Identification of mitophagy-related gene signatures for predicting delayed graft function and renal allograft loss post-kidney transplantation.

Autor: Mao K; Department 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., Lin F; Department of Kidney Transplantation, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China., Pan Y; Division of Nephrology, Department of Nursing, The University of Hong Kong-Shenzhen Hospital, Shenzhen City, Guangdong Province, China., Lu Z; Division of Urology, Department of Surgery, The University of Hong Kong-Shenzhen Hospital, Shenzhen City, Guangdong Province, China., Luo B; Division of Urology, Department of Surgery, The University of Hong Kong-Shenzhen Hospital, Shenzhen City, Guangdong Province, China., Zhu Y; Division of Urology, Department of Surgery, The University of Hong Kong-Shenzhen Hospital, Shenzhen City, Guangdong Province, China., Fang J; Division of Urology, Department of Surgery, The University of Hong Kong-Shenzhen Hospital, Shenzhen City, Guangdong Province, China., Ye J; Department of Kidney Transplantation, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China. Electronic address: yejunsh@126.com.
Jazyk: angličtina
Zdroj: Transplant immunology [Transpl Immunol] 2024 Dec; Vol. 87, pp. 102148. Date of Electronic Publication: 2024 Nov 14.
DOI: 10.1016/j.trim.2024.102148
Abstrakt: 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.
Competing Interests: Declaration of competing interest The authors have declared that no competing interests exist.
(Copyright © 2024 Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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