Background: Lower-grade glioma (LGG) is a highly heterogeneous disease that presents challenges in accurately predicting patient prognosis. Mitochondria play a central role in the energy metabolism of eukaryotic cells and can influence cell death mechanisms, which are critical in tumorigenesis and progression. However, the prognostic significance of the interplay between mitochondrial function and cell death in LGG requires further investigation. Methods: We employed a robust computational framework to investigate the relationship between mitochondrial function and 18 cell death patterns in a cohort of 1467 LGG patients from six multicenter cohorts worldwide. A total of 10 commonly used machine learning algorithms were collected and subsequently combined into 101 unique combinations. Ultimately, we devised the mitochondria-associated programmed cell death index (mtPCDI) using machine learning models that exhibited optimal performance. Results: The mtPCDI, generated by combining 18 highly influential genes, demonstrated strong predictive performance for prognosis in LGG patients. Biologically, mtPCDI exhibited a significant correlation with immune and metabolic signatures. The high mtPCDI group exhibited enriched metabolic pathways and a heightened immune activity profile. Of particular importance, our mtPCDI maintains its status as the most potent prognostic indicator even following adjustment for potential confounding factors, surpassing established clinical models in predictive strength. Conclusion: Our utilization of a robust machine learning framework highlights the significant potential of mtPCDI in providing personalized risk assessment and tailored recommendations for metabolic and immunotherapy interventions for individuals diagnosed with LGG. Of particular significance, the signature features highly influential genes that present further prospects for future investigations into the role of PCD within mitochondrial function.

Gliomas are complex and heterogeneous central nervous system tumors with poor prognosis. Despite the increasing development of aggressive combination therapies, the prognosis of glioma is generally unsatisfactory. Exosomal microRNA (miRNA) has been successfully used in other diseases as a reliable biomarker and even therapeutic target. Recent studies show that exosomal miRNA plays an important role in glioma occurrence, development, invasion, metastasis, and treatment resistance. However, the association of exosomal miRNA between glioma has not been systemically characterized. This will provide a theoretical basis for us to further explore the relationship between exosomal miRNAs and glioma and also has a positive clinical significance in the innovative diagnosis and treatment of glioma.

Neurodevelopmental disorders are mostly studied using mice as models. However, the mouse brain lacks similar cell types and structures as those of the human brain. In recent years, emergence of three‐dimensional brain organoids derived from human embryonic stem cells or induced pluripotent stem cells allows for controlled monitoring and evaluation of early neurodevelopmental processes and has opened a window for studying various aspects of human brain development. However, such organoids lack original anatomical structure of the brain during maturation, and neurodevelopmental maturation processes that rely on unique cellular interactions and neural network connections are limited. Consequently, organoids are difficult to be used extensively and effectively while modelling later stages of human brain development and disease progression. To address this problem, several methods and technologies have emerged that aim to enhance the sophisticated regulation of brain organoids developmental processes through bioengineering approaches, which may alleviate some of the current limitations. This review discusses recent advances and application areas of human brain organoid culture methods, aiming to generalize optimization strategies for organoid systems, improve the ability to mimic human brain development, and enhance the application value of organoids.

Metastasis is one of the important biological features of malignant tumors and one of the main factors responsible for poor prognosis. Although the widespread application of newer clinical technologies and their continuous development have significantly improved survival in patients with brain metastases, there is no uniform standard of care. More effective therapeutic measures are therefore needed to improve prognosis. Understanding the mechanisms of tumor cell colonization, growth, and invasion in the central nervous system is of particular importance for the prevention and treatment of brain metastases. This process can be plausibly explained by the “seed and soil” hypothesis, which essentially states that tumor cells can interact with various components of the central nervous system microenvironment to produce adaptive changes; it is this interaction that determines the development of brain metastases. As a novel form of intercellular communication, exosomes play a key role in the brain metastasis microenvironment and carry various bioactive molecules that regulate receptor cell activity. In this paper, we review the roles and prospects of brain metastatic tumor cells, the brain metastatic tumor microenvironment, and exosomes in the development and clinical management of brain metastases.

Background: N6-methyladenosine (m6A) RNA methylation is an important epigenetic modification affecting alternative splicing (AS) patterns of genes to regulate gene expression. AS drives protein diversity and its imbalance may be an important factor in tumorigenesis. However, the clinical significance of m6A RNA methylation regulator-related AS in the tumor microenvironment has not been investigated in low-grade glioma (LGG).Methods: We used 12 m6A methylation modulatory genes (WTAP, FTO, HNRNPC, YTHDF2, YTHDF1, YTHDC2, ALKBH5, YTHDC1, ZC3H13, RBM15, METTL14, and METTL3) from The Cancer Genome Atlas (TCGA) database as well as the TCGA-LGG (n = 502) dataset of AS events and transcriptome data. These data were downloaded and subjected to machine learning, bioinformatics, and statistical analyses, including gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Univariate Cox, the Least Absolute Shrinkage and Selection Operator (LASSO), and multivariable Cox regression were used to develop prognostic characteristics. Prognostic values were validated using Kaplan-Maier survival analysis, proportional risk models, ROC curves, and nomograms. The ESTIMATE package, TIMER database, CIBERSORT method, and ssGSEA algorithm in the R package were utilized to explore the role of the immune microenvironment in LGG. Lastly, an AS-splicing factor (SF) regulatory network was examined in the case of considering the role of SFs in regulating AS events.Results: An aggregate of 3,272 m6A regulator-related AS events in patients with LGG were screened using six machine learning algorithms. We developed eight AS prognostic characteristics based on splice subtypes, which showed an excellent prognostic prediction performance. Furthermore, quantitative prognostic nomograms were developed and showed strong validity in prognostic prediction. In addition, prognostic signatures were substantially associated with tumor immune microenvironment diversity, ICB-related genes, and infiltration status of immune cell subtypes. Specifically, UGP2 has better promise as a prognostic factor for LGG. Finally, splicing regulatory networks revealed the potential functions of SFs.Conclusion: The present research offers a novel perspective on the role of AS in m6A methylation. We reveal that m6A methylation regulator-related AS events can mediate tumor progression through the immune-microenvironment, which could serve as a viable biological marker for clinical stratification of patients with LGG so as to optimize treatment regimens.