Five-gene signature predicts acute kidney injury in early kidney transplant patients

• Published in: Aging (Albany, NY.) NY.), 2022-03, Vol.14 (6), p.2628-2644
• Main Authors: Zhai, Xia, Lou, Hongqiang, Hu, Jing
• Format: Article
• Language: English
• Subjects: Acute Kidney Injury - diagnosis; Acute Kidney Injury - genetics; Area Under Curve; Biomarkers - metabolism; Humans; Kidney Transplantation - adverse effects; MicroRNAs - genetics; MicroRNAs - metabolism; Phosphatidylinositol 3-Kinases - metabolism; Research Paper
• ISSN: 1945-4589; 1945-4589
• DOI: 10.18632/aging.203962

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.