Identification of Biomarkers for Predicting Allograft Rejection Following Kidney Transplantation Based on the Weighted Gene Coexpression Network Analysis

Hindawi BioMed Research International Volume 2021, Article ID 9933136, 14 pages https://doi.org/10.1155/2021/9933136

Research Article Identification of Biomarkers for Predicting Allograft Rejection following Kidney Transplantation Based on the Weighted Gene Coexpression Network Analysis

Li-Jun Wang,1 Xiao-Bo Ma,2 Hong-Ying Xia,3 Xun Sun,1 Lu Yu,4 Qian Yang,4 Zong-Qiang Hu,5 Yong-Heng Zhao,1 Wei Hu,1 and Jiang-Hua Ran 5

1Department of Urinary Surgery, The Affiliated Calmette Hospital of Kunming Medical University, The First People’s Hospital of Kunming, Calmette Hospital, Kunming, Yunnan Province, China 2Department 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 3Department of Pharmacy, Yan’an Hospital Affiliated to Kunming Medical University, Kunming, Yunnan Province, China 4Department of Pathology, The Affiliated Calmette Hospital of Kunming Medical University, The First People’s Hospital of Kunming, Calmette Hospital, Kunming, Yunnan Province, China 5Department of Hepatopancreatobiliary Surgery, The Affiliated Calmette Hospital of Kunming Medical University, The First People’s Hospital of Kunming, Calmette Hospital, Kunming, Yunnan Province, China

Correspondence should be addressed to Jiang-Hua Ran; [email protected]

Received 24 March 2021; Accepted 3 July 2021; Published 29 July 2021

Academic Editor: Junyan Liu

Copyright © 2021 Li-Jun Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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.

