Evaluating the effectiveness of whole blood plasma versus protein precipitates in ovarian cancer detection through infrared spectroscopy

IF 2.7 3区 化学 Q2 CHEMISTRY, ANALYTICAL
Ana C. O. Neves, Maria Paraskevaidi, Pierre Martin-Hirsch and Kássio M. G. de Lima
{"title":"Evaluating the effectiveness of whole blood plasma versus protein precipitates in ovarian cancer detection through infrared spectroscopy","authors":"Ana C. O. Neves, Maria Paraskevaidi, Pierre Martin-Hirsch and Kássio M. G. de Lima","doi":"10.1039/D4AY02321H","DOIUrl":null,"url":null,"abstract":"<p >Early diagnosis of ovarian cancer remains challenging due to the absence of effective screening tests. The success of treatment and 5 year survival rates are significantly reliant on identifying the disease at a non-advanced stage, which highlights the urgent need for novel early detection and diagnostic approaches. Blood-based spectroscopic techniques, combined with chemometrics, have the potential to be used as tools for screening and diagnostic purposes in this context. In this study, we utilised attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy to analyse blood plasma samples from benign (<em>n</em> = 15) and ovarian cancer (<em>n</em> = 15) cases. We conducted multivariate discrimination models to compare the results in terms of sensitivity, specificity, and diagnostic accuracy when using either plasmatic protein precipitates or whole plasma to distinguish between benign and ovarian cancer. Notably, diagnostic accuracy values of 96% (sensitivity and specificity of 96%) and 92% (sensitivity and specificity of 88% and 96%, respectively) were achieved for the protein precipitates and whole plasma datasets respectively using genetic algorithms with linear and quadratic discriminant analysis. Furthermore, this methodology demonstrated its capability to categorise samples within the ovarian cancer class, distinguishing between early stage (FIGO I) and advanced stage (FIGO II–III), with excellent accuracy exceeding 97% for protein precipitate dataset. These findings highlight the utilisation of a specific class of biomolecules in a proteomic-like approach based on infrared spectroscopy and chemometrics for detecting ovarian cancer using blood plasma samples.</p>","PeriodicalId":64,"journal":{"name":"Analytical Methods","volume":" 11","pages":" 2477-2486"},"PeriodicalIF":2.7000,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Analytical Methods","FirstCategoryId":"92","ListUrlMain":"https://pubs.rsc.org/en/content/articlelanding/2025/ay/d4ay02321h","RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CHEMISTRY, ANALYTICAL","Score":null,"Total":0}
引用次数: 0

Abstract

Early diagnosis of ovarian cancer remains challenging due to the absence of effective screening tests. The success of treatment and 5 year survival rates are significantly reliant on identifying the disease at a non-advanced stage, which highlights the urgent need for novel early detection and diagnostic approaches. Blood-based spectroscopic techniques, combined with chemometrics, have the potential to be used as tools for screening and diagnostic purposes in this context. In this study, we utilised attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy to analyse blood plasma samples from benign (n = 15) and ovarian cancer (n = 15) cases. We conducted multivariate discrimination models to compare the results in terms of sensitivity, specificity, and diagnostic accuracy when using either plasmatic protein precipitates or whole plasma to distinguish between benign and ovarian cancer. Notably, diagnostic accuracy values of 96% (sensitivity and specificity of 96%) and 92% (sensitivity and specificity of 88% and 96%, respectively) were achieved for the protein precipitates and whole plasma datasets respectively using genetic algorithms with linear and quadratic discriminant analysis. Furthermore, this methodology demonstrated its capability to categorise samples within the ovarian cancer class, distinguishing between early stage (FIGO I) and advanced stage (FIGO II–III), with excellent accuracy exceeding 97% for protein precipitate dataset. These findings highlight the utilisation of a specific class of biomolecules in a proteomic-like approach based on infrared spectroscopy and chemometrics for detecting ovarian cancer using blood plasma samples.

Abstract Image

求助全文
约1分钟内获得全文 求助全文
来源期刊
Analytical Methods
Analytical Methods CHEMISTRY, ANALYTICAL-FOOD SCIENCE & TECHNOLOGY
CiteScore
5.10
自引率
3.20%
发文量
569
审稿时长
1.8 months
期刊介绍: Early applied demonstrations of new analytical methods with clear societal impact
×
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