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

作者：Yi‐Hsin Yang, Zhiyuan Bo, Jingxian Wang, Bo Chen, Qing Su, Yiran Lian, Yimo Guo, Jinhuan Yang, Chong-ming Zheng, Juejin Wang, Hao Zeng, Junxi Zhou, Yaqing Chen, Gang Chen, Yi Wang · 发表于：BMC Cancer · 年份：2024 · DOI：10.1186/s12885-024-13161-1 · 被引用次数：7 · 研究领域：Gut microbiota and health、Alcohol Consumption and Health Effects、Liver Disease Diagnosis and Treatment

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, 9...