BACKGROUND: Oral squamous cell carcinoma (OSCC) represents a significant global health burden with heterogeneous clinical outcomes. Understanding the molecular and cellular complexity of OSCC is crucial for developing precision therapeutic strategies. METHODS: We performed comprehensive integrative analysis combining single-cell RNA sequencing (scRNA-seq) and bulk transcriptomics to characterize the cellular landscape of OSCC. Samples from HPV-positive, HPV-negative, recurrent, and non-recurrent cases were analyzed using advanced computational approaches including dimensionality reduction, trajectory inference, cell-cell communication analysis, and spatial transcriptomics. Survival analysis was performed using Kaplan-Meier methods, and pathway enrichment was assessed using GSEA. Immunotherapy response predictors were evaluated based on PD-L1 expression, tumor mutational burden, and immune cell infiltration patterns. RESULTS: Our analysis identified distinct molecular subtypes of OSCC with differential survival outcomes (p < 0.001). Single-cell profiling revealed significant cellular heterogeneity with 6-8 major cell clusters including cancer epithelial cells, cancer-associated fibroblasts (CAFs), tumor-associated macrophages (TAMs), T cells, and B cells. Epithelial-mesenchymal transition (EMT) pathway showed significant enrichment (NES = 2.5, pFDR = 0.001) in aggressive tumor subtypes. Trajectory analysis identified branching developmental paths from pre-malignant states through dysplasia to invasive carcinoma and recurrent disease. Cell-cell communication networks revealed extensive interactions between immune cells, fibroblasts, and cancer cells through key ligand-receptor pairs including VEGF-VEGFR2, EGF-EGFR, and Collagen-CD44 (p = 0.00001). Spatial transcriptomics analysis demonstrated distinct tumor microenvironmental niches with specific cellular compositions. Methylation analysis revealed strong negative correlation with gene expression (R = -0.70, p < 0.001), indicating epigenetic regulation. A six-gene prognostic signature effectively stratified patients into high-risk and low-risk groups. Immunotherapy response prediction identified PD-L1 expression (likelihood score 0.75) and tumor mutational burden as key biomarkers. Cell type proportions significantly correlated with recurrence rates (R² = 0.65, p < 0.001). CONCLUSIONS: This comprehensive multi-omics analysis provides unprecedented insights into OSCC cellular heterogeneity, tumor microenvironment dynamics, and disease progression mechanisms. Our findings identify novel prognostic biomarkers and therapeutic targets, particularly highlighting the potential of immunotherapy in specific patient subsets. The integrated single-cell and spatial transcriptomics approach offers a framework for precision medicine in oral cancer management.
Zhang et al. (2026) studied this question.