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

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BMC Cancer

Published in:

Open Access 01-12-2024 | Respiratory Microbiota | Research

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

Authors: 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

Published in: BMC Cancer | Issue 1/2024

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.

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Title: Machine learning based on alcohol drinking-gut microbiota-liver axis in predicting the occurrence of early-stage hepatocellular carcinoma
Authors: 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
Publication date: 01-12-2024
Publisher: BioMed Central
Keywords: Respiratory Microbiota
Hepatocellular Carcinoma
Hepatocellular Carcinoma
Published in: BMC Cancer / Issue 1/2024
Electronic ISSN: 1471-2407
DOI: https://doi.org/10.1186/s12885-024-13161-1

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

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