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pmid:35281025
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
Frontiers in immunology · 2022 doi:10.3389/fimmu.2022.859693
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Complete file
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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.\nResults: 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.\nConclusion: 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.", "claims": [{"id": "claim-method", "statement": "Then, univariate and multiple Cox regression analysis were performed in the GSE21374 dataset.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35281025", "locator": "sections[6] \"Construction of the Prognostic Gene Signature\" paragraph 2", "quote": "Then, univariate and multiple Cox regression analysis were performed in the GSE21374 dataset."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets presented in this study can be found in online repositories. 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