ORIGINAL RESEARCH article
Front. Mol. Biosci.
Sec. Molecular Diagnostics and Therapeutics
This article is part of the Research TopicPrecision Medicine: Innovations in Molecular Diagnosis and TreatmentView all 5 articles
Construction of a Robust Sepsis Prognostic Classifier Based on E3 Ubiquitin Ligase-Related Genes
Provisionally accepted
- 1Peking University People's Hospital, Beijing, China
- 2University College London, London, United Kingdom
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Sepsis is a life-threatening disease with high mortality and one of the leading causes of death worldwide. Although studies have shown that ubiquitylation is closely related to the occurrence and development of sepsis, the prognostic and diagnostic value of ubiquitylation-related genes in sepsis remains unclear. In this study, we obtained sepsis datasets from the Gene Expression Omnibus (GEO) database and identified ubiquitylation-related genes from the Ubiquitin and Ubiquitin-like Conjugation Database (iUUCD). We identified 159 differentially expressed genes related to ubiquitylation between sepsis patients and healthy individuals, and the prognosis of sepsis subgroups distinguished by these ubiquitylation genes showed significant differences, demonstrating the importance of the ubiquitylation phenotype in sepsis. To explore the prognostic value of ubiquitination-related genes in sepsis, we constructed a ubiquitylation-related score (URS) through LASSO analysis, random forest, and Cox regression analysis. Importantly, we validated the reliability of this model in both the GEO database cohort and the external cohort data from our unit. Finally, we identified LTB and TCP11L2 as key ubiquitylation-related genes, explored their expression patterns and potential biological context through single-cell RNA sequence analysis, and validated their expression and diagnostic value using patient blood samples. Our study suggests a link between ubiquitylation and sepsis, which can be used as a potential biomarker to guide the diagnosis, treatment, and prognosis of sepsis and proposes new ideas for future clinical research.
Keywords: immune cells, machine learning, Predict model, Sepsis, Ubiquitination
Received: 16 Oct 2025; Accepted: 06 Feb 2026.
Copyright: © 2026 Xue, Chen, Zhao and Zhu. 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: Fengxue Zhu
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