Identification and Verification of a Prognostic Risk Signature in Oral Squamous Cell Carcinoma

Introduction: Oral squamous cell carcinoma (OSCC) is a prevalent malignant condition. This study aimed to investigate the role of mTORC1 signaling and develop a prognostic model for OSCC. Materials and Methods: The single-sample gene set enrichment analysis (ssGSEA) algorithm was utilized to calculate the Z-Score of Hallmarks in OSCC, followed by univariate Cox regression analysis to identify processes associated with prognosis. Weighted gene co-expression network analysis (WGCNA) was performed using transcriptomic data from the cancer genome atlas (TCGA) cohort to identify genes correlated with mTORC1 signaling. A six-gene prognostic model was constructed using multifactorial Cox regression analysis and validated using an external dataset. Results: The study uncovered a strong linkage between mTORC1, glycolysis, hypoxia, and the prognosis of OSCC. mTORC1 signaling emerged as the most significant risk factor, negatively impacting patient survival. Additionally, a six-gene prognostic risk score model was developed which provided a quantitative measure of patients' survival probabilities. Interestingly, within the context of these findings, TP53 gene mutations were predominantly observed in the high-risk group, potentially underlining the genetic complexity of this patient subgroup. Additionally, differential immune cell infiltration and an integrated nomogram were also reported. Conclusion: This study highlights the importance of mTORC1 signaling in OSCC prognosis and presents a robust prognostic model for predicting patient outcomes.