基于饮酒-肠道微生物群-肝脏轴的机器学习预测早期肝细胞癌的发生。

Machine learning based on alcohol drinking-gut microbiota-liver axis in predicting the occurrence of early-stage hepatocellular carcinoma.

机构信息

Department of Epidemiology and Biostatistics, School of Public Health, Wenzhou Medical University, Wenzhou, 325035, China.

Department of Clinical Laboratory, Second Affiliated Hospital, Department of Epidemiology and Biostatistics, School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, China.

出版信息

BMC Cancer. 2024 Nov 29;24(1):1468. doi: 10.1186/s12885-024-13161-1.

DOI:10.1186/s12885-024-13161-1

PMID:39609660

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11606210/

Abstract

BACKGROUND

Alcohol drinking and gut microbiota are related to hepatocellular carcinoma (HCC), but the specific relationship between them remains unclear.

AIMS

We aimed to establish the alcohol drinking-gut microbiota-liver axis and develop machine learning (ML) models in predicting the occurrence of early-stage HCC.

METHODS

Two hundred sixty-nine patients with early-stage HCC and 278 controls were recruited. Alcohol drinking-gut microbiota-liver axis was established through the mediation/moderation effect analyses. Eight ML algorithms including Classification and Regression Tree (CART), Gradient Boosting Machine (GBM), K-Nearest Neighbor (KNN), Logistic Regression (LR), Neural Network (NN), Random Forest (RF), Support Vector Machine (SVM), and eXtreme Gradient Boosting (XGBoost) were applied.

RESULTS

A total of 160 pairs of individuals were included for analyses. The mediation effects of Genus_Catenibacterium (P = 0.024), Genus_Tyzzerella_4 (P < 0.001), and Species_Tyzzerella_4 (P = 0.020) were discovered. The moderation effects of Family_Enterococcaceae (OR = 0.741, 95%CI:0.160-0.760, P = 0.017), Family_Leuconostocaceae (OR = 0.793, 95%CI:0.486-3.593, P = 0.010), Genus_Enterococcus (OR = 0.744, 95%CI:0.161-0.753, P = 0.017), Genus_Erysipelatoclostridium (OR = 0.693, 95%CI:0.062-0.672, P = 0.032), Genus_Lactobacillus (OR = 0.655, 95%CI:0.098-0.749, P = 0.011), Species_Enterococcus_faecium (OR = 0.692, 95%CI:0.061-0.673, P = 0.013), and Species_Lactobacillus (OR = 0.653, 95%CI:0.086-0.765, P = 0.014) were uncovered. The predictive power of eight ML models was satisfactory (AUCs:0.855-0.932). The XGBoost model had the best predictive ability (AUC = 0.932).

CONCLUSIONS

ML models based on the alcohol drinking-gut microbiota-liver axis are valuable in predicting the occurrence of early-stage HCC.

摘要

背景

饮酒与肠道微生物群与肝细胞癌（HCC）有关，但它们之间的具体关系尚不清楚。

目的

我们旨在建立饮酒-肠道微生物群-肝脏轴，并开发机器学习（ML）模型来预测早期 HCC 的发生。

方法

招募了 269 名早期 HCC 患者和 278 名对照者。通过中介/调节效应分析建立饮酒-肠道微生物群-肝脏轴。应用了 8 种 ML 算法，包括分类回归树（CART）、梯度提升机（GBM）、K-最近邻（KNN）、逻辑回归（LR）、神经网络（NN）、随机森林（RF）、支持向量机（SVM）和极端梯度提升（XGBoost）。

结果

共纳入 160 对个体进行分析。发现了属_Catenibacterium（P=0.024）、属_Tyzzerella_4（P<0.001）和种_Tyzzerella_4（P=0.020）的中介效应。发现了科_肠球菌科（OR=0.741，95%CI：0.160-0.760，P=0.017）、科_乳球菌科（OR=0.793，95%CI：0.486-3.593，P=0.010）、属_肠球菌（OR=0.744，95%CI：0.161-0.753，P=0.017）、属_Erysipelatoclostridium（OR=0.693，95%CI：0.062-0.672，P=0.032）、属_乳杆菌（OR=0.655，95%CI：0.098-0.749，P=0.011）、种_屎肠球菌（OR=0.692，95%CI：0.061-0.673，P=0.013）和种_乳杆菌（OR=0.653，95%CI：0.086-0.765，P=0.014）的调节效应。八种 ML 模型的预测能力令人满意（AUCs：0.855-0.932）。XGBoost 模型具有最佳的预测能力（AUC=0.932）。