Identification of a prognostic model based on cuproptosis and ferroptosis-related genes in patients with head and neck squamous cell carcinoma

Background

Head and neck squamous cell carcinoma (HNSCC) is a common invasive malignant tumor that lacks powerful predictive or prognostic biomarkers. Ferroptosis and cuproptosis are two new forms of programmed cell death. Our study was aimed at constructing a prognostic model with a combination of cuproptosis and ferroptosis-related genes (CFRGs) for the early clinical detection of HNSCC.

Methods

We obtained the information of CFRGs, including the RNASeq data and corresponding clinical data in HNSCC patients from the TCGA and GEO databases. We assessed 28 CFRGs, and analyzed the relationship between those genes and their clinical features and prognosis of HNSCC. The consensus cluster analysis was employed to generate three CFRGclusters. Then, we investigated the association of molecular patterns and prognostic significance in these subtypes. The clinical indicators of the prognosis-related genes were identified and prognostic CFRG_score were constructed. We then built a predictive nomogram with confirmed consistency and reliability by calibration curve analysis. At last, we verified the expression of CFRGs in HNSCC tissues by qRT-PCR and immunohistochemical results.

Results

The DEGs were different between the normal and HNSCC tumor tissues and we screened out 28 CFRGs related to the prognosis in HNSCC. Associations between the clinical information and prognosis were found in the molecular subtypes related to prognosis. We utilized enrichment analysis of the differential genes and showed that those DEGs were mostly enriched in the biological processes associated with the pathways of neurodegeneration-multiple diseases, Alzheimer disease, Prion disease, Parkinson disease and Amyotrophic lateral sclerosis. CFRG_score was established to predict the survival of HNSCC patients and found that higher CFRG_score suggested favorable OS for patients, indicating the prediction of better prognosis. Moreover, we created highly reliable nomogram which could predict well for the expected prognosis. In addition, we confirmed that the expression of EGFR, VEGFA, HSPA5, SLC3A2, CAV1 and CD44 were consistent with qRT-PCR and immunohistochemical analysis in HNSCC tissues by qRT-PCR.

Conclusions

This prognostic model based on prognostic differential CFRG_score is strongly related to clinical characteristics, prognosis, and therapy in HNSCC patients and could be used as a promising tool which is dedicated to guiding the treatment of HNSCC.