An integrated proteomics and metabolomics approach to assess graft quality and predict early allograft dysfunction after liver transplantation: a retrospective cohort study

作者：Yimou Lin, Haitao Huang, Jiaying Cao, Ke Zhang, Ruihan Chen, Jingyu Jiang, Xuewen Yi, Shi Feng, Jimin Liu, Shusen Zheng, Qi Ling · 发表于：International Journal of Surgery · 年份：2024 · DOI：10.1097/js9.0000000000001292 · 被引用次数：16 · 研究领域：Organ Transplantation Techniques and Outcomes、Renal Transplantation Outcomes and Treatments、Liver Disease and Transplantation

BACKGROUND: Early allograft dysfunction (EAD) is a common complication after liver transplantation (LT) and is associated with poor prognosis. Graft itself plays a major role in the development of EAD. We aimed to reveal the EAD-specific molecular profiles to assess graft quality and establish EAD predictive models. METHODS: A total of 223 patients who underwent LT were enrolled and divided into training ( n =73) and validation ( n =150) sets. In the training set, proteomics was performed on graft biopsies, together with metabolomics on paired perfusates. Differential expression, enrichment analysis, and protein-protein interaction network were used to identify the key molecules and pathways involved. EAD predictive models were constructed using machine learning and verified in the validation set. RESULTS: A total of 335 proteins were differentially expressed between the EAD and non-EAD groups. These proteins were significantly enriched in triglyceride and glycerophospholipid metabolism, neutrophil degranulation, and the MET-related signaling pathway. The top 12 graft proteins involved in the aforementioned processes were identified, including GPAT1, LPIN3, TGFB1, CD59, and SOS1. Moreover, downstream metabolic products, such as lactate dehydrogenase, interleukin-8, triglycerides, and the phosphatidylcholine/phosphorylethanolamine ratio in the paired perfusate displayed a close relationship with the graft proteins. To predict the occurrence of EAD, an integrated model using pe...