Identification and validation of a prognostic signature comprising inflammation and pyroptosis-related genes in oral squamous cell carcinoma

Abstract

Background Oral squamous cell carcinoma (OSCC) represents a common malignancy characterized by significant morbidity and mortality rates, highlighting the critical necessity for novel therapeutic approaches. Consequently, investigating differentially expressed genes linked to inflammation and pyroptosis may identify potential prognostic biomarkers and therapeutic targets. Methods To address this research gap, our study utilized an extensive bioinformatics approach by analyzing the Cancer Genome Atlas Head and Neck Squamous Cell Carcinoma (TCGA-HNSC) dataset through differential expression analysis to identify genomic features associated with OSCC. Subsequent analyses included Gene Ontology and pathway enrichment assessments, along with survival analyses using Cox regression models, to evaluate the prognostic significance of the identified differentially expressed genes. Furthermore, immune infiltration analysis and somatic mutation assessments were conducted to elucidate the relationship between immune cell types and key prognostic genes. Additionally, copy number variation analysis was performed to highlight genomic alterations associated with immune-related and prognostic-related differentially expressed genes(DEGs). Results Our analysis identified a total of 3,495 differentially expressed genes, among which 53 immune-related and prognosis-related differentially expressed genes demonstrated a significant correlation with the prognosis of OSCC. Immune infiltration analysis further revealed the presence of 28 immune cell types within OSCC samples, with a notable prevalence of activated CD8 T cells and regulatory T cells, underscoring their association with critical prognostic genes. Additionally, pathway analysis highlighted the activation of cytokine signaling pathways and their associations with processes relevant to systemic lupus erythematosus. A prognostic risk model derived from these findings effectively stratified patients based on overall survival, identifying four key genes—CTSG,HKDC1,PTX3 and SPP1—as crucial prognostic indicators. This analysis may uncover potential prognostic biomarkers and therapeutic targets. Conclusion This study established a novel OSCC prognostic risk model based on inflammation- and pyroptosis-related interactive genes. CTSG, HKDC1, PTX3 and SPP1 were validated as independent prognostic biomarkers, and the risk score was closely associated with tumor microenvironment features, metabolic activity, and therapeutic sensitivity. This work may provides substantial references for the exploration of novel biomarkers for OSCC treatment and facilitates clinical decision-making.