Quantitative proteomics revealed protein biomarkers to distinguish malignant pleural effusion from benign pleural effusion

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS
Tingyan Dong , Yueming Liang , Hui Chen , Yanling Li , Zhiping Li , Xinglin Gao
{"title":"Quantitative proteomics revealed protein biomarkers to distinguish malignant pleural effusion from benign pleural effusion","authors":"Tingyan Dong ,&nbsp;Yueming Liang ,&nbsp;Hui Chen ,&nbsp;Yanling Li ,&nbsp;Zhiping Li ,&nbsp;Xinglin Gao","doi":"10.1016/j.jprot.2024.105201","DOIUrl":null,"url":null,"abstract":"<div><p>To identify protein biomarkers capable of early prediction regarding the distinguishing malignant pleural effusion (MPE) from benign pleural effusion (BPE) in patients with lung disease. A four-dimensional data independent acquisition (4D-DIA) proteomic was performed to determine the differentially expressed proteins in samples from 20 lung adenocarcinoma MPE and 30 BPE. The significantly differential expressed proteins were selected for Gene Ontology (GO) enrichment and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway analysis. Protein biomarkers with high capability to discriminate MPE from BPE patients were identified by Random Forest (RF) algorithm prediction model, whose diagnostic and prognostic efficacy in primary tumors were further explored in public datasets, and were validated by ELISA experiment. 50 important proteins (30 up-regulated and 20 down-regulated) were selected out as potential markers to distinguish the MPE from BPE group. GO analysis revealed that those proteins involving the most important cell component is extracellular space. KEGG analysis identified the involvement of cellular adhesion molecules pathway. Furthermore, the Area Under Curve (AUC) of these proteins were ranged from 0.717 to 1.000,with excellent diagnostic properties to distinguish the MPE. Finally, significant survival and gene and protein expression analysis demonstrated BPIFB1, DPP4, HPRT1 and ABI3BP had high discriminating values.</p></div><div><h3>Significance</h3><p>We performed a 4D-DIA proteomics to determine the differentially expressed proteins in pleural effusion samples from MPE and BPE. Some potential protein biomarkers were identified to distinguish the MPE from BPE patients., which may provide helpful diagnostic and therapeutic insights for lung cancer. This is significant because the median survival time of patients with MPE is usually 4–12 months, thus, it is particularly important to diagnose MPE early to start treatments promptly. The most common causes of MPE are lung cancers, while pneumonia and tuberculosis are the main causes of BPE. If more diagnostic markers could be identified periodically, there would be an important significance to clinical diagnose and treatment with drugs in lung cancer patients.</p></div>","PeriodicalId":2,"journal":{"name":"ACS Applied Bio Materials","volume":null,"pages":null},"PeriodicalIF":4.6000,"publicationDate":"2024-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1874391924001337/pdfft?md5=6126a0743b7c1dc09ec98e259205d7b8&pid=1-s2.0-S1874391924001337-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Applied Bio Materials","FirstCategoryId":"99","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1874391924001337","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATERIALS SCIENCE, BIOMATERIALS","Score":null,"Total":0}
引用次数: 0

Abstract

To identify protein biomarkers capable of early prediction regarding the distinguishing malignant pleural effusion (MPE) from benign pleural effusion (BPE) in patients with lung disease. A four-dimensional data independent acquisition (4D-DIA) proteomic was performed to determine the differentially expressed proteins in samples from 20 lung adenocarcinoma MPE and 30 BPE. The significantly differential expressed proteins were selected for Gene Ontology (GO) enrichment and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway analysis. Protein biomarkers with high capability to discriminate MPE from BPE patients were identified by Random Forest (RF) algorithm prediction model, whose diagnostic and prognostic efficacy in primary tumors were further explored in public datasets, and were validated by ELISA experiment. 50 important proteins (30 up-regulated and 20 down-regulated) were selected out as potential markers to distinguish the MPE from BPE group. GO analysis revealed that those proteins involving the most important cell component is extracellular space. KEGG analysis identified the involvement of cellular adhesion molecules pathway. Furthermore, the Area Under Curve (AUC) of these proteins were ranged from 0.717 to 1.000,with excellent diagnostic properties to distinguish the MPE. Finally, significant survival and gene and protein expression analysis demonstrated BPIFB1, DPP4, HPRT1 and ABI3BP had high discriminating values.

Significance

We performed a 4D-DIA proteomics to determine the differentially expressed proteins in pleural effusion samples from MPE and BPE. Some potential protein biomarkers were identified to distinguish the MPE from BPE patients., which may provide helpful diagnostic and therapeutic insights for lung cancer. This is significant because the median survival time of patients with MPE is usually 4–12 months, thus, it is particularly important to diagnose MPE early to start treatments promptly. The most common causes of MPE are lung cancers, while pneumonia and tuberculosis are the main causes of BPE. If more diagnostic markers could be identified periodically, there would be an important significance to clinical diagnose and treatment with drugs in lung cancer patients.

定量蛋白质组学揭示了区分恶性胸腔积液和良性胸腔积液的蛋白质生物标志物。
目的:找出能够早期预测肺部疾病患者恶性胸腔积液(MPE)与良性胸腔积液(BPE)区别的蛋白质生物标志物。研究人员采用四维数据独立采集(4D-DIA)蛋白质组学方法,确定了 20 例肺癌 MPE 和 30 例良性胸腔积液样本中差异表达的蛋白质。选择差异表达明显的蛋白质进行基因本体(GO)富集和京都基因组百科全书(KEGG)通路分析。通过随机森林(RF)算法预测模型确定了具有较强区分 MPE 和 BPE 患者能力的蛋白质生物标志物,并通过 ELISA 实验进一步探讨了这些蛋白质在原发性肿瘤中的诊断和预后功效。筛选出的 50 个重要蛋白质(30 个上调,20 个下调)是区分 MPE 和 BPE 组的潜在标志物。GO分析显示,这些蛋白质涉及的最重要细胞成分是细胞外空间。KEGG 分析确定了细胞粘附分子通路的参与。此外,这些蛋白质的曲线下面积(AUC)介于 0.717 至 1.000 之间,在区分 MPE 方面具有极佳的诊断特性。最后,重要的生存和基因及蛋白表达分析表明,BPIFB1、DPP4、HPRT1 和 ABI3BP 具有很高的鉴别价值。意义:我们开展了一项 4D-DIA 蛋白组学研究,以确定 MPE 和 BPE 胸腔积液样本中表达不同的蛋白质。研究发现了一些区分 MPE 和 BPE 患者的潜在蛋白质生物标志物,这些标志物可能有助于肺癌的诊断和治疗。这一点意义重大,因为MPE患者的中位生存期通常为4-12个月,因此早期诊断MPE并及时开始治疗尤为重要。导致 MPE 的最常见原因是肺癌,而导致 BPE 的主要原因是肺炎和肺结核。如果能定期发现更多的诊断标志物,将对肺癌患者的临床诊断和药物治疗具有重要意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
×
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