Impact of the Tumor Microenvironment on the Gene Expression Profile in Papillary Thyroid Cancer

Transcriptome of papillary thyroid cancer (PTC) is well characterized and correlates with some prognostic and genotypic factors, but data addressing the interaction between PTC and tumor microenvironment (TME) are scarce. Therefore, in the present study, we aimed to assess the impact of TME on gene expression profile in PTC. We evaluated the gene expression profile in PTC and normal thyroid cells isolated by laser capture microdissection and in whole tissue slides corresponding to the entire tumor. We included 26 microdissected samples for gene expression analysis (HG-U133 PLUS 2.0, Affymetrix, currently Thermo Fisher Scientific USA): 15 PTC samples, 11 samples of normal thyrocytes, and 30 whole slides (15 PTC and 15 normal thyroid). Transcripts were divided into three groups: differentially expressed both in microdissected and whole slides, transcripts differently expressed in microdissected samples and not changed in whole slides, and transcripts differentially expressed in whole slides and not changed in microdissected samples. Eleven genes were selected for validation in an independent set of samples; among them, four genes differentiated only microdissected PTC and normal cells. Two genes (PTCSC and CTGF) were confirmed. One gene (FOS) was not confirmed by the validation, whereas EGR1 was also significant in whole slide analysis. The other seven genes (TFF3, FN1, MPPED2, MET, KCNJ2, TACSTD2, and GALE) showed differentiated expression in microdissected thyrocytes and in whole tumor slides. Most of identified genes were related to the tumor-microenvironment interaction and confirmed the crosstalk between TME and cancer cells.

Transcriptomic population markers for human population discrimination

Background

Numerous studies have demonstrated significant differences in the expression level across continental human populations. Most of published results were performed on B-cell lines materials examined under specific laboratory conditions, without further validation in a primary biological material. The goal of our study was to identify mRNA markers characterized by a significant and stable difference in the gene expression profile in Caucasian and Chinese populations, both in the commercially available B-lymphocyte cell lines and in the primary samples of the peripheral blood.

Results

The preliminary selection of population-differentiating transcripts was based on Illumina expression microarray analysis of the representative group of ethnically-specified B-lymphocyte cell lines. Twenty genes with the inter-population difference in the mean expression characterized by the at least 1.5-fold change and FDR <  0.05 were identified. Subsequently, a two-step validation procedure was carried out. In the first step, a subset of selected population- differentiating transcripts was tested in the independent set of B-lymphocyte cell lines, using TLDA cards. Based on TLDA analysis, three transcripts representing Fch > 2 were chosen for validation. The differentiating status was confirmed for all of them: UTS2, UGT2B17 and SLC7A7. The mean expression of UTS2 was higher in CHB (25.8-fold change compared to CEU), while the expression of UGT2B17 and SLC7A7 was higher in CEU (3.2- and 2.2-fold change, respectively).

In the next validation step, two transcripts were verified in the primary biological material. As an ultimate result of our study, two mRNA markers (UTS2 and UGT2B17) exhibiting population differences in the expression level in both B-cell line and in the blood were identified. Further statistical analysis confirmed the discriminatory potential of these two markers.

Conclusions

An inter-population differences on the level of gene expression were identified in both B-cell lines and peripheral blood samples. These findings may have a practical application in the field of forensic science. In particular, these transcripts, targeted by specific probes, may be used as population-specific targets in the efforts aiming to separate mixture of blood from individuals of different populations. Notwithstanding, these results have to be confirmed on extended population group.

Electronic supplementary material

The online version of this article (10.1186/s12863-018-0663-2) contains supplementary material, which is available to authorized users.

Unsupervised analysis of follicular thyroid tumours transcriptome by oligonucleotide microarray gene expression profiling

INTRODUCTION

Mechanisms driving the invasiveness of follicular thyroid cancer (FTC) are not fully understood. In our study, we undertook an unsupervised analysis of the set of follicular thyroid tumours (adenomas (FTA) and carcinomas) to verify whether the malignant phenotype influences major sources of variability in our dataset.

MATERIAL AND METHODS

The core set of samples consisted of 52 tumours (27 FTC, 25 FTA). Total RNA was analysed by oligonucleotide microarray (HG-U133 Plus 2.0). Principal Component Analysis (PCA) was applied as a main method of unsupervised analysis.

RESULTS

An analysis of biological character of genes correlated to the first six PCs was performed. When genes correlated to the first PC were used to cluster FTC and FTA, they appeared in two branches; one, relatively enriched in adenomas, with homogenous expression of subset of genes, and the other containing mainly carcinomas, with down-regulation of these genes and heterogeneous up-regulation in a smaller cluster of transcripts. Genes highly up-regulated in adenomas included some thyroid-specific transcripts. The second cluster of genes, up-regulated in carcinomas, contained mainly immunity-related transcripts. Immune response genes were found in the first, third and sixth principal components, improving the discrimination between carcinomas and adenomas.

CONCLUSIONS

Our unsupervised analysis indicates that invasiveness of follicular tumours might be considered as the major source of variability in transcriptome analysis. However, the distance between both groups is small and the clusters are overlapping, thus, unsupervised analysis is not sufficient to properly classify them.