Abstract
For end-stage renal diseases, kidney transplantation is the most efficient treatment. However, the unexpected rejection caused by inflammation usually leads to allograft failure. Thus, a systems-level characterization of inflammation factors can provide potentially diagnostic biomarkers for predicting renal allograft rejection. Serum of kidney transplant patients with different immune status were collected and classified as transplant patients with stable renal function (ST), impaired renal function with negative biopsy pathology (UNST), acute rejection (AR), and chronic rejection (CR). The expression profiles of 40 inflammatory proteins were measured by quantitative protein microarrays and reduced to a lower dimensional space by the partial least squares (PLS) model. The determined principal components (PCs) were then trained by the support vector machines (SVMs) algorithm for classifying different phenotypes of kidney transplantation. There were 30, 16, and 13 inflammation proteins that showed statistically significant differences between CR and ST, CR and AR, and CR and UNST patients. Further analysis revealed a protein-protein interaction (PPI) network among 33 inflammatory proteins and proposed a potential role of intracellular adhesion molecule-1 (ICAM-1) in CR. Based on the network analysis and protein expression information, two PCs were determined as the major contributors and trained by the PLS-SVMs method, with a promising accuracy of 77.5 % for classification of chronic rejection after kidney transplantation. For convenience, we also developed software packages of GPS-CKT (Classification phenotype of Kidney Transplantation Predictor) for classifying phenotypes. By confirming a strong correlation between inflammation and kidney transplantation, our results suggested that the network biomarker but not single factors can potentially classify different phenotypes in kidney transplantation.
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Abbreviations
- AR:
-
Acute rejection
- BLC:
-
B lymphocyte chemoattractant
- CR:
-
Chronic rejection
- G-CSF:
-
Granulocyte colony-stimulating factor
- GM-CSF:
-
Granulocyte/macrophage colony-stimulating factor
- UNST:
-
Impaired renal function with negative biopsy pathology
- ICAM-1:
-
Intracellular adhesion molecule-1
- LOO:
-
Leave-one-out
- PLS:
-
Partial least squares
- PCs:
-
Principal components
- PPI:
-
Protein-protein interaction
- M-CSF:
-
Macrophage colony-stimulating factor-1
- TIMP-2:
-
Metalloproteinase inhibitor 2
- MCP-1:
-
Monocyte chemoattractant protein-1
- ROC:
-
Receiver operation characteristic
- ST:
-
Stable renal function
- SVMs:
-
Support vector machines
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Acknowledgments
This work was sponsored by the National Natural Science Foundation of China (81500569) to D.Z.; National Natural Science Foundation of China (81100534) and Shanghai Rising-Star Program (13QA1400800) to D.W.; grants from the National Basic Research Program (973 project) (2012CB910101 and 2013CB933903), and Natural Science Foundation of China (31171263, 81272578) to Y.X. The authors have no commercial or other associations that might pose a conflict of interest in connection with the submitted material.
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All samples were obtained with informed consent and ethics approval by the ethics board of Zhongshan Hospital, Fudan University. The study has been carried out in accordance with The Code of Ethics of the World Medical Association (Declaration of Helsinki) for experiments involving humans.
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Dong Zhu and Zexian Liu contributed equally to this work.
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Zhu, D., Liu, Z., Pan, Z. et al. A new method for classifying different phenotypes of kidney transplantation. Cell Biol Toxicol 32, 323–332 (2016). https://doi.org/10.1007/s10565-016-9337-x
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DOI: https://doi.org/10.1007/s10565-016-9337-x