Construction of a prediction model for hepatocellular carcinoma based on machine learning and its prognostic characteristics.

ABSTRACT: Hepatocellular carcinoma (HCC), as a cancer with high morbidity and mortality, urgently requires the development of a clinical prediction model with high robustness and generalizability and its prognostic study of the tumor microenvironment to provide personalized clinical treatment for patients. Key prognostic genes were screened by analyzing mRNA expression data from GTEx and The Cancer Genome Atlas (TCGA) using limma difference analysis, Cox analysis, and machine learning (ML) algorithms. TCGA database was used as a training set, and the International Cancer Genome Consortium database was used as a test set to screen the best prognostic modeling algorithms using a combination of 101 ML algorithms for training and constructing Nomo score plots based on the algorithmic risk scores as well

SUBMITTER: Wang J 

PROVIDER: S-EPMC12517959 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature