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

Ling Zhou (), Lu Tang (), Angela T. Song (), Diane M. Cibrik () and Peter X.-K. Song ()
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Ling Zhou: University of Michigan
Lu Tang: University of Michigan
Angela T. Song: University of Michigan
Diane M. Cibrik: University of Michigan
Peter X.-K. Song: University of Michigan

Statistics in Biosciences, 2017, vol. 9, issue 2, No 7, 452 pages

Abstract: 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.

Keywords: $$L_1$$ L 1 -norm penalty; Post-selection inference; Renal allograft loss; Regularized estimation; Survival analysis (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/s12561-016-9170-z

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