A combined gene signature model for predicting radiotherapy response and relapse-free survival in laryngeal squamous cell carcinoma

Abstract

Abstract Background Radioresistance is a major challenge in radiotherapy for laryngeal squamous cell carcinoma (LSCC), and there is currently no effective method to predict radiosensitivity in LSCC patients. This study aimed to establish a prediction model for radiotherapy response based on gene expression. Methods The datasets of LSCC were obtained from the ENT department of Shanghai Ruijin Hospital and The Cancer Genome Atlas (TCGA). Lasso regression and Cox regression were used to establish the prediction model based on gene expression. Weighted gene coexpression network analysis (WGCNA) was used to analyze the correlation between gene expression and clinical characteristics. RT-qPCR was used to detect gene expression in tumor tissue to verify the accuracy of the prediction model. Results Using a cohort of LSCC cases receiving radiotherapy collected in the TCGA database, the 3 protein-coding genes (PCGs) signature model was identified for the first time as the predictor of relapse-free survival and radiosensitivity in LSCC patients. And we explored the potential clinical value of 3 PCGs and screened out 2 long non-coding RNAs (lncRNAs) potential associated with 3 PCGs. More importantly, the LSCC cases collected by our department were used to preliminarily verify the predictive power of the 3 PCGs signature model for the radiosensitivity of LSCC, and the significant correlation between the expression levels of the 3 PCGs and the 2 lncRNAs. Conclusion We successfully establish a radiosensitivity prediction model based on the 3 PCGs Riskscore, which provides a theoretical basis for the decision-making of LSCC treatment options. Meantime, we preliminarily screen the potential associated lncRNAs of the 3 PCGs for further basic and clinical research.

Keywords

• Laryngeal squamous cell carcinoma
• Radiation sensitivity prediction model
• Lasso regression
• Cox regression
• WGCNA