Molecular Classification of HNSCC Based on Inflammatory Response-Related Genes - Integrated Single-Cell and Bulk RNA-Seq Analysis

Objective: Tumor cells, inflammatory cells, and chemical factors collaboratively orchestrate a sophisticated signaling network, culminating in the formation of the inflammatory tumor microenvironment (TME). The present study sought to explore the nature of the inflammatory response in HNSCC and to decipher its influence on immunotherapeutic. Materials and Methods: A thorough analysis was performed utilizing the TCGA cohort along with two GEO cohorts. Unsupervised clustering of 200 inflammatory response-related genes (IRGs) was applied using the k-means algorithm to explore the heterogeneity of HNSCC. Additionally, a prognostic signature based on IRGs genes was constructed using Lasso regression. Meanwhiles, the expression of IRGs were identified in tumors and paracancerous tissues at the single-cell level. The crosstalk between IRGs was explored using CellChat and the patterns of incoming and outgoing signals were identified. Finally, qPCR was used to verify the expression of hub genes. Results: There were significant differences in immune-cell function and immune-cell infiltration among three inflammatory response clusters. Additionally, we also constructed a prognostic model which could predicted the responses of common chemotherapeutic drugs and immunotherapy. Furthermore, qPCR and sc-RNA seq corroborated that the expression profiles of the prognostic genes were largely in alignment with the findings from the bioinformatics analysis. Ultimately, the molecular docking demonstrated favorable binding affinities between the pivotal gene-SCC7 and four chemotherapeutic drugs. Conclusion: This research has uniquely shed light on the intricate connection between the inflammatory response profiles and the immune infiltration patterns in HNSCC.