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.

There are 5 main histological types of thyroid cancers (TCs): papillary, follicular (also known as differentiated), poorly differentiated, anaplastic (the most aggressive form), and medullary TC, and only the latter arises from thyroid C cells. These different forms of TCs show significant variability, both among and within tumours. This great variation is particularly notable among the first 4 types, which all originate from thyroid follicular cells. Importantly, this heterogeneity is not limited to histopathological diversity only but is also manifested as variation in several genetic and/or epigenetic alterations, the numbers of interactions between the tumour and surrounding microenvironment, and interpatient differences, for example. All these factors contribute to the great complexity in the development of a tumour from cancer cells. In the present review, we summarise the knowledge accumulated about the heterogeneity of TCs. Further research in this direction should help to gain a better understanding of the underlying mechanisms contributing to the development and diversity of TCs, paving the way toward more effective treatment strategies.

Background: Methods: Results: Conclusion:

Papillary thyroid cancer (PTC) offers excellent prognosis, however relapse risk or persistent disease is related to ~30%. Currently, attention is paid to the possibility of patient group selection of different risk of unfavorable outcome to match a particular therapeutic approach. Therefore, interest in new prognostic and predictive markers known preoperatively is observed. BRAF V600E mutation is such a marker. Many studies analyzing the prevalence of the mutation and its relationship with other clinico-pathological risk factors were reported but with controversial conclusions. The prognostic significance of BRAF mutation was confirmed by some single centre studies, a few meta-analyses and a large multicenter retrospective international study. They confirmed a correlation between the mutation and the risk of recurrence. The strongest argument against using BRAF mutation as an independent prognostic and predictive factor in PTC is its high prevalence (30–80%). At present it seems that BRAF mutation is one of the factors influencing the prognosis and it should be analyzed in correlation with other prognostic factors. The most recent ATA recommendations do not indicate a routine application of BRAF status for initial risk stratification in differentiated thyroid cancer due to a lack of evident confirmation of a direct influence of mutation on the increase in relapse risk. However, ATA demonstrates the continuous risk scale for the relapse risk assessment, considering BRAF and/or TERT status. At present, researchers are working on determining the role of BRAF mutation in patients from a low-risk group and its correlations with others molecular events. Currently, BRAF mutation cannot be used as a single, independent predictive factor. However, its usefulness in the context of other molecular and clinico-pathological risk factors cannot be excluded. They may be used to make modern prognostic scales of relapse risk and be applied to individualized diagnostic and therapeutic strategy for PTC patients.

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.

Selection of novel molecular markers is an important goal of cancer genomics studies. The aim of our analysis was to apply the multivariate bioinformatical tools to rank the genes – potential markers of papillary thyroid cancer (PTC) according to their diagnostic usefulness. We also assessed the accuracy of benign/malignant classification, based on gene expression profiling, for PTC. We analyzed a 180-array dataset (90 HG-U95A and 90 HG-U133A oligonucleotide arrays), which included a collection of 57 PTCs, 61 benign thyroid tumors, and 62 apparently normal tissues. Gene selection was carried out by the support vector machines method with bootstrapping, which allowed us 1) ranking the genes that were most important for classification quality and appeared most frequently in the classifiers (bootstrap-based feature ranking, BBFR); 2) ranking the samples, and thus detecting cases that were most difficult to classify (bootstrap-based outlier detection). The accuracy of PTC diagnosis was 98.5% for a 20-gene classifier, its 95% confidence interval (CI) was 95.9–100%, with the lower limit of CI exceeding 95% already for five genes. Only 5 of 180 samples (2.8%) were misclassified in more than 10% of bootstrap iterations. We specified 43 genes which are most suitable as molecular markers of PTC, among them some well-known PTC markers (MET, fibronectin 1, dipeptidylpeptidase 4, or adenosine A1 receptor) and potential new ones (UDP-galactose-4-epimerase, cadherin 16, gap junction protein 3, sushi, nidogen, and EGF-like domains 1, inhibitor of DNA binding 3, RUNX1, leiomodin 1, F-box protein 9, and tripartite motif-containing 58). The highest ranking gene, metallophosphoesterase domain-containing protein 2, achieved 96.7% of the maximum BBFR score.