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Evaluating the significance of ECSCR in the diagnosis of ulcerative colitis and drug efficacy assessment

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Title: Evaluating the significance of ECSCR in the diagnosis of ulcerative colitis and drug efficacy assessment
Authors: Bin Feng, Yanqiu Zhang, Longwei Qiao, Qingqin Tang, Zheng Zhang, Sheng Zhang, Jun Qiu, Xianping Zhou, Chao Huang, Yuting Liang
Source: Frontiers in Immunology, Vol 15 (2024)
Publisher Information: Frontiers Media S.A., 2024.
Publication Year: 2024
Collection: LCC:Immunologic diseases. Allergy
Subject Terms: ulcerative colitis, ECSCR, machine learning, diagnosis, biomarker, Immunologic diseases. Allergy, RC581-607
More Details: BackgroundThe main challenge in diagnosing and treating ulcerative colitis (UC) has prompted this study to discover useful biomarkers and understand the underlying molecular mechanisms.MethodsIn this study, transcriptomic data from intestinal mucosal biopsies underwent Robust Rank Aggregation (RRA) analysis to identify differential genes. These genes intersected with UC key genes from Weighted Gene Co-expression Network Analysis (WGCNA). Machine learning identified UC signature genes, aiding predictive model development. Validation involved external data for diagnostic, progression, and drug efficacy assessment, along with ELISA testing of clinical serum samples.ResultsRRA integrative analysis identified 251 up-regulated and 211 down-regulated DEGs intersecting with key UC genes in WGCNA, yielding 212 key DEGs. Subsequently, five UC signature biomarkers were identified by machine learning based on the key DEGs—THY1, SLC6A14, ECSCR, FAP, and GPR109B. A logistic regression model incorporating these five genes was constructed. The AUC values for the model set and internal validation data were 0.995 and 0.959, respectively. Mechanistically, activation of the IL-17 signaling pathway, TNF signaling pathway, PI3K-Akt signaling pathway in UC was indicated by KEGG and GSVA analyses, which were positively correlated with the signature biomarkers. Additionally, the expression of the signature biomarkers was strongly correlated with various UC types and drug efficacy in different datasets. Notably, ECSCR was found to be upregulated in UC serum and exhibited a positive correlation with neutrophil levels in UC patients.ConclusionsTHY1, SLC6A14, ECSCR, FAP, and GPR109B can serve as potential biomarkers of UC and are closely related to signaling pathways associated with UC progression. The discovery of these markers provides valuable information for understanding the molecular mechanisms of UC.
Document Type: article
File Description: electronic resource
Language: English
ISSN: 1664-3224
Relation: https://www.frontiersin.org/articles/10.3389/fimmu.2024.1426875/full; https://doaj.org/toc/1664-3224
DOI: 10.3389/fimmu.2024.1426875
Access URL: https://doaj.org/article/d789131268f54fb19158e51a9c091bc3
Accession Number: edsdoj.789131268f54fb19158e51a9c091bc3
Database: Directory of Open Access Journals
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  Data: Evaluating the significance of ECSCR in the diagnosis of ulcerative colitis and drug efficacy assessment
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  Data: <searchLink fieldCode="AR" term="%22Bin+Feng%22">Bin Feng</searchLink><br /><searchLink fieldCode="AR" term="%22Yanqiu+Zhang%22">Yanqiu Zhang</searchLink><br /><searchLink fieldCode="AR" term="%22Longwei+Qiao%22">Longwei Qiao</searchLink><br /><searchLink fieldCode="AR" term="%22Qingqin+Tang%22">Qingqin Tang</searchLink><br /><searchLink fieldCode="AR" term="%22Zheng+Zhang%22">Zheng Zhang</searchLink><br /><searchLink fieldCode="AR" term="%22Sheng+Zhang%22">Sheng Zhang</searchLink><br /><searchLink fieldCode="AR" term="%22Jun+Qiu%22">Jun Qiu</searchLink><br /><searchLink fieldCode="AR" term="%22Xianping+Zhou%22">Xianping Zhou</searchLink><br /><searchLink fieldCode="AR" term="%22Chao+Huang%22">Chao Huang</searchLink><br /><searchLink fieldCode="AR" term="%22Yuting+Liang%22">Yuting Liang</searchLink>
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  Data: Frontiers in Immunology, Vol 15 (2024)
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  Data: LCC:Immunologic diseases. Allergy
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  Data: <searchLink fieldCode="DE" term="%22ulcerative+colitis%22">ulcerative colitis</searchLink><br /><searchLink fieldCode="DE" term="%22ECSCR%22">ECSCR</searchLink><br /><searchLink fieldCode="DE" term="%22machine+learning%22">machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22diagnosis%22">diagnosis</searchLink><br /><searchLink fieldCode="DE" term="%22biomarker%22">biomarker</searchLink><br /><searchLink fieldCode="DE" term="%22Immunologic+diseases%2E+Allergy%22">Immunologic diseases. Allergy</searchLink><br /><searchLink fieldCode="DE" term="%22RC581-607%22">RC581-607</searchLink>
– Name: Abstract
  Label: Description
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  Data: BackgroundThe main challenge in diagnosing and treating ulcerative colitis (UC) has prompted this study to discover useful biomarkers and understand the underlying molecular mechanisms.MethodsIn this study, transcriptomic data from intestinal mucosal biopsies underwent Robust Rank Aggregation (RRA) analysis to identify differential genes. These genes intersected with UC key genes from Weighted Gene Co-expression Network Analysis (WGCNA). Machine learning identified UC signature genes, aiding predictive model development. Validation involved external data for diagnostic, progression, and drug efficacy assessment, along with ELISA testing of clinical serum samples.ResultsRRA integrative analysis identified 251 up-regulated and 211 down-regulated DEGs intersecting with key UC genes in WGCNA, yielding 212 key DEGs. Subsequently, five UC signature biomarkers were identified by machine learning based on the key DEGs—THY1, SLC6A14, ECSCR, FAP, and GPR109B. A logistic regression model incorporating these five genes was constructed. The AUC values for the model set and internal validation data were 0.995 and 0.959, respectively. Mechanistically, activation of the IL-17 signaling pathway, TNF signaling pathway, PI3K-Akt signaling pathway in UC was indicated by KEGG and GSVA analyses, which were positively correlated with the signature biomarkers. Additionally, the expression of the signature biomarkers was strongly correlated with various UC types and drug efficacy in different datasets. Notably, ECSCR was found to be upregulated in UC serum and exhibited a positive correlation with neutrophil levels in UC patients.ConclusionsTHY1, SLC6A14, ECSCR, FAP, and GPR109B can serve as potential biomarkers of UC and are closely related to signaling pathways associated with UC progression. The discovery of these markers provides valuable information for understanding the molecular mechanisms of UC.
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        Value: 10.3389/fimmu.2024.1426875
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      – Text: English
    Subjects:
      – SubjectFull: ulcerative colitis
        Type: general
      – SubjectFull: ECSCR
        Type: general
      – SubjectFull: machine learning
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      – SubjectFull: Immunologic diseases. Allergy
        Type: general
      – SubjectFull: RC581-607
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      – TitleFull: Evaluating the significance of ECSCR in the diagnosis of ulcerative colitis and drug efficacy assessment
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