What does this research mean for the field?

The genes NXT1 and UXS1 form a novel immune-escape signature that serves as a highly accurate diagnostic biomarker for sepsis (AUC > 0.9) and characterizes distinct molecular subtypes. Novelty: ClaimNovelty.NOVEL_FINDING. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

To identify immune-escape molecular mechanisms in sepsis and develop an accurate diagnostic gene signature using integrative bioinformatics.

September 3, 2026Physiological GenomicsOpen Access

A Novel Immune-Escape Signature and Classifier for Predicting Sepsis Constructed by Integrative Bioinformatics

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Authors

ZHZhu Hengling HZHengbao Zhu DLDongxin LiuChina Academy of Chinese Medical Sciences

Discussion

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Implication

Bioinformatic analysis identifies a two-gene immune-escape signature in sepsis datasets, highlighting molecular pathways for enhanced clinical diagnosis.

Key Points

To identify immune-escape molecular mechanisms in sepsis and develop an accurate diagnostic gene signature using integrative bioinformatics.
Analyzed transcriptomic data from the Gene Expression Omnibus, utilizing GSE65682 as the training cohort and GSE95233 as an external validation cohort.
Combined differential gene expression analysis with Weighted Gene Co-expression Network Analysis (WGCNA) to identify key feature genes.
Constructed a diagnostic classifier evaluated with ROC curves, nomograms, and decision curves, alongside immune cell infiltration profiling and consensus clustering.
Screened three feature genes to establish a two-gene diagnostic model consisting of NXT1 and UXS1, both demonstrating high diagnostic performance (AUC > 0.9).
Characterized distinct immune cell infiltration landscapes between sepsis cases and controls, mapping associated miRNA and lncRNA competing endogenous RNA networks.
Identified two distinct sepsis molecular subtypes that exhibited contrasting immune microenvironmental and transcriptomic characteristics.

Cite This Study

Hengling et al. (2026) studied this question.

synapsesocial.com/papers/6a993586636c6408cfa7dce5 https://doi.org/10.1152/physiolgenomics.00328.2025

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September 3, 2026Physiological GenomicsOpen Access

A Novel Immune-Escape Signature and Classifier for Predicting Sepsis Constructed by Integrative Bioinformatics

View Full Paper

Ask AI

Bookmark

Authors

ZHZhu Hengling HZHengbao Zhu DLDongxin LiuChina Academy of Chinese Medical Sciences

Discussion

Member takes

Implication

Bioinformatic analysis identifies a two-gene immune-escape signature in sepsis datasets, highlighting molecular pathways for enhanced clinical diagnosis.

Key Points

To identify immune-escape molecular mechanisms in sepsis and develop an accurate diagnostic gene signature using integrative bioinformatics.
Analyzed transcriptomic data from the Gene Expression Omnibus, utilizing GSE65682 as the training cohort and GSE95233 as an external validation cohort.
Combined differential gene expression analysis with Weighted Gene Co-expression Network Analysis (WGCNA) to identify key feature genes.
Constructed a diagnostic classifier evaluated with ROC curves, nomograms, and decision curves, alongside immune cell infiltration profiling and consensus clustering.
Screened three feature genes to establish a two-gene diagnostic model consisting of NXT1 and UXS1, both demonstrating high diagnostic performance (AUC > 0.9).
Characterized distinct immune cell infiltration landscapes between sepsis cases and controls, mapping associated miRNA and lncRNA competing endogenous RNA networks.
Identified two distinct sepsis molecular subtypes that exhibited contrasting immune microenvironmental and transcriptomic characteristics.

Cite This Study

Hengling et al. (2026) studied this question.

synapsesocial.com/papers/6a993586636c6408cfa7dce5 https://doi.org/10.1152/physiolgenomics.00328.2025

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