Identifying GADD45A as a potential R-loop regulator in sepsis via machine-learning and multi-omics analysis

Sepsis is a life-threatening syndrome characterized by dysregulated immune responses, while the molecular basis of immune dysfunction remains unclear. Increasing evidence suggests that R-loop accumulation and DNA damage may contribute to immune disorders, but their roles in sepsis have not been systematically investigated.

Four GEO microarray datasets were integrated after batch-effect correction and normalization. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were performed to identify sepsis-related genes. Overlapping R-loop-related genes (RLRGs) were obtained by intersecting differentially expressed genes, key module genes, and curated RLRGs. Machine learning was then applied to prioritize hub genes and construct an optimal diagnostic model, which was validated in external cohorts. A nomogram, single-cell RNA-sequencing analysis, survival analysis, and in vivo/in vitro experiments were further used for validation.

Five overlapping RLRGs (EXOSC4, GADD45A, HMGB2, LDHA, and PRKDC) were identified, and all were consistently retained by top-performing machine learning models, supporting their robustness. The five-gene nomogram showed good calibration and clinical utility. Single-cell analysis revealed cell-type-specific expression across neutrophils, monocytes, and T cells. Among the five genes, only GADD45A was significantly associated with poor survival. Experimental validation showed that GADD45A was upregulated in a CLP-induced sepsis mouse model. In HL-60 cells, GADD45A silencing reduced LPS-induced inflammatory cytokine expression, R-loop accumulation, and DNA damage.

GADD45A is a promising biomarker for the diagnosis and prognosis of sepsis and may promote disease progression by enhancing R-loop accumulation and DNA damage.