Single-cell and machine learning approaches uncover intrinsic immune-evasion genes in the prognosis of hepatocellular carcinoma.

ABSTRACT:

Background and aims

Hepatocellular carcinoma (HCC) is a tumor of high heterogeneity and complexity, which poses significant challenges to effective treatment and patient prognosis because of its immune evasion characteristics. To address these issues, single-cell technology and machine learning methods have emerged as a promising approach to identify genes associated with immune escape in HCC. This study aimed to develop a prognostic risk score model for HCC by identifying intrinsic immune-evasion genes (IIEGs) through single-cell technology and machine learning, providing insights into immune infiltration, enhancing predictive accuracy, and facilitating the development of more effective treatment strategies.

Materials and methods

The study utilized data from The Cancer Geno

SUBMITTER: Wang J 

PROVIDER: S-EPMC11771279 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature