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Updated: Dec 29, 2025
Author Spotlight: Exploring the Role of Inflammation in the Co-occurrence of Primary Sjogren's Syndrome and Lung Adenocarcinoma
Published on: September 20, 2024
IRGS: an immune-related gene classifier for lung adenocarcinoma prognosis
Xiaoshun Shi1,2, Ruidong Li3, Xiaoying Dong4
1Department of Thoracic Surgery, Nanfang Hospital, Southern Medical University, No. 1838 of North Guangzhou Avenue, Guangzhou, 510515, People's Republic of China. sxs917@163.com.
Background:
Tumour cells interfere with normal immune functions by affecting the expression of some immune-related genes, which play roles in the prognosis of cancer patients. In recent years, immunotherapy for tumours has been widely studied, but a practical prognostic model based on immune-related genes in lung adenocarcinoma comparable to existing model has not been established and reported.
Methods:
We first obtained publicly accessible lung adenocarcinoma RNA expression data from The Cancer Genome Atlas (TCGA) for differential gene expression analysis and then filtered immune-related genes based on the ImmPort database. By using the lasso algorithm and multivariate Cox Proportional-Hazards (CoxPH) regression analysis, we identified candidate genes for model development and validation. The robustness of the model was further examined by comparing the model with three established gene models.
Results:
Gene expression data from a total of 524 lung adenocarcinoma patients from TCGA were used for model development. We identified four biomarkers (MAP3K8, CCL20, VEGFC, and ANGPTL4) that could predict overall survival in lung adenocarcinoma (HR = 1.98, 95% CI 1.48 to 2.64, P = 4.19e-06) and this model could be used as a classifier for the evaluation of low-risk and high-risk groups. This model was validated with independent microarray data and was highly comparable with previously reported gene expression signatures for lung adenocarcinoma prognosis.
Conclusions:
In this study, we identified a practical and robust four-gene prognostic model based on an immune gene dataset with cross-platform compatibility. This model has potential value in improving TNM staging for survival predictions in patients with lung adenocarcinoma.
Impact:
The study provides a method of immune relevant gene prognosis model and the identification of immune gene classifier for the prediction of lung adenocarcinoma prognosis with RNA sequencing and microarray compatibility.
