A combination of RNA-Seq and CHIP-Seq analysis for determining super enhancer- associated genes to evaluate prognosis and immunity of glioma

Abstract Gliomas are one of the most prevalent primary malignant tumors seen in adults. Despite the use of surgical resection, radiation and chemotherapies, a clinical relapse or advancement is practically inevitable, and finding new therapeutic targets is urgent to increase the overall survival period. Super enhancers are known to be ultra-long cis-acting elements that have enhanced transcriptional activities, which mediate higher grades of transcription to promote the growth of glioma and are expected to be a new therapeutic direction for glioma. Based on this, we adopted transcriptional RNA-seq and H3K27ac Chip-seq of 44 patient-derived glioblastoma stem cells and 9 neural stem cells, by integrating two methods of analysis, we screened 33 high-risk genes and imported these genes into TCGA and CGGA glioma databases. The first step is prognosis evaluation. In order to build the risk model, we applied the Lasso and COX analyses and used the KM and ROC curves to detect the accuracy of the risk model, Consensus cluster exhibited suitable groupings and unique molecular differences. The second step is functional analysis, utilizing the GSEA program, GO and KEGG to explore mechanisms between these genes and glioma. The third step is immune evaluation, including immune microenvironment and immune checkpoints, to look for potential therapeutic strategies. Finally, the function of the gene was verified in vitro experiments with molecules and cells. Collectively, we identified SE-associated high-risk genes through multi-omics analysis, bringing more possibilities for risk assessment and treatment strategies for glioma.