• Ming Sun, Sihan Chen, and Min Fu.
• Department of Otolaryngology, the Second Affiliated Hospital of Dalian Medical University, Dalian.
• Medicine (Baltimore). 2021 Jan 15; 100 (2): e24263e24263.

BackgroundLaryngeal squamous cell carcinoma (LSCC) is one of the most common malignant tumors of the head and neck in the world. At present, the treatment methods include surgery, radiotherapy, and chemotherapy, but the 5-year survival rate is still not ideal and the quality of life of the patients is low. Due to the relative lack of immunotherapy methods, this study aims to build a risk prediction model of related immune genes, which can be used to effectively predict the prognosis of laryngeal cancer patients, and provide targets for subsequent immunotherapy.MethodsWe collected the 111 cases of laryngeal squamous cell carcinoma and 12 matched normal samples in the The Cancer Genome Atlas Database (TCGA) gene expression quantification database. The differentially expressed related immune genes were screened by R software version 3.5.2. The COX regression model of immune related genes was constructed, and the sensitivity and specificity of the model were evaluated. The risk value was calculated according to the model, and the risk curve was drawn to verify the correlation between related immune genes, risk score, and clinical traits.ResultsWe selected 8 immune-related genes that can predict the prognosis of LSCC in a COX regression model and plotted the Kaplan-Meier survival curve. The 5-year survival rate of the high-risk group was 16.5% (95% CI: 0.059-0.459), and that of the low-risk group was 72.9% (95% CI: 0.555-0.956). The area under the receiver operating characteristic (ROC) curve was used to confirm the accuracy of the model (AUG = 0.887). After univariate and multivariate regression analysis, the risk score can be used as an independent risk factor for predicting prognosis. The risk score (P = .021) was positively correlated with the clinical Stage classification.ConclusionWe screened out 8 immune genes related to prognosis: RBP1, TLR2, AQP9, BTC, EPO, STC2, ZAP70, and PLCG1 to construct risk value models, which can be used to speculate the prognosis of the disease and provide new targets for future immunotherapy.Copyright © 2021 the Author(s). Published by Wolters Kluwer Health, Inc.

52 keywords...

hide…