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Immune-Related Genes for Predicting Future Kidney Graft Loss: A Study Based on GEO Database

  • Meng Dou
  • , Chenguang Ding
  • , Bingxuan Zheng
  • , Ge Deng
  • , Kun Zhu
  • , Cuixiang Xu
  • , Wujun Xue
  • , Xiaoming Ding
  • , Jin Zheng
  • , Puxun Tian
  • The First Affiliated Hospital of Xi’an Jiaotong University
  • Shaanxi Provincial People's Hospital

科研成果: 期刊稿件文章同行评审

10 引用 (Scopus)

摘要

Objective: We aimed to identify feature immune-related genes that correlated with graft rejection and to develop a prognostic model based on immune-related genes in kidney transplantation. Methods: Gene expression profiles were obtained from the GEO database. The GSE36059 dataset was used as a discovery cohort. Then, differential expression analysis and a machine learning method were performed to select feature immune-related genes. After that, univariate and multivariate Cox regression analyses were used to identify prognosis-related genes. A novel Riskscore model was built based on the results of multivariate regression. The levels of these feature genes were also confirmed in an independent single-cell dataset and other GEO datasets. Results: 15 immune-related genes were expressed differently between non-rejection and rejection kidney allografts. Those differentially expressed immune-related genes (DE-IRGs) were mainly associated with immune-related biological processes and pathways. Subsequently, a 5-immune-gene signature was constructed and showed favorable predictive results in the GSE21374 dataset. Recipients were divided into the high-risk and low-risk groups according to the median value of RiskScore. The GO and KEGG analysis indicated that the differentially expressed genes (DEGs) between high-risk and low-risk groups were mainly involved in inflammatory pathways, chemokine-related pathways, and rejection-related pathways. Immune infiltration analysis demonstrated that RiskScore was potentially related to immune infiltration. Kaplan-Meier survival analysis suggested that recipients in the high-risk group had poor graft survival. AUC values of 1- and 3-year graft survival were 0.804 and 0.793, respectively. Conclusion: Our data suggest that this immune-related prognostic model had good sensitivity and specificity in predicting the 1- and 3-year kidney graft survival and might act as a useful tool for predicting kidney graft loss.

源语言英语
文章编号859693
期刊Frontiers in Immunology
13
DOI
出版状态已出版 - 25 2月 2022
已对外发布

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