A six-gene panel to label follicular adenoma, low- and high-risk follicular thyroid carcinoma

in Endocrine Connections
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Anello Marcello Poma Department of Surgical, Medical, Molecular Pathology and Critical Area, University of Pisa, Pisa, Italy

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Riccardo Giannini Department of Surgical, Medical, Molecular Pathology and Critical Area, University of Pisa, Pisa, Italy

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Paolo Piaggi National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, Arizona, USA

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Clara Ugolini Department of Laboratory Medicine, Section of Pathology, Azienda Ospedaliero-Universitaria Pisana, Pisa, Italy

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Gabriele Materazzi Department of Surgical, Medical, Molecular Pathology and Critical Area, University of Pisa, Pisa, Italy

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Paolo Miccoli Department of Surgical, Medical, Molecular Pathology and Critical Area, University of Pisa, Pisa, Italy

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Paolo Vitti Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy

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Fulvio Basolo Department of Surgical, Medical, Molecular Pathology and Critical Area, University of Pisa, Pisa, Italy

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The distinction between follicular thyroid carcinomas (FTCs) and follicular-patterned benign lesions is almost impossible on fine-needle aspiration cytology. Furthermore, minimally invasive FTCs (MI-FTCs) with less than 4 vascular invasion foci generally have an excellent prognosis, but there are exceptions and, so far, no molecular marker appears able to identify them reliably. We aimed to distinguish benign lesions from low- and high-risk FTCs by a small-scale combination of genes. The expression analysis of 75 selected genes was performed on 18 follicular adenomas (FAs), 14 MI-FTCs and 6 widely invasive FTC (WI-FTCs). The mutational status of the RAS genes, TERT promoter and PAX8-PPARG rearrangements was also investigated. Seven samples were mutated, namely 3 MI-FTCs and 4 WI-FTCs. Twenty-five genes were differentially expressed (FDR <0.05) between FAs and WI-FTCs. Six of these (ECM1, RXRG, SDPR, SLC26A4, TIFF3, TIMP1) were also differently expressed among MI-FTCs and FAs or WI-FTCs and were considered to build a classification model, which was tested to classify samples according to their histological class. Hence, 31 out of 38 were correctly classified, and accuracy remained high after cross-validation (27/38). The 2 MI-FTCs incorrectly classified as WI-FTCs harbored both RAS and TERT promoter mutations. The capability of these six genes to stratify benign, low- and high-risk lesions appears to be promising in supporting the diagnosis of indeterminate thyroid nodules.

Abstract

The distinction between follicular thyroid carcinomas (FTCs) and follicular-patterned benign lesions is almost impossible on fine-needle aspiration cytology. Furthermore, minimally invasive FTCs (MI-FTCs) with less than 4 vascular invasion foci generally have an excellent prognosis, but there are exceptions and, so far, no molecular marker appears able to identify them reliably. We aimed to distinguish benign lesions from low- and high-risk FTCs by a small-scale combination of genes. The expression analysis of 75 selected genes was performed on 18 follicular adenomas (FAs), 14 MI-FTCs and 6 widely invasive FTC (WI-FTCs). The mutational status of the RAS genes, TERT promoter and PAX8-PPARG rearrangements was also investigated. Seven samples were mutated, namely 3 MI-FTCs and 4 WI-FTCs. Twenty-five genes were differentially expressed (FDR <0.05) between FAs and WI-FTCs. Six of these (ECM1, RXRG, SDPR, SLC26A4, TIFF3, TIMP1) were also differently expressed among MI-FTCs and FAs or WI-FTCs and were considered to build a classification model, which was tested to classify samples according to their histological class. Hence, 31 out of 38 were correctly classified, and accuracy remained high after cross-validation (27/38). The 2 MI-FTCs incorrectly classified as WI-FTCs harbored both RAS and TERT promoter mutations. The capability of these six genes to stratify benign, low- and high-risk lesions appears to be promising in supporting the diagnosis of indeterminate thyroid nodules.

