Identification of Immune-Related Genes and Construction of Metastasis Prediction Model for Non-Small Cell Lung Cancer

Abstract

Abstract Objective: The aim of the study was to identify the immune-related genes (IRGs) associated with non-small cell lung cancer (NSCLC) metastasis, and establish a risk score prediction model. Methods: Gene expression information and clinical data of NSCLC patients were downloaded from The Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) were screened based on tumor group and normal group. DEGs were intersected with IRGs from the ImmPort database to obtain differentially expressed IRGs (DEIRGs). Weighted gene coexpression network analysis (WGCNA) was applied to determine the hub DEIRGs (HubDEIRGs) related to immune scores. Risk score was calculated based on the significant HubDEIRGs through logistic regression analyses. Logistic regression analysis was performed to analyze the influencing factors for metastasis with age, gender, T stage and risk score as covariates. A metastasis risk nomogram was constructed. The correlation between risk score and immune cells infiltration was examined. Results: A total of 477 HubDEIRGs were identified. PDK1, PROC, IL11, SH2D1B, S100A5, AGT, WFDC2, CRHR2 and EREG were metastasis-associated immune genes. Age, T stage and risk score served as independent risk factors for metastasis. The areas under the curve (AUC) of the nomogram were 0.714 and 0.643 in the training and validation sets. The calibration curve was close to the ideal diagonal line. The high-risk group had a greater degree of immune infiltration than the low-risk group. Conclusion: The risk scoring model for predicting the risk of metastasis in NSCLC patients based on 9 immune genes in this study had importantly potential clinical application value.