Preliminary Investigation of the Biomarkers of Acute Renal Transplant Rejection Using Integrated Proteomics Studies, Gene Expression Omnibus Datasets, and RNA Sequencing

Abstract

A kidney transplant is often the best treatment for end-stage renal disease. Although immunosuppressive therapy sharply reduces the occurrence of acute allograft rejection (AR), it remains the main cause of allograft dysfunction. We aimed to identify effective biomarkers for AR instead of invasive kidney transplant biopsy. We integrated the results of several proteomics studies related to AR and utilized public data sources. Gene ontology (GO) and pathway analyses were used to identify important biological processes and pathways. The performance of the identified proteins was validated using several public gene expression omnibus (GEO) datasets. Samples that performed well were selected for further validation through RNA sequencing of peripheral blood mononuclear cells of patients with AR (n = 16) and non-rejection (n = 19) from our medical center. A total of 25 differentially expressed proteins (DEPs) overlapped in proteomic studies of urine and blood samples. GO analysis showed that the DEPs were mainly involved in the immune system and blood coagulation. Pathway analysis showed that the complement and coagulation cascade pathways were well enriched. We found that immunoglobulin heavy constant alpha 1 (IGHA1) and immunoglobulin κ constant (IGKC) showed good performance in distinguishing AR from non-rejection groups validated with several GEO datasets. Through RNA sequencing, the combination of IGHA1, IGKC, glomerular filtration rate, and donor age showed good performance in the diagnosis of AR with ROC AUC 91.4% (95% CI: 82-100%). Our findings may contribute to the discovery of potential biomarkers for AR monitoring.

Keywords: RNA sequencing; biomarkers; gene expression omnibus datasets; proteomics studies; renal allograft rejection.

FIGURE 1

GO analysis with DAVID. GO analysis of the DEPs of urine samples, blood samples, and 25 shared DEPs based on proteomics studies are shown. (A–C) biological process (BP) items. (D–F) molecular function (MF) items. (G–I) cellular component (CC) items.

FIGURE 2

Pathway enrichment analysis and Nephroseq usage. (A) Pathway enrichment analysis of the DEPs was performed with DAVID. The top 10 pathways of urinary DEPs are shown. As for analysis of the DEPs from blood samples, only two pathways were identified with an adjusted p-value < 0.05. (B) Heat map of the expression of potential biomarkers based on Nephroseq. Compared with non-rejection and normal kidney tissue groups, acute rejection showed that IGKC, IGHA1, B2M and CFB were significantly different with a p-value < 0.05 and fold change > 1.5.

FIGURE 3

Receiver operating characteristic (ROC) curve of B2M, CFB, IGHA1, and IGKC in different GEO data sets. (A) ROC of B2M in GSE147089, GSE14328, and GSE21374 with AUC values of 81.7, 80.7, and 76.8, respectively. (B) ROC of CFB in GSE21374, GSE147089, and GSE48581 with AUC values of 72, 73.9, and 69.3, respectively. (C) ROC of IGHA1 in GSE14328 and GSE147089 with AUC values of 84.5, 65.8, and 62.8, respectively. (D) ROC of IGKC in GSE14328, GSE21374, and GSE48581 with AUC values of 80.6, 63.1, and 63.9, respectively.

FIGURE 4

Boxplot of the expressions of B2M, CFB, IGHA1, and IGKC in different GEO data sets. (A–C) According to GSE21374, GSE147089, and GSE48581, the expression of B2M, IGHA1, IGKC, and CFB were significantly higher in the rejection group than in the non-rejection group. (D) According to GSE21374, the expressions of B2M and IGKC were significantly higher; however, IGHA1 was significantly lower in the rejection group than in the non-rejection group. Each point represents a patient. *p < 0.05.

FIGURE 5

Receiver operating characteristic (ROC) curves of B2M, CFB, IGHA1, and IGKC based on RNA sequencing in PBMCs of renal allograft transplant patients. (A) ROC of B2M, CFB, IGHA1, and IGKC merely based on the expression of RNA-Seq. (B) ROC of different combinations of IGHA1, IGKC, GFR, and donor age. D_Age represents the donor age. IGHA1 + IGKC + D_Age + GFR AUC 91.4% (95% CI: 82–100%). IGHA1 + D_Age + GFR AUC 88.8% (95% CI: 77.8–99.9%). IGKC + D_Age + GFR AUC 83.6% (95% CI: 70.1–97%).