Dataset Information

Comprehensive proteomics analysis to distinguish follicular adenoma and follicular thyroid carcinoma from indeterminate thyroid nodules.Comprehensive proteomics analysis to distinguish follicular adenoma and follicular thyroid carcinoma from indeterminate thyroid nodules

ABSTRACT: In this study, a total of 202 formalin fixed paraffin embedded (FFPE) thyroid tissues including 62 of benign nodules, 72 of follicular adenoma, and 68 of follicular thyroid carcinoma were used for the analyses. Here, through global FFPE proteomic analysis, proteins differentially expressed by group were identified and quantitative analysis was performed by LC-MS/MS.

INSTRUMENT(S):

ORGANISM(S): Homo Sapiens (human)

SUBMITTER: Hee-Sung Ahn

LAB HEAD: Kyunggon Kim

PROVIDER: PXD043154 | Pride | 2025-10-13

REPOSITORIES: Pride

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Dataset's files

Source:

			Action	DRS
	20190728_Thyroid_FFPE_FA_07.mgf	Mgf
	20190728_Thyroid_FFPE_FA_07.mzML	Mzml
	20190728_Thyroid_FFPE_FA_07.pepXML	Pepxml
	20190728_Thyroid_FFPE_FA_08.mgf	Mgf
	20190728_Thyroid_FFPE_FA_08.mzML	Mzml

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Publications

Comprehensive Proteomics and Machine Learning Analysis to Distinguish Follicular Adenoma and Follicular Thyroid Carcinoma from Indeterminate Thyroid Nodules.

Ahn Hee-Sung HS Song Eyun E Kim Chae A CA Jeon Min Ji MJ Lee Yu-Mi YM Sung Tea-Yon TY Song Dong Eun DE Yu Jiyoung J Shin Ji Min JM Choi Yeon-Sook YS Kim Kyunggon K Kim Won Gu WG

Endocrinology and metabolism (Seoul, Korea) 20250410 4

<h4>Backgruound</h4>The preoperative diagnosis of follicular thyroid carcinoma (FTC) is challenging because it cannot be readily distinguished from follicular adenoma (FA) or benign follicular nodular disease (FND) using the sonographic and cytological features typically employed in clinical practice.<h4>Methods</h4>We employed comprehensive proteomics and machine learning (ML) models to identify novel diagnostic biomarkers capable of classifying three subtypes: FTC, FA, and FND. Bottom-up prote ...[more]

PMID: 40205804

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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.