BackgroundThe 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.MethodsThe 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 was adopted to determine the DEGs, and further established the clustering and risk score models. The association between genomic alterations and risk score was investigated using CNV and somatic mutation data. GSVA was adopted to identify the immunological pathways, immune infiltration and inflammatory profiles related to the signature genes. The Tumor Immune Dysfunction and Exclusion (TIDE) algorithm and GDSC database were used to predict the patient’s response to immunotherapy and chemotherapy, respectively.ResultsThe prognosis of LGG patients with epilepsy was associated with the immune score. Three prognostic DEGs (ABCC3, PDPN, and INA) were screened out. The expression of signature genes was regulated by DNA methylation. The clustering and risk score models could stratify glioma patients into distinct prognosis groups. The risk score was an independent predictor in prognosis, with a high risk-score indicating a poor prognosis, more malignant clinicopathological and genomic aberration features. The nomogram had the better predictive ability. Patients at high risk had a higher level of macrophage infiltration and increased inflammatory activities associated with T cells and macrophages. While the higher percentage of NK CD56bright cell and more active inflammatory activity associated with B cell were present 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.ConclusionAn 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.

Background: Long noncoding RNAs (lncRNAs) are promising cancer biomarkers and therapeutic targets . And lipid metabolism reprogramming is a trait of cancer metabolism. However, the relationship between lncRNAs and lipid metabolism and their prognostic value are still unclear in glioma. Hence, we screened for prognostic lncRNAs associated with lipid metabolism and explored their potential biological functions and effects on glioma. Methods: The glioma data were obtained from TCGA and CGGA databases. Correlation analysis was used to identify the lncRNAs associated with lipid metabolism . The Cox and LASSO regression analysis were adopted to find prognostic lncRNAs, and further established ceRNA network, clustering and risk score models. CNV and somatic mutation data were used to explore the correlation between risk score and genomic alterations. GSVA and GSEA was adopted to reveal the potential biological functions of prognostic lncRNAs. The abundance of 22 immune cells was inferred by CIBERSORT algorithm . The TIDE algorithm and GDSC database were used to predict the patient's response to immunotherapy and chemotherapy, respectively. Human glioma cell lines were used for further experimental validation. CCK-8 and colony forming assays were adopted to evaluate the cell viability. The cell proliferation was detected by EdU assay. Transwell assay was used to evaluate the ability of cell invasion and migration. Results: A total of twenty prognostic lncRNAs associated with lipid metabolism were found, and a prognostic lncRNA signature was established. A high risk-score suggested a poor prognosis , more malignant clinicopathological and genomic aberrations features. The risk score was also an independent prognos tic factor. The high-risk patients were more likely to benefit from anti-PD1 treatment. The biological function of signature lncRNAs was mainly to regulate the biosynthesis and transportation of lipid (especially the fatty acid). Among signature lncRNAs, LINC01614 was associated with prognosis, genomic aberrations characteristics, m6A methylation modification, immune infiltration status, and chemotherapy response of patients with glioma. In vitro experiments, silencing the expression of LINC01614 could inhibit the viability, migration , and proliferation of glioma cells. Conclusions: A prognostic lncRNA signature containing twenty lncRNAs associated with lipid metabolism was established in glioma. The signature lncRNAs play an important role in the development of glioma by regulating lipid biosynthesis and transportation. Our study also provides a new perspective for understanding the lipid metabolism and the biological role of lncRNAs in glioma.