Identification and analysis of a cell communication prognostic signature for oral squamous cell carcinoma at bulk and single-cell levels

Head and neck squamous cancer (HNSC) is a heterogenous malignant tumour disease with poor prognosis and has become the current major public health concern worldwide. Oral squamous cell carcinoma (OSCC) is the majority of HNSC. It is still in lack of comprehensive tumour immune microenvironment analysis and prognostic model development for OSCC's clinic practice. Single-cell sequencing data analysis was conducted to identify immune cell subtypes and illustrate cell–cell interaction status in OSCC via R package ‘Seurat’, ‘Harmony’, ‘elldex’ and ‘CellChat’. Base on the bulk sequencing data, WGCNA analysis was employed to identify the CD8⁺ T cell related gene module. XGBoost was used to construct the gene prognostic model for OSCC. Validation sets and immunotherapy data sets were analysed to further evaluate the model's effectiveness and immunotherapy responsiveness predicting potential. siRNA was used to down regulate FCRL4 expression. Real-time PCR and Western blot were used to validate target gene expression. The effects of FCRL4 on OSCC cells were detected by wound healing, Trans well and clone formation assays. Communication between epithelial cells and tissue stem cells may be the potential key regulators for OSCC progression. By integrating single-cell sequencing data analysis and bulk sequencing data analysis, we constructed a novel immune-related gene prognostic model. The model can effectively predict the prognosis and immunotherapy responsiveness of OSCC patients. In addition, the effects of FCRL4 on OSCC cells were validated. We comprehensively interpreted the immune microenvironment pattern of OSCC based on the single-cell sequencing data and bulk sequencing data analysis. A robust immune feature-based prognostic model was developed for the precise treatment and prognosis evaluation of OSCC.