Evidence map›Paper›PMID 39414957›Full record

ArticleScientific reports2024

Integrative biomarker discovery and immune profiling for ulcerative colitis: a multi-methodological approach.

Lai Jiang, Shengke Zhang, Chenglu Jiang, Haiqing Chen, Jinbang Huang, Jinyan Yang, Hao Chi, Qibiao Wu, Guanhu Yang

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. UBE2L6 drives ulcerative colitis progression by promoting BIRC2 degradation and activating non-canonical NF-κB signalling.Inflammation research : official journal of the European Histamine Research Society ... [et al.] · 2026
    Article
  2. Review
  3. Review
  4. Review
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
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

9 authors.

Lai Jiang *Faculty of Chinese Medicine, State Key Laboratory of Quality Research in Chinese Medicine, and University Hospital, Macau University of Science and Technology, Macau, Macao SAR, China.
Shengke Zhang *Clinical Medical College, Southwest Medical University, Luzhou, 646000, China.
Chenglu Jiang *Clinical Medical College, Southwest Medical University, Luzhou, 646000, China.
Haiqing ChenClinical Medical College, Southwest Medical University, Luzhou, 646000, China.
Jinbang HuangClinical Medical College, Southwest Medical University, Luzhou, 646000, China.
Jinyan YangClinical Medical College, Southwest Medical University, Luzhou, 646000, China.
Hao ChiClinical Medical College, Southwest Medical University, Luzhou, 646000, China. chihao7511@163.com.
Qibiao WuFaculty of Chinese Medicine, State Key Laboratory of Quality Research in Chinese Medicine, and University Hospital, Macau University of Science and Technology, Macau, Macao SAR, China. qbwu@must.edu.mo.
Guanhu YangFaculty of Chinese Medicine, State Key Laboratory of Quality Research in Chinese Medicine, and University Hospital, Macau University of Science and Technology, Macau, Macao SAR, China. guanhuyang@gmail.com.

Funding

Macau University of Science and Technology 0098/2021/A2Macau University of Science and Technology Foundation FRG-23-003-FCNational Natural Science Foundation of China 82361168663
6 · The paper itself

Abstract

Background We aimed to pinpoint biomarkers, create a diagnostic model for ulcerative colitis (UC), and delve into its immune features to better understand this autoimmune condition. Methods The sequencing data for both the UC and the control groups were obtained from GEO, including both bulk and single-cell data. Using GSE87466 as training group, we applied differential analysis, WGCNA, PPI, LASSO, RF, and SVM-RFE for biomarker selection. A neural network shaped our diagnostic model, corroborated by GSE92415 as the validation cohort with ROC assessment. Immune cell profiling was conducted using CIBERSORT. Results 53 disease-associated genes were screened. Enrichment analysis highlighted roles in complement cascades and cell adhesion. Eight biomarkers were finally identified through multiple machine learning and PPI: B4GALNT2, PDZK1IP1, FAM195A, REG4, MTMR11, FLJ35024, CD55, and CD44. The diagnostic model had AUCs of 0.984 (training group) and 0.957 (validation group). UC tissues revealed heightened plasma cells, CD8 T cells, and other immune cells. Two unique UC immune patterns emerged, with certain T and NK cells central to immune modulation. Conclusion We identified eight biomarkers of UC by various methods, constructed a diagnostic model through neural networks, and explored the immune complexity of the disease, which contributes to the diagnosis and treatment of UC.

Indexed as

BiomarkersColitis, UlcerativeCD8-Positive T-LymphocytesComputational BiologyGene Expression ProfilingHumansMachine LearningROC CurveBiomarkersDiagnostic modelsImmune infiltrationMachine learningSingle cell sequencingTherapeutic targetsUlcerative colitis

Identifiers

PMID39414957
PMCPMC11484944

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.