ArticleFrontiers in immunology2026
Identification of biomarkers for pediatric sepsis based on machine learning and bioinformatics analysis.
Article in Frontiers in immunology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Pediatric sepsis is a systemic inflammatory syndrome caused by dysregulated host immune responses, with a high mortality rate and a lack of effective biomarkers, posing significant challenges for early diagnosis and treatment. This study integrated six bulk RNA-seq datasets related to pediatric sepsis, including 497 patients and 116 healthy control samples. Weighted gene co-expression network analysis was used to identify gene modules significantly associated with pediatric sepsis, and 237 high-confidence biomarkers were screened based on 14 machine learning models, among which RORA and GPR183 stood out in multiple models. Functional analysis indicated that these biomarkers were mainly involved in biological processes such as transcription and translation, the immune system, and cellular senescence. Immune infiltration analysis revealed a significant reduction in adaptive immune cells such as B cells and CD8
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