Establishment of a genomic model for predicting the likelihood of poor early graft function after liver transplantation
10.3760/cma.j.cn421203-20210223-00065
- VernacularTitle:早期肝移植物功能不良相关预测基因筛选及风险评分模型构建
- Author:
Bin YU
1
;
Tian XIA
;
Yanfeng WANG
Author Information
1. 武汉大学中南医院 武汉大学肝胆疾病研究院 武汉大学移植医学中心 移植医学技术湖北省重点实验室，武汉 430071
- Keywords:
Liver transplantation;
Poor early liver graft function;
biomarker
- From:
Chinese Journal of Organ Transplantation
2021;42(6):359-364
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To explore the poor early liver graft function(PEGF)-related biomarkers and establish a genomic model for PEGF prediction specific to liver transplantation(LT)with allografts of donation after brain death(DBD).Methods:By data-mining a public GSE23649 dataset from the database of Gene Expression Omnibus(GEO), key PEGF-related genes in DBD liver biopsies after 2h reperfusion were identified by differential expression analysis.And LASSO-penalized Logistic regression model was utilized for selecting an optimal gene set.Receiver operating characteristic curves with its area under the curve(AUC)and a nomogram were generated for evaluating and visualizing its predictive capability for PEGF.Gene set enrichment analysis(GSEA) was performed for exploring the biological pathways underlying PEGF.Results:Six key PEGF-related genes in DBD-LT were initially identified, including 4 up-regulated genes(HBB, PFDN5, RPS3A & RPS5)and 2 down-regulated genes(RPL22 & FAM62B). A six-mRNA-based risk-scoring model was further established with an excellent predictive capability(AUC=1.000, P=0.0008). Four PEGF-related biological pathways in DBD livers, such as "VEGF" and "natural killer cell-mediated cytotoxicity" , were identified by GSEA(all P<0.05). Conclusions:The genomic model may effectively predict the likelihood of PEGF immediately after DBD-LT or even prior to transplantation in the context of normothermic machine perfusion.
