Published online Aug 26, 2024. doi: 10.12998/wjcc.v12.i24.5568
Revised: May 21, 2024
Accepted: June 12, 2024
Published online: August 26, 2024
Processing time: 93 Days and 1.6 Hours
Hepatocellular carcinoma (HCC) is the most common subtype of liver cancer. The primary treatment strategies for HCC currently include liver transplantation and surgical resection. However, these methods often yield unsatisfactory outcomes, leading to a poor prognosis for many patients. This underscores the urgent need to identify and evaluate novel therapeutic targets that can improve the prognosis and survival rate of HCC patients.
To construct a radiomics model that can accurately predict the EZH2 expression in HCC.
Gene expression, clinical parameters, HCC-related radiomics, and fibroblast-related genes were acquired from public databases. A gene model was developed, and its clinical efficacy was assessed statistically. Drug sensitivity analysis was conducted with identified hub genes. Radiomics features were extracted and machine learning algorithms were employed to generate a radiomics model related to the hub genes. A nomogram was used to illustrate the prognostic significance of the computed Radscore and the hub genes in the context of HCC patient outcomes.
EZH2 and NRAS were independent predictors for prognosis of HCC and were utilized to construct a predictive gene model. This model demonstrated robust performance in diagnosing HCC and predicted an unfavorable prognosis. A negative correlation was observed between EZH2 expression and drug sensitivity. Elevated EZH2 expression was linked to poorer prognosis, and its diagnostic value in HCC surpassed that of the risk model. A radiomics model, developed using a logistic algorithm, also showed superior efficiency in predicting EZH2 expression. The Radscore was higher in the group with high EZH2 expression. A nomogram was constructed to visually demonstrate the significant roles of the radiomics model and EZH2 expression in predicting the overall survival of HCC patients.
EZH2 plays significant roles in diagnosing HCC and therapeutic efficacy. A radiomics model, developed using a logistic algorithm, efficiently predicted EZH2 expression and exhibited strong correlation with HCC prognosis.
Core Tip: This study integrated radiomics molecular analysis based on computed tomography images. It aimed to identify important molecular biomarkers associated with hepatocellular carcinoma (HCC), particularly EZH2, and establish a radiomics model to predict EZH2 expression and its association with the prognosis of HCC patients. The results of this study demonstrated a close correlation between the radiomics model, EZH2 expression, and HCC patient prognosis, suggesting that a radiomics analysis can provide additional molecular information and offer a new approach to clinical treatment of HCC.