Proteomic analysis of plasma and duodenal tissue in celiac disease patients reveals potential noninvasive diagnostic biomarkers
Abstract The pathogenesis of celiac disease (CeD) remains incompletely understood. Traditional diagnostic techniques for CeD include serological testing and endoscopic examination; however, they have limitations. Therefore, there is a need to identify novel noninvasive biomarkers for CeD diagnosis....
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Nature Portfolio
2024-12-01
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| Online Access: | https://doi.org/10.1038/s41598-024-80391-5 |
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| author | Na Li Ayinuer Maimaitireyimu Tian Shi Yan Feng Weidong Liu Shenglong Xue Feng Gao |
| author_facet | Na Li Ayinuer Maimaitireyimu Tian Shi Yan Feng Weidong Liu Shenglong Xue Feng Gao |
| author_sort | Na Li |
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| description | Abstract The pathogenesis of celiac disease (CeD) remains incompletely understood. Traditional diagnostic techniques for CeD include serological testing and endoscopic examination; however, they have limitations. Therefore, there is a need to identify novel noninvasive biomarkers for CeD diagnosis. We analyzed duodenal and plasma samples from CeD patients by four-dimensional data-dependent acquisition (4D-DIA) proteomics. Differentially expressed proteins (DEPs) were identified for functional analysis and to propose blood biomarkers associated with CeD diagnosis. In duodenal and plasma samples, respectively, 897 and 140 DEPs were identified. Combining weighted gene co-expression network analysis(WGCNA) with the DEPs, five key proteins were identified across three machine learning methods. FGL2 and TXNDC5 were significantly elevated in the CeD group, while CHGA expression showed an increasing trend, but without statistical significance. The receiver operating characteristic curve results indicated an area under the curve (AUC) of 0.7711 for FGL2 and 0.6978 for TXNDC5, with a combined AUC of 0.8944. Exploratory analysis using Mfuzz and three machine learning methods identified four plasma proteins potentially associated with CeD pathological grading (Marsh classification): FABP, CPOX, BHMT, and PPP2CB. We conclude that FGL2 and TXNDC5 deserve exploration as potential sensitive, noninvasive diagnostic biomarkers for CeD. |
| format | Article |
| id | doaj-art-c7c9c3a83c2b4b6180b849a6859b86ce |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Nature Portfolio |
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| spelling | doaj-art-c7c9c3a83c2b4b6180b849a6859b86ce2024-12-08T12:26:59ZengNature PortfolioScientific Reports2045-23222024-12-0114111210.1038/s41598-024-80391-5Proteomic analysis of plasma and duodenal tissue in celiac disease patients reveals potential noninvasive diagnostic biomarkersNa Li0Ayinuer Maimaitireyimu1Tian Shi2Yan Feng3Weidong Liu4Shenglong Xue5Feng Gao6Xinjiang Medical University, Xinjiang Uygur Autonomous RegionDepartment of Gastroenterology, People’s Hospital of Xinjiang Uygur Autonomous Region, Xinjiang Uygur Autonomous RegionDepartment of Gastroenterology, People’s Hospital of Xinjiang Uygur Autonomous Region, Xinjiang Uygur Autonomous RegionDepartment of Gastroenterology, People’s Hospital of Xinjiang Uygur Autonomous Region, Xinjiang Uygur Autonomous RegionDepartment of Gastroenterology, People’s Hospital of Xinjiang Uygur Autonomous Region, Xinjiang Uygur Autonomous RegionCollege of Life Science and Technology, Xinjiang UniversityDepartment of Gastroenterology, People’s Hospital of Xinjiang Uygur Autonomous Region, Xinjiang Uygur Autonomous RegionAbstract The pathogenesis of celiac disease (CeD) remains incompletely understood. Traditional diagnostic techniques for CeD include serological testing and endoscopic examination; however, they have limitations. Therefore, there is a need to identify novel noninvasive biomarkers for CeD diagnosis. We analyzed duodenal and plasma samples from CeD patients by four-dimensional data-dependent acquisition (4D-DIA) proteomics. Differentially expressed proteins (DEPs) were identified for functional analysis and to propose blood biomarkers associated with CeD diagnosis. In duodenal and plasma samples, respectively, 897 and 140 DEPs were identified. Combining weighted gene co-expression network analysis(WGCNA) with the DEPs, five key proteins were identified across three machine learning methods. FGL2 and TXNDC5 were significantly elevated in the CeD group, while CHGA expression showed an increasing trend, but without statistical significance. The receiver operating characteristic curve results indicated an area under the curve (AUC) of 0.7711 for FGL2 and 0.6978 for TXNDC5, with a combined AUC of 0.8944. Exploratory analysis using Mfuzz and three machine learning methods identified four plasma proteins potentially associated with CeD pathological grading (Marsh classification): FABP, CPOX, BHMT, and PPP2CB. We conclude that FGL2 and TXNDC5 deserve exploration as potential sensitive, noninvasive diagnostic biomarkers for CeD.https://doi.org/10.1038/s41598-024-80391-5Celiac disease4D-DIA proteomicsMachine learningWGCNAMarsh classificationBiomarker |
| spellingShingle | Na Li Ayinuer Maimaitireyimu Tian Shi Yan Feng Weidong Liu Shenglong Xue Feng Gao Proteomic analysis of plasma and duodenal tissue in celiac disease patients reveals potential noninvasive diagnostic biomarkers Scientific Reports Celiac disease 4D-DIA proteomics Machine learning WGCNA Marsh classification Biomarker |
| title | Proteomic analysis of plasma and duodenal tissue in celiac disease patients reveals potential noninvasive diagnostic biomarkers |
| title_full | Proteomic analysis of plasma and duodenal tissue in celiac disease patients reveals potential noninvasive diagnostic biomarkers |
| title_fullStr | Proteomic analysis of plasma and duodenal tissue in celiac disease patients reveals potential noninvasive diagnostic biomarkers |
| title_full_unstemmed | Proteomic analysis of plasma and duodenal tissue in celiac disease patients reveals potential noninvasive diagnostic biomarkers |
| title_short | Proteomic analysis of plasma and duodenal tissue in celiac disease patients reveals potential noninvasive diagnostic biomarkers |
| title_sort | proteomic analysis of plasma and duodenal tissue in celiac disease patients reveals potential noninvasive diagnostic biomarkers |
| topic | Celiac disease 4D-DIA proteomics Machine learning WGCNA Marsh classification Biomarker |
| url | https://doi.org/10.1038/s41598-024-80391-5 |
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