ArticleFrontiers in toxicology2026
Machine learning-integrated network toxicology uncovers glioma targets of DEHP.
Article in Frontiers in toxicology, 2026. 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
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
1 citing paper in PubMed.
- Computational prioritization of candidate TCDD-associated targets in retinoblastoma using network toxicology and transcriptomic analysis.Frontiers in genetics · 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
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Di (2-ethylhexyl) phthalate (DEHP), a ubiquitous environmental plasticizer, is increasingly linked to neurotoxicity and carcinogenesis. However, its role in glioma pathogenesis remains poorly understood. This study integrates network toxicology and machine learning to identify molecular targets of DEHP in glioma. Methods: Potential DEHP targets were identified through four databases (CHEMBL, CTD, SwissTargetPrediction, PharmMapper). Glioma-related genes were screened using differential expression analysis and weighted gene co-expression network analysis (WGCNA) on GEO and TCGA datasets. Overlapping genes were subjected to functional enrichment, followed by 127 machine learning models to prioritize core genes. SHAP analysis interpreted model contributions, while COX regression assessed prognostic value. Molecular docking and dynamics simulations evaluated binding stability between DEHP and target proteins. Results: A total of 77 overlapping genes were identified, enriched in neuroactive ligand-receptor interactions, GABAergic synapses, and ion channel activity. Machine learning prioritized 12 key genes (e.g., RELA, ABCA1, HIF1A), forming a parsimonious 12-gene diagnostic model with strong external discrimination (pooled validation AUC = 0.994). A high DEHP-related risk score was associated with poorer survival in the TCGA cohort and showed similar prognostic stratification across the external CGGA_325, CGGA_693, and GSE16011 cohorts. Molecular simulations confirmed stable binding between DEHP and core proteins. Experimental validation demonstrated dose- and time-dependent upregulation of RELA, ABCA1, and HIF1A in DEHP-exposed U87 cells. Conclusion: This integrative approach provides a systems-level framework to prioritize DEHP-associated target genes and molecular signatures in glioma, extending beyond the previously reported PER3-related observation and offering candidate biomarkers for early detection and prognosis under environmental exposure.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.