English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

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.

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