ArticlePloS one2026
Bioinformatics analysis reveals the association of bile acid metabolism-related genes with sepsis.
Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSepsis, is a life-threatening syndrome triggered by infection. Bile acid metabolism may be involved in the pathogenesis of sepsis, the underlying association has not yet been elucidated. Thus, we aimed to screen for bile acid metabolism-related biomarkers of sepsis and discover the potential association.
methodsSepsis-related datasets were downloaded from the Gene Expression Omnibus (GEO) database. We identified differentially expressed genes (DEGs) and bile acid metabolism-related differentially expressed genes (BAMRDEGs), then identified hub genes using protein-protein interaction (PPI) network analysis and CytoHubba algorithm. After GO/KEGG enrichment analysis, a sepsis risk prediction model based on key genes was subsequently constructed using support vector machine recursive feature elimination (SVM-RFE) machine learning and least absolute shrinkage and selection operator (LASSO) regression. CIBERSORTx analysis was performed to assess immune cell infiltration and its association with key genes. Finally, the transcriptional levels of key genes in sepsis samples were detected by Quantitative Real-time PCR (qRT-PCR).
results9785 DEGs were identified, including 5138 upregulated and 4647 downregulated genes. Additionally, 25 hub genes were identified. Gene enrichment analysis indicated that the hub genes participate in multiple biological pathways. Key genes (ABCC2, PECR, EPHX2, PEX2, and AGXT) exert central roles in the development of sepsis, indicating the involvement of bile acid metabolism. Significant correlations existed between the expression of key BAMRDEGs and the levels of different immune cell types. qRT-PCR suggested significant up-regulation on ABCC2 and AGXT in sepsis samples versus controls.
conclusionThis study revealed novel insights into the correlation between sepsis and bile acid metabolism, and identified 5 key genes involved in the development of sepsis, providing molecular targets and novel strategies for the diagnosis and treatment of sepsis.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.