Hee-Sung Ahn, Eyun Song, Chae A Kim, Min Ji Jeon, Yu-Mi Lee, Tea-Yon Sung, Dong Eun Song, Jiyoung Yu, Ji Min Shin, Yeon-Sook Choi, Kyunggon Kim, Won Gu Kim
{"title":"综合蛋白质组学和机器学习分析区分滤泡性腺瘤和滤泡性甲状腺癌与不确定甲状腺结节。","authors":"Hee-Sung Ahn, Eyun Song, Chae A Kim, Min Ji Jeon, Yu-Mi Lee, Tea-Yon Sung, Dong Eun Song, Jiyoung Yu, Ji Min Shin, Yeon-Sook Choi, Kyunggon Kim, Won Gu Kim","doi":"10.3803/EnM.2024.2208","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>Close spectrum-spectrum matching quantified 6,332 proteins, with approximately 9% (780 proteins) differentially expressed among the groups. When applying an ML model to the proteomics data from samples with preoperative indeterminate cytopathology (n=183), we identified distinct protein panels: five proteins (CNDP2, DNAAF5, DYNC1H1, FARSB, and PDCD4) for the FND prediction model, six proteins (DNAAF5, FAM149B1, RPS9, TAGLN2, UPF1, and UQCRC1) for the FA model, and seven proteins (ACTN4, DSTN, MACROH2A1, NUCB1, SPTAN1, TAGLN, and XRCC5) for the FTC model. The classifiers' performance, evaluated by the median area under the curve values of the random forest models, was 0.832 (95% confidence interval [CI], 0.824 to 0.839) for FND, 0.826 (95% CI, 0.817 to 0.835) for FA, and 0.870 (95% CI, 0.863 to 0.877) for FTC.</p><p><strong>Conclusion: </strong>Quantitative proteome analysis combined with an ML model yielded an optimized multi-protein panel that can distinguish FTC from benign subtypes. Our findings indicate that a proteomic approach holds promise for the differential diagnosis of FTC.</p>","PeriodicalId":11636,"journal":{"name":"Endocrinology and Metabolism","volume":" ","pages":""},"PeriodicalIF":3.9000,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Comprehensive Proteomics and Machine Learning Analysis to Distinguish Follicular Adenoma and Follicular Thyroid Carcinoma from Indeterminate Thyroid Nodules.\",\"authors\":\"Hee-Sung Ahn, Eyun Song, Chae A Kim, Min Ji Jeon, Yu-Mi Lee, Tea-Yon Sung, Dong Eun Song, Jiyoung Yu, Ji Min Shin, Yeon-Sook Choi, Kyunggon Kim, Won Gu Kim\",\"doi\":\"10.3803/EnM.2024.2208\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>Close spectrum-spectrum matching quantified 6,332 proteins, with approximately 9% (780 proteins) differentially expressed among the groups. When applying an ML model to the proteomics data from samples with preoperative indeterminate cytopathology (n=183), we identified distinct protein panels: five proteins (CNDP2, DNAAF5, DYNC1H1, FARSB, and PDCD4) for the FND prediction model, six proteins (DNAAF5, FAM149B1, RPS9, TAGLN2, UPF1, and UQCRC1) for the FA model, and seven proteins (ACTN4, DSTN, MACROH2A1, NUCB1, SPTAN1, TAGLN, and XRCC5) for the FTC model. The classifiers' performance, evaluated by the median area under the curve values of the random forest models, was 0.832 (95% confidence interval [CI], 0.824 to 0.839) for FND, 0.826 (95% CI, 0.817 to 0.835) for FA, and 0.870 (95% CI, 0.863 to 0.877) for FTC.</p><p><strong>Conclusion: </strong>Quantitative proteome analysis combined with an ML model yielded an optimized multi-protein panel that can distinguish FTC from benign subtypes. Our findings indicate that a proteomic approach holds promise for the differential diagnosis of FTC.</p>\",\"PeriodicalId\":11636,\"journal\":{\"name\":\"Endocrinology and Metabolism\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":3.9000,\"publicationDate\":\"2025-04-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Endocrinology and Metabolism\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.3803/EnM.2024.2208\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENDOCRINOLOGY & METABOLISM\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Endocrinology and Metabolism","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.3803/EnM.2024.2208","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENDOCRINOLOGY & METABOLISM","Score":null,"Total":0}
Comprehensive Proteomics and Machine Learning Analysis to Distinguish Follicular Adenoma and Follicular Thyroid Carcinoma from Indeterminate Thyroid Nodules.
Background: 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.
Methods: 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.
Results: Close spectrum-spectrum matching quantified 6,332 proteins, with approximately 9% (780 proteins) differentially expressed among the groups. When applying an ML model to the proteomics data from samples with preoperative indeterminate cytopathology (n=183), we identified distinct protein panels: five proteins (CNDP2, DNAAF5, DYNC1H1, FARSB, and PDCD4) for the FND prediction model, six proteins (DNAAF5, FAM149B1, RPS9, TAGLN2, UPF1, and UQCRC1) for the FA model, and seven proteins (ACTN4, DSTN, MACROH2A1, NUCB1, SPTAN1, TAGLN, and XRCC5) for the FTC model. The classifiers' performance, evaluated by the median area under the curve values of the random forest models, was 0.832 (95% confidence interval [CI], 0.824 to 0.839) for FND, 0.826 (95% CI, 0.817 to 0.835) for FA, and 0.870 (95% CI, 0.863 to 0.877) for FTC.
Conclusion: Quantitative proteome analysis combined with an ML model yielded an optimized multi-protein panel that can distinguish FTC from benign subtypes. Our findings indicate that a proteomic approach holds promise for the differential diagnosis of FTC.
期刊介绍:
The aim of this journal is to set high standards of medical care by providing a forum for discussion for basic, clinical, and translational researchers and clinicians on new findings in the fields of endocrinology and metabolism. Endocrinology and Metabolism reports new findings and developments in all aspects of endocrinology and metabolism. The topics covered by this journal include bone and mineral metabolism, cytokines, developmental endocrinology, diagnostic endocrinology, endocrine research, dyslipidemia, endocrine regulation, genetic endocrinology, growth factors, hormone receptors, hormone action and regulation, management of endocrine diseases, clinical trials, epidemiology, molecular endocrinology, neuroendocrinology, neuropeptides, neurotransmitters, obesity, pediatric endocrinology, reproductive endocrinology, signal transduction, the anatomy and physiology of endocrine organs (i.e., the pituitary, thyroid, parathyroid, and adrenal glands, and the gonads), and endocrine diseases (diabetes, nutrition, osteoporosis, etc.).