Tingyan Dong , Yueming Liang , Hui Chen , Yanling Li , Zhiping Li , Xinglin Gao
{"title":"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。","authors":"Tingyan Dong , Yueming Liang , Hui Chen , Yanling Li , Zhiping Li , 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":"{\"title\":\"Quantitative proteomics revealed protein biomarkers to distinguish malignant pleural effusion from benign pleural effusion\",\"authors\":\"Tingyan Dong , Yueming Liang , Hui Chen , Yanling Li , Zhiping Li , 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}","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}
Quantitative proteomics revealed protein biomarkers to distinguish malignant pleural effusion from benign pleural effusion
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.