Background: Gliomas, originating from the most common non-neuronal cells in the brain (glial cells), are the most common brain tumors and are associated with high mortality and poor prognosis. Glioma cells exhibit a tendency to disrupt normal cell-cycle regulation, leading to abnormal proliferation and malignant growth. This study investigated the predictive potential of in gliomas and explored its relationship with the cell cycle.
Methods: Retrospective analysis of RNA-seq and single-cell sequencing data was conducted using the Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA) databases. The differential expression of in gliomas with various pathological features and in different non-neuronal cell groups was analyzed. Functional data were examined using gene set variation analysis (GSVA). Furthermore, CellMiner was used to evaluate the relationship between expression and predicted treatment response across these databases.
Results: expression was enriched in high-grade gliomas and 1p/19q non-codeletion gliomas. enrichment was observed in classical and mesenchymal subtypes within the TCGA glioma subtype group. In single-cell subgroup analysis, expression was higher in glioma tissues compared to other non-neuronal cells. Additionally, the TCGA classical subtype of glioma cells exhibited more expression than the other subgroups. emerged as an independent prognostic factor for overall survival in glioma. GSVA unveiled potential mechanisms by which may impact cell-cycle regulation in glioma. Finally, a significant correlation was observed between expression and the sensitivity of multiple anti-cancer drugs.
Conclusion: These findings confirmed as a novel biomarker and provided insights into the differential gene expression in non-neuronal cells and the impact of the cell cycle on gliomas. Consequently, may be used to predict glioma prognosis and has potential therapeutic value.
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| http://dx.doi.org/10.3389/fncel.2024.1440409 | DOI Listing |
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