TRANSCRIPTOMICS ASSOCIATION WITH ACUTE REJECTION AFTER KIDNEY TRANSPLANTATION: INTEGRATED ANALYSIS OF GENE EXPRESSION-BASED MICROARRAY DATA

Y CAO1, A LEE1, S SHAZANFAR1, S ALEXANDER2, J CRAIG2, G WONG2, J YANG3
1The University of Sydney, Camperdown, Australia, 2Centre for Kidney Research, Kids Research Institute, Westmead Hospital; College of Medicine and Public Health, Flinders University, , Australia, 3Charles Perkins Centre, The University of Sydney; School of Mathematics and Statistics, University of Sydney, Camperdown, Australia

Aim: To develop a validated set of models that identify transcriptomics-based biomarkers for predicting acute rejection after kidney transplantation.
Background: Identification of biomarkers using transcriptional profiling may allow prediction of recipients who are at risk of developing allograft injury such as acute rejection after transplantation.
Methods: Comprehensive search using Gene Expression Omnibus was conducted for publicly available datasets that contained gene expression and acute rejection data after kidney transplantation. With each selected dataset, a set of biomarkers for acute rejection was derived using five algorithms including, but not limited to XgBoost, Lasso and Random Forest. Biomarkers for each dataset are then cross-validated on the remaining independent datasets.
Results: A total of six datasets (n = 738) were included in the meta-analyses. For each dataset, a set of genes was identified for prediction of acute rejection patients within its own dataset, with optimal performance of the modeling achieved using XgBoost classifier (mean accuracy of six datasets > 84%, SD = 0.053). A set of 129 gene loci that included CXCL6, CXCL11 and OLFM4 achieved accuracy over 70% for prediction of acute rejection patients across three of the six datasets. These 129 gene loci (with 175 gene-gene interaction, based on String) were significantly enriched for genes in both the innate and cognate immune system including a significant number of molecules involved in immune trafficking such as adhesion molecules, chemokines and chemokine receptors and a number of as yet uncharacterised but intriguing zinc finger transcription factors.
Conclusions: A set of 129 gene loci with many found in immune-driven molecular pathways including adhesion, trafficking and activation achieved satisfactory performance in predicting acute rejection after kidney transplantation.


Biography:
I am currently in my Honours year of the Bachelor of Science (Advanced) degree at the University of Sydney. I double major in Molecular Biology and Genetics and Computer Science. I enjoy working with biological/clinical data, and is particularly intrigued by how programming can be utilised to discover patterns and drive solutions.

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The ASM is hosted by Australian and New Zealand Society of Nephrology.

The aims of the Society are to promote and support the study of the kidney and urinary tract in health and disease, and to ensure the highest professional standards for the practice of nephrology in Australia and New Zealand.

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