Int J Med Sci 2020; 17(10):1393-1405. doi:10.7150/ijms.47301

Research Paper

Development and validation of a novel immune-related prognostic model in lung squamous cell carcinoma

Zeyu Liu1*, Yuxiang Wan1*, Yuqin Qiu1, Xuewei Qi1, Ming Yang2, Jinchang Huang1✉, Qiaoli Zhang1✉

1. Third Affiliated Hospital, Beijing University of Chinese Medicine, Beijing, China.
2. School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China.
*These authors have contributed equally to this work.

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Citation:
Liu Z, Wan Y, Qiu Y, Qi X, Yang M, Huang J, Zhang Q. Development and validation of a novel immune-related prognostic model in lung squamous cell carcinoma. Int J Med Sci 2020; 17(10):1393-1405. doi:10.7150/ijms.47301. Available from http://www.medsci.org/v17p1393.htm

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Abstract

Background: The immune system plays an important role in the development of lung squamous cell carcinoma (LUSC). Therefore, immune-related genes (IRGs) expression may be an important predictor of LUSC prognosis. However, a prognostic model based on IRGs that can systematically assess the prognosis of LUSC patients is still lacking. This study aimed to construct a LUSC immune-related prognostic model by using IRGs.

Methods: Gene expression data about LUSC were obtained from The Cancer Genome Atlas (TCGA). Differential expression analysis and univariate Cox regression analysis were performed to identify prognostic differentially expressed IRGs. A prognostic model was constructed using the Lasso and multivariate Cox regression analyses. Then we validated the performance of the prognostic model in training and test cohorts. Furthermore, associations with clinical variables and immune infiltration were also analyzed.

Results: 593 differentially expressed IRGs were identified, and 8 of them were related to prognosis. Then a transcription factor regulatory network was established. A prognostic model consisted of 4 immune-related genes was constructed by using Lasso and multivariate Cox regression analyses. The prognostic value of this model was successfully validated in training and test cohorts. Further analysis showed that the prognostic model could be used independently to predict the prognosis of LUSC patients. The relationships between the risk score and immune cell infiltration indicated that the model could reflect the status of the tumor immune microenvironment.

Conclusions: We constructed a risk model using four PDIRGs that can accurately predict the prognosis of LUSC patients. The risk score generated by this model can be used as an independent prognostic indicator. Moreover, the model can predict the infiltration of immune cells in patients, which is conducive to the prediction of patient sensitivity to immunotherapy.

Keywords: immune-related genes, prognostic model, lung squamous cell carcinoma, The Cancer Genome Atlas, bioinformatics