{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T04:15:41Z","timestamp":1787026541915,"version":"build-2736575974"},"reference-count":36,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T00:00:00Z","timestamp":1780012800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Immunol."],"abstract":"\n Background<\/jats:title>\n Neonatal sepsis is a life-threatening condition with high mortality. Ferroptosis and cuproptosis, oxidative stress-related cell death pathways, are implicated in sepsis pathogenesis, but their role in neonatal sepsis remains unclear. This study aimed to identify and validate diagnostic biomarkers for neonatal sepsis associated with ferroptosis and cuproptosis pathways using integrated bioinformatics and machine learning approaches, and to explore potential therapeutic targets.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n \n Transcriptomic data from neonatal sepsis patients (GSE69686, GSE25504) were analyzed. Differential expression analysis, weighted gene co\u2212expression network analysis (WGCNA), and protein\u2212protein interaction (PPI) networks were performed to identify cuproptosis\u2212 and ferroptosis\u2212related genes (CFRGs). Three machine learning algorithms\u2014LASSO, SVM\u2212RFE, and XGBoost\u2014were applied for feature selection. Immune infiltration was assessed via CIBERSORT. Molecular docking was used to screen FDA\u2212approved drugs against candidate targets.\n In vitro<\/jats:italic>\n validation was conducted using LPS\u2212stimulated THP\u22121\u2212derived macrophages, with gene expression measured by RT\u2212qPCR and drug effects assessed by CCK\u22128 assay.\n <\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n Three biomarkers\u2014\n PGD<\/jats:italic>\n ,\n MAPK14<\/jats:italic>\n , and\n KRAS<\/jats:italic>\n \u2014were consistently identified by all machine learning models and showed strong diagnostic performance (AUC &gt; 0.79 in external validation). Immune infiltration analysis revealed increased neutrophils and Tregs, and decreased CD8\n +<\/jats:sup>\n T cells in sepsis. Molecular docking identified dasatinib and gefitinib as high\u2212affinity binders to\n MAPK14<\/jats:italic>\n and\n KRAS<\/jats:italic>\n .\n In vitro<\/jats:italic>\n , LPS stimulation significantly upregulated\n PGD<\/jats:italic>\n ,\n MAPK14<\/jats:italic>\n , and\n KRAS<\/jats:italic>\n expression, and candidate drugs effectively inhibited macrophage viability.\n <\/jats:p>\n <\/jats:sec>\n \n Conclusion<\/jats:title>\n \n PGD<\/jats:italic>\n ,\n MAPK14<\/jats:italic>\n , and\n KRAS<\/jats:italic>\n are promising diagnostic biomarkers for neonatal sepsis, closely linked to ferroptosis, cuproptosis, and immune dysregulation. This computational\u2212experimental framework supports their translational potential and highlights dasatinib and gefitinib as compounds with potential anti-inflammatory activity that warrant further investigation.\n <\/jats:p>\n <\/jats:sec>","DOI":"10.3389\/fimmu.2026.1808072","type":"journal-article","created":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T06:11:40Z","timestamp":1780035100000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Computational discovery of PGD, MAPK14, and KRAS as diagnostic biomarkers for neonatal sepsis through integrated machine learning, immune infiltration analysis, and molecular docking"],"prefix":"10.3389","volume":"17","author":[{"given":"Li","family":"Luo","sequence":"first","affiliation":[{"name":"Department of Neonatology Nursing, West China Second University Hospital, Sichuan University","place":["Chengdu, China"]},{"name":"Key Laboratory of Birth Defects and Related 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