1. Introduction occurrence of allograft rejection following kidney transplantation greatly limits the success rate by resulting in degrada- Kidney transplant is a promising method of kidney replace- tion of kidney function and graft loss [4, 5]. Although the ment therapy for chronic kidney disease and end-stage current advancements in the development of immunosup- kidney disease [1, 2]. Increasing evidence has demonstrated pressive medications targeting T cell-mediated immunity that kidney transplantation can effectively improve the qual- have effectively inhibited the occurrence of rejection reaction ity of life and survival rates of patients with chronic kidney [6], the currently available immunosuppressive medications disease and end-stage kidney disease [3]. However, the are ineffective against the adaptive humoral immune 2 BioMed Research International response. Moreover, to date, the detection of allograft rejec- GSE15296 was corrected with Limma [19] and SVA package tion following kidney transplantation mainly relies on the [20] in R. biopsies, which is prone to misdiagnosis. Therefore, there is a continuing need for better understanding of the mechanism 2.2. Construction of Gene Coexpression Network. WGCNA of allograft rejection following kidney transplantation and package [15] in R was selected to construct the weighted gene screening of novel biomarkers for the diagnosis and treatment coexpression network based on all the genes involved in all of allograft rejection following kidney transplantation. samples in the training cohort. Firstly, we checked the associ- Currently, it has been suggested that understanding the ation of all samples in the training cohort by performing underlying mechanisms of allograft rejection following kid- sample cluster analysis and removed the outlier samples to ney transplantation can contribute to identify potential ther- ensure the accuracy of the analysis. Then, to ensure that the apeutic targets. Based on the key role of anti-HLA antibodies gene interaction maximally conforms to the scale-free distri- in the occurrence of rejection, many promising treatment bution, we performed the soft threshold selection analysis to methods have already being tested in clinical trials, such as select the appropriate soft threshold. Moreover, hierarchical B cell-depleting rituximab treatment and proteasome inhibi- clustering was carried out to identify modules by a dynamic tion using bortezomib [7]. However, these treatment tree cutting algorithm with a minimum module size of 30 methods also have some limitations because of the individual for the gene dendrogram [21]. Finally, the dissimilarity of heterogeneity [8]. Moreover, most therapies only focused on module eigengenes (MES) was calculated for module den- the immune cell level, a few studies on the molecular level [9, drogram and some modules (the dissimilarity of module 10]. Recent researches have showed that CD20, VWF, and eigengenes < 0:25) were merged to obtain the ultimate FOXP3 genes were upregulated in the peripheral blood of network. rejection patients [9]. Moreover, it has been raised that the expression of VWF, DARC, and CAV1 can effectively distin- 2.3. Identification of Rejection-Related Modules and Genes. guish excluded rejection samples from nonrejection samples To detect the independence between two different modules, [10]. Therefore, genes especially immune-related genes may we analyzed the association among different modules. Mod- play a vital role in the occurrence of allograft rejection follow- ule eigengenes were used to evaluate the possible relationship ing kidney transplantation. Nevertheless, it remains largely between gene modules and clinical traits and to identify unclear how these genes influence the occurrence of allograft rejection-related modules. Finally, the genes in rejection- rejection. related modules were defined as rejection-related genes. More and more approaches have been developed for identifying the disease-related modules and genes, which 2.4. Functional and Pathway Enrichment Analysis. Gene were highly effective for helping search for the diagnostic Ontology (GO) annotation, including biological processes and therapeutic markers in clinical practice [11–14]. As an (BP), molecular function (MF), and cellular component approach for screening disease-related modules, weighted (CC), and Kyoto Encyclopedia of Genes and Genomes gene coexpression network analysis (WGCNA) is the most (KEGG) pathway enrichment analysis were performed to common and useful method to reveal the association investigate the biological function of rejection-related genes between genes and clinical features [15]. In previous study, by clusterProfiler package [22] in R, and P <0:05 was consid- WGCNA has been used to study the complex diseases, such ered to be significantly enriched. Besides, the results of as glioblastoma multiforme [16], cardiovascular disease in enrichment analysis were plotted by ggplot2 package [23] diabetic patients [17], and Sjögren’s syndrome [18]. How- in R. ever, relative study in kidney transplant is still scarce. In the present study, kidney transplant gene expression 2.5. Identification of Immune-Related Genes. Given the key data were downloaded from the NCBI Gene Expression role of immune immunoreaction in the allograft rejection Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/ following kidney transplantation, immune-related genes geo/query/acc.cgi). Next, WGCNA was constructed to iden- involved in the immune-related GO and pathways were tify rejection-related genes. Moreover, we further explored selected and reserved for the subsequent analysis based on the potential molecular mechanism of rejection-related genes the results of enrichment analysis. and provided the candidate biomarkers for the diagnosis of rejection, which will contribute to the therapy of allograft 2.6. Differential Expression Analysis of Immune-Related rejection following kidney transplantation. Genes. The Wilcoxon rank sum test was used to identify differentially expressed and immune-related genes between 2. Materials and Methods rejection samples and nonrejection samples in the training cohort and the validation cohort, respectively, and P <0:05 2.1. Data Processing. Three kidney transplant-related gene was considered to be statistically significant. datasets, including GSE46474, GSE15296, and GSE14067, were obtained from the NCBI GEO database. The detailed 2.7. Identification of Hub Genes. Receiver operator character- information of the three gene datasets is listed in Table 1. istic curve was plotted using pROC package [24] in R to iden- Next, GSE46474 and GSE15296 were combined as the train- tify the hub genes based on the capability of distinguishing ing cohort, and GSE14067 was selected as the independent rejection samples and nonrejection samples in the training validation cohort. The batch effect between GSE46474 and cohort which was evaluated by the area under the curve BioMed Research International 3

Table 1: The information of three transplant-related gene datasets from the GEO database.

