Identification of Biomarkers for Predicting Allograft Rejection following Kidney Transplantation Based on the Weighted Gene Coexpression Network Analysis
机构：[1]Department of Urinary Surgery, The Affiliated Calmette Hospital of Kunming Medical University, The First People’s Hospital of Kunming, Calmette Hospital, Kunming, Yunnan Province, China[2]Department of Clinical Laboratory, Yunnan Institute of Experimental Diagnosis, The First Affiliated Hospital of Kunming Medical University, Yunnan Key Laboratory of Laboratory Medicine, Kunming, Yunnan Province, China昆明医科大学附属第一医院[3]Department of Pharmacy, Yan’an Hospital Affiliated to Kunming Medical University, Kunming, Yunnan Province, China[4]Department of Pathology, The Affiliated Calmette Hospital of Kunming Medical University, The First People’s Hospital of Kunming, Calmette Hospital, Kunming, Yunnan Province, China[5]Department of Hepatopancreatobiliary Surgery, The Affiliated Calmette Hospital of Kunming Medical University, The First People’s Hospital of Kunming, Calmette Hospital, Kunming, Yunnan Province, China
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.
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外文
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中科院(CAS)分区：
出版当年[2021]版：
大类|3 区生物
小类|3 区生物工程与应用微生物4 区医学：研究与实验
最新[2023]版：
无
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出版当年[2020]版：
Q2BIOTECHNOLOGY & APPLIED MICROBIOLOGYQ3MEDICINE, RESEARCH & EXPERIMENTAL
最新[2023]版：
Q3BIOTECHNOLOGY & APPLIED MICROBIOLOGYQ3MEDICINE, RESEARCH & EXPERIMENTAL
第一作者机构：[1]Department of Urinary Surgery, The Affiliated Calmette Hospital of Kunming Medical University, The First People’s Hospital of Kunming, Calmette Hospital, Kunming, Yunnan Province, China
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推荐引用方式(GB/T 7714)：
Li-Jun Wang,Xiao-Bo Ma,Hong-Ying Xia,et al.Identification of Biomarkers for Predicting Allograft Rejection following Kidney Transplantation Based on the Weighted Gene Coexpression Network Analysis[J].BIOMED RESEARCH INTERNATIONAL.2021,2021:doi:10.1155/2021/9933136.
APA：
Li-Jun Wang,Xiao-Bo Ma,Hong-Ying Xia,Xun Sun,Lu Yu...&Jiang-Hua Ran.(2021).Identification of Biomarkers for Predicting Allograft Rejection following Kidney Transplantation Based on the Weighted Gene Coexpression Network Analysis.BIOMED RESEARCH INTERNATIONAL,2021,
MLA：
Li-Jun Wang,et al."Identification of Biomarkers for Predicting Allograft Rejection following Kidney Transplantation Based on the Weighted Gene Coexpression Network Analysis".BIOMED RESEARCH INTERNATIONAL 2021.(2021)
