Dataset Information

MicroRNAs Classify Different Disease Behavior Phenotypes of Crohn's Disease and May Have Prognostic Utility.

ABSTRACT:

Background

There is a dire need for reliable prognostic markers that can guide effective therapeutic intervention in Crohn's disease (CD). We examined whether different phenotypes in CD can be classified based on colonic microRNA (miRNA) expression and whether miRNAs have prognostic utility for CD.

Methods

High-throughput sequencing of small and total RNA isolated from colon tissue from patients with CD and controls without Inflammatory Bowel Disease (non-IBD) was performed. To identify miRNAs associated with specific phenotypes of CD, patients were stratified according to disease behavior (nonstricturing, nonpenetrating; stricturing; penetrating), and miRNA profiles in each subset were compared with those of the non-IBD group. Validation assays were performed using quantitative reverse transcription polymerase chain reaction. These miRNAs were further evaluated by quantitative reverse transcriptase polymerase chain reaction on formalin-fixed, paraffin-embedded tissue (index biopsies) of patients with nonpenetrating CD at the time of diagnosis that either retained the nonpenetrating phenotype or progressed to penetrating/fistulizing CD.

Results

We found a suite of miRNAs, including miR-31-5p, miR-215, miR-223-3p, miR-196b-5p, and miR-203 that stratify patients with CD according to disease behavior independent of the effect of inflammation. Furthermore, we also demonstrated that expression levels of miR-215 in index biopsies of patients with CD might predict the likelihood of progression to penetrating/fistulizing CD. Finally, using a novel statistical simulation approach applied to colonic RNA-sequencing data for patients with CD and non-IBD controls, we identified miR-31-5p and miR-203 as candidate master regulators of gene expression profiles associated with CD.

Conclusions

miRNAs may serve as clinically useful prognostic markers guiding initial therapy and identifying patients who would benefit most from effective intervention.

SUBMITTER: Peck BC

PROVIDER: S-EPMC4603665 | biostudies-literature | 2015 Sep

REPOSITORIES: biostudies-literature

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Publications

MicroRNAs Classify Different Disease Behavior Phenotypes of Crohn's Disease and May Have Prognostic Utility.

Peck Bailey C E BC Weiser Matthew M Lee Saangyoung E SE Gipson Gregory R GR Iyer Vishal B VB Sartor Ryan B RB Herfarth Hans H HH Long Millie D MD Hansen Jonathan J JJ Isaacs Kim L KL Trembath Dimitri G DG Rahbar Reza R Sadiq Timothy S TS Furey Terrence S TS Sethupathy Praveen P Sheikh Shehzad Z SZ

Inflammatory bowel diseases 20150901 9

<h4>Background</h4>There is a dire need for reliable prognostic markers that can guide effective therapeutic intervention in Crohn's disease (CD). We examined whether different phenotypes in CD can be classified based on colonic microRNA (miRNA) expression and whether miRNAs have prognostic utility for CD.<h4>Methods</h4>High-throughput sequencing of small and total RNA isolated from colon tissue from patients with CD and controls without Inflammatory Bowel Disease (non-IBD) was performed. To id ...[more]

PMID: 26164662

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Project description:BackgroundCrohn's disease (CD) has an unclear etiology, but there is growing evidence of a direct link with a dysbiotic microbiome. Many gut microbes have previously been associated with CD, but these have mainly been confounded with patients' ongoing treatments. Additionally, most analyses of CD patients' microbiomes have focused on microbes in stool samples, which yield different insights than profiling biopsy samples.ResultsWe sequenced the 16S rRNA gene (16S) and carried out shotgun metagenomics (MGS) from the intestinal biopsies of 20 treatment-naïve CD and 20 control pediatric patients. We identified the abundances of microbial taxa and inferred functional categories within each dataset. We also identified known human genetic variants from the MGS data. We then used a machine learning approach to determine the classification accuracy when these datasets, collapsed to different hierarchical groupings, were used independently to classify patients by disease state and by CD patients' response to treatment. We found that 16S-identified microbes could classify patients with higher accuracy in both cases. Based on follow-ups with these patients, we identified which microbes and functions were best for predicting disease state and response to treatment, including several previously identified markers. By combining the top features from all significant models into a single model, we could compare the relative importance of these predictive features. We found that 16S-identified microbes are the best predictors of CD state whereas MGS-identified markers perform best for classifying treatment response.ConclusionsWe demonstrate for the first time that useful predictors of CD treatment response can be produced from shotgun MGS sequencing of biopsy samples despite the complications related to large proportions of host DNA. The top predictive features that we identified in this study could be useful for building an improved classifier for CD and treatment response based on sufferers' microbiome in the future. The BISCUIT project is funded by a Clinical Academic Fellowship from the Chief Scientist Office (Scotland)-CAF/08/01.

Project description:BACKGROUND AND PURPOSE:Multifocal glioblastomas (ie, glioblastomas with multiple foci, unconnected in postcontrast pretreatment T1-weighted images) represent a challenge in clinical practice due to their poor prognosis. We wished to obtain imaging biomarkers with prognostic value that have not been found previously. MATERIALS AND METHODS:A retrospective review of 1155 patients with glioblastomas from 10 local institutions during 2006-2017 provided 97 patients satisfying the inclusion criteria of the study and classified as having multifocal glioblastomas. Tumors were segmented and morphologic features were computed using different methodologies: 1) measured on the largest focus, 2) aggregating the different foci as a whole, and 3) recording the extreme value obtained for each focus. Kaplan-Meier, Cox proportional hazards, correlations, and Harrell concordance indices (c-indices) were used for the statistical analysis. RESULTS:Age (P < .001, hazard ratio = 2.11, c-index = 0.705), surgery (P < .001, hazard ratio = 2.04, c-index = 0.712), contrast-enhancing rim width (P < .001, hazard ratio = 2.15, c-index = 0.704), and surface regularity (P = .021, hazard ratio = 1.66, c-index = 0.639) measured on the largest focus were significant independent predictors of survival. Maximum contrast-enhancing rim width (P = .002, hazard ratio = 2.05, c-index = 0.668) and minimal surface regularity (P = .036, hazard ratio = 1.64, c-index = 0.600) were also significant. A multivariate model using age, surgery, and contrast-enhancing rim width measured on the largest foci classified multifocal glioblastomas into groups with different outcomes (P < .001, hazard ratio = 3.00, c-index = 0.853, median survival difference = 10.55 months). Moreover, quartiles with the highest and lowest individual prognostic scores based on the focus with the largest volume and surgery were identified as extreme groups in terms of survival (P < .001, hazard ratio = 18.67, c-index = 0.967). CONCLUSIONS:A prognostic model incorporating imaging findings on pretreatment postcontrast T1-weighted MRI classified patients with glioblastoma into different prognostic groups.

Dataset Information

MicroRNAs Classify Different Disease Behavior Phenotypes of Crohn's Disease and May Have Prognostic Utility.

Background

Methods

Results

Conclusions

Publications

MicroRNAs Classify Different Disease Behavior Phenotypes of Crohn's Disease and May Have Prognostic Utility.

Similar Datasets

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