GEO number Rejection Nonrejection Platforms Description GSE46474 20 20 GPL570 Affymetrix Human Genome U133 Plus 2.0 Array GSE15296 51 24 GPL570 Affymetrix Human Genome U133 Plus 2.0 Array GSE14067 38 37 GPL570 Affymetrix Human Genome U133 Plus 2.0 Array

Original

GSM382252 GSM382248 GSM382279 GSM382304 GSM1130816 (a) Batch corrected 14

GSM382252 GSM382248 GSM382279 GSM382304 GSM1130816 (b)

Figure 1: The batch eﬀect correction of GSE46474 and GSE15296. (a) The distribution of gene expression for the samples before correction. (b) The distribution of gene expression for the samples after correction. 4

Height 100 120 20 40 60 80 Scale free topology model ft, signed R2 0.0 0.2 0.4 0.6 0.8 GSM382283 GSM382279 GSM382270 GSM382250 GSM382291 GSM382306 1 GSM1130819 GSM1130834 2 GSM382269 GSM382272

3 GSM382282 GSM1130831

4 GSM1130841 GSM382285

5101520 GSM382292 5

Sof threshold(power) GSM382300 GSM1130828 Scale independence Scale 6 GSM1130845 GSM1130814 7 GSM1130830 GSM1130850

8 GSM1130851 GSM1130833

9 GSM1130843 GSM1130812 10 GSM1130821 GSM1130827

11 GSM382273 GSM382281

12 GSM382294 GSM382305

13 GSM1130816 GSM1130836 GSM382314 14 GSM1130848 GSM382319 15 GSM382255 GSM382320 Figure 16

GSM382322 Sample clusteringtodetectoutliers GSM382310

17 GSM382316 GSM382260

18 GSM382309 GSM382278 GSM382274 19 GSM382261 GSM1130847 20

:Continued. 2: GSM1130838 GSM382276 (b) (a) GSM382286 GSM1130817 GSM382280 GSM1130818 Mean connectivity GSM1130842 GSM1130820 GSM1130826 GSM382258 GSM382253 1000 2000 3000 4000 5000 GSM1130823 GSM1130837 GSM382301 GSM382252 0 GSM382254 GSM382287 GSM1130825 GSM382308

1 GSM1130822 GSM1130824 GSM1130832 2 GSM382303 GSM1130815 3 GSM1130839 GSM382251 4 GSM382271 GSM382307 1015205 5 GSM382313 Sof threshold(power) GSM382304

6 GSM382275 Mean connectivity GSM382277 GSM382284 7 GSM382293 GSM382248 8 GSM382259 GSM382298 9 GSM382299 GSM1130813 10 GSM1130829 GSM1130844 11 GSM1130846 GSM382249 12 GSM382256 GSM382265 13

GSM382264 International Research BioMed GSM382267

14 GSM1130849 GSM382289

15 GSM382315 GSM382317 GSM382311 16 GSM382312 GSM1130835 17 GSM382318 GSM382266 18 GSM382321 GSM382297

19 GSM1130840 GSM382262

20 GSM382268 GSM382288 GSM382302 GSM382257 GSM382263 GSM382290 GSM382295 GSM382296 BioMed Research International 5

Cluster dendrogram 1.0

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Figure 2: Gene coexpression network construction. (a) Sample cluster analysis identiﬁed GSM382283 as the outlier samples in the training cohort. (b) Soft threshold selection analysis to select the appropriate soft threshold. (c) 24 modules were identiﬁed by setting the dissimilarity of module eigengenes < 0:25.

