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Immune-Related Genes for Predicting Future Kidney Graft Loss: A Study Based on GEO Database
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 disco...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8913884/ https://www.ncbi.nlm.nih.gov/pubmed/35281025 http://dx.doi.org/10.3389/fimmu.2022.859693 |
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author | Dou, Meng Ding, Chenguang Zheng, Bingxuan Deng, Ge Zhu, Kun Xu, Cuixiang Xue, Wujun Ding, Xiaoming Zheng, Jin Tian, Puxun |
author_facet | Dou, Meng Ding, Chenguang Zheng, Bingxuan Deng, Ge Zhu, Kun Xu, Cuixiang Xue, Wujun Ding, Xiaoming Zheng, Jin Tian, Puxun |
author_sort | Dou, Meng |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8913884 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-89138842022-03-12 Immune-Related Genes for Predicting Future Kidney Graft Loss: A Study Based on GEO Database Dou, Meng Ding, Chenguang Zheng, Bingxuan Deng, Ge Zhu, Kun Xu, Cuixiang Xue, Wujun Ding, Xiaoming Zheng, Jin Tian, Puxun Front Immunol Immunology 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. Frontiers Media S.A. 2022-02-25 /pmc/articles/PMC8913884/ /pubmed/35281025 http://dx.doi.org/10.3389/fimmu.2022.859693 Text en Copyright © 2022 Dou, Ding, Zheng, Deng, Zhu, Xu, Xue, Ding, Zheng and Tian https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Immunology Dou, Meng Ding, Chenguang Zheng, Bingxuan Deng, Ge Zhu, Kun Xu, Cuixiang Xue, Wujun Ding, Xiaoming Zheng, Jin Tian, Puxun Immune-Related Genes for Predicting Future Kidney Graft Loss: A Study Based on GEO Database |
title | Immune-Related Genes for Predicting Future Kidney Graft Loss: A Study Based on GEO Database |
title_full | Immune-Related Genes for Predicting Future Kidney Graft Loss: A Study Based on GEO Database |
title_fullStr | Immune-Related Genes for Predicting Future Kidney Graft Loss: A Study Based on GEO Database |
title_full_unstemmed | Immune-Related Genes for Predicting Future Kidney Graft Loss: A Study Based on GEO Database |
title_short | Immune-Related Genes for Predicting Future Kidney Graft Loss: A Study Based on GEO Database |
title_sort | immune-related genes for predicting future kidney graft loss: a study based on geo database |
topic | Immunology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8913884/ https://www.ncbi.nlm.nih.gov/pubmed/35281025 http://dx.doi.org/10.3389/fimmu.2022.859693 |
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