| Autor: |
Kim IW; College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul National University, Seoul 08826, Republic of Korea., Kim JH; College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul National University, Seoul 08826, Republic of Korea., Han N; College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul National University, Seoul 08826, Republic of Korea., Kim S; Department of Bioinformatics and Life Science, Soongsil University, Seoul 06978, Republic of Korea., Kim YS; Kidney Research Institute, Seoul National University College of Medicine, Seoul 03080, Republic of Korea., Oh JM; College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul National University, Seoul 08826, Republic of Korea. |
| Abstrakt: |
Antibody‑mediated rejections (AMRs) are one of the most challenging complications that result in the deterioration of renal allograft function and graft loss in a large majority of cases. The purpose of the present study was to characterize a meta‑signature of differentially expressed RNAs associated with AMR in cases of kidney transplantation. Gene Expression Omnibus (GEO) dataset searches up to September 11, 2017, using Medical Subject Heading terms and keywords associated with kidney transplantation, AMR and mRNA arrays were downloaded from the GEO dataset. Using a computational analysis, a meta‑signature was determined that characterized the significant intersection of differentially expressed genes (DEGs). Gene‑set and network analyses were also performed to identify gene sets and sub‑networks associated with the AMR‑related traits. A statistically significant mRNA meta‑signature of upregulated and downregulated gene expression levels that were significantly associated with AMR was identified. C‑X‑C motif chemokine ligand 10 (CXCL10), CXCL9 and guanylate binding protein 1 were the most significantly associated with AMR. DEGs were efficiently identified and were found to be able to predict the occurrence of AMR according to a meta‑analysis approach from publicly available datasets. These methods and results can be applied for a more accurate diagnosis of AMR in transplant cases. |