(ROC) value, and these genes with ROC > 0:7 were defined as 3.2. Association between Any Two Modules and Identification hub genes. of Hub Modules. To further quantify the coexpression simi- larity among 27 modules, the correlation between any two 2.8. PPI Network Construction and Gene Correlation modules was calculated, and the results suggested that 27 Analysis. To further investigate the interactions between modules could be divided into two main clusters based on hub genes and other rejection-related genes at the protein their correlation (Figure 3(a)). Moreover, we also randomly level, the Search Tool for the Retrieval of Interacting Genes selected 400 genes to construct the gene cluster tree and to (STRING, https://string-db.org/) was used to build the plot the network heat map. Based on the heat map plot, we protein-protein interaction (PPI) network. Next, Cytoscape could see that each module showed independence to other version 3.7.1 [25] was used to visualize and analyze the inter- modules. Therefore, the 27 modules were effective and repre- actions of the proteins, and the cutoff value was set to sentative (Figure 3(b)). In our study, nonrejection and confidence = 0:4. Moreover, psych package in R was used to rejection were selected as two clinical parameters to identify compare the association between any two hub genes. rejection-related modules. Based on P <0:05, blue module, cyan module, dark turquoise module, grey60 module, ivory 2.9. Statistical Analysis. Statistical analysis was performed module, pale turquoise module, saddle brown module, and using R software v 4.0.2. Unless otherwise stipulated, yellow green module were selected as the rejection-related P <0:05 was considered statistically significant. modules and 3065 genes in these 8 modules were defined as rejection-related genes (Figure 3(c)). 3. Results 3.3. Functional and Pathway Enrichment Analysis. To further 3.1. Identification of Gene Coexpression Networks. The results investigate the GO function and KEGG pathways which of batch correction clearly showed that the batch effect involved rejection-related genes, functional and pathway between GSE46474 and GSE15296 was effectively eliminated enrichment analysis was performed using the clusterProfiler and the combined dataset, including 44 nonrejection samples package in R. The GO enrichment results showed that for and 71 rejection samples, could be used for further analysis BP, the rejection-related genes were mainly associated with (Figure 1). Next, the sample cluster analysis showed that T cell activation, T cell differentiation, and positive regula- GSM382283 was an outlier sample in the combined dataset. tion of defense response; for CC, the rejection-related genes Hence, GSM382283 was removed and remaining samples were mainly related to tertiary granule; and that for MF, the were used for subsequent analysis (Figure 2(a)). To construct rejection-related genes were mainly involved in activation a scale-free network, soft threshold selection analysis of immune receptors and chemokine receptor binding suggested that β =7 (scale free R2 =0:90) was optimal soft (Figure 4(a), Supplementary material 1). In addition, the thresholds (Figure 2(b)). Finally, by setting the dissimilarity results of KEGG pathway enrichment analysis also showed of module eigengenes < 0:25, 24 modules were identified that rejection-related genes were mainly related to the (Figure 2(c)). interaction of viral proteins with cytokines and cytokine 6 eeto f40gns c ouecreainanalysis. correlation Module (c) genes. 400 of selection Figure :Mdl orlto nlss a ouecutrn n etmpo h iegn daec.()Gn lseigrslsb random by results clustering Gene (b) adjacency. eigengene the of map heat and clustering Module (a) analysis. correlation Module 3: MEdarkolivegreen MElightsteelblue1 MEdarkturquoise MEpaleturquoise MEmidnightblue MEsaddlebrown MEgreenyellow MEyellowgreen MElightyellow MElightcyan1 MEdarkgreen MElightgreen 0.2 0.4 0.6 0.8 1.0 1.2 MEroyalblue MEdarkgrey MEmagenta MEorange MEgrey60 MEpurple MEbrown MEwhite MEivory MEcyan MEgrey MEblue MEorange MEivory MEmagenta MEbrown MEdarkolivegreen MElightcyan1 MElightyellow MEdarkgrey MEmidnightblue MEyellowgreen (a) MEblue MElightgreen MEwhite MEdarkgreen Reject −0.0085 (9e−04) (1e−05) (0.006) (0.008) (0.006) −0.044 −0.025 MEpurple (0.02) (0.03) (0.06) (0.07) (0.01) (0.09) (0.03) −0.12 −0.24 −0.31 −0.13 −0.13 −0.14 0.096 (0.6) (0.2) (0.3) (0.9) (0.3) (0.5) (0.8) (0.2) (0.2) (0.3) (0.2) (0.1) −0.1 −0.2 0.26 0.22 0.18 0.25 0.06 0.17 0.12 0.16 0.39 0.25 0.2 0.1 MEpaleturquoise MEcyan MEgrey60 MEroyalblue MElightsteelblue1

