Identification of Biomarkers for Predicting Allograft Rejection following Kidney Transplantation Based on the Weighted Gene Coexpression Network Analysis
| Autor: | Jiang-Hua Ran, Zong-Qiang Hu, Xun Sun, Yong-Heng Zhao, Xiao-Bo Ma, Hong-Ying Xia, Lu Yu, Qian Yang, Li-Jun Wang, Wei Hu |
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| Rok vydání: | 2021 |
| Předmět: |
Graft Rejection
0301 basic medicine VAV2 Article Subject 030230 surgery Bioinformatics General Biochemistry Genetics and Molecular Biology 03 medical and health sciences 0302 clinical medicine Databases Genetic Humans Medicine Gene Regulatory Networks Protein Interaction Maps KEGG Gene Kidney transplantation General Immunology and Microbiology business.industry Gene Expression Profiling Molecular Sequence Annotation General Medicine Allografts medicine.disease Gene coexpression Kidney Transplantation Gene Ontology 030104 developmental biology Gene Expression Regulation ROC Curve Allograft rejection Identification (biology) business Biomarkers Research Article Kidney disease |
| Zdroj: | BioMed Research International BioMed Research International, Vol 2021 (2021) |
| ISSN: | 2314-6141 2314-6133 |
| DOI: | 10.1155/2021/9933136 |
| Popis: | Kidney transplantation is the promising treatment of choice for chronic kidney disease and end-stage kidney disease and can effectively improve the quality of life and survival rates of patients. However, the allograft rejection following kidney transplantation has a negative impact on transplant success. Therefore, the present study is aimed at screening novel biomarkers for the diagnosis and treatment of allograft rejection following kidney transplantation for improving long-term transplant outcome. In the study, a total of 8 modules and 3065 genes were identified by WGCNA based on the GSE46474 and GSE15296 dataset from the Gene Expression Omnibus (GEO) database. Moreover, the results of Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis showed that these genes were mainly involved in the immune-related biological processes and pathways. Thus, 317 immune-related genes were selected for further analysis. Finally, 5 genes (including CD200R1, VAV2, FASLG, SH2D1B, and RAP2B) were identified as the candidate biomarkers based on the ROC and difference analysis. Furthermore, we also found that in the 5 biomarkers an interaction might exist among each other in the protein and transcription level. Taken together, our study identified CD200R1, VAV2, FASLG, SH2D1B, and RAP2B as the candidate diagnostic biomarkers, which might contribute to the prevention and treatment of allograft rejection following kidney transplantation. |
| Databáze: | OpenAIRE |
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