Dataset Information

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

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.

SUBMITTER: Dou M

PROVIDER: S-EPMC8913884 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Dou Meng M Ding Chenguang C Zheng Bingxuan B Deng Ge G Zhu Kun K Xu Cuixiang C Xue Wujun W Ding Xiaoming X Zheng Jin J Tian Puxun P

Frontiers in immunology 20220225

<h4>Objective</h4>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.<h4>Methods</h4>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 regres ...[more]

PMID: 35281025

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Dataset Information

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

Objective

Methods

Results

Conclusion

Publications

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

Similar Datasets

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