An anoikis-based signature for predicting prognosis in hepatocellular carcinoma with machine learning - Guizhen, Zhang; Weiwei, Zhu; Yun, Wang; Guangying, Cui; Yize, Zhang; Zujiang, Yu (Frontiers in Pharmacology, 2023)
Hepatocellular carcinoma (HCC) is a common malignancy with high mortality worldwide. Despite advancements in diagnosis and treatment in recent years, there is still an urgent unmet need to explore the underlying mechanisms and novel prognostic markers. Anoikis has received considerable attention because of its involvement in the progression of human malignancies. However, the potential mechanism of anoikis-related genes (ANRGs) involvement in HCC progression remains unclear. In this study, we determined the dysregulation of ANRGs in HCC and their prognostic value via comprehensive bioinformatics analyses. Then, a nine-gene signature based on ANRGs was developed by least absolute shrinkage and selection operator (Lasso) Cox regression analysis. We found that the signature presented a strong ability of risk stratification and prediction for overall survival in HCC patients. Additionally, high risk scores were closely correlated with unfavorable clinical features such as advanced pathological stage, poor histological differentiation and vascular invasion. Additionally, machine learning was applied to identify the prominent ANRGs to the risk score. The results showed that DAP3 was an important risk score contributor. Further immunohistochemistry determined the elevated expression of DAP3 in HCC tissues compared with nontumor tissues. Finally, functional analyses showed that DAP3 may promote HCC progression through multiple cancer-related pathways and suppress immune infiltration. In conclusion, the anoikis-based signature can be utilized as a novel prognostic biomarker for HCC, and DAP3 may play an important role in the development and progression of HCC.