Evidence map›Paper›PMID 41947789›Full record

ArticleFrontiers in cellular and infection microbiology2026

Integrated bioinformatics analysis of molecular signatures and therapeutic targets in early sepsis.

Xiaowei Gai, Yaqing Li, Yanan Wang, Dan Gao, Shanshan Wu, Yanan Geng, Jiamin Zhang, Minghui Yao, Gaiqi Yao, Qiuyan Wang

Abstract read
In one paragraph

Article in Frontiers in cellular and infection microbiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

10 authors.

Xiaowei GaiDepartment of Critical Care Medicine, Qinhuangdao Hospital of Peking University Third Hospital, Qinhuangdao, Hebei, China.
Yaqing LiDepartment of Infectious Diseases, Hebei General Hospital, Shijiazhuang, Hebei, China.
Yanan WangDepartment of Critical Care Medicine, Qinhuangdao Hospital of Peking University Third Hospital, Qinhuangdao, Hebei, China.
Dan GaoDepartment of Critical Care Medicine, Qinhuangdao Hospital of Peking University Third Hospital, Qinhuangdao, Hebei, China.
Shanshan WuDepartment of Critical Care Medicine, Qinhuangdao Hospital of Peking University Third Hospital, Qinhuangdao, Hebei, China.
Yanan GengDepartment of Critical Care Medicine, Qinhuangdao Hospital of Peking University Third Hospital, Qinhuangdao, Hebei, China.
Jiamin ZhangDepartment of Critical Care Medicine, Qinhuangdao Hospital of Peking University Third Hospital, Qinhuangdao, Hebei, China.
Minghui YaoDepartment of Critical Care Medicine, Qinhuangdao Hospital of Peking University Third Hospital, Qinhuangdao, Hebei, China.
Gaiqi YaoDepartment of Critical Care Medicine, Peking University Third Hospital, Beijing, China.
Qiuyan WangDepartment of Critical Care Medicine, Qinhuangdao Hospital of Peking University Third Hospital, Qinhuangdao, Hebei, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: This study aimed to identify key molecular signatures and therapeutic targets in early sepsis through integrated bioinformatics analysis. Methods: We analyzed three independent blood transcriptomic datasets (GSE95233, GSE137340, GSE57065) from the Gene Expression Omnibus. Differential expression, functional enrichment, and protein-protein interaction network analyses were performed to identify overlapping differentially expressed genes (DEGs) and hub genes. The Connectivity Map (cMAP) database was queried to predict potential therapeutic compounds. An independent dataset validated the diagnostic performance and cellular correlates of the hub genes. Results: We identified 330 overlapping differentially expressed genes enriched in immune pathways like T-cell receptor signaling. Ten hub genes central to T-cell function were pinpointed (CD4, CD247, CD3E, CD2, FYN, ZAP70, CD3G, ITK, LAT, CD5). Independent validation confirmed that these hub genes were significantly down-regulated in sepsis patients, with their expression levels strongly correlating with T-cell exhaustion and inversely correlating with myeloid cell infiltration. While these genes demonstrated excellent diagnostic accuracy (AUC: 0.908-0.999), this high predictive performance likely reflects differences in immune cell composition rather than disease-specific molecular signatures. Additionally, cMAP analysis predicted six potential therapeutic agents (e.g., anastrozole, etofenamate), offering a theoretical framework for drug repurposing in sepsis. Conclusion: This study independently validates the significant down-regulation of T-cell-related genes in early sepsis, reflecting a profound disruption of adaptive immunity. Our findings confirm the robustness of these genes as surrogate markers of early immune imbalance, establishing a critical foundation for future investigations into T-cell-focused immunomodulatory strategies in sepsis.

Indexed as

Computational BiologySepsisBiomarkersGene Expression ProfilingGene Regulatory NetworksHumansProtein Interaction MapsT-LymphocytesTranscriptomeBiomarkersbioinformaticsbiomarkersdrug repurposingimmunologysepsistranscriptomics

Identifiers

PMID41947789
PMCPMC13050901

What OpenQuestion 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 OpenQuestion graph.