Combined single-cell RNA-seq and bulk RNA-seq construction of M2 TAMs signature for predicting HNSCC prognosis and immunotherapy

Abstract

Tumor associated macrophages (TAMs) in Head and neck squamous cell carcinoma (HNSCC), particularly M2-polarized subtypes, are pivotal drivers of tumorigenesis, angiogenesis, and metastasis, contributing to adverse clinical outcomes. Current prognostic markers lack precision, underscoring the need for novel biomarkers and risk stratification models. Single-cell RNA sequencing (scRNA-seq) was applied to profile the transcriptional landscape of TAMs in HNSCC at single-cell resolution. 1,208 M2 TAMs were integrated from scRNA-seq data with bulk RNA sequencing to identify molecular signatures. Weighted correlation network analysis (WGCNA) and Uniform Manifold Approximation and Projection (UMAP) analysis were applied to dissect TAMs heterogeneity and interactions within the tumor microenvironment. In vivo experiments validated the efficacy of the prognostic signature model. In this study, high infiltration of M2 TAMs was strongly associated with advanced clinical stages, lymph node metastasis, and reduced overall survival (P<0.001). TCGA datasets were utilized for cross-platform verification. Multivariate Cox regression and survival analyses were performed to establish prognostic relevance. 11 prognostic signature genes (FCGBP, GIMAP5, WIPF1, RASGEF1B, GIMAP7, IGFLR1, GPR35, NCF1, CLECL1, HEXB, IL10) were identified through integrative analysis, which formed the basis of a robust risk stratification model. The distribution of biomarkers in the high-risk group, as determined by the signature we constructed, can serve as a better indicator for assessing poor prognosis. In clinical samples, prognosis signature has the potential to predict the prognosis effectively in patients with HNSCC.M2 TAMs-driven prognostic signature for HNSCC offers a clinically actionable tool for risk stratification and outcome prediction.

Keywords: head and neck squamous cell carcinoma; immune profile; single-cell RNA sequencing; tumor-associated macrophages; weighted correlation network analysis.