A LASSO Method to Identify Protein Signature Predicting Post-transplant Renal Graft Survival

A LASSO Method to Identify Protein Signature Predicting Post-transplant Renal Graft Survival Identifying novel biomarkers to predict renal graft survival is important in post-transplant clinical practice. Serum creatinine, currently the most popular surrogate biomarker, offers limited information on the underlying allograft profiles. It is known to perform unsatisfactorily to predict renal function. In this paper, we apply a LASSO machine-learning algorithm in the Cox proportional hazards model to identify promising proteins that are associated with the hazard of allograft loss after renal transplantation, motivated by a clinical pilot study that collected 47 patients receiving renal transplants at the University of Michigan Hospital. We assess the association of 17 proteins previously identified by Cibrik et al. (PROTEOMICS Clin Appl 7(11–12): 839–849, 2013) with allograft rejection in our regularized Cox regression analysis, where the LASSO variable selection method is applied to select important proteins that predict the hazard of allograft loss. We also develop a post-selection inference to further investigate the statistical significance of the proteins on the hazard of allograft loss, and conclude that two proteins KIM-1 and VEGF-R2 are important protein markers for risk prediction. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Statistics in Biosciences Springer Journals

A LASSO Method to Identify Protein Signature Predicting Post-transplant Renal Graft Survival

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Publisher
Springer US
Copyright
Copyright © 2016 by International Chinese Statistical Association
Subject
Statistics; Statistics for Life Sciences, Medicine, Health Sciences; Biostatistics; Theoretical Ecology/Statistics
ISSN
1867-1764
eISSN
1867-1772
D.O.I.
10.1007/s12561-016-9170-z
Publisher site
See Article on Publisher Site

Abstract

Identifying novel biomarkers to predict renal graft survival is important in post-transplant clinical practice. Serum creatinine, currently the most popular surrogate biomarker, offers limited information on the underlying allograft profiles. It is known to perform unsatisfactorily to predict renal function. In this paper, we apply a LASSO machine-learning algorithm in the Cox proportional hazards model to identify promising proteins that are associated with the hazard of allograft loss after renal transplantation, motivated by a clinical pilot study that collected 47 patients receiving renal transplants at the University of Michigan Hospital. We assess the association of 17 proteins previously identified by Cibrik et al. (PROTEOMICS Clin Appl 7(11–12): 839–849, 2013) with allograft rejection in our regularized Cox regression analysis, where the LASSO variable selection method is applied to select important proteins that predict the hazard of allograft loss. We also develop a post-selection inference to further investigate the statistical significance of the proteins on the hazard of allograft loss, and conclude that two proteins KIM-1 and VEGF-R2 are important protein markers for risk prediction.

Journal

Statistics in BiosciencesSpringer Journals

Published: Oct 3, 2016

References

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