Background

Systems-level characterization of inflammation in kidney transplantation remains incomplete. The present study aimed at identifying the inflammation proteins in phenotype transformation and assessing potential biomarkers for monitoring disease progressing.

Methods

Serum of kidney transplantation patients with different immunologic status were collected: transplantation patients with stable renal function (ST), impaired renal function with negative biopsy pathology (UNST), or acute rejection (AR), chronic rejection(CR). Protein concentration was measured by using quantitative protein array. All differentially expressed proteins between groups were analyzed by PPI to highlight the proteins interactions in different phenotypes.

By using Support Vector Machine regression and ROC curve, we evaluate the classification efficiency of these phenotypes related proteins.

Results

Of 40 inflammatory factors, there are 30, 16 and 13 proteins showed statistically significant difference between Cr and ST, CR and AR, CR and UNST patients. We classified transplants patients into three levels. There were 12 common proteins among three levels. The classifying potency of 11 level-specific proteins were better than the effect of total 40 proteins.

We identify active networks in different inflammation stages by network screening and the comprehensive survey of the consistency between the PPI networks. NOA analysis reveals that active JAK-STAT cascade identified in patients with abnormal kidney function.

Conclusions

The study determined the special profile of inflammatory factors in different stages of kidney transplantation patients and demonstrate the potential value of molecular network-based approaches to understand inflammation-derived mechanisms and findphenotype-related biomarkers.

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