Evidence map›Paper›PMID 42266692›Full record

ArticleFrontiers in immunology2026

Multi-cohort transcriptomics integration for building and validating a diagnostic model of peripheral blood septic shock.

Ling Li, Kexun Li, Weiwei Qian, Hui Jiang, Hongqiong Peng, Yang Zhang, Zhengjun Chen, Xia Zeng

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Ling Li *Emergency Department, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
Kexun Li *Emergency Department, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
Weiwei Qian *Emergency Department, Shangjinnanfu Hospital, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
Hui JiangEmergency Department, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
Hongqiong PengEmergency Department, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
Yang ZhangEmergency Department, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
Zhengjun ChenRobotic Minimally Invasive Surgery Center, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, Sichuan, China.
Xia ZengEmergency Department, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To integrate multi-cohort transcriptomic, single-cell, and experimental data to identify diagnostic signature genes for septic shock, establish a peripheral blood molecular diagnostic model, and elucidate the m6A regulatory mechanisms of key genes. Methods: Candidate genes were identified from five GEO peripheral blood cohorts through batch effect-corrected differential expression analysis and WGCNA, followed by parallel GO/DO enrichment analysis. Feature genes were selected using PPI networks combined with LASSO, SVM-RFE, and random forest algorithms. A 5-gene artificial neural network (ANN) diagnostic model was constructed and validated using ROC and logistic regression in GSE95233, GSE131761, and clinical cohorts. Immune cell composition and expression of characteristic genes in neutrophils were analyzed using CIBERSORT and GSE167363 single-cell data. The METTL14/YTHDF1-S100A12 m6A axis was elucidated via qRT-PCR, Western blot, MeRIP-qPCR, RIP-qPCR, and Actinomycin D experiments. In CLP mice, siMETTL14 was administered for Results: A total of 76 sepsis-shock-associated candidate genes were identified, enriched in the bacterial defense pathway. Five robust candidate genes (S100A12, MMP8, PGLYRP1, CEACAM8, MMP9) were selected by integrating PPI and three machine learning algorithms. The constructed ANN achieved high AUC across multiple cohorts, and all five genes showed significantly elevated mRNA and protein levels in peripheral blood from clinical sepsis patients. CIBERSORT and single-cell results indicated significant neutrophil expansion, with the five genes predominantly enriched in neutrophils and progressively elevated with worsening outcomes. m6A-related experiments demonstrated that METTL14 mediates m6A modification and stabilizes S100A12 mRNA through YTHDF1 recognition; knocking down either METTL14 or YTHDF1 accelerated its degradation. Conclusion: In this study, a diagnostic signature was established for septic shock comprising five neutrophil-associated genes and an ANN model, revealing the regulatory role of the METTL14/YTHDF1-mediated m6A-S100A12 axis in neutrophils. This suggests the METTL14/m6A pathway as a potential diagnostic and therapeutic target.

Indexed as

Shock, SepticTranscriptomeAnimalsBiomarkersCohort StudiesDisease Models, AnimalGene Expression ProfilingHumansMiceNeutrophilsBiomarkersartificial neural networkdiagnostic gene signaturem6a modificationperipheral blood transcriptomeseptic shock

Identifiers

PMID42266692
PMCPMC13243033

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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 Socratic graph.