Aging and head and neck cancer insights from single cell and spatial transcriptomic analyses

Abstract

Background Head and neck squamous cell carcinoma(HNSCC) is the sixth most common malignancy worldwide, with more than 890,000 new cases and 450,000 deaths annually. Its major risk factors include smoking, alcohol abuse, aging, and poor oral hygiene. Due to the lack of early and effective detection and screening methods, many patients are diagnosed at advanced stages with a five-year survival rate of less than 50%. In this study, we deeply explored the expression of Aging-related genes(ARGs) in HNSCC and analyzed their prognostic significance using single-cell sequencing and spatial transcriptomics analysis. This research aims to provide new theoretical support and directions for personalized treatment. Annually, more than 890,000 new cases of head and neck squamous cell carcinoma (HNSCC) are diagnosed globally, leading to 450,000 deaths, making it the sixth most common malignancy worldwide. The primary risk factors for HNSCC include smoking, alcohol abuse, aging, and poor oral hygiene. Many patients are diagnosed at advanced stages due to the absence of early and effective detection and screening methods, resulting in a five-year survival rate of less than 50%. In this research, single cell sequencing and spatial transcriptome analysis were used to investigate the expression of Aging-related genes (ARGs) in HNSCC and to analyse their prognostic significance. This research aims to provide new theoretical support and directions for personalized treatment. Methods In this study, we investigated the association between HNSCC and AGRs by utilizing the GSE139324 series in the GEO database alongside the TCGA database, combined with single-cell sequencing and spatial transcriptomics analysis. The data were analyzed using Seurat and tSNE tools to reveal intercellular communication networks. For the spatial transcriptome data, SCTransform and RunPCA were applied to examine the metabolic activities of the cells. Gene expression differences were determined through spacerxr and RCTD tools, while the limma package was employed to identify differentially expressed genes and to predict recurrence rates using Cox regression analysis and column line plots. These findings underscore the potential importance of molecular classification, prognostic assessment, and personalized treatment of HNSCC. Results This study utilized HNSCC single-cell sequencing data to highlight the significance of ARGs in the onset and prognosis of HNSCC. It revealed that the proportion of monocytes and macrophages increased, while the proportion of B cells decreased. Notably, high expression of the APOE gene in monocytes was closely associated with patient prognosis. Additionally, a Cox regression model was developed based on GSTP1 and age to provide personalized prediction tools for clinical use in predicting patient survival. Conclusions We utilized single-cell sequencing and spatial transcriptomics to explore the cellular characteristics ...