Introduction

The incidence of follicular thyroid carcinoma (FTC) has decreased over the last years (1), but still accounts for 10–15% of all thyroid cancers (2). The distinction between follicular adenoma (FA) and FTC, conventionally classified as minimally invasive (MI-FTC) and widely invasive (WI-FTC) (3), cannot be determined by fine-needle aspiration cytology (FNAC), since the demonstration of malignancy in these types of lesions is based on the presence of capsular and/or vascular invasion, which can be assessed only after diagnostic surgery and histological observation (4). However, in these cases, postoperative diagnoses reveal a high rate of unnecessary or inadequate surgeries (5). Several authors have suggested to further distinguish angioinvasive MI-FTCs (or moderately invasive FTCs) from MI-FTCs with tumor capsular invasion only (4, 6). However, it has been demonstrated that the extensive circumferential evaluation of the capsule not only allows to reach a higher effectiveness in the distinction between benign and malignant follicular neoplasms, but also yields a more adequate assessment of invasive vascular foci that are often underestimated by random histological section sampling. These results have highlighted that the presence of capsular invasion could be an indicator of vascular invasion (7). Although follicular lesions with vascular invasion were associated with a higher risk than those with solely capsular invasion (8, 9), only extensive vascular invasion (i.e. 4 or more vascular invasion foci) strongly correlated with a worse outcome (10, 11). Latest guidelines of the American Thyroid Association (ATA) then suggest a cutoff of 4 intracapsular vascular invasion foci to distinguish low- from high-risk MI-FTC (12). Even the degree of tumor capsule invasion for the diagnosis of MI-FTC is controversial; in fact, some authors have questioned whether a partial invasion may be sufficient for FTC diagnosis (13). However, some studies have justified FTC diagnosis in the presence of capsular invasion (even partial) without vascular involvement, since these tumors, although rarely, can develop recurrences and metastatic disease (14, 15). Despite the unresolved issues in MI-FTC diagnosis, it has been well demonstrated that MI-FTCs generally have a better outcome than WI-FTCs, with a low rate of recurrence, metastatic disease and disease-related mortality (2, 16, 17). For these reasons, the latest guidelines of the ATA suggested that lobectomy may be a sufficient treatment for low-risk FTC (i.e. MI-FTC with less than 4 vascular invasion foci) (12). However, molecular markers such as TERT promoter mutations were found to be an independent poor prognosis factor in differentiated thyroid cancer (DTC) (18, 19), especially in coexistence with BRAF or RAS mutations (20). Then, ATA guidelines have suggested that these molecular markers, alone or in combination, may be helpful to provide a more accurate risk-stratification of DTC, and in addition to the other clinico-pathological risk factors, may redefine the surgical approach to these lesions (12). In the last years, large-scale screening studies have disclosed many other potential biomarkers for the preoperative diagnostics of FTC, including both somatic mutations (21) and expression profiling (22, 23, 24), but none of them have been found to be conclusive. In the present study, we sought to find molecular biomarkers, which could help distinguish MI-FTCs from both FAs and WI-FTCs. Furthermore, the combination of gene expression analysis and genotyping could be useful for the stratification of MI-FTCs with similar clinico-pathological features.

Materials and methods

Study group

The study included 40 patients with diagnoses of FA, MI-FTC or WI-FTC, who underwent total/near-total thyroidectomy at the Department of Surgical, Medical, Molecular Pathology and Critical Area of the University of Pisa, Italy, between 2013 and 2015. Hematoxylin and eosin-stained sections of neoplasms obtained from the archives of the section of Pathology of the University Hospital of Pisa were re-evaluated independently by two pathologists (C U, F B). A diagnostic concordance rate of 98% was achieved between the two investigators. Discordant cases were eliminated. Tumors were diagnosed and classified according to the WHO 2004 histopathological criteria (3). For all FAs and MI-FTCs, the neoplastic capsules were examined in toto. Moreover, the vascular invasiveness and the infiltration of thyroid parenchyma and extra-thyroid tissues were evaluated. Hürthle cells neoplasms or neoplasms with oncocytic aspects were excluded from the study. To avoid contamination with other cell types, cases with any grade of thyroiditis were excluded, and only neoplasms with almost 50% of neoplastic cellularity were considered for the examination. This retrospective work was performed on archival material, and it was conducted anonymously; therefore, ethical committee approval was not required. The study conforms to the Principles of the Helsinki Declaration of 1975. Both informed and surgical consent were achieved one day before the operation.

