在不同肝脏背景的患者中,准确评估部分肝切除术(PH)后未来的肝脏残余生长是一个紧迫的临床问题。氨基酸(AA)代谢在肝脏再生中起着至关重要的作用。在这项研究中,我们将代谢组学和机器学习(ML)结合起来,开发了一种针对多种肝脏背景的广义未来肝脏残留评估模型.在健康小鼠和患有非酒精性脂肪性肝炎或肝纤维化的小鼠中,在70%PH后0、6、24、48、72和168小时计算肝脏指数。使用UPLC-MS/MS测量39个氨基酸(AAs)的血清水平。数据集以2:1的比例随机分为训练集和测试集,使用正交偏最小二乘回归(OPLS)和R中的最小偏变量选择(MUVR)来选择AA的代谢物特征。为了评估肝脏残余生长,建立了九个ML模型,并使用决定系数(R2)进行评估,平均绝对误差(MAE),和均方根误差(RMSE)。通过与理想解决方案(TOPSIS)相似的顺序偏好后帕累托技术用于对ML算法进行排名,并利用堆叠技术在上级算法之间建立共识。与OPLS相比,由MUVR识别的签名AAs集(Thr,Arg,EtN,Phe,Asa,3MHs,阿布,Asp,Tyr,Leu,Ser,和baib)更简洁。帕累托TOPSIS后排名表明,与MUVR组合的大多数ML算法优于OPLS算法。建立的SVM-KNN一致性模型表现最好,测试集的R2为0.79,MAE为0.0029,RMSE为0.0035。这项研究确定了12种AA的代谢物特征,并构建了SVM-KNN共识模型,以评估具有不同肝脏背景的小鼠PH后未来的肝脏残留生长。我们的临床前研究有望建立肝脏再生的替代和通用评估方法。
Accurate assessment of future liver remnant growth after partial hepatectomy (PH) in patients with different liver backgrounds is a pressing clinical issue. Amino acid (AA) metabolism plays a crucial role in liver regeneration. In this study, we combined metabolomics and machine learning (ML) to develop a generalized future liver remnant assessment model for multiple liver backgrounds. The liver index was calculated at 0, 6, 24, 48, 72 and 168 h after 70 % PH in healthy mice and mice with nonalcoholic steatohepatitis or liver fibrosis. The serum levels of 39 amino acids (AAs) were measured using UPLC-MS/MS. The dataset was randomly divided into training and testing sets at a 2:1 ratio, and orthogonal partial least squares regression (OPLS) and minimally biased variable selection in R (MUVR) were used to select a metabolite signature of AAs. To assess liver remnant growth, nine ML models were built, and evaluated using the coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE). The post-Pareto technique for order preference by similarity to the ideal solution (TOPSIS) was employed for ranking the ML algorithms, and a stacking technique was utilized to establish consensus among the superior algorithms. Compared with those of OPLS, the signature AAs set identified by MUVR (Thr, Arg, EtN, Phe, Asa, 3MHis, Abu, Asp, Tyr, Leu, Ser, and bAib) are more concise. Post-Pareto TOPSIS ranking demonstrated that the majority of ML algorithm in combinations with MUVR outperformed those with OPLS. The established SVM-KNN consensus model performed best, with an R2 of 0.79, an MAE of 0.0029, and an RMSE of 0.0035 for the testing set. This study identified a metabolite signature of 12 AAs and constructed an SVM-KNN consensus model to assess future liver remnant growth after PH in mice with different liver backgrounds. Our preclinical study is anticipated to establish an alternative and generalized assessment method for liver regeneration.