Evidence map›Paper›PMID 38791570›Full record

Observational studyInternational journal of molecular sciences2024

Insights into Therapeutic Response Prediction for Ustekinumab in Ulcerative Colitis Using an Ensemble Bioinformatics Approach.

Kanellos Koustenis, Nikolas Dovrolis, Nikos Viazis, Alexandros Ioannou, Giorgos Bamias, George Karamanolis, Maria Gazouli

Abstract readObservational Study
PubMed Full text at PMC Publisher
In one paragraph

Observational study in International journal of molecular sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Artificial intelligence in the management of inflammatory bowel disease: What's next?World journal of gastrointestinal pharmacology and therapeutics · 2026
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Kanellos KoustenisGastroenterology Department, Evangelismos-Polykliniki General Hospital, 115 27 Athens, Greece.
Nikolas DovrolisLaboratory of Biology, Department of Basic Medical Sciences, Medical School, National and Kapodistrian University of Athens, Michalakopoulou 176, 115 27 Athens, Greece.ORCID 0000-0003-3832-9939
Nikos ViazisGastroenterology Department, Evangelismos-Polykliniki General Hospital, 115 27 Athens, Greece.ORCID 0000-0002-0918-1587
Alexandros IoannouGastroenterology Unit, Alexandra Hospital, 115 28 Athens, Greece.
Giorgos BamiasGI-Unit, 3rd Academic Department of Internal Medicine, National and Kapodistrian University of Athens, Sotiria Hospital, 115 27 Athens, Greece.
George KaramanolisGastroenterology Unit, Second Department of Surgery, Aretaieio Hospital, Medical School, National and Kapodistrian University of Athens, 115 27 Athens, Greece.
Maria GazouliLaboratory of Biology, Department of Basic Medical Sciences, Medical School, National and Kapodistrian University of Athens, Michalakopoulou 176, 115 27 Athens, Greece.ORCID 0000-0002-3295-6811

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionOptimizing treatment with biological agents is an ideal goal for patients with ulcerative colitis (UC). Recent data suggest that mucosal inflammation patterns and serum cytokine profiles differ between patients who respond and those who do not. Ustekinumab, a monoclonal antibody targeting the p40 subunit of interleukin (IL)-12 and IL-23, has shown promise, but predicting treatment response remains a challenge. We aimed to identify prognostic markers of response to ustekinumab in patients with active UC, utilizing information from their mucosal transcriptome.

methodsWe performed a prospective observational study of 36 UC patients initiating treatment with ustekinumab. Colonic mucosal biopsies were obtained before treatment initiation for a gene expression analysis using a microarray panel of 84 inflammatory genes. A differential gene expression analysis (DGEA), correlation analysis, and network centrality analysis on co-expression networks were performed to identify potential biomarkers. Additionally, machine learning (ML) models were employed to predict treatment response based on gene expression data.

resultsSeven genes, including BCL6, CXCL5, and FASLG, were significantly upregulated, while IL23A and IL23R were downregulated in non-responders compared to responders. The co-expression analysis revealed distinct patterns between responders and non-responders, with key genes like BCL6 and CRP highlighted in responders and CCL11 and CCL22 in non-responders. The ML algorithms demonstrated a high predictive power, emphasizing the significance of the IL23R, IL23A, and BCL6 genes.

conclusionsOur study identifies potential biomarkers associated with ustekinumab response in UC patients, shedding light on its underlying mechanisms and variability in treatment outcomes. Integrating transcriptomic approaches, including gene expression analyses and ML, offers valuable insights for personalized treatment strategies and highlights avenues for further research to enhance therapeutic outcomes for patients with UC.

Indexed as

Colitis, UlcerativeComputational BiologyUstekinumabAdultFemaleGene Expression ProfilingHumansInterleukin-23 Subunit p19Intestinal MucosaMachine LearningMaleMiddle AgedPrognosisProspective StudiesReceptors, InterleukinTranscriptomeIL23A protein, humanIL23R protein, humanInterleukin-23 Subunit p19Receptors, InterleukinUstekinumabbioinformaticsprognostic markersulcerative colitisustekinumab

Identifiers

PMID38791570
PMCPMC11122545

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.