Evidence map›Paper›PMID 42769250›Full record

ArticleFrontiers in immunology2026

Identification of MMP8, DDX24, RNASE2, and EMB as a novel diagnostic gene panel for sepsis: a transcriptome-based modeling study.

Zhiwen Gong, Xinyi Liu, Xiaoyu Xiang, Tingting Li, Zhongxue Feng, Jing Yang, Lietao Wang, Lijun Wang, Wei Zhang

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Article in Frontiers in immunology, 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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1 · What the graph read from it

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Zhiwen Gong *Institute of Critical Care Medicine, West China Hospital Sichuan University, Chengdu, China.
Xinyi Liu *Department of Critical Care Medicine, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, Chengdu, China.
Xiaoyu XiangInstitute of Critical Care Medicine, West China Hospital Sichuan University, Chengdu, China.
Tingting LiInstitute of Critical Care Medicine, West China Hospital Sichuan University, Chengdu, China.
Zhongxue FengInstitute of Critical Care Medicine, West China Hospital Sichuan University, Chengdu, China.
Jing YangDepartment of Critical Care Medicine, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University and Collaborative Innovation Center of Biotherapy, Chengdu, Sichuan, China.
Lietao WangInstitute of Critical Care Medicine, West China Hospital Sichuan University, Chengdu, China.
Lijun WangInstitute of Critical Care Medicine, West China Hospital Sichuan University, Chengdu, China.
Wei ZhangInstitute of Critical Care Medicine, West China Hospital Sichuan University, Chengdu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: Sepsis is a life-threatening condition with high mortality and complex pathology. Early diagnosis is critical but remains challenging due to a lack of effective biomarkers. This study aims to identify specific diagnostic markers to distinguish sepsis from non-sepsis, and to develop a robust diagnostic model. Methods: PBMC transcriptome data from our cohort (24 healthy controls, 29 common infections, 51 sepsis patients) were analyzed to identify genes with expression levels increasing or decreasing with infection severity. Low-expression genes were excluded, and candidate markers were evaluated using multiple GEO datasets. Top-performing genes were selected to build a LASSO regression-based diagnostic model. Model performance was assessed by AUC, BSS, ROC and calibration curves, nomogram, DCA, and CIC curves. Functional analyses (GO, KEGG, immune infiltration, GSEA, PPI network) were performed to explore underlying immune mechanisms. Results: A total of 114 genes showing expression changes with infection severity were identified. Four genes-MMP8, DDX24, RNASE2, and EMB-were selected based on diagnostic performance. The resulting model performed well in both our cohort and public datasets (AUC: 0.811-1; BSS: -0.464 - 0.964). In the GSE69686 dataset, the model also showed predictive value for neonatal and pediatric sepsis (AUC: 0.666-0.852; BSS: -1.34 - -0.293). Immune infiltration and GSEA revealed enrichment of these genes in neutrophils, monocytes, and T cells, reflecting key immune features of sepsis. Conclusion: The four identified genes-MMP8, DDX24, RNASE2, and EMB-collectively form a diagnostic model that effectively distinguishes sepsis patients from those with non-sepsis individuals.

Indexed as

Matrix Metalloproteinase 8SepsisTranscriptomeAdultBiomarkersFemaleGene Expression ProfilingHumansMaleMiddle AgedBiomarkersMatrix Metalloproteinase 8MMP8 protein, humanbiomarkerdiagnostic modelearly diagnosisimmunemachine learningsepsis

Identifiers

PMID42769250
PMCPMC13590547

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.