Bioinformatics Analysis of Tumor-Associated Macrophages in Hepatocellular Carcinoma and Establishment of a Survival Model Based on Transformer.

Abstract

Hepatocellular carcinoma (HCC) ranks among the most prevalent malignancies globally. Although treatment strategies have improved, the prognosis for patients with advanced HCC remains unfavorable. Tumor-associated macrophages (TAMs) play a dual role, exhibiting both anti-tumor and pro-tumor functions. In this study, we analyzed single-cell RNA sequencing data from 10 HCC tumor cores and 8 adjacent non-tumor liver tissues available in the dataset GSE149614. Using dimensionality reduction and clustering approaches, we identified six major cell types and nine distinct TAM subtypes. We employed Monocle2 for cell trajectory analysis, hdWGCNA for co-expression network analysis, and CellChat to investigate functional communication between TAMs and other components of the tumor microenvironment. Furthermore, we estimated TAM abundance in TCGA-LIHC samples using CIBERSORT and observed that the relative proportions of specific TAM subtypes were significantly correlated with patient survival. To identify TAM-related genes influencing patient outcomes, we developed a high-dimensional, gene-based transformer survival model. This model achieved superior concordance index (C-index) values across multiple datasets, including TCGA-LIHC, OEP000321, and GSE14520, outperforming other methods. Our results emphasize the heterogeneity of tumor-associated macrophages in hepatocellular carcinoma and highlight the practicality of our deep learning framework in survival analysis.