Integrating Transcriptomics and Machine Learning to Uncover the FLI1-PARP14-Immune Axis in Ulcerative Colitis...

Area of Science:

• Gastroenterology
• Immunology
• Bioinformatics

Background:

• Ulcerative colitis (UC) is a chronic inflammatory bowel disease with incompletely understood molecular mechanisms.
• Current understanding of UC pathophysiology requires further elucidation of molecular drivers and diagnostic markers.

Purpose of the Study:

• To identify diagnostic biomarkers for UC activity.
• To unravel the pathogenic causes of UC through integrated multi-omics and machine learning approaches.
• To investigate the role of gene expression and immune cell infiltration in UC.

Main Methods:

• Utilized weighted gene co-expression network analysis (WGCNA) and differential gene expression (DEG) analysis on public gene expression datasets.
• Applied protein-protein interaction networks and ensemble machine learning to identify key genes.
• Conducted summary-data-based Mendelian Randomization (SMR) and immune infiltration analyses.

Main Results:

• Identified eight characteristic genes, with CXCL11, PARP14, and IFITM1 as significant hub genes demonstrating high diagnostic accuracy (AUC > 0.83).
• SMR analysis suggested a causal link between elevated PARP14 expression and UC susceptibility.
• Hub genes correlated strongly with immune cells like M1 macrophages and NK cells, with FLI1 identified as a key upstream regulator.

Conclusions:

• Established a FLI1-PARP14-immune axis central to UC pathogenesis.
• Highlighted PARP14 as a potential diagnostic biomarker and therapeutic target for ulcerative colitis.
• Provided novel insights into the molecular mechanisms underlying UC activity.