Nucleic acids extraction and purification

The most representative FFPE tissue block was considered for each specimen. Two micrometer sections were taken from each block, stained with hematoxylin and eosin and histologically examined to confirm the presence of tumor tissue. The tumor area was marked and the tumor cell percentage was indicated by the pathologist. For each sample, 4 unstained 10 μm sections and 5 μm sections were used for DNA and RNA extraction, respectively. Unstained sections were deparaffinized with xylene and rehydrated in decreasing-grade ethanol solution. Manual micro-dissection was performed to maximize the amount of tumor cells. DNA was extracted and purified by using the QIAamp DNA Mini Kit (Qiagen), according to the manufacturer’s protocol. DNA was eluted in 50 μL of elution buffer. RNA was isolated using the RNeasy FFPE Kit (Qiagen) in conformance to the manufacturer’s instructions. RNA was eluted in 20 μL of RNase-free water. RNA and DNA quantification and quality were assessed by means of a spectrophotometer (Xpose Trinean, Gentbrugge, Belgium).

Detection of point mutations and rearrangements

The mutational status of NRAS (exons 2 and 3), HRAS (exons 2 and 3), KRAS (exons 2 and 3) and of the TERT promoter was tested by direct sequencing (3130 Genetic Analyzer, Applied Biosystems), according to the standard procedures (25). In addition, the presence of PAX8-PPARG rearrangements (exons 7 and 9) was evaluated by a two-step RT-PCR. Firstly, 600 ng of total RNA were used to synthesize cDNA by the RevertAid First Strand cDNA Synthesis Kit (Thermo Scientific) in a reaction volume of 20 μL. Secondly, 75 ng of cDNA were amplified in a 20-μL volume by Premix Ex Taq (Takara Bio), 4 ng of each primer (25) and 8 ng of probe (25), following the manufacturer’s suggestions. Amplification was performed in 40 cycles (denaturation at 95°C for 5 s and annealing and elongation at 56.5°C for 30 s) according to the manufacturer’s protocol. Each sample was amplified in duplicate, cDNA adequacy was tested by amplifying GAPDH housekeeping gene in double copy for each sample, and each assay included a double copy of the positive controls. Only the samples in which both GAPDH copies were amplified were considered suitable for rearrangement-positivity evaluation. The amplification of both GAPDH and PAX8-PPARG was positively considered when the cycle threshold was less than 35 cycles. The cases resulting positive for rearrangements were confirmed by direct sequencing (3130 Genetic Analyzer, Applied Biosystems).

NanoString analysis

The nCounter custom code set used in this study was designed and synthesized by NanoString Technologies (Seattle, Washington, USA). This code set consisted of reporter and capture probe pairs specific for the 75 genes and 5 housekeeping genes, which was already used by our group (26). In this assay, 150 ng of total RNA were hybridized with reporter and capture probes for 16 h at 65°C in a SensoQuest thermal cycler (SensoQuest, Gottingen, Germany). Sample clean-up and the counts of digital reports were performed as recommended by the manufacturer’s instructions. Five MI-FTCs were analyzed in triplicate to evaluate their potential intra-tumor RNA heterogeneity, by employing the RNA extracted from 3 different areas of the lesion. The first included the RNA from the entire tumor, the second from the non-invasive area and the third from the invasive foci.

