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

Autor: Yi Yang, Zhiyuan Bo, Jingxian Wang, Bo Chen, Qing Su, Yiran Lian, Yimo Guo, Jinhuan Yang, Chongming Zheng, Juejin Wang, Hao Zeng, Junxi Zhou, Yaqing Chen, Gang Chen, Yi Wang
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: BMC Cancer, Vol 24, Iss 1, Pp 1-12 (2024)
Druh dokumentu: article
ISSN: 1471-2407
DOI: 10.1186/s12885-024-13161-1
Popis: 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
Databáze: Directory of Open Access Journals
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