background (hereditary vs sporadic). We studied 86 MTC samples. We demonstrated that there were no distinct differences in the gene expression profiles of hereditary and sporadic MTCs. This suggests a homogeneous nature of MTC. We also noticed that the site of the RET gene mutation slightly influenced the gene expression profile of MTC. We found a significant association between the localization of RET mutations and the expression of three genes: NNAT (suggested to be a tumour suppressor gene), CDC14B (involved in cell cycle control) and NTRK3 (tyrosine receptor kinase that undergoes rearrangement in papillary thyroid cancer). This study suggests that these genes are significantly deregulated in tumours with MEN2A-like and MEN2B-like mutations; however, further investigations are necessary to demonstrate any clinical impact of these findings., Competing Interests: The authors declare no competing financial interests.

Purpose: Following the nuclear accidents in Chernobyl and later in Fukushima, the nuclear community has been faced with important issues concerning how to search for and diagnose biological consequences of low-dose internal radiation contamination. Although after the Chernobyl accident an increase in childhood papillary thyroid cancer (PTC) was observed, it is still not clear whether the molecular biology of PTCs associated with low-dose radiation exposure differs from that of sporadic PTC., Methods: We investigated tissue samples from 65 children/young adults with PTC using DNA microarray (Affymetrix, Human Genome U133 2.0 Plus) with the aim of identifying molecular differences between radiation-induced (exposed to Chernobyl radiation, ECR) and sporadic PTC. All participants were resident in the same region so that confounding factors related to genetics or environment were minimized., Results: There were small but significant differences in the gene expression profiles between ECR and non-ECR PTC (global test, p < 0.01), with 300 differently expressed probe sets (p < 0.001) corresponding to 239 genes. Multifactorial analysis of variance showed that besides radiation exposure history, the BRAF mutation exhibited independent effects on the PTC expression profile; the histological subset and patient age at diagnosis had negligible effects. Ten genes (PPME1, HDAC11, SOCS7, CIC, THRA, ERBB2, PPP1R9A, HDGF, RAD51AP1, and CDK1) from the 19 investigated with quantitative RT-PCR were confirmed as being associated with radiation exposure in an independent, validation set of samples., Conclusion: Significant, but subtle, differences in gene expression in the post-Chernobyl PTC are associated with previous low-dose radiation exposure.

Background: Differential diagnosis between malignant follicular thyroid cancer (FTC) and benign follicular thyroid adenoma (FTA) is a great challenge for even an experienced pathologist and requires special effort. Molecular markers may potentially support a differential diagnosis between FTC and FTA in postoperative specimens. The purpose of this study was to derive molecular support for differential post-operative diagnosis, in the form of a simple multigene mRNA-based classifier that would differentiate between FTC and FTA tissue samples., Methods: A molecular classifier was created based on a combined analysis of two microarray datasets (using 66 thyroid samples). The performance of the classifier was assessed using an independent dataset comprising 71 formalin-fixed paraffin-embedded (FFPE) samples (31 FTC and 40 FTA), which were analysed by quantitative real-time PCR (qPCR). In addition, three other microarray datasets (62 samples) were used to confirm the utility of the classifier., Results: Five of 8 genes selected from training datasets (ELMO1, EMCN, ITIH5, KCNAB1, SLCO2A1) were amplified by qPCR in FFPE material from an independent sample set. Three other genes did not amplify in FFPE material, probably due to low abundance. All 5 analysed genes were downregulated in FTC compared to FTA. The sensitivity and specificity of the 5-gene classifier tested on the FFPE dataset were 71% and 72%, respectively., Conclusions: The proposed approach could support histopathological examination: 5-gene classifier may aid in molecular discrimination between FTC and FTA in FFPE material.

Introduction: Causes of treatment failure in acute lymphoblastic leukemia (ALL) are still poorly understood. Microarray technology gives new possibilities for the analysis of the biology of leukemias. We hypothesize that drug sensitivity in pediatric ALL is driven by specific molecular mechanisms that correlate with gene expression profiles assessed by microarray analysis., Objective: The aim of the study was to determine the ex vivo resistance profiles of 20 antileukemic drugs and gene expression profiles, with relation to response to initial therapy., Patients and Methods: Lymphoblasts were analyzed after bone marrow biopsy was obtained from 56 patients. The profile of in vitro resistance to drugs was determined in the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-tetrazoliumbromide (MTT) cytotoxicity assay. High-quality total RNA was prepared and hybridized to oligonucleotide arrays HG-U133A 2.0 Chip (Affymetrix). The expression of selected genes was tested by qualitative reverse transcription polymerase chain reaction (qRT-PCR)., Results and Conclusions: The exposure of leukemic blasts to drugs initiates a complex cellular response, which reflects global changes in gene expression. Changes in the expression of several genes are highly correlated with drug resistance.

