Aflatoxin B1 and liver carcinogenesis

Dear Editor, I recently read with great interest the study by Gao et al[1] titled “Integrating machine learning and molecular docking to decipher the molecular network of aflatoxin B1-induced hepatocellular carcinoma,” along with the insightful comments from Wang et al[2], Chen et al[3], and Teng et al[4]. Their contributions provide valuable insights into the carcinogenic mechanisms of aflatoxin B1 (AFB1)-induced hepatocellular carcinoma (HCC). However, I believe that the clinical relevance of the findings could be significantly enhanced by incorporating recent evidence on the synergistic effects of AFB1 and hepatitis B virus (HBV) in HCC development. The model presented by Gao et al primarily focuses on AFB1-specific perturbations but overlooks the well-established mechanisms of HBV-AFB1 co-carcinogenesis, which plays a critical role in the etiology of HCC, particularly in endemic regions. Recent studies have demonstrated that HBV infection can upregulate YTHDF2 and enhance m6A methylation of PARP1 mRNA, which leads to increased AFB1-induced DNA damage[5]. Additionally, dysregulation of BUB1B has been linked to accelerated genomic instability in models co-exposed to both AFB1 and HBV[6], while suppression of the FTCD-AS1-PXR-MASP1 axis exacerbates liver injury associated with dual exposure[7]. Excluding these critical pathways may oversimplify the pathogenesis of AFB1-driven HCC. To strengthen the findings of Gao et al’s study and enhance their clinical applicability, I recommend incorporating HBV-interacting proteins, such as HBx and HBsAg, into network analyses to better reflect the biological context. Furthermore, it is crucial to validate the top predicted targets, including PARP1 and BUB1B, in microenvironments that simulate co-exposure to AFB1 and HBV. Training machine-learning algorithms using multi-omics data from patients with concurrent HBV and AFB1 exposure would significantly improve the predictive power of the model. We appreciate the foundational computational framework provided by Gao et al for understanding the molecular networks involved in AFB1-induced HCC. However, integrating HBV-aware modeling and rigorous wet-lab validation may reveal clinically actionable targets for HCC prevention in high-risk populations. Given that the synergistic action of HBV and AFB1 is a crucial driver of HCC in high-incidence areas, we believe that incorporating multifactorial mechanistic models will substantially enhance the clinical translational potential of this study. We look forward to the continued advancement of this important field of study.