{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Ahn HS"],"funding":["Ministry of Science and ICT","Asan Institute for Life Sciences, Asan Medical Center","National Research Foundation of Korea","NCIRD CDC HHS"],"pagination":["623-636"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12409165"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["40(4)"],"pubmed_abstract":["<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 proteomics techniques were applied to quantify proteins in formalin-fixed, paraffin-embedded (FFPE) thyroid tissues. In total, 202 FFPE tissue samples, comprising 62 FNDs, 72 FAs, and 68 FTCs, were analyzed.<h4>Results</h4>Close spectrum-spectrum matching quantified 6,332 proteins, with approximately 9% "],"journal":["Endocrinology and metabolism (Seoul, Korea)"],"pubmed_title":["Comprehensive Proteomics and Machine Learning Analysis to Distinguish Follicular Adenoma and Follicular Thyroid Carcinoma from Indeterminate Thyroid Nodules."],"pmcid":["PMC12409165"],"funding_grant_id":["U01 IP000054","RS-2024-00454407","RS-2022-NR069128","2024IP0054"],"pubmed_authors":["Song DE","Ahn HS","Sung TY","Kim WG","Jeon MJ","Lee YM","Kim K","Kim CA","Song E","Yu J","Shin JM","Choi YS"],"additional_accession":[]},"is_claimable":false,"name":"Comprehensive Proteomics and Machine Learning Analysis to Distinguish Follicular Adenoma and Follicular Thyroid Carcinoma from Indeterminate Thyroid Nodules.","description":"<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 proteomics techniques were applied to quantify proteins in formalin-fixed, paraffin-embedded (FFPE) thyroid tissues. In total, 202 FFPE tissue samples, comprising 62 FNDs, 72 FAs, and 68 FTCs, were analyzed.<h4>Results</h4>Close spectrum-spectrum matching quantified 6,332 proteins, with approximately 9% ","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Aug","modification":"2026-05-29T21:47:08.048Z","creation":"2026-04-08T06:06:23.52Z"},"accession":"S-EPMC12409165","cross_references":{"pubmed":["40205804"],"doi":["10.3803/EnM.2024.2208"]}}