Is the voltage-gated sodium channel β3 subunit (SCN3B) a biomarker for glioma?

Christopher L-H Huang, Hengrui Liu, Jieling Weng, Antony P Jackson

Journal: Functional & integrative genomics 2024;24(5):162

PMID: 39289188

Abstract

Recent studies suggest a need for reliable biomarkers enhancing prognosis prediction and treatment strategies in cancer. Here, we performed a data analysis bearing on the expression of SCN3B, voltage-gated sodium channel (VGSC) β3 subunit, as a possible candidate for the development of a glioma biomarker for the first time. This extends our previous review article that mentioned the potential of SCN3B as a prognostic biomarker for glioma survival, further examining its association with existing indicators and immune responses. We utilized clinical and genomic data from multiple glioma cohorts. These include the Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA). We employed analytical techniques including time-dependent receiver operating characteristic (ROC) analysis, decision curves analysis (DCA), and correlation studies with immune checkpoint markers. Our findings indicate a differential SCN3B expression between glioma grades, and that this significantly correlates with patient survival, particularly in oligodendroglioma subtypes. The DCA curves suggested that the inclusion of SCN3B in the prognostic model would improve decision-making in these subtypes. Moreover, SCN3B expression positively correlated with the presence of key immune cells and negatively correlated with several immune checkpoint inhibitors. This suggests potential roles in modulating immune responses in glioma. Thus, SCN3B emerges as a promising potential prognostic biomarker for glioma, especially for oligodendroglioma. Its dual correlations with prognosis and immune regulation present a compelling case for further experimental and clinical investigations to establish its utility in enhancing glioma management strategies. These findings underscore the importance of integrating novel biomarkers with traditional prognostic models to refine treatment paradigms and improve patient outcomes.

© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

Address: Department of Biochemistry, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QW, UK. [email protected].; Department of Pathology, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.; Department of Biochemistry, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QW, UK.; Physiological Laboratory, University of Cambridge, Downing Street, Cambridge, CB2 3EG, UK.; Department of Biochemistry, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QW, UK. [email protected].

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