Table_1_Construction of a Prognostic Immune Signature for Squamous-Cell Lung Cancer to Predict Survival.docx (22.95 kB)

Table_1_Construction of a Prognostic Immune Signature for Squamous-Cell Lung Cancer to Predict Survival.docx

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posted on 15.09.2020 by Rui-Lian Chen, Jing-Xu Zhou, Yang Cao, Ling-Ling Sun, Shan Su, Xiao-Jie Deng, Jie-Tao Lin, Zhi-Wei Xiao, Zhuang-Zhong Chen, Si-Yu Wang, Li-Zhu Lin
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.

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