ArticlePeerJ2020
The mRNA landscape profiling reveals potential biomarkers associated with acute kidney injury AKI after kidney transplantation.
Article in PeerJ, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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.
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Who cites it
5 citing papers in PubMed.
- ETV1 Couples Reciprocal FGF23 and PTH Signaling to RenalResearch square · 2026Article
- Pressure cycling technology-assisted data-independent acquisition proteomics reveals molecular alterations and potential therapeutic targets in minor glomerular abnormalities.Precision clinical medicine · 2026Article
- Identification of Multiple Hub Genes in Acute Kidney Injury after Kidney Transplantation by Bioinformatics Analysis.Medicina (Kaunas, Lithuania) · 2022Review
- A Review of Current and Emerging Trends in Donor Graft-Quality Assessment Techniques.Journal of clinical medicine · 2022Review
- Assessing the Global Impact on the Mouse Kidney After Traumatic Brain Injury: A Transcriptomic Study.Journal of inflammation research · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThis study aims to identify potential biomarkers associated with acute kidney injury (AKI) post kidney transplantation. MATERIAL AND
methodsTwo mRNA expression profiles from Gene Expression Omnibus repertory were downloaded, including 20 delayed graft function (DGF) and 68 immediate graft function (IGF) samples. Differentially expressed genes (DEGs) were identified between DGF and IGF group. The Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis of DEGs were performed. Then, a protein-protein interaction analysis was performed to extract hub genes. The key genes were searched by literature retrieval and cross-validated based on the training dataset. An external dataset was used to validate the expression levels of key genes. Receiver operating characteristic curve analyses were performed to evaluate diagnostic performance of key genes for AKI.
resultsA total of 330 DEGs were identified between DGF and IGF samples, including 179 up-regulated and 151 down-regulated genes. Of these,
conclusion
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Registered trials
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