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

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 of 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. [5] 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.

Identification and validation of a prognostic four-genes signature for hepatocellular carcinoma: integrated ceRNA network analysis

“…Lasso has two important characteristics, one is feature selection: automatic selection of features, it will learn to remove features without information and precisely set the weights of these features to zero, especially for high-dimensional data. Another one is interpretability: models are easier to explain, for example, we can find the independent variables that provide the most important information in the model when we have a lot of independent variables [23–26]. Li et al identified 13 differentially expressed miRNAs in the serum of HER2 + MBC patients with distinct responses to trastuzumab using miRNA microarrays and constructed a four-miRNA signature to predict survival using a LASSO model [27].…”

Prospects for the use of artificial neural networks for problem solving in clinical transplantation

“…Значительная часть работ по использованию ИИ в трансплантологии посвящена решению проблем выживаемости и отторжения, главным образом у реципиентов почки или печени [21][22][23][24][25][26][27][28][29]. Другими важными задачами для решения методами машинного обучения являются подбор совместимых пар донора и реципиента [7,30], прогнозирование дисфункции трансплантата [31][32][33], а также подбор оптимального режима иммуносупрессии [8,34,35].…”

Construction and analysis of a competing endogenous RNA network to reveal potential prognostic biomarkers for Oral Floor Squamous Cell Carcinoma

“…The DEmRNAs were evaluated using univariate Cox'sproportional hazard regression model. Genes with P-value <0.05 were considered as candidate variables and entered into a stepwise LASSO regression [12][13][14][15] and multivariate Cox regression analysis.…”