An anoikis-based signature for predicting prognosis in hepatocellular carcinoma with machine learning.

ABSTRACT: Background: 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. Methods: We use comprehensive bioinformatics analyses to determine the expression profile of ANRGs and their prognostic implications in HCC. Next, a risk score model was established by least absolute shrinkage and selection operator (Lasso) Cox regression analysis. Then, the prognostic value o

SUBMITTER: Guizhen Z 

PROVIDER: S-EPMC9846167 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature