ArticleFrontiers in molecular biosciences2025
Identifying potential biomarkers and molecular mechanisms related to arachidonic acid metabolism in vitiligo.
Article in Frontiers in molecular biosciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Numerous studies have reported that dysregulation of fatty acid metabolic pathways is associated with the pathogenesis of vitiligo, in which arachidonic acid metabolism (AAM) plays an important role. However, the molecular mechanisms of AAM in the pathogenesis of vitiligo have not been clarified. Therefore, we aimed to identify the biomarkers and molecular mechanisms associated with AAM in vitiligo using bioinformatics methods. Methods: The GSE75819 and GSE65127 datasets were used in this study as the training and validation sets, respectively, along with 58 AAM-related genes (AAM-RGs). The differentially expressed genes (DEGs) between the lesional and control groups in the training set were identified through differential expression analysis. A biomarker-based nomogram was constructed to predict the risk of vitiligo. Results: 15 overlapping candidate genes were obtained between the DEGs and AAM-RGs. Machine-learning algorithms were used to identify six key genes as Conclusion: In conclusion,
Indexed as
Identifiers
What Socratic holds
Registered trials
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 Socratic graph.