ArticleFrontiers in cell and developmental biology2026
Integrative machine learning and transcriptomic analysis reveal a ferroptosis gene signature for pediatric
Article in Frontiers in cell and developmental biology, 2026. 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
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Methods: We analyzed comprehensive transcriptomic data from two independent publicly deposited pediatric whole-blood cohorts: GSE103119 (discovery cohort: 30 MPP cases and 20 healthy controls; Illumina HumanHT-12 v4.0) and E-MTAB-14588 (validation cohort: 9 MPP cases and 19 healthy controls; RNA-seq). Through systematic comparative analysis and FerrDb annotation, we identified ferroptosis-associated differentially expressed genes and calculated ferroptosis score. Machine learning (L2 logistic regression with nested cross-validation) was applied to develop a ferroptosis gene panel for patient stratification. Functional enrichment analysis, protein-protein interaction networks, and cross-cohort meta-analysis were performed to characterize ferroptosis transcriptomic changes during MPP. Five key genes (ATG7, SLC2A3, PGD, SAT1, OXSR1) were validated in Results: Bioinformatics analysis identified 259 FerrDb-annotated ferroptosis-related genes in the discovery cohort, with ATG7 showing the strongest upregulated ferroptosis signal. The ferroptosis score in the MPP group showed an increasing trend compared with the control group, replicated in E-MTAB-14588. Cross-cohort meta-analysis of 85 shared ferroptosis genes demonstrated strong effect-direction concordance (Spearman ρ = 0.734, Conclusion: This study provides comprehensive molecular characterization suggesting ferroptosis involvement in pediatric MPP through integrative transcriptomic profiling and machine learning, complemented by
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.