Systems biology-driven miR-mRNA integration identifies potential steroid- refractoriness biomarkers in ulcerative colitis and reveals a conserved mechanism across species

Background and Aims: Approximately 60% of patients with ulcerative colitis (UC) exhibit steroid resistance or dependence, highlighting the need for reliable biomarkers to predict therapeutic response. This study employed a systems biology and machine learning approach to integrate microRNA (miR) and mRNA expression data from both rectal biopsies and plasma samples from UC patients undergoing corticosteroid therapy, aiming to uncover the molecular mechanisms underlying steroid refractoriness. Methods: Whole-transcriptome and miR profiling were performed at baseline and after three days of corticosteroid therapy. Corticosteroid-treated UC patients were classified as responders (R) or non-responders (NR) after seven days of treatment. Mathematical modelling and protein-miR interaction mapping were used to identify mechanistically relevant biomarker candidates. Selected findings were validated in a TNBS-induced colitis mouse model. Results: Key transcriptional co-regulators such as NCOA3, CBP, NCOR1, and NRIP1 were differentially expressed between R and NR, influencing glucocorticoid receptor (GCR) signalling. Multiple miRNAs, including miR-145-5p, miR-10b-5p, and miR-16-5p, were identified as potential biomarkers and regulators of inflammatory and GCR-related pathways. The cross-correlation between plasma and tissue miRs revealed consistent molecular patterns, some of which were also conserved in the murine model, supporting the existence of cross-species steroid response mechanisms. Conclusions: This integrative multi-omic approach provides new insights into molecular steroid-refractoriness in UC and offers a promising framework for developing predictive tools and advancing personalised therapeutic strategies in inflammatory bowel disease.