Study on the prediction model of liver cancer based on chronic liver disease and the related molecular mechanism.

INTRODUCTION AND

OBJECTIVE

Due to the high heterogeneity of HCC, which leads to poor prognostic outcomes for patients, there is a need to develop a novel predictive model for accurate classification of HCC in order to improve patient survival rates.

MATERIALS AND

METHODS

The data of the HCV, cirrhosis, and HCC were obtained from TCGA and GEO databases. Multivariable Cox regression analysis and survival analysis was conducted to assess the prognostic relevance of these differentially expressed genes. Single-cell sequencing was used to explore the intercellular interaction patterns and identify relevant signaling pathways. Drug sensitivity analysis was conducted to determine personalized treatment strategies for patients.

RESULTS

In this study, we conducted integrated analysis of hepatitis, cirrhosis, and hepatocellular carcinoma datasets and identified 10 liver disease progression genes associated with prognosis. These genes exhibited significant downregulation in expression as the disease advanced, suggesting their crucial involvement in HCC development. By performing multivariable Cox analysis, we established a prognostic model for liver disease progression to predict the prognosis of HCC patients. The model was validated using ROC analysis, demonstrating good accuracy and stability in prognostic evaluation. Single-cell sequencing analysis revealed that these genes primarily exert their effects through the MIF signaling pathway during HCC progression. Furthermore, we observed that patients in the low-risk group exhibited higher sensitivity to TACE treatment, while patients in the high-risk group showed better response to sorafenib treatment.

CONCLUSIONS

In summary, we have elucidated the key genes involved in the progression of liver diseases and established a precise prognostic model for assessing the prognosis of HCC patients. Our study provides novel insights and strategies for the treatment of HCC.