Statistical analysis and bioinformatics tools

The differential expression analysis among FAs, MI-FTCs and WI-FTCs was performed using the procedures of the Limma R package (27). The design matrix contrasted all three group pairings by the voom transformation of counts (28). The Bayes function was used to compute the empirical Bayes moderated differential expression statistics. Genes with a false discovery rate (FDR) below 0.05 were considered to be differentially expressed between groups. Multiple discriminant analysis and leave-one-out cross-validation were then used to quantify the accuracy of classification of differentially expressed genes. In brief, two linear and orthogonal discriminant functions were calculated, based on the log2 expression values of differentially expressed genes to maximize the distances between the averages (centroids) of the three groups. Specifically, the two discriminant functions were used to calculate a classification score for each sample, and centroids were calculated for each discriminant function as the average score of the samples belonging to the specific group. Each sample was then assigned to the group with the shortest distance to the closest group centroid. The accuracy of classification was calculated as the proportion of samples correctly classified within the actual group. Finally, leave-one-out cross-validation was done by computing the two discriminant functions in 37 samples (training set), and the remaining sample was used to test the accuracy of classification of the discriminant functions. Cross-validation was then run 38 times, each time with a different training set, and overall accuracy was calculated as the proportion of samples correctly classified within the actual group over all the 38 runs. Unsupervised hierarchical clustering was performed on normalized data with the nSolver 2.5 Analysis Software (NanoString Technologies). Clustering was applied independently to both samples (columns) and genes (rows) by Pearson’s correlation (r). The prediction of the effects of the newly reported amino acid substitution was performed using the PredictSNP tool (29).

Results

Clinico-pathological features

A total of 38 samples were eligible for inclusion in the study: 18 FAs, 14 MI-FTCs and 6 WI-FTCs. Every case had a complete follicular growth pattern and a total absence of oncocytic aspects and thyroiditis. Each selected tumor area had more than 60% of neoplastic cells. Seven of the 14 MI-FTCs showed capsular invasion only, while the others had both capsular and vascular invasion foci (less than 4). Furthermore, 7 MI-FTCs had only a partial invasion of the capsule. Four of the 6 WI-FTCs had extraglandular spread, whereas 2 were intrathyroidal tumors. Mean age at diagnosis was 48.1 ± 13.7 years, and mean tumor size was 29.1 ± 13.1 mm. Thirty cytological diagnoses, classified according to the Bethesda system (30), were available: 21 were category III, 8 were category IV and 1 was category V.

Genotyping results

All samples but 3 were suitable for genotyping analysis. None of the FAs harbored mutations, whereas 3 MI-FTCs were mutated (one had a PAX8-PPARG rearrangement and 2 were mutated both in TERT promoter and RAS genes, NRAS in one case and KRAS in the other); finally, 4 WI-FTCs harbored NRAS mutations (Table 1). The newly reported NRAS M72L mutation was found in heterozygosis and should not be deleterious according to the PredictSNP result.

Table 1

Genotyping results of 18 follicular adenomas, 14 minimally invasive follicular thyroid carcinomas and 6 widely invasive follicular thyroid carcinomas.

NRAS KRAS TERT promoter PAX8-PPARG TOTAL no. of mutated samples
Q61R, n Q61K, n M72L, n Q61R, n C228T, n ex.7, n
FAs 0 0 0 0 0 0 0
MI-FTCs 0 1 1 1 2 1 3
WI-FTCs 4 0 0 0 0 0 4
Total 4 1 1 1 2 1 7

Fas, follicular adenomas; MI-FTCs, minimally invasive follicular thyroid carcinomas; WI-FTCs, widely invasive follicular thyroid carcinomas.

Gene expression analysis of the 75-gene panel

All samples passed the quality control and were included in the differential gene expression analysis. Raw expression data were normalized using both housekeeping genes and positive controls (31). Samples analyzed in triplicate showed an average Pearson’s correlation of 0.90 (data not shown). Normalized expression levels were used to identify differentially expressed genes (DEG) among FAs, MI-FTCs and WI-FTCs based on a FDR <0.05. Twenty-five genes were deregulated between FAs and WI-FTCs; specifically, 14 and 11 were upregulated and downregulated in WI-FTCs compared to FAs, respectively; five were also differentially expressed between MI-FTCs and WI-FTCs; in particular, ECM1 and RXRG were upregulated and SLC26A4, SDPR and TFF3 were downregulated in WI-FTCs. Finally, TIMP1 was upregulated in MI-FTCs and WI-FTCs compared to FAs (Fig. 1 and Supplementary Table 1, see section on supplementary data given at the end of this article).

Figure 1
Figure 1

Differentially expressed genes. Fas, follicular adenomas; MI-FTCs, minimally invasive follicular thyroid carcinomas; WI-FTCs, widely invasive follicular thyroid carcinomas.

Citation: Endocrine Connections 7, 1; 10.1530/EC-17-0261

Unsupervised hierarchical clustering

Unsupervised hierarchical clustering was performed on DEG normalized data using Pearson’s correlation (r). Two main clusters were obtained (Fig. 2): cluster 1 (r = 0.20) included 24 samples (18 FAs and 6 MI-FTCs), and cluster 2 (r = 0.17) consisted of 14 samples (8 MI-FTCs and 6 WI-FTCs). No correlations were found between MI-FTCs in cluster 1 and MI-FTCs in cluster 2 considering age, size, gender, presence of vascular invasion and degree of capsular invasion (partial or entire thickness). All mutated samples but one (1 MI-FTC harboring a PAX8-PPARG rearrangement) were encompassed in cluster 2.

Figure 2
Figure 2

Unsupervised hierarchical clustering. Differentially expressed genes (rows) and samples (columns) were independently clustered by Pearson’s correlation (r). Red and green equal to high and low expression levels, respectively.

Citation: Endocrine Connections 7, 1; 10.1530/EC-17-0261

6-gene-based classification

The six DEGs (ECM1, RXRG, TIMP1, SLC26A4, SDPR and TFF3) between MI-FTCs and the other two types of lesions were taken into account to determine the centroids of each histological class (FAs, MI-FTCs and WI-FTCs), and each sample was classified according to the nearest class centroid. By following these criteria, 31/38 samples were appropriately classified (Table 2). A jackknife resampling was then used to cross-validate the results obtained, and 27/38 samples were correctly classified.

Table 2

Six-gene-based classification of samples based on histology compared to age, size, capsule invasion degree, vascular invasion foci, cytological diagnosis (Bethesda system) and genotype.

Sample ID Histology Age (years) Size (mm) Capsule invasion degree Vascular invasion foci Bethesda category Genotype Prediction Probability (%)
FA1 FA 41 35 None None III WT FA 76
FA13 FA 58 28 None None III WT FA 65
FA14 FA 19 18 None None IV WT FA 65
FA2 FA 55 25 None None III WT FA 82
FA25 FA 35 34 None None III WT FA 75
FA26 FA 34 18 None None IV WT MI-FTC 51
FA3 FA 31 44 None None IV WT FA 78
FA39 FA 50 20 None None III WT FA 54
FA40 FA 47 50 None None III WT FA 91
FA41 FA 51 15 None None III WT FA 63
FA42 FA 53 10 None None NA WT FA 90
FA70 FA 40 26 None None III WT FA 79
FA80 FA 41 30 None None III WT FA 90
FA81 FA 45 36 None None III WT MI-FTC 88
FA82 FA 49 25 None None NA WT FA 58
FA83 FA 50 42 None None III WT FA 81
FA85 FA 40 28 None None III WT FA 80
FA91 FA 35 52 None None NA WT FA 100
MI-FTC19 MI-FTC 45 15 Entire <4 III WT MI-FTC 80
MI-FTC22 MI-FTC 64 26 Entire <4 III NRAS Q61K WI-FTC 75
NRAS M72L
TERT C228T
MI-FTC30 MI-FTC 34 22 Partial None IV WT MI-FTC 91
MI-FTC33 MI-FTC 59 22 Partial <4 III WT MI-FTC 58
MI-FTC36 MI-FTC 63 34 Partial <4 NA PAX8-PPARG ex. 7 MI-FTC 62
MI-FTC50 MI-FTC 45 50 Entire None III WT MI-FTC 67
MI-FTC51 MI-FTC 69 28 Partial None III WT MI-FTC 92
MI-FTC58 MI-FTC 55 22 Partial None IV WT MI-FTC 73
MI-FTC59 MI-FTC 28 18 Entire <4 IV WT MI-FTC 83
MI-FTC60 MI-FTC 42 10 Partial <4 NA WT FA 56
MI-FTC61 MI-FTC 39 18 Entire None NA WT MI-FTC 84
MI-FTC92 MI-FTC 41 22 Entire None III WT MI-FTC 74
MI-FTC93 MI-FTC 38 35 Entire None NA WT FA 63
MI-FTC94 MI-FTC 72 22 Partial <4 III KRAS Q61R WI-FTC 93
TERT C228T
WI-FTC4 WI-FTC 63 38 Entire >4 III WT WI-FTC 70
WI-FTC48 WI-FTC 29 15 Entire <4 III NRAS Q61R WI-FTC 100
WI-FTC49 WI-FTC 63 60 Entire >4 IV WT FA 72
WI-FTC5 WI-FTC 53 NA Entire >4 NA NRAS Q61R WI-FTC 100
WI-FTC69 WI-FTC 77 NA Entire >4 V NRAS Q61R WI-FTC 100
WI-FTC75 WI-FTC 73 57 Entire >4 IV NRAS Q61R WI-FTC 95

Probability refers to the reliability of classification according to the distance from histological class centroids.

FA, follicular adenoma; MI-FTC, minimally invasive follicular thyroid carcinoma; NA, not available; WI-FTC, widely invasive follicular thyroid carcinoma.

Discussion

In the present study, we performed a molecular characterization of 18 FAs, 14 MI-FTCs and 6 WI-FTCs, in order to find molecular biomarkers allowing to discriminate among these three types of lesions, which generally deserve a different surgical and therapeutic approach. Firstly, the potential intra-tumor mRNA heterogeneity of MI-FTCs was tested by analyzing five samples in triplicate: the expression profiles obtained from the RNA gathered from the entire tumor, from the invasive foci and from the non-invasive area were very similar (r = 0.90). The normalized gene expression levels were then used to determine DEGs among FAs, MI-FTCs and WI-FTCs. Twenty-five out of the 75 genes of the panel were differentially expressed (FDR <0.05) between FAs and WI-FTCs (Fig. 1), highlighting a great difference between these lesions, even from a molecular point of view. Five of these genes (ECM1, RXRG, SLC26A4, SDPR and TFF3) were also differentially expressed between MI-FTCs and WI-FTCs; in particular ECM1 and RXRG were upregulated in WI-FTCs, and SLC26A4, SDPR and TFF3 were downregulated. TIMP1, upregulated in MI-FTCs vs FAs, was the only differentially expressed gene in this comparison. These results were confirmed by an unsupervised clustering approach, which mirrored the DEG analysis: FAs and WI-FTCs were separated into two different groups, whereas MI-FTCs were split into the two main groups (Fig. 2). However, MI-FTCs that segregated in the two different clusters did not show differences considering age, size, degree of capsule invasion and even presence or absence of vascular invasion. The six genes differentially expressed between MI-FTCs and FAs or WI-FTCs (ECM1, RXRG, SLC26A4, SDPR, TFF3 and TIMP1) were then taken into account as a mini-panel, by testing the accuracy of their combination in predicting the histological class of the samples. Following this approach, 31 samples out of 38 were properly classified (Table 2), and more importantly, the prediction accuracy remained high after cross-validation (27/38). Furthermore, 2 MI-FTCs erroneously classified as WI-FTCs harbored both RAS and TERT mutations, the coexistence of which is strongly associated with recurrence and mortality (20); consequently, these 2 MI-FTCs should not be considered low-risk lesions, as suggested by the recent ATA guidelines (12). Then, the six-gene classification seemed able to identify high-risk MI-FTCs that did not show histopathological characteristics useful to stratify these lesions (e.g. diffuse vascular invasion, large size). Although the majority of inaccuracies were FA–MI-FTC misclassification, there was one exception: the WI-FTC classified as FA. This error underlines that the model needs to be further tested in order to evaluate how it could be useful in the clinical practice. Nevertheless, the faithfulness of the panel-based risk-stratification is supported by previous studies (32, 33, 34, 35, 36, 37) describing the biological role of these six genes in cancer progression and associating them with poor prognosis in many cancer models. TIMP1 protein is primarily an inhibitor of secreted and anchored metalloproteinases (38); however, its upregulation has been reported in several cancers, including melanoma, glioblastoma and breast cancer, in which it was also associated with poor prognosis (39, 40, 41). This apparent controversy could be explained by the protease-independent functions (32) and by the weak metalloproteinase-inhibitory capacity compared to other TIMPs (32). So far, TIMP1 expression has not been associated with adverse outcomes in thyroid cancer, but an increased expression with respect to normal tissue or benign lesions has already been described (23, 42). In our series, TIMP1 was also overexpressed in both MI-FTCs and WI-FTCs compared to FAs.

