ArticleMedicine2025
Exploring the biomarkers for diagnostic accuracy associated with glycolysis and macrophage polarization in pediatric sepsis and performing mechanistic studies.
Article in Medicine, 2025. 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
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The pediatric sepsis (PS) is characterized by severe clinical symptoms and high mortality. There was a lack of studies on the mechanisms of glycolysis and macrophage polarization (MP) in PS. The focus of this study was to identify biomarkers associated with glycolysis and MP in PS and to conduct mechanistic studies. The GSE26440 and GSE13904 datasets, glycolysis-related genes and macrophage polarization-related genes (MPRGs) were used in this study for analysis. First, to identify differentially expressed genes in GSE26440. Next, the weighted gene co-expression network analysis was taken to obtain key modular genes related between glycolysis and MP, and differentially expressed genes were overlapped with key modular genes to identify candidate genes. Subsequently, biomarkers were detected to biomarkers by constructing a protein-protein interaction network, machine learning and expression validation. Finally, based on the biomarkers, functional enrichment, regulatory network, immune microenvironment analysis, and the quantitative real-time polymerase chain reaction were analyzed. After screening, 4 biomarkers (a disintegrin and a metalloprotease9 [ADAM9], transforming growth factor alpha [TGFA], G protein subunit alpha q [GNAQ], and decaprenyl diphosphate synthase subunit 1 [PDSS1]) were obtained. The lysosome and fc gamma r mediated phagocytosis co-targeted 4 biomarkers in gene set enrichment analysis. Spearman correlation analysis showed that ADAM9, TGFA, GNAQ, and PDSS1 were significantly positively related to activated dendritic cells, and had a significantly negative correlation with activated B cells. Next, a long noncoding RNA (lncRNAs)-MicroRNAs (miRNAs)-mRNA network containing 7 microRNAs and 23 lncRNAs was constructed. The hsa-miR-302c-5P co-targeted ADAM9, TGFA, and PDSS1. Finally, quantitative real-time polymerase chain reaction showed that TGFA, GNAQ, and PDSS1 expression levels were significantly elevated in PS samples. In this study, 4 biomarkers (ADAM9, TGFA, GNAQ, and PDSS1) associated with glycolysis and MP were identified to provide scientific theories for the clinical management of PS.
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.