Background: Kidney transplant rejection is considered as a vital factor of kidney transplant failure. Therefore, it's necessary to search for effective biomarkers for kidney transplant surveillance.
Methods: In this study, we conducted time-series gene expression profiles analysis of samples from kidney transplant patients with different post-transplant days through weighted gene co-expression network analysis (WGCNA). Associations between gene co-expression modules and days post-transplant were determined through spearman rank correlation analysis. Potential kidney transplant rejection-related modules were subjected to gene functional enrichment analysis through clusterProfiler and protein-protein interaction analysis via STRING database.
Results: A total of 11 gene co-expression modules were identified, and the pink module which was mainly involved in "energy derivation by oxidation of organic compounds" and "Huntington disease" showed significant correlation with the phenotypic trait "days post-transplant". CYC1, SDHA, UQCRC1, UQCRQ, and SDHB in the pink module exhibited high scores in the protein-protein interaction network analysis.
Conclusions: We reported several potential genes may be associated with the kidney transplant rejection, which should provide novel biomarkers for kidney transplant surveillance.
Keywords: Biomarker; CYC1; Gene co-expression network; Kidney transplant rejection; SDHA.
Copyright © 2020 The Authors. Published by Elsevier B.V. All rights reserved.