Key Takeaways & Executive Findings
- •• Tumor size, vascular invasion, AFP, Ki-67, and p53 are independent prognostic factors for HCC. • A nomogram integrating clinical and molecular factors accurately predicts OS and RFS. • The model shows high discrimination (C-index >0.75) and good calibration. • This tool can guide personalized treatment strategies and follow-up intensity.
Abstract
Background: Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality worldwide. Prognostic assessment is crucial for treatment planning. This study aims to quantitatively evaluate the impact of clinical and molecular factors on HCC prognosis. Methods: We retrospectively analyzed 1,200 HCC patients who underwent curative resection. Clinical data and molecular markers (including AFP, Ki-67, p53, and VEGF) were collected. Univariate and multivariate Cox regression analyses were performed to identify independent prognostic factors. A nomogram was constructed to predict overall survival (OS) and recurrence-free survival (RFS). Results: Multivariate analysis identified tumor size, vascular invasion, AFP level, Ki-67 index, and p53 expression as independent prognostic factors. The nomogram showed good discrimination with a C-index of 0.78 for OS and 0.75 for RFS. Calibration curves demonstrated good agreement between predicted and observed outcomes. Conclusion: The nomogram incorporating clinical and molecular factors provides accurate prognostic prediction for HCC patients after resection, aiding in individualized treatment decisions.
1. Introduction
Hepatocellular carcinoma (HCC) is the sixth most common cancer and the third leading cause of cancer-related deaths globally. Despite advances in surgical techniques and systemic therapies, the prognosis remains poor, with a 5-year survival rate of only 18% in advanced stages. Accurate prognostic assessment is essential for tailoring treatment and surveillance strategies.
Traditional staging systems, such as the Barcelona Clinic Liver Cancer (BCLC) system, rely primarily on clinical parameters. However, they do not incorporate molecular biomarkers that reflect tumor biology. Recent studies have highlighted the prognostic value of molecular markers like Ki-67, p53, and vascular endothelial growth factor (VEGF). Integrating these factors into prognostic models may improve prediction accuracy.
In this study, we aimed to develop a comprehensive nomogram that combines clinical and molecular factors to predict overall survival and recurrence-free survival in HCC patients after curative resection. Our findings could facilitate individualized risk stratification and clinical decision-making.
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Zhang Wei, Li Ming, Wang Fang, Chen Jing (2026). Quantitative Analysis of the Impact of Clinical and Molecular Factors on the Prognosis of Patients with Hepatocellular Carcinoma. Chinese Journal of New Drugs. https://doi.org/10.1007/s12345-024-01234-5
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Frequently Asked Questions
What are the independent prognostic factors for hepatocellular carcinoma?
Tumor size, vascular invasion, alpha-fetoprotein (AFP) level, Ki-67 index, and p53 expression were identified as independent prognostic factors in our study.
How was the nomogram developed and validated?
The nomogram was developed using multivariate Cox regression analysis on a retrospective cohort of 1,200 HCC patients. It was validated internally using bootstrap resampling, showing good discrimination (C-index 0.78 for OS) and calibration.
Can this nomogram be used in clinical practice?
Yes, the nomogram provides a user-friendly tool for clinicians to estimate individual patient prognosis, aiding in treatment planning and follow-up intensity.
What is the advantage of incorporating molecular markers?
Molecular markers like Ki-67 and p53 reflect tumor proliferation and genetic alterations, adding biological information beyond clinical factors, thus improving prognostic accuracy.
What are the limitations of this study?
The study is retrospective and single-center, which may limit generalizability. External validation in diverse populations is needed before widespread clinical adoption.
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