Integrative machine learning and transcriptomic analysis reveal a ferroptosis gene signature for pediatric Mycoplasma pneumoniae pneumonia

Abstract

Background Mycoplasma pneumoniae pneumonia (MPP) is one of the most common causes of community-acquired pneumonia in children, affecting approximately 10%–40% of pediatric pneumonia cases worldwide. Despite advances in diagnostic and therapeutic approaches, the pathogenesis of severe MPP remains incompletely understood, and patients demonstrate highly heterogeneous responses to treatment. Recent evidence suggests that ferroptosis, an iron-dependent form of regulated cell death, may play a critical role in the pathophysiology of MPP. This study aimed to elucidate the molecular mechanisms underlying the interaction between MPP and ferroptosis using bioinformatics approaches, and to identify novel ferroptosis-related biomarkers and therapeutic targets. 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 Mycoplasma pneumoniae-infected THP-1 cell line model by qRT-PCR. 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, P = 1.36 × 10−15). Gene Ontology and pathway enrichment analyses demonstrated significant activation of iron metabolism, lipid peroxidation, oxidative stress response, and inflammatory pathways. A five-gene ferroptosis panel (ATG7, SLC2A3, PGD, SAT1, OXSR1) achieved AUC = 0.890 (95% CI 0.800–0.980) in discovery cross-validation and AUC = 0.860 (95% CI 0.693–1.000) in external validation. In vitro qRT-PCR showed that mRNA expression levels of ATG7, SLC2A3, PGD, SAT1 and OXSR1 were significantly upregulated in Mycoplasma pneumoniae-infected THP-1 compared to control group (all P < 0.05). Conclusion This study provides comprehensive molecular characterization suggesting ferroptosis involvement in pediatric MPP through integrative transcriptomic profiling and machine learning, complemented by in vitro qRT-PCR experimental validation. The identification of consistent ferroptosis transcriptomic signatures across independent cohorts, a high-performance diagnostic gene panel, and in vitro mRNA-level evidence for ferroptosis-associated gene dysregulation advances our understanding of MPP pathogenesis. ATG7, SLC2A3, PGD, SAT1 and OXSR1 represent potential candidate diagnostic biomarkers and therapeutic targets for personalized treatment strategies, with protein-level, functional, in vivo, and clinical validation required before clinical translation. These findings pave the way for developing ferroptosis-targeted interventions to improve patient outcomes in severe pediatric MPP.