Identifying biomarkers for diagnosis and disease activity monitoring in PSC-IBD and UC through proteomic profiling: A prospective, biomarker discovery single-center study protocol

TL;DR

This study aims to discover new biomarkers for diagnosing and monitoring PSC-IBD and UC using proteomic profiling of biopsy samples from patients and healthy controls.

Contribution

The study introduces a novel proteomic approach to identify biomarkers for PSC-IBD and UC, linking proteomic profiles to disease phenotypes.

Findings

Proteomic profiling will identify novel biomarkers for PSC-IBD and UC.

External validation will use targeted proteomics and immunohistochemistry to confirm findings.

The study design supports improved diagnostic accuracy and disease activity assessment.

Abstract

Inflammatory bowel diseases (IBD) and primary sclerosing cholangitis (PSC) are chronic inflammatory conditions with limited biomarker-driven diagnostic tools. Proteomic profiling offers a promising approach to uncover specific biomarkers that could refine diagnostic accuracy, monitor disease activity, and guide therapeutic strategies. Our primary aim is to identify novel biomarkers for PSC-IBD and conventional ulcerative colitis (UC) via proteomic approach. The secondary aim is to advance the etiopathogenic understanding of the diseases by linking specific proteomic profiles with disease phenotypes. This single-center, prospective, biomarker-discovery study will involve 50 participants with PSC-IBD, 50 with UC, and 50 healthy controls. Biopsy samples from five bowel segments will be analyzed for proteomic signatures by an untargeted approach. The findings will subsequently undergo multi-step external validation in separate cohorts of 30 patients with PSC-IBD and 30 with UC, utilizing targeted proteomics, immunohistochemistry, and ELISA in bowel mucosa and peripheral blood, respectively. This proposed study aims to identify novel biomarkers to improve the diagnostic accuracy of PSC-IBD and UC and refine the disease activity assessment. Its robust design and large sample size provide a strong foundation for successful biomarker identification, with the potential to enhance clinical management of patients.

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