Transcription Factor Signatures May Predict the Prognosis and Status of the Immune Microenvironment of Primary Lower-Grade Gliomas

Aim: Transcription factor (TF) in glioma, including proliferation, invasion/migration, and tumor microenvironment, has been receiving increasing attention. However, there are still no systematical analyses based on global TF. Herein, using global TF target gene sets, we comprehensively investigated their relationship with prognosis and potential biological effect in lower-grade glioma (LGG). We aimed to develop a less-biased prognostic model and provide new insight for personalized management of this disease. Methods: TF target gene sets were collected from MSigDB and GRID database followed by ssGSEA calculating normalized enrichment score. Comprehensive survival analysis was combined with Kaplan-Meier and Cox algorithms. Consensus cluster and lasso regression were performed to develop prognostic signatures with validation of ROC and independent external cohort. Approaches of xCell/CIBERSORT/TIMER were involved in analyzing the immune microenvironment. We also correlated identified prognostic signatures with tumor mutational burden (TMB) and m6A genes. Results: Fourteen TFs were significantly screened based on survival. Patients were classified into 2 prognosis-related clusters based on 14-TFs features. The function of differentially expressed TF target genes between Cluster1/2 was enriched mostly on glioma invasion/ migration. The prognostic model was trained by 6 out of 14-TFs followed by generating risk-score as an independent prognostic indicator. We found differences between the high/low-risk group in TMB and the immune microenvironment, where the high-risk group repre-sented hot-tumor. Besides, 6-TFs were correlated with m6A regulation genes. Conclusion: Our findings suggested that the 6-TFs model could be used to predict prog-nosis and predict the status of the immune microenvironment in LGG.

Automated summary

By investigating the relationship between transcription factors in glioma and prognosis as well as potential biological effects, a less-biased prognostic model was developed to provide new insights for personalized management of glioma. Through comprehensive analysis, it was found that a 6-transcription factors model could predict prognosis and immune microenvironment status in lower-grade glioma (LGG).