Background: Several studies have indicated that the anoikis effector Bcl-2 inhibitor of transcription 1 (Bit1) can promote or inhibit tumor progression depending on the nature of the malignancy. However, its regulatory effects on gliomas are unknown. Methods: This study aimed at assessing Bit1 expression in glioma tissues and cells, its subsequent effects on glioma cell apoptosis, proliferation, invasion, and migration, and the underlying molecular mechanisms. Results: The findings showed that lower Bit1 expressions in glioma tissues as well as a negative correlation between Bit1 expression and glioma grade. Additional findings also revealed that Bit1 silencing significantly inhibited anoikis and enhanced glioma cell proliferation, invasion, and migration. Further analysis showed that the decrease in Bit1 expressions led to malignancy proliferation and anoikis resistance through activation of the IL-6/ STAT3 signaling pathway. Conclusion: Our data suggested that Bit1 may play an anti-oncogenic role in glioma cells and that a decrease in its expressions might induce glioma cell proliferation, migration, and invasion through the IL-6/STAT3 signaling pathway.

Background: Pyroptosis, also known as inflammatory necrosis, is a programmed cell death that manifests itself as a continuous swelling of cells until the cell membrane breaks, leading to the liberation of cellular contents, which triggers an intense inflammatory response. Pyroptosis might be a panacea for a variety of cancers, which include immunotherapy and chemotherapy-insensitive tumors such as glioma. Several findings have observed that long non-coding RNAs (lncRNAs) modulate the bio-behavior of tumor cells by binding to RNA, DNA and protein. Nevertheless, there are few studies reporting the effect of lncRNAs in pyroptosis processes in glioma. Methods: The principal goal of this study was to identify pyroptosis-related lncRNAs (PRLs) utilizing bioinformatic algorithm and to apply PCR techniques for validation in human glioma tissues. The second goal was to establish a prognostic model for predicting the overall survival patients with glioma. Predict algorithm was used to construct prognosis model with good diagnostic precision for potential clinical translation. Results: Noticeably, molecular subtypes categorized by the PRLs were not distinct from any previously published subtypes of glioma. The immune and mutation landscapes were obviously different from previous subtypes of glioma. Analysis of the sensitivity (IC50) of patients to 30 chemotherapeutic agents identified 22 agents as potential therapeutic agents for patients with low riskscores. Conclusions: We established an exact prognostic model according to the expression profile of PRLs, which may facilitate the assessment of patient prognosis and treatment patterns and could be further applied to clinical.