Identification and Validation of a Four-Gene Ferroptosis Signature for Predicting Overall Survival of Lung Squamous Cell Carcinoma - Wang, Qi; Chen, Yaokun; Gao, Wen; Feng, Hui; Zhang, Biyuan; Wang, Haiji; Lu, Haijun; Tan, Ye; Dong, Yinying; Xu, Mingjin (Frontiers in Oncology, 2022)
Background: Lung squamous cell carcinoma (LUSC) represents 30% of all non-small cell lung carcinoma. Targeted therapy is not sufficient for LUSC patients because of the low frequency of targeted-effective mutation in LUSC whereas immunotherapy offers more options for patients with LUSC. We explored a ferroptosis-related prognostic signature that can potentially assess the prognosis and immunotherapy efficacy of LUSC patients. Methods: A total of 502 LUSC patients were downloaded from TCGA. The external validation data was obtained from GEO: GSE73403. Then we identified the candidate genes and constructed the prognostic signature through the Cox survival regression analyses and LASSO. Risk score plot, Kaplan–Meier curve, ROC curve were used to assess the prognostic power and performance of the model. CIBERSORT algorithm estimated the fraction of immune cell types. TIDE was utilized to predict the response to immunotherapy. IMvigor210 was used to explore the association between the risk scores and immunotherapy outcomes. A nomogram combined selected clinical characteristics and the risk scores was constructed. Results: We screened 132 differentially expresesed ferroptosis-related genes. According to KEGG and GO pathway analyses, these genes were mainly engaged in the positive regulation of cytokine production, cytokine metabolic process, and oxidoreductase activity. We then constructed a prognostic model via LASSO regression. The proportions of CD8+ T cells, CD4+ activated T cells, and follicular helper T cells were significantly different between low-risk and high-risk group. TIDE algorithm indicated low-risk LUSC patients might profit more from immune checkpoint inhibitors. The predictive value of ferroptosis gene model in immunotherapy response was further confirmed in IMvigor210. Finally, we combined the clinical characteristics with a LASSO regression model to construct a nomogram that could be easily applied in clinical practice. Conclusion: We identified prognostic model provides an accurate and objective basis for guiding individualized treatment decisions for LUSC.