Multi-Omics Data Integration Analysis of an Immune-Related Gene Signature in LGG Patients With Epilepsy - Cheng, Quan; Duan, Weiwei; He, Shiqing; Li, Chen; Cao, Hui; Liu, Kun; Ye, Weijie; Yuan, Bo; Xia, Zhiwei (Frontiers in Cell and Developmental Biology, 2021)
Background: The tumor immune microenvironment significantly affects tumor occurrence, progression, and prognosis, but its impact on the prognosis of low-grade glioma (LGG) patients with epilepsy has not been reported. Hence, the purpose of this study is to explore its effect on LGG patients with epilepsy. Methods: The data of LGG patients derived from the TCGA database. The level of immune cell infiltration and the proportion of 22 immune cells were evaluated by ESTIMATE and CIBERSORT algorithms, respectively. The COX and LASSO regression analysis were adopted to determine the DEGs, and further established the clustering and risk score models. Using CNV and somatic mutation data to explore the correlation between genomic alterations and risk score. GSVA was adopted to identify the immunological pathways, immune infiltration and inflammatory profiles related to the signature genes. The TIDE algorithm and GDSC database were used to predict the patient's response to immunotherapy and chemotherapy, respectively. Results: The prognosis of LGG patients with epilepsy was associated with the immune score. Three prognostic DEGs (ABCC3, PDPN, and INA) were found. The expression of signature genes was regulated by DNA methylation. The clustering and risk score models could stratify the glioma patients into distinct prognosis groups. The risk score was an independent predictor in prognosis, and the high risk-score suggested a poor prognosis, more malignant clinicopathological and genomic aberration features. The nomogram had better predictive ability. In high-risk patients, the infiltration level of macrophage was higher; the inflammatory activities related to T cell and macrophage were more active. While the higher percentage of NK CD56bright cell and more active inflammatory activity associated with B cell were found in the low-risk patients. The signature genes participated in the regulation of immune-related pathways, such as IL6-JAK-STAT3 signaling, IFN-α response, IFN-γ response, and TNFA-signaling-via-NFKB pathways. The high-risk patients were more likely to benefit from anti-PD1 and temozolomide (TMZ) treatment. Conclusions: An immune-related gene signature was established based on ABCC3, PDPN, and INA, which can be used to predict the prognosis, immune infiltration status, immunotherapy and chemotherapy response of LGG patients with epilepsy.