Construction of a robust sepsis prognostic classifier based on E3 ubiquitin ligase-related genes

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.

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Publication Details

Journal
Frontiers in Molecular Biosciences
Published
2026-02-17
DOI
https://doi.org/10.3389/fmolb.2026.1726356
Primary Topic
Single-cell and spatial transcriptomics
Type
article
Field-Weighted Citation Impact
0.00

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article

Construction of a robust sepsis prognostic classifier based on E3 ubiquitin ligase-related genes

Lie Chen, X S Zhao, Fengxue Zhu, Haiyan Xue
Frontiers in Molecular Biosciences
Single-cell and spatial transcriptomics
article

Construction of a robust sepsis prognostic classifier based on E3 ubiquitin ligase-related genes

Lie Chen, X S Zhao, Fengxue Zhu, Haiyan Xue
article en

Abstract

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.

Frontiers in Molecular BiosciencesVol. 13
Peking University (CN), Peking University People's Hospital (CN), University College London (GB)
Peking University, Peking University People's Hospital
Good health and well-being
Openalex Percentile: Top 53%
Single-cell and spatial transcriptomics
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