Recent studies have demonstrated that long non-coding RNAs (lncRNAs) are implicated in the regulation of tumor cell ferroptosis. However, the prognostic value of ferroptosis-related lncRNAs has never been comprehensively explored in glioma. In this study, the transcriptomic data and clinical information of glioma patients were downloaded from TCGA, CGGA and Rembrandt databases. We identified 24 prognostic ferroptosis-related lncRNAs, 15 of which (SNAI3-AS1, GDNF-AS1, WDFY3-AS2, CPB2-AS1, WAC-AS1, SLC25A21-AS1, ARHGEF26-AS1, LINC00641, LINC00844, MIR155HG, MIR22HG, PVT1, SNHG18, PAXIP1-AS2, and SBF2-AS1) were used to construct a ferroptosis-related lncRNAs signature (FRLS) according to the least absolute shrinkage and selection operator (LASSO) regression. The validity of this FRLS was verified in training (TCGA) and validation (CGGA and Rembrandt) cohorts, respectively. The Kaplan-Meier curves revealed a significant distinction of overall survival (OS) between the high- and low-risk groups. The receiver operating characteristic (ROC) curves exhibited robust prognostic capacity of this FRLS. A nomogram with improved accuracy for predicting OS was established based on independent prognostic factors (FRLS, age, and WHO grade). Besides, patients in the high-risk group had higher immune, stroma, and ESTIMATE scores, lower tumor purity, higher infiltration of immunosuppressive cells, and higher expression of immune checkpoints. Patients in the low-risk group benefited significantly from radiotherapy, while no survival benefit of radiotherapy was observed for those in the high-risk group. In conclusion, we identified the prognostic ferroptosis-related lncRNAs in glioma, and constructed a prognostic signature which was associated with the immune landscape of glioma microenvironment and radiotherapy response.

Improving the chemotherapy resistance of temozolomide (TMZ) is of great significance in the treatment of glioblastoma multiforme (GBM). Long non‐coding RNA just proximal to the X‐inactive specific transcript (JPX) has been proven to be involved in cancer progression. However, the intrinsic significance and molecular mechanism by which JPX orchestrates GBM progression and TMZ chemotherapy resistance remain poorly understood. Here, JPX was found to be significantly elevated in GBM tissues and cell lines, and patients with high expressions of JPX showed significantly worse prognoses. Functional experiments revealed its carcinogenic roles in GBM cell proliferation, TMZ chemoresistance, anti‐apoptosis, DNA damage repair, and aerobic glycolysis. Mechanistically, JPX formed a complex with phosphoinositide dependent kinase‐1 (PDK1) messenger RNA (mRNA) and promoted its stability and expression. Furthermore, an RNA immunoprecipitation (RIP) experiment showed that JPX interacted with N6‐methyladenosine (m6A) demethylase FTO alpha‐ketoglutarate dependent dioxygenase (FTO) and enhanced FTO‐mediated PDK1 mRNA demethylation. JPX exerted its GBM‐promotion effects through the FTO/PDK1 axis. Taken together, these findings reveal the key role of JPX in promoting GBM aerobic glycolysis and TMZ chemoresistance in an m6A‐dependent manner. Thus, it comprises a promising novel therapeutic target for GBM chemotherapy.

Background: Ferroptosis has been linked to tumor progression and resistance to antineoplastic therapy. Long noncoding RNA (lncRNA) exerts a regulatory role in various biological processes of tumor cells, while the function and molecular mechanism of lncRNA in ferroptosis are yet to be clarified in glioma. Methods: Both gain-of-function and loss-of-function experiments were employed to investigate the effects of SNAI3-AS1 on the tumorigenesis and ferroptosis susceptibility of glioma in vitro and in vivo. Bioinformatics analysis, Bisulfite sequencing PCR, RNA pull-down, RIP, MeRIP and dual-luciferase reporter assay were performed to explore the low expression mechanism of SNAI3-AS1 and the downstream mechanism of SNAI3-AS1 in ferroptosis susceptibility of glioma. Results: We found that ferroptosis inducer erastin downregulates SNAI3-AS1 expression in glioma by increasing the DNA methylation level of SNAI3-AS1 promoter. SNAI3-AS1 functions as a tumor suppressor in glioma. Importantly, SNAI3-AS1 enhances the anti-tumor activity of erastin by promoting ferroptosis both in vitro and in vivo. Mechanistically, SNAI3-AS1 competitively binds to SND1 and perturbs the m⁶A-dependent recognition of Nrf2 mRNA 3’UTR by SND1, thereby reducing the mRNA stability of Nrf2. Rescue experiments confirmed that SND1 overexpression and silence can rescue the gain- and loss-of-function ferroptotic phenotypes of SNAI3-AS1, respectively. Conclusions: Our findings elucidate the effect and detailed mechanism of SNAI3-AS1/SND1/Nrf2 signalling axis in ferroptosis, and provide a theoretical support for inducing ferroptosis to improve glioma treatment.

