Colonic MicroRNA Profiles, Identified by a Deep Learning Algorithm, That Predict Responses to Therapy of Patients With Acute Severe Ulcerative Colitis

Clin Gastroenterol Hepatol. 2019 Apr;17(5):905-913. doi: 10.1016/j.cgh.2018.08.068. Epub 2018 Sep 14.


Background & aims: Acute severe ulcerative colitis (ASUC) is a life-threatening condition managed with intravenous steroids followed by infliximab, cyclosporine, or colectomy (for patients with steroid resistance). There are no biomarkers to identify patients most likely to respond to therapy; ineffective medical treatment can delay colectomy and increase morbidity and mortality. We aimed to identify biomarkers of response to medical therapy for patients with ASUC.

Methods: We performed a retrospective analysis of 47 patients with ASUC, well characterized for their responses to steroids, cyclosporine, or infliximab, therapy at 2 centers in France. Fixed colonic biopsies, collected before or within the first 3 days of treatment, were used for microarray analysis of microRNA expression profiles. Deep neural network-based classifiers were used to derive candidate biomarkers for discriminating responders from non-responders to each treatment and to predict which patients would require colectomy. Levels of identified microRNAs were then measured by quantitative PCR analysis in a validation cohort of 29 independent patients-the effectiveness of the classification algorithm was tested on this cohort.

Results: A deep neural network-based classifier identified 9 microRNAs plus 5 clinical factors, routinely recorded at time of hospital admission, that associated with responses of patients to treatment. This panel discriminated responders to steroids from non-responders with 93% accuracy (area under the curve, 0.91). We identified 3 algorithms, based on microRNA levels, that identified responders to infliximab vs non-responders (84% accuracy, AUC = 0.82) and responders to cyclosporine vs non-responders (80% accuracy, AUC = 0.79).

Conclusion: We developed an algorithm that identifies patients with ASUC who respond vs do not respond to first- and second-line treatments, based on microRNA expression profiles in colon tissues.

Keywords: Acute Severe Ulcerative Colitis; IBD; Neural Network; Prognostic Factor.

MeSH terms

  • Adult
  • Aged
  • Aged, 80 and over
  • Biomarkers / analysis*
  • Colitis, Ulcerative / drug therapy*
  • Colitis, Ulcerative / pathology*
  • Colon / pathology*
  • Deep Learning
  • Drug Monitoring / methods*
  • Female
  • France
  • Gene Expression Profiling / methods*
  • Hospitals
  • Humans
  • Male
  • MicroRNAs / analysis*
  • Middle Aged
  • Treatment Outcome
  • Young Adult


  • Biomarkers
  • MicroRNAs