Construction of a Prognostic Immune Signature for Squamous-Cell Lung Cancer to Predict Survival - Chen, Rui-Lian; Zhou, Jing-Xu; Cao, Yang; Sun, Ling-Ling; Su, Shan; Deng, Xiao-Jie; Lin, Jie-Tao; Xiao, Zhi-Wei; Chen, Zhuang-Zhong; Wang, Si-Yu; Lin, Li-Zhu (Frontiers in Immunology, 2020)
Background: Limited treatment strategies are available for squamous-cell lung cancer (SQLC) patients. Few studies have addressed whether immune-related genes (IRGs) or the tumor immune microenvironment can predict the prognosis for SQLC patients. Our study aimed to construct a signature predict prognosis for SQLC patients based on IRGs. Methods: We constructed and validated a signature from SQLC patients in The Cancer Genome Atlas (TCGA) using bioinformatics analysis. The underlying mechanisms of the signature were also explored with immune cells and mutation profiles. Results: A total of 464 eligible SQLC patients from TCGA dataset were enrolled, and were randomly divided into the training cohort (n=232) and the testing cohort (n=232). Eight differentially expressed IRGs were identified and applied to construct the immune signature in the training cohort. The signature showed a significant difference in overall survival (OS) between low-risk and high-risk cohorts (P<0.001), with an area under the curve of 0.76. The predictive capability was verified with the testing and total cohorts. Multivariate analysis revealed that the 8-IRG signature served as an independent prognostic factor for OS in SQLC patients. Naive B cells, resting CD4 memory T cells, follicular helper T cells and M2 Macrophages were found to significantly associate with OS. There was no statistical difference in terms of tumor mutational burden between the high-risk and low-risk cohorts. Conclusions: Our study constructed and validated an 8-IRG signature prognostic model that predicts clinical outcomes for SQLC patients. However, this signature model needs further validation with a larger number of patients.