ArticleHealth science reports2025
Mendelian Randomization and Colocalization Analysis Reveal New Drug Targets for Oral Ulcer: A Mendelian Randomization Analysis.
Article in Health science reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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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.
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.
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Who cites it
2 citing papers in PubMed.
- Disulfidptosis- and Ferroptosis-Related Gene Signatures in Rheumatoid Arthritis: Association Analysis and Diagnostic Model Construction.The Kaohsiung journal of medical sciences · 2026Article
- [Multi-omics analysis of the relationship between oxidative stress-related gene and prostate cancer].Beijing da xue xue bao. Yi xue ban = Journal of Peking University. Health sciences · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and Aims: Oral ulcer (OU) is a complex issue with limited effective treatments. This study uses multi-omics data through summary Mendelian randomization (SMR) and colocalization analysis to identify specific gene associations with OU, aiming to find new therapeutic targets, repurpose existing drugs, and develop new treatment options. Methods: Our study consists of two phases: first, extracting data from Genome-Wide Association Studies and using blood mQTL, eQTL, and pQTL data as exposure factors, then integrating these with OU gene data through SMR analysis. Then, we validate the results with UK Biobank data and perform colocalization analysis to confirm shared genetic variants. Results: Genetically predicted levels of four circulating proteins are associated with OU. Under strong supportive evidence from mQTL, eQTL, and pQTL, genetically predicted levels of NFKB1 are negatively correlated with the risk of OU. With moderate supportive evidence from mQTL and pQTL, genetically predicted levels of FAIM3 are negatively correlated with the risk of OU. Meanwhile, under low supportive evidence from eQTL and pQTL, higher genetically predicted levels of JUND and lower levels of IL12β are associated with a higher risk of OU. Conclusion: SMR approach employed in this study has pinpointed several proteins with tangible associations to the risk of OU. NFKB1, FAIM3, JUND, and IL12β stand out as promising therapeutic targets for OU, beckoning further exploration and research.
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