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Identification of PDCD1 as a potential biomarker in acute rejection after kidney transplantation via comprehensive bioinformatic analysis

Authors :
Yucheng Wang
Xiaoli Lin
Cuili Wang
Xinyu Liu
Xiaoying Wu
Yingying Qiu
Ying Chen
Qin Zhou
Haige Zhao
Jianghua Chen
Hongfeng Huang
Source :
Frontiers in Immunology, Vol 13 (2023)
Publication Year :
2023
Publisher :
Frontiers Media S.A., 2023.

Abstract

BackgroundAcute rejection is a determinant of prognosis following kidney transplantation. It is essential to search for novel noninvasive biomarkers for early diagnosis and prompt treatment.MethodsGene microarray data was downloaded from the Gene Expression Omnibus (GEO) expression profile database and the intersected differentially expressed genes (DEGs) was calculated. We conducted the DEGs with Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Distribution of immune cell infiltration was calculated by CIBERSORT. A hub gene marker was identified by intersecting the rejection-related genes from WGCNA and a selected KEGG pathway—T cell receptor signaling pathway (hsa04660), and building a protein-protein interaction network using the STRING database and Cytoscape software. We performed flow-cytometry analysis to validate the hub gene.ResultsA total of 1450 integrated DEGs were obtained from five datasets (GSE1563, GSE174020, GSE98320, GSE36059, GSE25902). The GO, KEGG and immune infiltration analysis results showed that AR was mainly associated with T cell activation and various T-cell related pathways. Other immune cells, such as B cells, Macrophage and Dendritic cells were also associated with the progress. After utilizing the WGCNA and PPI network, PDCD1 was identified as the hub gene. The flow-cytometry analysis demonstrated that both in CD4+ and CD8+ T cells, PD1+CD57-, an exhausted T cell phenotype, were downregulated in the acute rejection whole blood samples.ConclusionsOur study illustrated that PDCD1 may be a candidate diagnostic biomarker for acute kidney transplant rejection via integrative bioinformatic analysis.

Details

Language :
English
ISSN :
16643224
Volume :
13
Database :
Directory of Open Access Journals
Journal :
Frontiers in Immunology
Publication Type :
Academic Journal
Accession number :
edsdoj.93aa2193cb7404b88391b9b47be972f
Document Type :
article
Full Text :
https://doi.org/10.3389/fimmu.2022.1076546