In this editorial, we comment on the article by Zhang et al entitled Development of a machine learning-based model for predicting the risk of early postoperative recurrence of hepatocellular carcinoma. Hepatocellular carcinoma (HCC), which is characterized by high incidence and mortality rates, remains a major global health challenge primarily due to the critical issue of postoperative recurrence. Early recurrence, defined as recurrence that occurs within 2 years posttreatment, is linked to the hidden spread of the primary tumor and significantly impacts patient survival. Traditional predictive factors, including both patient- and treatment-related factors, have limited predictive ability with respect to HCC recurrence. The integration of machine learning algorithms is fueled by the exponential growth of computational power and has revolutionized HCC research. The study by Zhang et al demonstrated the use of a groundbreaking preoperative prediction model for early postoperative HCC recurrence. Chall-enges persist, including sample size constraints, issues with handling data, and the need for further validation and interpretability. This study emphasizes the need for collaborative efforts, multicenter studies and comparative analyses to validate and refine the model. Overcoming these challenges and exploring innovative approaches, such as multi-omics integration, will enhance personalized oncology care. This study marks a significant stride toward precise, effi-cient, and personalized oncology practices, thus offering hope for improved patient outcomes in the field of HCC treatment.

• Hepatocellular carcinoma
• Early recurrence
• Machine learning
• XGBoost model
• Predictive precision medicine
• Clinical utility
• Personalized interventions

• Humans
• Carcinoma, Hepatocellular/diagnosis
• Carcinoma, Hepatocellular/prevention & control
• Carcinoma, Hepatocellular/surgery
• Liver Neoplasms/diagnosis
• Liver Neoplasms/prevention & control
• Liver Neoplasms/surgery
• Algorithms
• Machine Learning
• Medical Oncology

AIM: To investigate in vitro effects and mechanisms of silibinin on hepatocellular carcinoma (HCC) cell growth.

METHODS: Human HCC cell lines were treated with different doses of silibinin. The effects of silibinin on HCC cell growth and proliferation, apoptosis, cell cycle progression, histone acetylation, and other related signal transductions were systematically examined.

RESULTS: We demonstrated that silibinin significantly reduced the growth of HuH7, HepG2, Hep3B, and PLC/PRF/5 human hepatoma cells. Silibinin-reduced HuH7 cell growth was associated with significantly up-regulated p21/CDK4 and p27/CDK4 complexes, down-regulated Rb-phosphorylation and E2F1/DP1 complex. Silibinin promoted apoptosis of HuH7 cells that was associated with down-regulated survivin and up-regulated activated caspase-3 and -9. Silibinin's anti-angiogenic effects were indicated by down-regulated metalloproteinase-2 (MMP2) and CD34. We found that silibinin-reduced growth of HuH7 cells was associated with increased activity of phosphatase and tensin homolog deleted on chromosome ten (PTEN) and decreased p-Akt production, indicating the role of PTEN/PI₃K/Akt pathway in silibinin-mediated anti-HCC effects. We also demonstrated that silibinin increased acetylation of histone H3 and H4 (AC-H3 and AC-H4), indicating a possible role of altered histone acetylation in silibinin-reduced HCC cell proliferation.

CONCLUSION: Our results defined silibinin's in vitro anti-HCC effects and possible mechanisms, and provided a rationale to further test silibinin for HCC chemoprevention.

• Hepatocellular carcinoma
• HuH7 cells
• Silibinin
• Chemoprevention
• Cell cycle
• Cell cycle progression
• Apoptosis
• Acetylation of histone

• Acetylation/drug effects
• Antioxidants/pharmacology
• Apoptosis/drug effects
• Carcinoma, Hepatocellular/metabolism
• Carcinoma, Hepatocellular/pathology
• Cell Line, Tumor
• Cell Proliferation/drug effects
• Histones/metabolism
• Humans
• Liver Neoplasms/metabolism
• Liver Neoplasms/pathology
• Oncogene Protein v-akt/metabolism
• PTEN Phosphohydrolase/metabolism
• Phosphatidylinositol 3-Kinases/metabolism
• Signal Transduction/drug effects
• Silybin
• Silymarin/pharmacology