ObjectiveTo explore the correlation between mitophagy and the tumor microenvironment (TME) in patients with head and neck squamous cell carcinoma (HNSCC), with an aim to enhance therapeutic efficacy for HNSCC.MethodsA machine learning-based multigene prognostic signature was developed based on mitophagy-related differentially expressed genes (MRGs) identified in The Cancer Genome Atlas cohort. This signature was correlated with the TME using gene set enrichment analysis. The association between this prognostic signature and various immunological features of the TME was explored, including status of tumor-infiltrating immune cells, expression of immune checkpoint molecules, and the immunoscore. Immunohistochemistry validated the expression of hub gene CSNK2A2 and assessed its relationship with immunomarker expression. Quantitative PCR validated CSNK2A2 knockdown in HNSCC cell lines. Functional experiments including Transwell assays to determine cell migration and invasion, Cell Counting Kit 8 assay, and 5-ethynyl-2-deoxyuridine assay were performed to confirm the role of CSNK2A2 in HNSCC. Finally, a subcutaneous xenograft model was generated in C3H mice to validate our findings.ResultsThe MRG-based prognostic signature showed excellent predictive performance. High-risk patients had significantly shorter progression-free and overall survival (P < 0.0001) than low-risk patients. CD8+ T cell infiltration was lower in high-risk groups, whereas low-risk groups showed higher immunological marker expression. Thus, the low-risk HNSCC subtype may benefit from immune therapy, while high-risk subtypes may benefit from chemotherapy (P < 0.001). CSNK2A2 was highly expressed and strongly correlated with CD8 and PD-L1 based on immunohistochemistry of the HNSCC tissue microarray. CSNK2A2 knockdown reduced cell migration, invasion, and proliferation, and arrested cells in G1 phase. In vivo, it led to slower tumor growth and smaller tumor volumes.ConclusionWe established a potential prognostic signature that could improve HNSCC management in the future. CSNK2A2 may be a new biomarker to predict immunotherapy efficacy in HNSCC.

Background: Head and neck squamous cell carcinoma (HNSCC) has a poor prognosis due to its high rates of recurrence and metastasis. Herein, we designed and validated an individualized ferroptosis-associated gene signature (FGS) and further probed the potential survival mechanisms along with therapeutic targets for HNSCC. Methods: The FGS risk score was constructed using stepwise regression analysis and validated in the GSE41613 cohort. Characterization of the tumor microenvironment (TME) in patients with HNSCC, involving immune cells and immunomodulatory genes, was performed to investigate the survival mechanisms and therapeutic targets associated with FGS. To validate the role of FGS in TME, multiplex fluorescent immunohistochemistry (mfIHC) was performed on tissue sections from 55 patients with oral squamous carcinoma. Results: The risk score obtained from FGS showed good predictive power as an independent predictor of overall survival. From the tumor immune dysfunction and exclusion (TIDE) prediction, it was found that patients at low risk may benefit from immunotherapy. Furthermore, FGS was significantly associated with CD276, which was highly expressed in fibroblasts that enriched in angiogenesis and epithelial-mesenchymal transition pathways at a single-cell resolution, suggesting CD276 may play a critical mediator of the immunosuppressive microenvi-ronment. Lastly, we identified ATG5 as a critical gene in FGS. And the immune-bioinformatics analysis combined with experimental validation showed a negative correlation between ATG5 expression and CD8 + T cells. Conclusion: The FGS model provides a novel and effective method to predict the prognosis of patients with HNSCC and their survival can be prolonged through TME-related therapeutic targets.

Metabolic reprogramming contributes to patient prognosis. Here, we aimed to reveal the comprehensive landscape in metabolism of head and neck squamous carcinoma (HNSCC), and establish a novel metabolism-related prognostic model to explore the clinical potential and predictive value on therapeutic response. We screened 4752 metabolism-related genes (MRGs) and then identified differentially expressed MRGs in HNSCC. A novel 10-MRGs risk model for prognosis was established by the univariate Cox regression analysis and the least absolute shrinkage and selection operator (Lasso) regression analysis, and then verified in both internal and external validation cohort. Kaplan-Meier analysis was employed to explore its prognostic power on the response of conventional therapy. The immune cell infiltration was also evaluated and we used tumor immune dysfunction and exclusion (TIDE) algorithm to estimate potential response of immunotherapy in different risk groups. Nomogram model was constructed to further predict patients’ prognoses. We found the MRGs-related prognostic model showed good prediction performance. Survival analysis indicated that patients suffered obviously poorer survival outcomes in high-risk group (p < 0.001). The metabolism-related signature was further confirmed to be the independent prognostic value of HNSCC (HR = 6.387, 95% CI = 3.281-12.432, p < 0.001), the efficacy of predictive model was also verified by internal and external validation cohorts. We observed that HNSCC patients would benefit from the application of chemotherapy in the low-risk group (p = 0.029). Immunotherapy may be effective for HNSCC patients with high risk score (p < 0.01). Furthermore, we established a predictive nomogram model for clinical application with high performance. Our study constructed and validated a promising 10-MRGs signature for monitoring outcome, which may provide potential indicators for metabolic therapy and therapeutic response prediction in HNSCC.