Evidence map›Paper›PMID 41868226›Full record

ArticleFrontiers in medicine2026

Development of a prognostic model for sepsis based on gut microbiota-associated genes and identification of potential targets.

Fangqiong Li, Minrong Xu, Huiqin Xiao, Ping Hu, Wei Zhang

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Article in Frontiers in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

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5 authors.

Fangqiong LiDepartment of Clinical Laboratory, Tongde Hospital of Zhejiang Province Affiliated to Zhejiang Chinese Medical University, Hangzhou, China.
Minrong XuDepartment of Critical Care Medicine, Tongde Hospital of Zhejiang Province, Hangzhou, China.
Huiqin XiaoDepartment of Critical Care Medicine, Tongde Hospital of Zhejiang Province, Hangzhou, China.
Ping HuDepartment of Critical Care Medicine, Tongde Hospital of Zhejiang Province, Hangzhou, China.
Wei ZhangDepartment of Critical Care Medicine, Tongde Hospital of Zhejiang Province, Hangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Gut microbiota dysbiosis drives sepsis progression by impairing intestinal barrier function and exacerbating systemic inflammation, but the microbiota-host-immune interaction mechanisms remain unclear. Methods: We integrated transcriptomic and single-cell RNA sequencing (scRNA-seq) data from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between sepsis patients and healthy controls were identified in GSE154918, then intersected with 248 gut microbiota-related genes from the GutMGene database to obtain candidate genes. A prognostic model named GMGscore was constructed via LASSO-Cox regression in GSE65682 and validated in GSE95233. Area under the curve (AUC) was used to evaluate the model performance. The expression of gut microbiota-related genes was validated in peripheral blood samples obtained from patients with sepsis through RT-qPCR. Furthermore, scRNA-seq data (GSE167363) was used to determine the cellular localization of key genes. Molecular docking predicted interactions between gut microbiota metabolites and the key target. Results: We identified 34 gut microbiota-related DEGs, which were enriched in pathways like inflammatory bowel disease and IL-17 signaling. The GMGscore, based on 6 genes (CYP1A2, FFAR2, IL4R, MUC1, RORA, ASPM), showed excellent prognostic performance (AUC = 0.903 in training set; AUC = 0.901 in validation set). High GMGscore correlated with poor survival, upregulated neutrophil degranulation and reduced neutrophils. RORA was identified as a key gut microbiota-related target, which was consistently downregulated in sepsis with the highest diagnostic AUC across datasets, mainly expressed in effector T cells and NK cells, and positively correlated with CD8 + T cell/NK cell infiltration ( Conclusion: The GMGscore is a robust prognostic tool for sepsis. RORA, targeted by gut microbiota metabolites, may regulate immune balance via effector T cells and NK cells. These findings advance understanding of gut microbiota-sepsis crosstalk and provide new avenues for precise prognosis and targeted therapy.

Indexed as

gut microbiotamolecular dockingprognostic signatureretinoic acid receptor-related orphan receptor alphasepsissingle-cell RNA sequencing

Identifiers

PMID41868226
PMCPMC12999399

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