Module−trait relationships MEdarkturquoise MEgreenyellow (c)

0 0.2 0.4 0.6 0.8 1 MEsaddlebrown Network heatmap plot, selected genes Non−reject (1e−05) (9e−04) (0.006) (0.008) (0.006) −0.096 0.0085 (0.02) (0.03) (0.06) (0.07) (0.01) (0.09) (0.03) −0.26 −0.22 −0.18 −0.25 −0.39 −0.17 −0.06 −0.12 −0.16 −0.25 0.044 0.025 (0.6) (0.2) (0.3) (0.5) (0.8) (0.2) (0.9) (0.3) (0.3) (0.2) (0.2) (0.1) −0.2 −0.1 0.31 0.12 0.24 0.13 0.13 0.14 0.1 0.2 (b) iMdRsac International Research BioMed −1 −0.5 0 0.5 1 7 t 0.01 0.02 p.adjus BP CC MF 5 07 55 (a) 4: Continued. 02 Figure T cell activation Tertiary granule Cytokine activity Chemokine activity NAD+ kinase activity Receptor ligand activity Immune receptor activity Cytokine receptor activity Cytokine receptor binding Tertiary granule membrane Alpha−beta T cell activation Leukocyte cell−cell adhesion Leukocyte cell−cell Chemokine receptor binding Regulation of T cell activation Regulation of Signaling receptor activator activity Positive regulation of T cell activation Positive regulation of T cell Positive regulation of defense response Positive regulation of defense Positive regulation of cell−cell adhesion Positive regulation of cell−cell Regulation of leukocyte cell−cell adhesion Regulation of Positive regulation of T cell diferentiation Positive regulation of T cell Positive regulation of leukocyte cell−cell adhesion Positive regulation of leukocyte Tumor necrosis factor receptor superfamily binding BioMed Research International 8 BioMed Research International

p.adjust

Cytokine−cytokine receptor interaction

Viral protein interaction with cytokine and cytokine receptor

T17 cell diferentiation 0.002

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T1 and T2 cell diferentiation 0.004

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Gene ratio Count 30 50 40 60 (b)

Figure 4: GO functional annotation and KEGG pathway enrichment analysis for the rejection-related genes. (a) Bar diagram shows the top 10 GO terms enriched by the rejection-related genes. (b) Bubble plots show the top 10 KEGG pathways enriched by the rejection-related genes.

Training cohort receptors, osteoclast differentiation, and immune-related pathways, for example, natural killer cell-mediated cell toxic- ity, IL-17 signaling pathway, and T cell receptor signaling pathway (Figure 4(b), Supplementary material 2). Hence, these results indicated that these genes might mediate the rejection by activating immune-related pathways. 51 45 76 3.4. Identification of Differentially Expressed and Immune- Related Genes. Based on the results of functional and pathway enrichment analysis, 317 immune-related genes which were involved in immune- and inflammation-related GO function and immune-related pathways were extracted for further analysis. The results of the Wilcoxon rank sum test Validation cohort showed that 96 immune-related genes were significantly Figure 5: Overlapping and differentially expressed immune-related differentially expressed in rejection samples compared to genes in the training cohort and the validation cohort identified by nonrejection samples in the training cohort. Moreover, 121 the Wilcoxon rank sum test. immune-related genes were significantly differentially expressed in rejection samples compared to nonrejection samples in the validation cohort. Thus, after overlapping the differentially expressed and immune-related genes in the training cohort and the validation cohort, 45 differentially BioMed Research International 9

CD200R1 MIR142 1.0 1.0

0.8 0.8

0.6 0.6 AUC: 0.786 AUC: 0.770 0.4 0.4 Sensitivity Sensitivity

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Specifcity Specifcity (a) (b) VAV2 FASLG 1.0 1.0

