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

IF 1.6 4区医学 Q4 IMMUNOLOGY

Transplant immunology Pub Date : 2024-11-14 DOI:10.1016/j.trim.2024.102148

Kaifeng Mao , Fenwang Lin , Yige Pan , Zhenquan Lu , Bingfeng Luo , Yifei Zhu , Jiaqi Fang , Junsheng Ye

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Abstract

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.

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鉴定与有丝分裂相关的基因特征，以预测肾移植后移植功能延迟和肾异体移植损失。

背景：缺血再灌注损伤（IRI）是肾移植术后不可避免的后果，它不可避免地会导致肾脏损伤。大量研究表明，有丝分裂与人类癌症有关。然而，人们对有丝分裂在肾移植中的功能仍知之甚少。本研究旨在开发有丝分裂相关基因（MRGs）特征，以预测肾移植后移植功能延迟（DGF）和肾移植损失：方法：对差异表达基因（DEGs）进行鉴定，并与MRGs交叉，得到有丝分裂相关DEGs（MRDEGs）。进行了功能富集分析。随后，采用随机森林和 SVM-RFE 机器学习来识别枢纽基因。利用 LASSO 回归分析构建了 DGF 诊断预测特征。肾移植预后预测特征是通过单变量 Cox 和 LASSO 回归分析建立的。此外，还进行了ROC曲线、免疫学特征、相关性分析和生存分析：结果：通过将 61 个 DEG 与 4897 个 MRG 相交，得到了 19 个 MRDEG。然后通过机器学习确定了七个中心基因。随后，建立了五基因 DGF 诊断预测特征，ROC 曲线显示其对 DGF 具有很高的诊断价值。免疫浸润分析表明，与即刻移植物功能（IGF）组相比，DGF 组的许多免疫细胞更为丰富。研究还发现了一种双基因预后特征，它能准确预测肾异体移植物的预后：有丝分裂相关基因特征对 DGF 和肾脏同种异体移植物丢失具有很高的预测准确性。我们的研究可为肾移植后的预后和治疗策略提供新的视角。

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来源期刊

Transplant immunology 医学-免疫学

CiteScore

2.10

自引率

13.30%

发文量

198

审稿时长

48 days

期刊介绍： Transplant Immunology will publish up-to-date information on all aspects of the broad field it encompasses. The journal will be directed at (basic) scientists, tissue typers, transplant physicians and surgeons, and research and data on all immunological aspects of organ-, tissue- and (haematopoietic) stem cell transplantation are of potential interest to the readers of Transplant Immunology. Original papers, Review articles and Hypotheses will be considered for publication and submitted manuscripts will be rapidly peer-reviewed and published. They will be judged on the basis of scientific merit, originality, timeliness and quality.