International Journal of General Medicine (Nov 2021)
Prediction of Survival Outcome in Lower-Grade Glioma Using a Prognostic Signature with 33 Immune-Related Gene Pairs
Abstract
Shaohua Chen,1,2,* Yongchu Sun,3,4,* Xiaodong Zhu,3– 5 Zengnan Mo1,2 1Department of Urology, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China; 2Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Nanning, People’s Republic of China; 3Department of Radiation Oncology, Guangxi Medical University Cancer Hospital, Nanning, People’s Republic of China; 4Guangxi Key Laboratory of Early Prevention and Treatment for Regional High Frequency Tumor, Guangxi Medical University, Nanning, People’s Republic of China; 5Department of Oncology, Affiliated Wuming, Hospital of Guangxi Medical University, Nanning, People’s Republic of China*These authors contributed equally to this workCorrespondence: Xiaodong ZhuDepartment of Radiation Oncology, Guangxi Medical University Cancer Hospital, No. 71 He Di Road, Nanning, 530021, People’s Republic of ChinaEmail [email protected] MoInstitute of Urology and Nephrology, The First Affiliated Hospital of Guangxi Medical University, No. 6 Shuangyong Road, Nanning, 530021, People’s Republic of ChinaEmail [email protected]: Lower-grade glioma (LGG) is one of the prevalent malignancies threatening human health, with considerable intrinsic heterogeneities in their biological behavior. Previous studies have revealed that the immune component is a key factor influencing the formation and development of malignancies. In this study, we aim to use a novel approach to develop a prognostic signature of immune-related gene pairs (IRGPs) to determine the survival outcome of patients with LGG.Methods: Transcriptomic profiles and clinical data for LGG were obtained from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases, and used as training and validation data sets, respectively. IRGPs influencing the overall survival (OS) of patients with LGG in the training data set were screened by performing univariate Cox regression analysis. Next, a prognostic IRGPs signature was constructed using least absolute shrinkage and selection operator (LASSO) regression. Finally, we cross-validated the two databases to verify the stability of the prognostic signature.Results: A total of 33 IRGPs influencing prognosis of LGG in the training data set were included in the prognostic signature. Patients with high risk scores (RSs) in the training and validation data sets had a poorer OS than those with low RSs. Moreover, significant differences were observed in tumor-infiltrating immune cells (TICs) between high- and low-RS groups. Functional enrichment analyses results revealed that genes in the high-RS group were enriched in the immune-related activities and developmental processes.Conclusion: The prognostic signature containing 33 IRGPs has a significant correlation with OS and relative levels of immune cells associated with LGG. The results of the present study provide new insights into the prediction of survival outcome and therapeutic response of LGG.Keywords: lower-grade glioma, immune-related gene pairs, tumor-infiltrating immune cells, prognostic signature
