{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,3]],"date-time":"2026-11-03T04:12:55Z","timestamp":1793679175750,"version":"4.5.1"},"reference-count":52,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,2,18]],"date-time":"2026-02-18T00:00:00Z","timestamp":1771372800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Immunol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Sepsis is a life-threatening disorder characterized by multiple organ dysfunction caused by dysregulated host responses to infection. The present study aimed to identify potential diagnostic biomarkers for sepsis and elucidate their molecular mechanisms through comprehensive bioinformatics and experimental analyses.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Five publicly available transcriptomic datasets (GSE13904, GSE26440, GSE28750, GSE95233, and GSE57065) containing sepsis and healthy control samples were utilized in the study. After quality control and normalization, the samples were divided into training and validation cohorts. Fourteen machine learning algorithms were applied to the training cohort to identify robust diagnostic biomarkers, and their predictive performance was subsequently verified in the validation cohorts. Single-cell RNA sequencing (scRNA-seq) data were further analyzed to determine the cellular distribution of the identified regulators among immune cell subsets.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      In total, the least absolute shrinkage and selection operator (LASSO) model exhibited the best performance in the validation set, demonstrating high reliability. Through consensus feature selection across multiple models, the m\n                      <jats:sup>6<\/jats:sup>\n                      A methylation regulator fat mass and obesity-associated protein (FTO) was identified as a key biomarker. scRNA-seq analysis revealed that FTO was primarily expressed in neutrophils and macrophages. Its expression levels were markedly altered in peripheral blood mononuclear cells (PBMCs) and neutrophils from sepsis patients compared with healthy controls, which was consistent with the findings in in vitro macrophage and neutrophil models. Functional experiments demonstrated that FTO promotes macrophage polarization toward the pro-inflammatory M1 phenotype and enhances neutrophil inflammatory and chemotactic responses, highlighting its critical role in orchestrating inflammatory regulation during sepsis.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>\n                      FTO, identified through consensus machine learning approaches, could serve as a potential diagnostic biomarker and m\n                      <jats:sup>6<\/jats:sup>\n                      A methylation regulator for sepsis. The discovery of FTO and its downstream targets provides new insights into sepsis pathogenesis and may offer a foundation for developing novel therapeutic strategies.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fimmu.2026.1756059","type":"journal-article","created":{"date-parts":[[2026,2,18]],"date-time":"2026-02-18T05:32:42Z","timestamp":1771392762000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Identification of FTO as a key m6A demethylase linking immune dysregulation to sepsis pathogenesis"],"prefix":"10.3389","volume":"17","author":[{"given":"Yi","family":"Jiao","sequence":"first","affiliation":[{"name":"Department of Emergency, China-Japan Friendship Hospital","place":["Beijing, China"]},{"name":"Institute of Clinical Medical Sciences, China-Japan Friendship Hospital","place":["Beijing, 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