ArticleAging2022
Five-gene signature predicts acute kidney injury in early kidney transplant patients.
Article in Aging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 7 citations in OpenAlex.
- Developing an interpretable clinical-radiomics machine learning model using whole transition zone MRI analysis for improving diagnosis of transition zone prostate cancer.Frontiers in oncology · 2026Article
- Proteomic analysis of human kidney biopsies unveils emerging acute kidney injury very early after liver graft reperfusion.Journal of translational medicine · 2025Article
- Identification of Multiple Hub Genes in Acute Kidney Injury after Kidney Transplantation by Bioinformatics Analysis.Medicina (Kaunas, Lithuania) · 2022Review
- Machine learning algorithm-based identification and verification of characteristic genes in acute kidney injury.Frontiers in medicine · 2022Article
- Characteristic Genes and Immune Infiltration Analysis for Acute Rejection after Kidney Transplantation.Disease markers · 2022Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Patients with acute kidney injury (AKI) show high morbidity and mortality, and a lack of effective biomarkers increases difficulty in its early detection. Weighted gene co-expression network analysis (WGCNA) detected a total of 22 gene modules and 6 miRNA modules, of which 4 gene modules and 3 miRNA modules were phenotypically co-related. Functional analysis revealed that these modules were related to different molecular pathways, which mainly involved PI3K-Akt signaling pathway and ECM-receptor interaction. The brown modules related to transplantation mainly involved immune-related pathways. Finally, five genes with the highest AUC were used to establish a diagnosis and prediction model of AKI. The model showed a high area under curve (AUC) in the training set and validation set, and their prediction accuracy for AKI was as high as 100%. Similarly, the prediction accuracy of AKI after 24 h in the 0 h transplant sample was 100%. This study may provide new features for the diagnosis and prediction of AKI after kidney transplantation, and facilitate the diagnosis and drug development of AKI in kidney transplant patients.
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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.