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Objective To identify M1 macrophage-related genes in rejection after kidney transplantation and construct a risk prediction model for renal allograft survival. Methods GSE36059 and GSE21374 datasets after kidney transplantation were downloaded from Gene Expression Omnibus (GEO) database. GSE36059 dataset included the samples from the recipients with rejection and stable allografts. Using this dataset, weighted gene co-expression network analysis (WGCNA) and differential analysis were conducted to screen the M1 macrophage-related differentially expressed gene (M1-DEG). Then, GSE21374 dataset (including the follow-up data of graft loss) was divided into the training set and validation set according to a ratio of 7∶3. In the training set, a multivariate Cox's model was constructed using the variables screened by least absolute shrinkage and selection operator (LASSO), and the ability of this model to predict allograft survival was evaluated. CIBERSORT was employed to analyze the differences of infiltrated immune cells between the high-risk group and low-risk group, and the distribution of human leukocyte antigen (HLA)-related genes was analyzed between two groups. Gene set enrichment analysis (GSEA) was used to further clarify the biological process and pathway enrichment in the high-risk group. Finally, the database was employed to predict the microRNA (miRNA) interacting with the prognostic genes. Results In the GSE36059 dataset, 14 M1-DEG were screened. In the GSE21374 dataset, Toll-like receptor 8 (TLR8), Fc gamma receptor 1B (FCGR1B), BCL2 related protein A1 (BCL2A1), cathepsin S (CTSS), guanylate binding protein 2(GBP2) and caspase recruitment domain family member 16 (CARD16) were screened by LASSO-Cox regression analysis, and a multivariate Cox's model was constructed based on these 6 M1-DEG. The area under curve (AUC) of receiver operating characteristic of this model for predicting the 1- and 3-year graft survival was 0.918 and 0.877 in the training set, and 0.765 and 0.736 in the validation set, respectively. Immune cell infiltration analysis showed that the infiltration of rest and activated CD4+ memory T cells, γδT cells and M1 macrophages were increased in the high-risk group (all P < 0.05). The expression level of HLA I gene was up-regulated in the high-risk group. GSEA analysis suggested that immune response and graft rejection were enriched in the high-risk group. CTSS interacted with 8 miRNA, BCL2A1 and GBP2 interacted with 3 miRNA, and FCGR1B interacted with 1 miRNA. Conclusions The prognostic risk model based on 6 M1-DEG has high performance in predicting graft survival, which may provide evidence for early interventions for high-risk recipients.
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Objective:To explore the temporal distribution of high level of BK virus(BKV) viruria after kidney transplantation(KT)and the association of high level of viruria with clinical factors and specific human leukocyte antigen(HLA)sites in donors and recipients.Methods:From January 1, 2017 to December 31, 2019, clinical data were retrospectively reviewed for 212 recipients of cadaveric KT.A high level of urinary BKV viruria was defined as urinary BKV-DNA quantification>10 7(copies/ml)after KT while 212 recipients with the same gender composition below the threshold during the same period were selected as low-level controls.Clinical data and HLA sites of two groups were statistically analyzed and risk factors for high level of viruria screened by univariate and multifactorial Logistic regressions. Results:The median time to initial high-level BKV infection in urine after RT was 125.5 days.Based upon univariate Logistic analysis, delayed graft function(DGF)and HLA-A24 of recipient were risk factors for high-level BKV infection in urine while HLA-DQ9 of donor acted as a protective factor.Through multivariate Logistic analysis, DGF( OR=2.18, 95% CI 1.18~4.01, P=0.012)and HLA-A24( OR=1.63, 95% CI 1.06~2.53, P=0.027)of recipient were independent risk factors for high-level BKV infection in urine.And HLA-DQ9 of donors( OR=0.58, 95% CI 0.36~0.91, P=0.019)was an independent protective factor. Conclusions:High level of BKV viruria after RT is associated with donor/recipient-specific HLA sites.Early risk factor stratification and protective factors of recipients can aid in tailoring postoperative immunosuppression and screening program and developing T cell-associated vaccines.