Identification of a telomere-related gene signature for the prognostic and immune landscape prediction in head and neck squamous cell carcinoma by integrated analysis of machine learning and Mendelian randomization

Telomere-related genes (TRGs) are vital in diverse tumor types. Nevertheless, there is a notable lack of in-depth research concerning their significance in head and neck squamous cell carcinoma (HNSCC). In this context, the present study aims to assess the predictive value of TRGs in HNSCC. Gene expression data and clinical data for HNSCC were sourced from The Cancer Genome Atlas and the Gene Expression Omnibus database. A new prognostic signature for TRGs was formulated through the application of machine learning techniques. Based on this signature, risk scores were computed for individual samples, effectively classifying individuals into low- and high-risk categories. The signature was evaluated in terms of its association with survival outcomes, tumor mutation burden, functional enrichment, immune cell infiltration, and its predictive capacity regarding immunotherapy efficacy. Additionally, Mendelian randomization analysis was utilized to ascertain the potential causal association between the expression of model genes and the development of HNSCC. A sum of 24 TRGs was recognized and utilized to develop the predictive signature. The areas under the receiver operating characteristic (ROC) curves for 1-, 3-, and 5-year overall survival were computed as 0.654, 0.734, and 0.711, respectively. Kaplan-Meier survival analysis demonstrated that individuals classified as high-risk had notably poorer prognoses relative to those placed in the low-risk. Those with lower risk scores demonstrated better survival outcomes, marked by elevated immune scores, augmented immune-related functions, and greater immune cell infiltration. Furthermore, these lower-risk patients exhibited an enhanced response to immunotherapy in comparison to high-risk patients. Mendelian randomization findings indicated a possible causal link between MAD1L1 expression and the occurrence of HNSCC. This research established an innovative TRG-based risk model to forecast the survival outcomes and immune landscape of individuals with HNSCC. This reliable and validated prognostic indicator has the potential to inform and enhance the creation of innovative treatment approaches for individuals with HNSCC.