AUTHOR=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 TITLE=Construction of a Prognostic Immune Signature for Squamous-Cell Lung Cancer to Predict Survival JOURNAL=Frontiers in Immunology VOLUME=11 YEAR=2020 URL=https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2020.01933 DOI=10.3389/fimmu.2020.01933 ISSN=1664-3224 ABSTRACT=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 memory CD4 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.

Conclusion

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.