ArticleFrontiers in immunology2022
Immune-Related Genes for Predicting Future Kidney Graft Loss: A Study Based on GEO Database.
Article in Frontiers in immunology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 10 citations in OpenAlex.
- Urine Proteomics as a Source of Biological Information and Outcome Predictor in Living Kidney Transplantation.Molecular & cellular proteomics : MCP · 2026Article
- Identification of lactylation and its hub genes in contributing immune activation and renal allograft fibrosis by integrative bioinformatics and machine learning.Frontiers in immunology · 2026Article
- Identification of Renal Ischemia-Reperfusion Injury Subtypes and Predictive Model for Graft Loss after Kidney Transplantation Based on Programmed Cell Death-Related Genes.Kidney diseases (Basel, Switzerland) · 2024Article
- The Use of Machine Learning in the Diagnosis of Kidney Allograft Rejection: Current Knowledge and Applications.Diagnostics (Basel, Switzerland) · 2024Review
- Review
- A novel immune score model predicting the prognosis and immunotherapy response of breast cancer.Scientific reports · 2023Article
- Identification of the Subtypes of Renal Ischemia-Reperfusion Injury Based on Pyroptosis-Related Genes.Biomolecules · 2023Article
- Construction of predictive model of interstitial fibrosis and tubular atrophy after kidney transplantation with machine learning algorithms.Frontiers in genetics · 2023Article
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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