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ORIGINAL RESEARCH article

Front. Immunol.

Sec. Inflammation

This article is part of the Research Topicm6A modification in immune cell-regulated inflammatory diseasesView all 7 articles

Identification of FTO as a key m6A demethylase linking immune dysregulation to sepsis pathogenesis

Provisionally accepted
Yi  JiaoYi Jiao1,2Rui  LianRui Lian1Jian  Wei ZhangJian Wei Zhang1,3Nan  GaoNan Gao1,3Qishun  GengQishun Geng2,4Tian  Tian DengTian Tian Deng2,5Ran  Zhao WangRan Zhao Wang2,3Tingting  DengTingting Deng2Cheng  XiaoCheng Xiao1,2*Guoqiang  ZhangGuoqiang Zhang1*
  • 1Department of Emergency, China-Japan Friendship Hospital, Beijing, China
  • 2Institute of Clinical Medical Sciences, China-Japan Friendship Hospital, Beijing, China
  • 3China-Japan Friendship Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
  • 4Department of Pharmacy, The First Affiliated Hospital of Zhengzhou University, Zhengzhou Henan, China
  • 5Beijing University of Chinese Medicine, School of Clinical Medicine, China-Japan Friendship Hospital, Beijing, China

The final, formatted version of the article will be published soon.

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. 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. 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 m6A 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. In conclusion, FTO, identified through consensus machine learning approaches, could serve as a potential diagnostic biomarker and m6A 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.

Keywords: FTO, M1 macrophages, N6-Methyladenosine, Neutrophils, Sepsis

Received: 28 Nov 2025; Accepted: 02 Feb 2026.

Copyright: © 2026 Jiao, Lian, Zhang, Gao, Geng, Deng, Wang, Deng, Xiao and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence:
Cheng Xiao
Guoqiang Zhang

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