Abstract
Macrophages played an important role in the progression and treatment of cancer. Nevertheless, there is a limited amount of research that has comprehensively elucidated the characteristics of macrophages associated genes in head and neck squamous cell carcinoma (HNSCC). We employed weighted gene co-expression network analysis (WGCNA) to identify macrophage-related genes (MRGs) and classify patients with HNSCC into two distinct subtypes. A macrophage-related risk signature (MRS) model, comprising nine genes: IGF2BP2, PPP1R14C, SLC7A5, KRT9, RAC2, NTN4, CTLA4, APOC1, and CYP27A1, was formulated by integrating 101 machine learning algorithm combinations. We observed lower overall survival (OS) in the high-risk group and the high-risk group showed elevated expression levels in most of the differentially expressed immune checkpoint and human leukocyte antigen (HLA) genes, suggesting a strong immune evasion capacity in these tumors. Correspondingly, TIDE score positively correlated with risk score, implying that high-risk tumors may resist immunotherapy more effectively. At the single-cell level, we noted macrophages in the TME predominantly stalled in the G2/M phase, potentially hindering epithelial-mesenchymal transition and playing a crucial role in the inhibition of tumor progression. Additionally, we validated MRS gene expression levels using RT-qPCR and immunohistochemistry (IHC). The current study constructed a novel MRS for HNSCC, which could serve as an indicator for predicting the prognosis, immune infiltration and immunotherapy benefits for HNSCC patients.