Machine learning-based tumor associated macrophages polarity signature predicts prognosis and treatment response in hepatocellular carcinoma.

ABSTRACT:

Background

Tumor-associated macrophages (TAMs) shape the tumor microenvironment and drive hepatocellular carcinoma (HCC) progression. However, the prognostic significance of TAM polarity-related genes, particularly based on the CXCL9:SPP1 signature, remains unclear.

Methods

We identified 372 TAM polarity-related genes in the TCGA-LIHC dataset. Prognostic candidates were selected using univariate Cox regression, bootstrap resampling, and the Boruta algorithm. Seven machine learning models were compared, and XGBoost was selected to construct a TAM polarity-related signature (TPS) consisting of 17 genes. TPS was validated in two external cohorts. Associations with clinical features, biological pathways, immune status, and drug sensitivity were explored. scRNA-seq and qRT-PCR we

SUBMITTER: Wang F 

PROVIDER: S-EPMC12627068 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature