Constructing a prognostic signature of tumor-associated B lymphocytes in hepatocellular carcinoma via machine learning integration.

ABSTRACT: B lymphocytes are known for their anti-tumor role in hepatocellular carcinoma (HCC), but recent studies suggest that tumor-tamed B cells can promote cancer progression in other malignancies. It remains unclear whether a similar subset exists in HCC. We analyzed single-cell RNA RNA-seq (GSE162616) to compare B lymphocytes in normal and HCC samples. Weighted correlation network analysis (WGCNA) identified tumor-associated B lymphocyte module genes. Furthermore, we utilized 101 machine learning algorithms to construct a prognostic model, which was further compared with other published prognostic models using statistical comparisons, including P-values and confidence intervals. The model was further tested for its ability to predict responses to HCC-specific immunotherapy and chemotherapy in a

SUBMITTER: Zhang S 

PROVIDER: S-EPMC12808728 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature