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

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.

SUBMITTER: Poma AM

PROVIDER: S-EPMC5754511 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

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

Poma Anello Marcello AM Giannini Riccardo R Piaggi Paolo P Ugolini Clara C Materazzi Gabriele G Miccoli Paolo P Vitti Paolo P Basolo Fulvio F

Endocrine connections 20180101 1

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 express ...[more]

PMID: 29298844

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Project description:Molecular mechanisms underlying the pathogenesis and progression of malignant thyroid cancers, such as follicular thyroid carcinomas (FTCs), and how these differ from benign thyroid lesions, are poorly understood. In this study, we employed network-based integrative analyses of FTC and benign follicular thyroid adenoma (FTA) lesion transcriptomes to identify key genes and pathways that differ between them. We first analysed a microarray gene expression dataset (Gene Expression Omnibus GSE82208, n = 52) obtained from FTC and FTA tissues to identify differentially expressed genes (DEGs). Pathway analyses of these DEGs were then performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) resources to identify potentially important pathways, and protein-protein interactions (PPIs) were examined to identify pathway hub genes. Our data analysis identified 598 DEGs, 133 genes with higher and 465 genes with lower expression in FTCs. We identified four significant pathways (one carbon pool by folate, p53 signalling, progesterone-mediated oocyte maturation signalling, and cell cycle pathways) connected to DEGs with high FTC expression; eight pathways were connected to DEGs with lower relative FTC expression. Ten GO groups were significantly connected with FTC-high expression DEGs and 80 with low-FTC expression DEGs. PPI analysis then identified 12 potential hub genes based on degree and betweenness centrality; namely, TOP2A, JUN, EGFR, CDK1, FOS, CDKN3, EZH2, TYMS, PBK, CDH1, UBE2C, and CCNB2. Moreover, transcription factors (TFs) were identified that may underlie gene expression differences observed between FTC and FTA, including FOXC1, GATA2, YY1, FOXL1, E2F1, NFIC, SRF, TFAP2A, HINFP, and CREB1. We also identified microRNA (miRNAs) that may also affect transcript levels of DEGs; these included hsa-mir-335-5p, -26b-5p, -124-3p, -16-5p, -192-5p, -1-3p, -17-5p, -92a-3p, -215-5p, and -20a-5p. Thus, our study identified DEGs, molecular pathways, TFs, and miRNAs that reflect molecular mechanisms that differ between FTC and benign FTA. Given the general similarities of these lesions and common tissue origin, some of these differences may reflect malignant progression potential, and include useful candidate biomarkers for FTC and identifying factors important for FTC pathogenesis.

Project description:To identify differences in gene expression profiles of infected cells between thyroid carcinoma (C), thyroid adenoma (A) and normal thyroid (N) epithelial cells, differentially expressed genes were identified using three pairwise comparisons with the GEO2R online tool. Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis were used to classify them at the functional level. The most significant cluster in the N vs. A pairwise comparison had four hub genes: Insulin-like growth factor 2, Von Willebrand factor (VWF), multimerin 1 (MMRN1) and complement factor D (CFD). In N vs. C, the most significant cluster had 19 genes: IGF2, early growth response 2, transcription factor 3, KIT proto?oncogene receptor tyrosine kinase, SMAD family member 9, MLLT3 super elongation complex subunit, runt related transcription factor 1, CFD, actinin ? 1, SWI/SNF related matrix associated actin dependent regulator of chromatin subfamily a member 4, JunD proto?oncogene AP?1 transcription factor subunit, serum response factor (SRF), FosB proto?oncogene, AP?1 transcription factor subunit, connective tissue growth factor (CTGF), SRC proto?oncogene, non?receptor tyrosine kinase, MMRN1, SRY?box 9, early growth response 3 and ETS variant 4. In A vs. C, the most significant cluster had 14 genes: BCL2-like 1, galectin 3, MCL1 BCL2 family apoptosis regulator, DNA damage inducible transcript 3, BCL2 apoptosis regulator, CTGF, matrix metallopeptidase 7, early growth response 1, kinase insert domain receptor, TIMP metallopeptidase inhibitor 1, apolipoprotein E, VWF, cyclin D1 and placental growth factor. Histological evidence was presented to confirm the makeup of the hubs prior to logistic regression analysis to differentiate benign and malignant neoplasms. The results of the present study may aid in the search for novel potential biomarkers for the differential diagnosis, prognosis and development of drug targets of thyroid neoplasm.

Dataset Information

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

Publications

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

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