“Glomeruloid” Follicular Thyroid Adenoma

2013 ◽  
Vol 21 (4) ◽  
pp. 376-376 ◽  
Author(s):  
Francesca Maria Bosisio ◽  
John T. Bickel
2021 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Lei Ou ◽  
Junhao Wu ◽  
Ji Wu ◽  
Chunru Mou ◽  
Chunyin Zhang

1992 ◽  
Vol 60 (1) ◽  
pp. 99-101 ◽  
Author(s):  
Paola Dal Cin ◽  
Walter Sneyers ◽  
Magdy Sayed Aly ◽  
Alexander Segers ◽  
Frans Ostijn ◽  
...  

2019 ◽  
Vol 20 (13) ◽  
pp. 3126 ◽  
Author(s):  
Martyna Borowczyk ◽  
Ewelina Szczepanek-Parulska ◽  
Szymon Dębicki ◽  
Bartłomiej Budny ◽  
Frederik A. Verburg ◽  
...  

We aimed to identify differences in mutational status between follicular thyroid adenoma (FTA) and follicular thyroid cancer (FTC). The study included 35 patients with FTA and 35 with FTC. DNA was extracted from formalin-fixed paraffin-embedded (FFPE) samples from thyroidectomy. Next-generation sequencing (NGS) was performed with the 50-gene Ion AmpliSeq Cancer Hotspot Panel v2. Potentially pathogenic mutations were found in 14 (40%) FTA and 24 (69%) FTC patients (OR (95%CI) = 3.27 (1.22−8.75)). The number of mutations was higher in patients with FTC than FTA (p-value = 0.03). SMAD4 and STK11 mutations were present only in patients with FTA, while defects in FBXW7, JAK3, KIT, NRAS, PIK3CA, SMARCB1, and TP53 were detected exclusively in FTC patients. TP53 mutations increased the risk of FTC; OR (95%CI) = 29.24 (1.64–522.00); p-value = 0.001. FLT3-positivity was higher in FTC than in the FTA group (51.4% vs. 28.6%; p-value = 0.051). The presence of FLT3 and TP53 with no RET mutations increased FTC detectability by 17.1%, whereas the absence of FLT3 and TP53 with a presence of RET mutations increased FTA detectability by 5.7%. TP53 and FLT3 are candidate markers for detecting malignancy in follicular lesions. The best model to predict FTA and FTC may consist of FLT3, TP53, and RET mutations considered together.


1991 ◽  
Vol 56 (2) ◽  
pp. 277-280 ◽  
Author(s):  
Gazanfer Belge ◽  
Brita Thode ◽  
Jörn Bullerdiek ◽  
Sabine Bartnitzke

Author(s):  
Martyna Borowczyk ◽  
Kosma Woliński ◽  
Barbara Więckowska ◽  
Elżbieta Jodłowska-Siewert ◽  
Ewelina Szczepanek-Parulska ◽  
...  

Author(s):  
Md. Ali Hossain ◽  
Tania Akter Asa ◽  
Md. Mijanur Rahman ◽  
Shahadat Uddin ◽  
Ahmed A. Moustafa ◽  
...  

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.


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