ORIGINAL RESEARCH article
Front. Immunol.
Sec. Autoimmune and Autoinflammatory Disorders : Autoimmune Disorders
Volume 16 - 2025 | doi: 10.3389/fimmu.2025.1570374
This article is part of the Research TopicBiomarkers and Beyond: Predicting Course and Tailoring Treatment in Inflammatory Bowel DiseasesView all 6 articles
Biomarker Discovery for Non-invasive Diagnosis of Inflammatory Bowel Disease Using Blood Transcriptomics
Provisionally accepted
- Shahid Beheshti University of Medical Sciences, Tehran, Tehran, Iran
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Attempts to identify reliable inflammatory biomarkers from transcriptomic data represent a promising approach for the development of clinical diagnostic tests. In this context, diagnosis of inflammatory bowel disease (IBD), conventionally performed using colonoscopy and pathological examinations, requires a non-invasive, cost-effective, and precise test. Hereby, a blood-based biomarker panel is proposed by integrating and analyzing publicly available transcriptomes of IBD patients. Our analysis revealed an altered immune cell profile in IBD, characterized by increased M0 macrophages, T regulatory, and CD4 naïve T cells, along with decreased B lymphocytes and activated natural killer cells. Besides, neutrophil-mediated immunity induction and mitochondrial membrane oxidative phosphorylation disruption along with NDUFB2 downregulation were highlighted as key mechanisms involved in blood inflammation in IBD. To develop a diagnostic panel, we used LASSO and SVM algorithms, achieving an accuracy of 84% with the combination of EIF5A, IL4R, and SLC9A8 as key biomarkers, which subsequently were validated in a real-life cohort with 66 samples, exhibiting an impressive accuracy of 99%. In summary, this study provided IBD-specific molecular and immune cell signatures, developed and validated a blood-based diagnostic biomarker panel, while contributing to our understanding of the disease's pathophysiology.
Keywords: Inflammatory bowel disease (IBD), diagnosis, Transcriptomics, biomarker, whole blood, machine learning, bioinformatics
Received: 03 Feb 2025; Accepted: 04 Jun 2025.
Copyright: © 2025 Derakhshan, Ghorbaninejad, Shahrokh and Meyfour. 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: Anna Meyfour, Shahid Beheshti University of Medical Sciences, Tehran, 198396-3113, Tehran, Iran
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