Rationale: Glioblastoma (GBM) is the most aggressive type of primary brain cancer and contains self-renewing GBM stem cells (GSCs) that contribute to tumor growth and therapeutic resistance. However, molecular determinants governing therapeutic resistance of GSCs are poorly understood.

Purpose: As an inflammatory form of programmed cell death, pyroptosis has been well established to be associated with tumorigenesis and tumor immune microenvironment. In this paper, we aimed at the construction of a pyroptosis-related gene prognostic index (PRGPI) for predicting prognosis and guiding individualized immunotherapy in glioma patients. Patients and Methods: Pyroptosis-related genes (PRGs) were identified based on a detailed review of published literatures. The transcriptome data and clinical information of glioma patients were obtained from CGGA and TCGA databases. PRGPI was constructed by using the multivariate Cox regression method. The immune cell infiltration level was analyzed via CIBERSORT algorithm. The tumor immune dysfunction and exclusion (TIDE) algorithm was applied to evaluate the potential response to immune checkpoint inhibitor (ICI) therapy. The expression patterns of PRGs in PRGPI were validated in cell lines and pathological specimens. Results: We identified a total of 31 PRGs. Among them, PRGs (CASP3, DPP9, MAPK8, PELP1 and TOMM20) were selected for the construction of PRGPI. In both training (CGGA693) and validation (CGGA325 and TCGA) cohorts, PRGPI-high patients showed an inferior survival outcome compared with PRGPI-low patients. ROC curves illustrated that the prognostic prediction power of PRGPI was robust. A nomogram was developed based on independent prognostic indicators (PRGPI, age and WHO grade), and also exhibited a strong forecasting ability for overall survival (OS). Additionally, PRGPI-high patients exhibited higher immune, stroma and ESTIMATE scores, lower tumor purity, higher infiltration of M2-type macrophages, lower infiltration of CD8⁺ T cells and activated NK cells, higher tumor mutation burden (TMB), and higher expression of immune checkpoints. TIDE showed that PRGPI-high group had more responders of ICI therapy than PRGPI-low group. Finally, the expression patterns of five selected PRGs in PRGPI were significantly different between normal and glioma. Conclusion: The constructed PRGPI can be used for predicting prognosis and guiding individualized immunotherapy in glioma patients.

Background: Emerging evidences have indicated that the aberrant liquid–liquid phase separation (LLPS) leads to the dysfunction of biomolecular condensates, thereby contributing to the tumorigenesis and progression. Nevertheless, it remains unclear whether or how the LLPS of specific molecules affects the prognosis and tumor immune microenvironment (TIME) of patients with lower-grade glioma (LGG). Methods: We integrated the transcriptome information of 3585 LLPS-related genes to comprehensively evaluate the LLPS patterns of 423 patients with LGG in The Cancer Genome Atlas (TCGA) cohort. Then, we systematically demonstrated the differences among four LLPS subtypes based on multi-omics analyses. In addition, we constructed the LLPS-related prognostic risk score (LPRS) for individualized integrative assessment. Results: Based on the expression profiles of 85 scaffolds, 355 regulators, and 3145 clients in LGG, we identified four LLPS subtypes, namely LS1, LS2, LS3 and LS4. We confirmed that there were significant differences in prognosis, clinicopathological features, cancer hallmarks, genomic alterations, TIME patterns and immunotherapeutic responses among four LLPS subtypes. In addition, a prognostic signature called LPRS was constructed for individualized integrative assessment. LPRS exhibited a robust predictive capacity for prognosis of LGG patients in multiple cohorts. Moreover, LPRS was found to be correlated with clinicopathological features, cancer hallmarks, genomic alterations and TIME patterns of LGG patients. The predictive power of LPRS in response to immune checkpoint inhibitor (ICI) therapy was also prominent. Conclusions: This study provided a novel classification of LGG patients based on LLPS. The constructed LPRS might facilitate individualized prognosis prediction and better immunotherapy options for LGG patients.