Construction of immune-related molecular diagnostic and predictive models of hepatocellular carcinoma based on machine learning.

ABSTRACT:

Background

To exploit hepatocellular carcinoma (HCC) diagnostic substances, we identify potential predictive markers based on machine learning and to explore the significance of immune cell infiltration in this pathology.

Method

Three HCC gene expression datasets were used for weighted gene co-expression network analysis (WGCNA) and differential expression analysis. Least Absolute Shrinkage and Selection Operator (LASSO) and Random Forest were applied to identify candidate biomarkers. The diagnostic value of HCC diagnostic gene biomarkers was further assessed by the area under the ROC curve observed in the validation dataset. CIBERSORT was used to analyze 22 immune cell fractions from HCC patients and to analyze their correlation with diagnostic markers. In addition, the pro

SUBMITTER: Zheng H 

PROVIDER: S-EPMC10835357 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature