ArticlePeerJ2025
Biomarkers of mitochondrial permeability transition-driven necrosis in the regulation of ulcerative colitis.
Article in PeerJ, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Integrating multi-omics, machine learning, and molecular dynamics simulations to identify glutamate metabolism-related biomarkers and drug candidates in rheumatoid arthritis.Frontiers in molecular biosciences · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Ulcerative colitis (UC) is a gastrointestinal condition characterized by chronic intestinal inflammation and damage to the mucosal barrier, with regulated cell death (RCD) playing a pivotal role in its pathogenesis. Among the various forms of RCD, mitochondrial permeability transition-driven necrosis (MPTDN) has not been thoroughly investigated in relation to UC in the current literature. Objective: The objective of this study was to identify genes associated with MPTDN that are relevant to UC and to explore their potential implications in the disease process. Methods: Data were obtained from the Gene Expression Omnibus (GEO) database. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were utilized to identify differentially expressed MPTD-related genes (MPTDEGs). Machine learning techniques, including Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine-Recursive Feature Elimination (SVM-RFE), and Random Forest (RF), were employed to isolate key hub genes. The diagnostic potential of these genes was evaluated through receiver operating characteristic (ROC) curve analysis, and their expression was validated using an external dataset. Additionally, immunoinfiltration analysis was conducted to investigate the relationship between differentially expressed immune cells and the identified diagnostic genes. The correlation between gene expression and response to anti-TNF therapy was also assessed. Finally, the differential expression of these genes was confirmed in a mouse model of UC induced by 2.5% dextran sulfate sodium (DSS). Results: A total of six MPTDEGs were identified. The genes CASP1 and CASP4, which were identified through machine learning algorithms, exhibited strong diagnostic performance, with area under the curve (AUC) values exceeding 0.7, indicating the effectiveness of the model. Immunoinfiltration analysis demonstrated a significant correlation between the expression of CASP1 and CASP4 and the presence of macrophages and neutrophils. Importantly, low expression levels of CASP1 were associated with a favorable response to infliximab treatment. Furthermore, the expression levels of CASP1 and CASP4 were significantly increased in UC mouse models ( Conclusions: The MPTDN-related genes CASP1 and CASP4 have been identified as potential biomarkers for the diagnosis of UC and are associated with abnormal immune cell infiltration in UC patients. Additionally, CASP1 may serve as a predictor of sensitivity to infliximab therapy.
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