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0.6 0.6 AUC: 0.748 AUC: 0.741 0.4

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Figure 6: ROC analysis identification of hub genes in the training cohort: (a) CD200R1, (b) MIR142, (c) VAV2, (d) FASLG, (e) SH2D1B, and (f) RAP2B. expressed and immune-related genes were selected for subse- and nonrejection samples, ROC curves of 45 genes were plot- quent analysis (Figure 5). ted to evaluate the capacity of genes in rejection samples and nonrejection samples. Finally, based on the AUC value of 3.5. Identification of Hub Genes. To further identify the hub each gene, 6 genes including CD200R1, MIR142, VAV2, genes which could effectively distinguish rejection samples FASLG, SH2D1B, and RAP2B were identified as hub genes 10 BioMed Research International

Training cohort Training cohort Validation cohort Validation cohort p p p Wilcoxon, = 2.2e−07 Wilcoxon, p = 0.00029 Wilcoxon, = 4e−07 Wilcoxon, = 0.01 4.4 9 1000 50 750 4.0 40 8 500 30 3.6 7 MIR142 expression MIR142 expression 250 CD200R1 expression CD200R1 expression 20

3.2 Nonrejection Rejection Nonrejection Rejection Nonrejection Rejection Nonrejection Rejection (a) (b)

Validation cohort Training cohort Wilcoxon, p = 0.0027 Wilcoxon, p = 2.9e−06 6.0 300

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Training cohort Validation cohort p Wilcoxon, p = 5.4e−05 Wilcoxon, = 0.00015

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Figure 7: The gene expression of hub genes in the training cohort and the validation cohort: (a) CD200R1, (b) MIR142, (c) VAV2, (d) FASLG, (e) SH2D1B, and (f) RAP2B. because they could better distinguish rejection samples and involved in the immune-related biological processes and nonrejection samples (AUC > 0:7, Figure 6). Notably, only pathways. Thus, 317 immune-related genes were preserved 5 genes (including CD200R1, VAV2, FASLG, SH2D1B, and for further analysis. Finally, based on the difference analysis RAP2B) showed the consistent expression trends between and ROC analysis, 5 genes (including CD200R1, VAV2, the training cohort and the validation cohort (Figures 7(a), FASLG, SH2D1B, and RAP2B) were selected as the candidate 7(c), and 7(d)–7(f)), but MIR142 showed the opposite diagnostic biomarkers (Figures 6 and 7). expression trends between the training cohort and the valida- It has been demonstrated that allograft rejection was tion cohort (Figure 7(b)). Thus, CD200R1, VAV2, FASLG, related to molecular changes [30, 31]. Our study found that SH2D1B, and RAP2B were retained as the candidate the expression of CD200R1, VAV2, FASLG, SH2D1B, and diagnostic biomarkers. RAP2B was both dissonant in rejection samples compared with nonrejection samples between the training cohort and 3.6. PPI Network Construction and Gene Correlation Analysis the validation cohort, which further elucidated that the for Hub Genes. To further investigate the interactions occurrence of allograft rejection following kidney transplan- between candidate diagnostic biomarkers and other tation was associated with the molecular changes. Consistent rejection-related genes in protein levels, the PPI network with our results, CD200R1, a member of the immunoglobu- was constructed for all immune-related genes using the lin superfamily, has been proved to predict immunosuppres- STRING database. The result of PPI network showed that sion and high-risk severity after kidney transplantation [32]. all 5 candidate diagnostic biomarkers had existing interac- Therefore, CD200R1 might play a key role in allograft rejec- tions with other proteins (Figure 8(a)). Thus, the alterations tion. VAV2 was confirmed to affect alloreactivity and in protein levels for the 5 genes might influence the expres- allograft survival in mice [33]. Our study suggested for the sion level of other proteins resulting in the occurrence of first time that VAV2 was associated with allograft rejection rejection. Moreover, we also calculated the association of following kidney transplantation in humans. FASLG was the expression for candidate diagnostic biomarkers in the found to be upregulated in renal transplant recipients after training cohort. Surprisingly, we found that SH2D1B had a the treatment of cyclosporine (CsA) and tacrolimus strong positive correlation with FASLG and RAP2B, which (FK506) [34]. Hence, FASLG was promising to become a could suggest that the expression of them might influence therapeutic target for allograft rejection following kidney each other (Figure 8(b)). transplantation. In addition, SH2D1B has already been pro- posed to be associated with the antibody-mediated rejection 4. Discussion in kidney transplantation and could be selected as a predictor for graft loss [35]. As for the rest of the hub genes, RAP2B Currently, allograft rejection following kidney transplanta- was not reported to be related to allograft rejection following tion has been regarded as a main cause of graft failure after kidney transplantation. Moreover, we also explored the rela- kidney transplantation [26–28], and the diagnosis of allograft tionships of protein interactions between 5 candidate rejection following kidney transplantation primarily relied diagnostic biomarkers and other immune-related genes and on the histological examination of a kidney biopsy [29]. found that 5 candidate diagnostic biomarkers have interac- However, kidney biopsy also had many errors. Moreover, tions with other proteins (Figure 8(a)). Furthermore, we also although other diagnosis biomarkers for allograft rejection found that there were strong correlations among FASLG, have also been researched, few biomarkers were identified SH2D1B, and RAP2B in the transcriptional level in clinical practice. Therefore, novel biomarkers in the (Figure 8(b)). Hence, we speculated that these genes might molecular level are essential for improving the early diagnosis influence each other in the transcriptional level resulting in and therapy of allograft rejection. the occurrence of allograft rejection. In the present study, 3065 rejection-related genes were As expected, these genes are mainly related to immune- identified based on WGCNA. The result of GO and KEGG related biological processes and pathways (Figure 4, enrichment analysis showed that these genes were mainly Supplementary materials 1 and 2). Immune response has 12 BioMed Research International

