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Updated: May 2, 2026
Non-invasive Imaging of Acute Allograft Rejection after Rat Renal Transplantation Using 18F-FDG PET
Published on: April 28, 2013
Identification of mitophagy-related gene signatures for predicting delayed graft function and renal allograft loss
Kaifeng Mao1, Fenwang Lin2, Yige Pan3
1Department of Kidney Transplantation, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China; Division of Urology, Department of Surgery, The University of Hong Kong-Shenzhen Hospital, Shenzhen City, Guangdong Province, China.
Background:
Ischemia-reperfusion injury (IRI) is an unavoidable consequence post-kidney transplantation, which inevitably leads to kidney damage. Numerous studies have demonstrated that mitophagy is implicated in human cancers. However, the function of mitophagy in kidney transplantation remains poorly understood. This study aims to develop mitophagy-related gene (MRGs) signatures to predict delayed graft function (DGF) and renal allograft loss post-kidney transplantation.
Methods:
Differentially expressed genes (DEGs) were identified and intersected with the MRGs to obtain mitophagy-related DEGs (MRDEGs). Functional enrichment analyses were conducted. Subsequently, random forest and SVM-RFE machine learning were employed to identify hub genes. The DGF diagnostic prediction signature was constructed using LASSO regression analysis. The renal allograft prognostic prediction signature was developed through univariate Cox and LASSO regression analysis. In addition, ROC curves, immunological characterization, correlation analysis, and survival analysis were performed.
Results:
Nineteen MRDEGs were obtained by intersecting 61 DEGs with 4897 MRGs. Seven hub genes were then identified through machine learning. Subsequently, a five-gene DGF diagnostic prediction signature was established, with ROC curves indicating its high diagnostic value for DGF. Immune infiltration analysis revealed that many immune cells were more abundant in the DGF group compared to the Immediate Graft Function (IGF) group. A two-gene prognostic signature was developed, which accurately predicted renal allografts prognosis.
Conclusions:
The mitophagy-related gene signatures demonstrated high predictive accuracy for DGF and renal allograft loss. Our study may provide new perspectives on prognosis and treatment strategies post-kidney transplantation.
Insights
This study developed gene signatures related to mitophagy to predict kidney transplant outcomes. These signatures accurately forecast delayed graft function and long-term graft loss, offering new insights for patient prognosis and treatment.
Area of Science:
- Molecular Biology
- Transplantation Immunology
- Bioinformatics
Background:
- Ischemia-reperfusion injury (IRI) is a major cause of kidney damage after transplantation.
- The role of mitophagy in kidney transplantation is not well understood.
- Mitophagy, a selective form of autophagy, is crucial for cellular quality control.
Purpose of the Study:
- To develop mitophagy-related gene (MRG) signatures for predicting delayed graft function (DGF).
- To establish MRG signatures for forecasting renal allograft loss post-kidney transplantation.
Main Methods:
- Identified differentially expressed genes (DEGs) and intersected them with MRGs to find MRDEGs.
- Employed machine learning (Random Forest, SVM-RFE) to identify hub genes.
- Constructed DGF diagnostic and renal allograft prognostic signatures using LASSO regression.
Main Results:
- Developed a five-gene signature with high diagnostic value for DGF.
- Identified a two-gene signature that accurately predicted renal allograft prognosis.
- Observed increased immune cell abundance in the DGF group compared to the immediate graft function (IGF) group.
Conclusions:
- Mitophagy-related gene signatures show high predictive accuracy for DGF and renal allograft loss.
- These signatures offer novel perspectives for prognosis and treatment strategies in kidney transplantation.
