Exploration of a predictive model based on genes associated with fatty acid metabolism and clinical treatment for head and neck squamous cell carcinoma.

Abstract

Head and neck squamous cell carcinoma (HNSCC) is one of the most prevalent malignant tumors of the head and neck and presents high risks of recurrence and poor prognosis postoperatively. The aim of this study was to establish a predictive model based on fatty acid metabolism (FAM) genes to forecast the prognosis of HNSCC patients and the subsequent treatment strategies. We accessed the TCGA and GEO databases for HNSCC genes and clinical data. The FAM risk score model was created and validated using a combination of univariate Cox analysis and least absolute shrinkage and selection operator (LASSO) regression analysis. Combining risk scores and clinical characteristics, a nomogram was established and assessed. Subsequently, the function, gene mutation, immune difference, and chemotherapeutic drug sensitivity of the groups with high- and low-risk scores were analyzed. Consequently, the mode's validity was evaluated comprehensively by combining single gene analysis. The FAM risk score model for predicting HNSCC prognosis had certain validity. Patients in the high- and low-risk groups had genetic mutations, and the prognosis was the poorest for the high-risk groups with high genetic mutations. The patients with low-risk scores were suitable for immunotherapy since they had increased infiltration of immune cells. In contrast, the patients in the other groups were more suitable for chemotherapy. The results of this study demonstrated that the FAM risk score model may predict the prognosis of HSNCC and has a certain therapeutic guidance value.