HLA-DOB ULBP3 GPR18 GPR55 KPNB1 KIR2DL1 YES1 CD247 KIR3DL3 VAV2 GSDMD CD226SH2D1A DNASE1L1 SLAMF6 RIPK3 NCR3 CCL4 OSCAR PNP CXCL1 CD38 YPEL5 FASLGSYK EPOR MSH2 BCL10 SH2D1BGZMB RIPK2 CD96 CCRL2 MMP9 DCLRE1C XIAP CD1D CD83 RNF41 NLRC5 TNF IL21R TRAF3 NFKBIA GLA CTSC IL17RA KLF4 LILRB3 ITGAX FBXW11 MAP2K7 SOCS1 CD47 APOE SKP1 TNIP2 VAT1 ACTR2 PSMD6 ADAM10 F3 PSMB10 CUL1 RNASE2 HK3LAMTOR3 SOCS6 IL4R FGF10SOCS5 CD200R1ADAM8 MCEMP1 RAP2BTMEM63A CBFB FES

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Figure 8: The PPI network and association of 5 candidate diagnostic biomarkers. (a) PPI network for the 5 candidate diagnostic biomarkers. (b) The association of 5 candidate diagnostic biomarkers. been considered as a major cause of graft loss [36, 37], result- 5. Conclusion ing in the patients’ need to receive immunosuppressive drug regimens [37]. Although all kinds of immune cells can con- In summary, the present study identified 6 immune-related tribute to graft rejection and failure, T cell- and antibody- genes that were associated with allograft rejection following mediated rejection usually in acute and chronic rejection has kidney transplantation. Although additional researches are the major role [27]. Our study found that 5 candidate diagnos- needed to demonstrate the roles for these gens, it is promis- tic biomarkers were mainly related to T cell activation and dif- ing that the findings will contribute to understand the ferentiation. 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