Machine Learning Based on Alcohol Drinking-gut Microbiota-liver Axis in Predicting the Occurrence of Early-stage Hepatocellular Carcinoma

Overview

Journal BMC Cancer

Publisher Biomed Central

Specialty Oncology

Date 2024 Nov 28

PMID 39609660

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

Affiliations

Soon will be listed here.

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.

References

Spann A, Yasodhara A, Kang J, Watt K, Wang B, Goldenberg A . Applying Machine Learning in Liver Disease and Transplantation: A Comprehensive Review. Hepatology. 2020; 71(3):1093-1105. DOI: 10.1002/hep.31103. View

Yang X, Yang C, Zhang S, Geng H, Zhu A, Bernards R . Precision treatment in advanced hepatocellular carcinoma. Cancer Cell. 2024; 42(2):180-197. DOI: 10.1016/j.ccell.2024.01.007. View

Liu H, Yuan K . New measures of effect size in moderation analysis. Psychol Methods. 2020; 26(6):680-700. DOI: 10.1037/met0000371. View

Cheng N, Ren Y, Zhou J, Zhang Y, Wang D, Zhang X . Deep Learning-Based Classification of Hepatocellular Nodular Lesions on Whole-Slide Histopathologic Images. Gastroenterology. 2022; 162(7):1948-1961.e7. DOI: 10.1053/j.gastro.2022.02.025. View

Zhang P, Liu J, Xiong B, Zhang C, Kang B, Gao Y . Microbiota from alginate oligosaccharide-dosed mice successfully mitigated small intestinal mucositis. Microbiome. 2020; 8(1):112. PMC: 7382812. DOI: 10.1186/s40168-020-00886-x. View

Sung H, Ferlay J, Siegel R, Laversanne M, Soerjomataram I, Jemal A . Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021; 71(3):209-249. DOI: 10.3322/caac.21660. View

Julien J, Ayer T, Bethea E, Tapper E, Chhatwal J . Projected prevalence and mortality associated with alcohol-related liver disease in the USA, 2019-40: a modelling study. Lancet Public Health. 2020; 5(6):e316-e323. DOI: 10.1016/S2468-2667(20)30062-1. View

Bajaj J . Alcohol, liver disease and the gut microbiota. Nat Rev Gastroenterol Hepatol. 2019; 16(4):235-246. DOI: 10.1038/s41575-018-0099-1. View

Chaudhary K, Poirion O, Lu L, Garmire L . Deep Learning-Based Multi-Omics Integration Robustly Predicts Survival in Liver Cancer. Clin Cancer Res. 2017; 24(6):1248-1259. PMC: 6050171. DOI: 10.1158/1078-0432.CCR-17-0853. View

10.

Ladd A, Duarte S, Sahin I, Zarrinpar A . Mechanisms of drug resistance in HCC. Hepatology. 2023; 79(4):926-940. DOI: 10.1097/HEP.0000000000000237. View

11.

Kim H, Lampertico P, Nam J, Lee H, Kim S, Sinn D . An artificial intelligence model to predict hepatocellular carcinoma risk in Korean and Caucasian patients with chronic hepatitis B. J Hepatol. 2021; 76(2):311-318. DOI: 10.1016/j.jhep.2021.09.025. View

12.

Adam G, Rampasek L, Safikhani Z, Smirnov P, Haibe-Kains B, Goldenberg A . Machine learning approaches to drug response prediction: challenges and recent progress. NPJ Precis Oncol. 2020; 4:19. PMC: 7296033. DOI: 10.1038/s41698-020-0122-1. View

13.

Asnicar F, Thomas A, Passerini A, Waldron L, Segata N . Machine learning for microbiologists. Nat Rev Microbiol. 2023; 22(4):191-205. DOI: 10.1038/s41579-023-00984-1. View

14.

Steyerberg E, Vickers A, Cook N, Gerds T, Gonen M, Obuchowski N . Assessing the performance of prediction models: a framework for traditional and novel measures. Epidemiology. 2009; 21(1):128-38. PMC: 3575184. DOI: 10.1097/EDE.0b013e3181c30fb2. View

15.

Yang J, Chittajallu P, Benhammou J, Patel A, Pisegna J, Tabibian J . Validation of a Machine Learning Algorithm, EVendo, for Predicting Esophageal Varices in Hepatocellular Carcinoma. Dig Dis Sci. 2024; 69(8):3079-3084. PMC: 11341647. DOI: 10.1007/s10620-024-08449-y. View

16.

He Y, Liang T, Chen Z, Mo S, Liao Y, Gao Q . Recurrence of Early Hepatocellular Carcinoma after Surgery May Be Related to Intestinal Oxidative Stress and the Development of a Predictive Model. Oxid Med Cell Longev. 2022; 2022:7261786. PMC: 9553367. DOI: 10.1155/2022/7261786. View

17.

Ma C, Han M, Heinrich B, Fu Q, Zhang Q, Sandhu M . Gut microbiome-mediated bile acid metabolism regulates liver cancer via NKT cells. Science. 2018; 360(6391). PMC: 6407885. DOI: 10.1126/science.aan5931. View

18.

Park S, Park J, Kim H, Park S . Review of Statistical Methods for Evaluating the Performance of Survival or Other Time-to-Event Prediction Models (from Conventional to Deep Learning Approaches). Korean J Radiol. 2021; 22(10):1697-1707. PMC: 8484151. DOI: 10.3348/kjr.2021.0223. View

19.

Kumar Y, Gupta S, Singla R, Hu Y . A Systematic Review of Artificial Intelligence Techniques in Cancer Prediction and Diagnosis. Arch Comput Methods Eng. 2021; 29(4):2043-2070. PMC: 8475374. DOI: 10.1007/s11831-021-09648-w. View

20.

Shalapour S, Lin X, Bastian I, Brain J, Burt A, Aksenov A . Inflammation-induced IgA+ cells dismantle anti-liver cancer immunity. Nature. 2017; 551(7680):340-345. PMC: 5884449. DOI: 10.1038/nature24302. View