This review describes the gene expression profile changes associated with the presence of different mutations that contribute to thyroid cell carcinogenesis. The results are discussed in the context of thyroid cancer biology and of the implications for disease prognosis, while the diagnostic aspect has been omitted. For papillary thyroid cancer (PTC), the most characteristic gene expression profile is associated with the presence of BRAF mutation. BRAF-associated PTC differ profoundly from RET/PTC or RAS-associated cancers. Simultaneously, they retain many characteristic gene expression features common for all PTCs, induced by the alternative mutations activating MAPK pathway. Although the difference between papillary and follicular thyroid cancer (FTC) is significant at the gene expression profile level, surprisingly, the RAS-related signature of FTC is not well specified. PAX8/peroxisome proliferator-activated receptor γ (PPARγ) rearrangements, which occur in FTC as an alternative to the RAS mutation, are associated with specific changes in gene expression. Furthermore, the difference between well-differentiated thyroid cancers and poorly differentiated and anaplastic thyroid cancers is mainly a reflection of tumor degree of differentiation and may not be attributed to the presence of characteristic mutations., (© 2011 Society for Endocrinology)

Background: The molecular mechanisms of cell cycle exit are poorly understood. Studies on lymphocytes at cell cycle exit after growth factor deprivation have predominantly focused on the initiation of apoptosis. We aimed to study gene expression profile of primary and immortalised IL-2-dependent human T cells forced to exit the cell cycle by growth factor withdrawal, before apoptosis could be evidenced., Results: By the Affymetrix microarrays HG-U133 2.0 Plus, 53 genes were distinguished as differentially expressed before and soon after IL-2 deprivation. Among those, PIM1, BCL2, IL-8, HBEGF, DUSP6, OSM, CISH, SOCS2, SOCS3, LIF and IL13 were down-regulated and RPS24, SQSTM1, TMEM1, LRRC8D, ECOP, YY1AP1, C1orf63, ASAH1, SLC25A46 and MIA3 were up-regulated. Genes linked to transcription, cell cycle, cell growth, proliferation and differentiation, cell adhesion, and immune functions were found to be overrepresented within the set of the differentially expressed genes., Conclusion: Cell cycle exit of the growth factor-deprived T lymphocytes is characterised by a signature of differentially expressed genes. A coordinate repression of a set of genes known to be induced during T cell activation is observed. However, growth arrest following exit from the cell cycle is actively controlled by several up-regulated genes that enforce the non-dividing state. The identification of genes involved in cell cycle exit and quiescence provides new hints for further studies on the molecular mechanisms regulating the non-dividing state of a cell, the mechanisms closely related to cancer development and to many biological processes.

The paper is devoted to two questions: whether distinction of causes versus effects of neoplasia leaves a signature in the cancer versus normal gene expression profiles and whether roles of genes, "causes" or "effects", can be inferred from repeated measurements of gene expressions. We model joint probability distributions of logarithms of gene expressions with the use of Bayesian networks (BN). Fitting our models to real data confirms that our BN models have the ability to explain some aspects of observational evidence from DNA microarray experiments. Effects of neoplastic transformation are well seen among genes with the highest power to differentiate between normal and cancer cells. Likelihoods of BNs depend on the biological role of selected genes, defined by Gene Ontology. Also predictions of our BN models are coherent with the set of putative causes and effects constructed based on our data set of papillary thyroid cancer.

Microarray technology has become a powerful tool to analyze the gene expression of tens of thousands of genes simultaneously. Microarray-based gene expression profiles are available for malignant thyroid tumors (i.e., follicular thyroid carcinoma, and papillary thyroid carcinoma), and for benign thyroid tumors (such as autonomously functioning thyroid nodules and cold thyroid nodules). In general, the two main foci of microarray investigations are improved understanding of the pathophysiology/molecular etiology of thyroid neoplasia and the detection of genetic markers that could improve the differential diagnosis of thyroid tumors. Their results revealed new features, not known from one-gene studies. Simultaneously, the increasing number of microarray analyses of different thyroid pathologies raises the demand to efficiently compare the data. However, the use of different microarray platforms complicates cross-analysis. In addition, there are other important differences between these studies: 1) some studies use intraindividual comparisons, whereas other studies perform interindividual comparisons; 2) the reference tissue is defined as strictly nonnodular healthy tissue or also contains benign lesions such as goiter, follicular adenoma, and hyperplastic nodules in some studies; and 3) the widely used Affymetrix GeneChip platform comprises several GeneChip generations that are only partially compatible. Moreover, the different studies are characterized by strong differences in data analysis methods, which vary from simple empiric filters to sophisticated statistic algorithms. Therefore, this review summarizes and compares the different published reports in the context of their study design. It also illustrates perspectives and solutions for data set integration and meta-analysis, as well as the possibilities to combine array analysis with other genetic approaches.

