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。

IF 3.9 3区 医学 Q2 ENDOCRINOLOGY & METABOLISM
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}
引用次数: 0

摘要

背景:术前诊断滤泡性甲状腺癌(FTC)是具有挑战性的,因为它不能很容易区分滤泡性腺瘤(FA)或良性滤泡结节病(FND)使用超声和细胞学特征通常用于临床实践。方法:我们采用综合蛋白质组学和机器学习(ML)模型来识别能够分类三种亚型的新型诊断生物标志物:FTC、FA和FND。自下而上的蛋白质组学技术用于定量福尔马林固定石蜡包埋(FFPE)甲状腺组织中的蛋白质。总共分析了202个FFPE组织样本,包括62个fnd, 72个FAs和68个FTCs。结果:密切的光谱-光谱匹配量化了6332个蛋白,其中约9%(780个)蛋白在组间差异表达。当将ML模型应用于术前细胞病理学不确定样本(n=183)的蛋白质组学数据时,我们鉴定出不同的蛋白质组:FND预测模型中的5个蛋白质(CNDP2、DNAAF5、DYNC1H1、FARSB和PDCD4), FA模型中的6个蛋白质(DNAAF5、FAM149B1、RPS9、TAGLN2、UPF1和UQCRC1), FTC模型中的7个蛋白质(ACTN4、DSTN、MACROH2A1、NUCB1、SPTAN1、TAGLN和XRCC5)。通过随机森林模型曲线值下的中位数面积来评估分类器的性能,FND为0.832(95%置信区间[CI], 0.824至0.839),FA为0.826 (95% CI, 0.817至0.835),FTC为0.870 (95% CI, 0.863至0.877)。结论:定量蛋白质组学分析结合ML模型建立了一个优化的多蛋白面板,可以区分FTC与良性亚型。我们的研究结果表明,蛋白质组学方法有望对FTC进行鉴别诊断。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Endocrinology and Metabolism
Endocrinology and Metabolism Medicine-Endocrinology, Diabetes and Metabolism
CiteScore
6.60
自引率
5.90%
发文量
145
审稿时长
24 weeks
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.).
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信