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ORIGINAL RESEARCH article

Front. Immunol.
Sec. Autoimmune and Autoinflammatory Disorders : Autoimmune Disorders
Volume 15 - 2024 | doi: 10.3389/fimmu.2024.1426875

Evaluating the Significance of ECSCR in the Diagnosis of Ulcerative Colitis and Drug Efficacy Assessment

Provisionally accepted
Bin Feng Bin Feng 1Yanqiu Zhang Yanqiu Zhang 2Longwei Qiao Longwei Qiao 3Qingqin Tang Qingqin Tang 1Zheng Zhang Zheng Zhang 1Sheng Zhang Sheng Zhang 1Jun Qiu Jun Qiu 1Xianping Zhou Xianping Zhou 2Chao Huang Chao Huang 3Yuting Liang Yuting Liang 1*
  • 1 The First Affiliated Hospital of Soochow University, Suzhou, China
  • 2 Anhui Medical University, Hefei, Anhui Province, China
  • 3 Suzhou Municipal Hospital, Suzhou, Jiangsu Province, China

The final, formatted version of the article will be published soon.

    The main challenge in diagnosing and treating ulcerative colitis (UC) has prompted this study to discover useful biomarkers and understand the underlying molecular mechanisms.In 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. Results: RRA 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.Conclusions: THY1, 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.

    Keywords: ulcerative colitis, ECSCR, machine learning, diagnosis, biomarker

    Received: 02 May 2024; Accepted: 03 Jul 2024.

    Copyright: © 2024 Feng, Zhang, Qiao, Tang, Zhang, Zhang, Qiu, Zhou, Huang and Liang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Yuting Liang, The First Affiliated Hospital of Soochow University, Suzhou, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.