ECM1 is a soluble protein (43) overexpressed in many malignant epithelial tumors including thyroid cancer (33, 44). We found ECM1 to be highly expressed only in WI-FTCs and in the two MI-FTCs harboring both TERT and RAS mutations, highlighting its association with high-risk lesions. In fact, it was demonstrated that ECM1 is actively involved in promoting the Warburg effect (45) and angiogenesis (46) and, accordingly, that it is also associated with poor prognosis (33). RXRG encodes for a member of the retinoid X receptor family (47). This isoform was expressed similarly to ECM1, with few exceptions, confirming a previously reported high expression in thyroid cancer compared to normal thyroid cells (34). Interestingly, treatment with retinoids showed thyroid tumor growth suppression in vitro, especially in those expressing γ isoform (34). TFF3 is a secretory protein of the trefoil family (48). Although it is overexpressed and also correlates with poor prognosis in many cancer models (49, 50), we reported a downregulation of TFF3. However, this is in agreement with many studies that clearly demonstrated a lower expression of TFF3 in thyroid cancer with respect to normal tissue and benign lesions (22, 42, 51). Moreover, the forced expression of TFF3 in transfected anaplastic thyroid cancer cells restored an epithelial-like cell morphology and the expression of differentiation markers of follicular thyroid cells (35). SDPR is a phospholipid-binding protein that is over-expressed in serum-starved cells (52). SDPR downregulation was previously described in thyroid cancer (24) as well as in our series; the same trend was also reported in breast, kidney and prostate cancer (53). Furthermore, functional studies in breast cancer highlighted an important role of SDPR in apoptosis promotion (36) and in the suppression of the epithelial–mesenchymal transition (54). Lastly, SLC26A4 encodes for pendrin, a chloride–iodide transporter associated with the Pendred syndrome (55). SLC26A4 mRNA and pendrin levels are both considerably lower in thyroid cancer than those in the follicular cells of normal tissue or in benign thyroid tumors (24, 56). Pendrin, normally expressed at the apical pole of thyrocytes, is located in the intracellular compartments of the DTC majority (56). Despite the lack of evidence of association between SLC26A4 expression and poor prognosis in thyroid cancer, we reported a downregulation of this gene only in widely invasive carcinomas. However, low levels of SLC26A4 mRNA were associated with papillary thyroid cancer that failed to uptake 131I (37). In conclusion, even if we are fully aware that follicular-patterned tumors also encompass the follicular variant of papillary thyroid carcinoma, in this preliminary study, we focused on FA and FTC. Herein, we present a small panel of genes able to stratify benign, low- and high-risk lesions. Although the validation in a larger cohort of cases and through long-term follow-up is needed, our data provide some points that deserve further investigations: (a) since lesions carrying TERT promoter mutations have gene expression profiles very similar to those of WI-FTCs, these alterations could be useful markers for stratifying MI-FTCs prognosis; (b) gene expression and this six-gene model have the potential to support the diagnosis of indeterminate nodules. In fact, the gene expression levels of selected genes seemed promising not only in differentiating benign from malignant lesions, which can rarely be distinguished by FNAC, but also in differentiating between low- and high-risk malignant lesions, providing further information that could be useful in the surgical and therapeutic approach.

Supplementary data

This is linked to the online version of the paper at https://doi.org/10.1530/EC-17-0261.

Declaration of interest

The authors declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research reported.

Funding

This work was supported by AIRC (IG_10316_2010), PRA (PRA_2016_19) and PRIN (2015HPMLFY).

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Supplementary Materials

 

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  • Differentially expressed genes. Fas, follicular adenomas; MI-FTCs, minimally invasive follicular thyroid carcinomas; WI-FTCs, widely invasive follicular thyroid carcinomas.

  • Unsupervised hierarchical clustering. Differentially expressed genes (rows) and samples (columns) were independently clustered by Pearson’s correlation (r). Red and green equal to high and low expression levels, respectively.