The Combined Detection of Immune Genes for Predicting the Prognosis of Patients With Non-Small Cell Lung Cancer

Abstract

Lung cancer is one of the leading causes of cancer-related death. In recent years, there has been an increasing interest in the fields of tumor and immunity. This study focused on the possible prognostic value of immune genes in non-small cell lung cancer patients. We used The Cancer Genome Atlas (TCGA) to download gene expression data and clinical information of lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC). The immune gene list was downloaded from the Immport database. We then constructed immune gene prognostic models on the basis of Cox regression analysis. We further evaluated the clinical significance of the models via survival analysis, receiver operating characteristic (ROC) curves, and independent prognostic factor analysis. Moreover, we analyzed the associations of prognostic models with both mutation burdens and neoantigens. Using the Gene Expression Omnibus (GEO) and Kaplan–Meier plotter databases, we evaluated the validity of the prognostic models. The prognostic model of LUAD included 13 immune genes, and the prognostic model of LUSC contained 10 immune genes. High-risk patients based on prognostic models had a lower 5-year survival rate than did low-risk patients. The ROC curve analysis demonstrated the prediction accuracy of the prognostic models, as the area under the curve (AUC) was 0.742, 0.707, and 0.711 for LUAD, and 0.668, 0.703, and 0.668 for LUSC, when the predicted survival times were 1, 3, and 5 years, respectively. The mutation burden analysis showed that mutation level was associated with the risk score in patients with LUAD. The analysis based on GEO and Kaplan–Meier plotter demonstrated the prognostic validity of the models. Therefore, immune gene-related models of LUAD and LUSC can predict prognosis. Further study of these genes may enable us to better distinguish between LUAD and LUSC and lead to improvement in immunotherapy for lung cancer.

Development and Validation of an Immune-Based Prognostic Risk Score for Patients With Resected Non-Small Cell Lung Cancer

“…Tian et al. obtained immune gene prognostic models for lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) based TCGA database by using gene expression and achieved the AUCs of 0.742, 0.707, and 0.711 for LUAD, and 0.668, 0.703, and 0.668 for LUSC ( 24 ). In our study, the proposed IMPRS integrated IHC-immune signature based on immune-related IHC digital images and clinicopathological factors (age, T stage, and N stage) yielded a C-index of 0.869, demonstrating a better prediction performance than that achieved by their studies.…”

Identification of Hypoxia Signature to Assess the Tumor Immune Microenvironment and Predict Prognosis in Patients with Ovarian Cancer

“…It is demonstrated that ANGPTL4 is overexpressed in OC and related to shorter relapse-free survival times in serous OC [ 35 – 37 ]. In addition, ANGPTL4 is significantly related to the poor prognosis of patients with non-small-cell lung cancer, lung adenocarcinoma, and cervical cancer [ 38 – 40 ]. CITED2 is significantly enriched in regulatory T cells and granulosa cells [ 41 , 42 ].…”

Identification and Functional Analysis of Individual-Specific Subpathways in Lung Adenocarcinoma

“…Lung cancer is one of the most malignant tumors. In both male and female patients, lung cancer is the leading cause of cancer-related deaths worldwide [ 1 ]. The incidence and mortality of lung cancer have been increasing year by year, with a significant burden on patients and society [ 2 ].…”