Evidence map›Paper›PMID 38076334›Full record

ArticleJournal of inflammation research2023

Analysis and Verification of Glycosylation Characteristics of Septic Acute Kidney Injury.

Si Chen, Hua Gan

Open access · goldAbstract read
In one paragraph

Article in Journal of inflammation research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.9field-weighted citation impact, top 24% of its field
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

4 citing papers in PubMed, 6 citations in OpenAlex.

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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

2 authors at 1 institution in 1 country.

Si ChenDepartment of Nephrology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, People's Republic of China.
Hua GanDepartment of Nephrology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, People's Republic of China.
The Affiliated Yongchuan Hospital of Chongqing Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Septic acute kidney injury (S-AKI) results from an imbalance in the regulation of systemic inflammatory responses. Glycosylation plays an important role in inflammatory responses. However, the relationship between S-AKI and glycosylation is unclear. Methods: The datasets of the public platform were analyzed using R language to obtain glycosylation-related differentially expressed genes (GRDEGs) in S-AKI. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses were performed for GRDEGs. Hub genes were obtained using three machine learning algorithms and their diagnostic values were evaluated using receiver operating characteristic (ROC) curves. The relationships between the hub genes, immune cells, and signaling pathways were analyzed, and the upstream miRNAs, transcription factors, and compounds of the hub genes were predicted. Mouse models of AKI with sepsis were constructed and the expression of the hub genes was verified. Results: We obtained 45 GRDEGs that were mainly enriched in glycoprotein metabolism and immune inflammatory response, such as "O-glycan biosynthesis", "phagosome", "pathogenic Escherichia coli infection", "glycosyltransferase activity", etc. Seven hub genes that have potential diagnostic value were identified and were associated with the regulation of immune cells. Through gene set enrichment analysis (GSEA) of hub genes, it was found that these genes may be involved in metabolism, signaling transduction, and inflammation-related signaling pathways, such as "metabolism of amino and derivatives", "RHO GTPase cycle", "transport of small molecules", "neutrophil degranulation", "immune system", etc. We then predicted 100 miRNAs, 60 TFs, and 23 compounds of the hub genes using forecasting tools. Finally, animal experiments confirmed the differential expression of ASGR1, UMOD, SPTBN1, and ADAMTS17. Conclusion: This study identified and validated four biomarkers associated with abnormal glycosylation that could be potential targets for AKI in sepsis.

Indexed as

acute kidney injurybioinformaticsglycosylationhub genessepsis

Identifiers

PMID38076334
PMCPMC10710256
OpenAlexW4389346523

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

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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.