Introduction: Medullary thyroid carcinoma occurs both as a sporadic and a familial disease. Inherited MTC (iMTC) patients usually exhibit better prognosis than patients with sporadic form of MTC (sMTC), however, in both subtypes the outcome is unpredictable. No molecular markers contributing to the prognosis or predicting the type of therapy have been introduced to clinical practice until now. The aim of this study was to analyze gene expression pattern of MTC by high density oligonucleotide microarray., Material and Methods: 24 samples were studied: 12 MTC and 12 corresponding normal tissues, (Affymetrix HG-U 133A). Among MTC patients there were half inherited cases and half sporadic ones., Results: First, the differences between MTC and thyroid tissue were analyzed by Singular Value Decomposition (SVD) which indicated three main modes determining the variability of gene expression profile: the first two were related to the tumor/normal tissue difference and the third one was related to the immune response. The characteristic expression pattern, beside of numerous changes within cancer- related genes, included many up-regulated genes specific for thyroid C cells. Further analysis of the second component revealed two subgroups of MTC, but the subdivision was not related to the iMTC/sMTC difference. Recursive Feature Replacement (RFR) confirmed the very similar expression profile in both forms of MTC. With subsequent ANOVA analysis some genes with differential expression could be specified, among them monoamine oxidase B (MAOB) and gamma-aminobutyric acid receptor (GABRR1) which were consistently up-regulated in sMTC. In contrary, some genes involved in regulation of cell proliferation: opioid growth factor receptor(OGFR) and synaptotagmin V (SYT 5) were up-regulated in iMTC., Conclusions: The obtained data indicate a very similar gene expression pattern in inherited and sporadic MTC. Minor differences in their molecular profile require further analysis.

In our study we present chosen elements of microarray analysis of gene expression profile in papillary thyroid cancer. The study group included 16 papillary thyroid cancer tissues and 16 corresponding normal tissues. Samples were analyzed on high density oligonucleotide microarrays (GeneChip HG-U133A) which contain 22.000 genes. 110 genes, which had significant changed expression, were selected by MAS 5.0 program. 3 genes were chosen to the deeper analysis: dipeptidylpeptidase 4 (DPP4), fibronectin 1 (FN1), tissue inhibitor of metalloproteinase 1 (TIMP1). DPP4-RNA were absent in normal tissue while in cancer tissue it was detected in large amount. FN1 and TIMP1 expression were detected in normal tissue but markedly increased in papillary thyroid cancer. Among these 3 genes DPP4 seems to be the best molecular marker for papillary thyroid cancer.

The paper presents gene expression profile analysis with DNA microarrays and compares two core technological platforms used for this purpose - high density oligonucleotide microarrays and cDNA microarrays. With this background recent results of papillary thyroid carcinoma analysis with DNA microarrays are presented.

Molecular mechanisms of distant metastases (M1) in papillary thyroid cancer (PTC) are poorly understood. We attempted to analyze the gene expression profile in PTC primary tumors to seek the genes associated with M1 status and characterize their molecular function. One hundred and twenty-three patients, including 36 M1 cases, were subjected to transcriptome oligonucleotide microarray analyses: (set A—U133, set B—HG 1.0 ST) at transcript and gene group level (limma, gene set enrichment analysis (GSEA)). An additional independent set of 63 PTCs, including 9 M1 cases, was used to validate results by qPCR. The analysis on dataset A detected eleven transcripts showing significant differences in expression between metastatic and non-metastatic PTC. These genes were validated on microarray dataset B. The differential expression was positively confirmed for only two genes: IGFBP3, (most significant) and ECM1. However, when analyzed on an independent dataset by qPCR, the IGFBP3 gene showed no differences in expression. Gene group analysis showed differences mainly among immune-related transcripts, indicating the potential influence of tumor immune infiltration or signal within the primary tumor. The differences in gene expression profile between metastatic and non-metastatic PTC, if they exist, are subtle and potentially detectable only in large datasets. [ABSTRACT FROM AUTHOR]

Background: Telomerase reverse transcriptase promoter (TERTp) mutations are related to a worse prognosis in various malignancies, including papillary thyroid carcinoma (PTC). Since mechanisms responsible for the poorer outcome of TERTp(+) patients are still unknown, searching for molecular consequences of TERTp mutations in PTC was the aim of our study. Methods: The studied cohort consisted of 54 PTCs, among them 24 cases with distant metastases. BRAF V600E, RAS, and TERTp mutational status was evaluated in all cases. Differences in gene expression profile between TERTp(+) and TERTp(−) PTCs were examined using microarrays. The evaluation of signaling pathways and gene ontology was based on the Gene Set Enrichment Analysis. Results: Fifty-nine percent (32/54) of analyzed PTCs were positive for at least one mutation: 27 were BRAF(+), among them eight were TERTp(+), and 1 NRAS(+), whereas five other samples harbored RAS mutations. Expression of four genes significantly differed in BRAF(+)TERTp(+) and BRAF(+)TERTp(−) PTCs. Deregulation of pathways involved in key cell processes was observed. Conclusions: TERTp mutations are related to higher PTC aggressiveness. CRABP2 gene was validated as associated with TERTp mutations. However, its potential use in diagnostics or risk stratification in PTC patients needs further studies. [ABSTRACT FROM AUTHOR]