Frontiers in Immunology (Sep 2020)

Construction of a Prognostic Immune Signature for Squamous-Cell Lung Cancer to Predict Survival

  • 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

DOI
https://doi.org/10.3389/fimmu.2020.01933
Journal volume & issue
Vol. 11

Abstract

Read online

BackgroundLimited 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.MethodsWe 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.ResultsA 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.ConclusionOur 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.

Keywords