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

TL;DR

A machine learning model using TAM polarity genes predicts survival and treatment response in liver cancer patients.

Contribution

A novel TAM polarity-related gene signature (TPS) was developed and validated for HCC prognosis and treatment guidance.

Findings

TPS stratified HCC patients into high- and low-risk groups with distinct survival outcomes.

High-risk patients showed oncogenic pathways and immune suppression, while low-risk patients had lipid and amino acid metabolism.

TPS identified potential drug targets like CDK1, PLK1, and statins, and validated SPP1 as a key signaling mediator.

Abstract

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.

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 were performed to investigate cellular expression and functional relevance.

TPS markedly different patients into high- and low-risk groups with significantly different survival outcomes (TCGA 1-, 3-, 5-year AUCs: 0.91, 0.89, 0.88). High-risk patients showed enrichment in glycan metabolism, DNA repair, and oncogenic pathways, whereas low-risk patients displayed elevated lipid and amino acid metabolism. Immune profiling revealed greater infiltration of immunosuppressive cells and higher expression of immune checkpoints in high-risk patients. Drug sensitivity analysis identified potential therapeutic targets and candidate compounds, including CDK1, PLK1, and statins. scRNA-seq analysis highlighted disrupted macrophage-immune interactions and identified SPP1 as a key signaling mediator. Silencing of TTC1 and G6PD suppressed HCC cell proliferation.

We developed and validated a robust TAM polarity-related signature that effectively stratifies HCC patients by prognosis. TPS provides insights into tumor immunity, metabolism, and drug response, and may serve as a valuable tool for precision medicine in HCC.

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