Analysis reveals PHF23-related prognosis signature predicts tumor malignancy in glioma, suggesting Entospletinib as an immunomodulatory option.
Gliomas exhibit considerable molecular heterogeneity and immunological complexity, emphasizing the need for effective biomarkers and therapeutic targets. In this study, the Chinese Glioma Genome Atlas (CGGA-325/693) and The Cancer Genome Atlas (TCGA-LGG/GBM) cohorts were used to explore the pathological role of PHD finger protein 23 (PHF23) in gliomas. Machine learning algorithms were performed to construct a PHF23-related prognosis signature (PHF23-RPS). Our analysis revealed significant upregulation of PHF23 in high-grade gliomas, while the PHF23-RPS exhibited strong predictive performance (AUC = 0.853). Two molecular subtypes were identified; Cluster 2 was characterized as “inflamed yet immunosuppressive”. This subtype displayed a tumor mutational burden (TMB) paradox, where elevated TMB failed to translate into survival benefits due to extensive M2 macrophage infiltration and checkpoint-mediated immune exhaustion. Pharmacogenomic screening and molecular dynamics simulations identified Entospletinib as a potential candidate targeting this immunosuppressive barrier, showing a stable binding affinity (−7.7 kcal/mol). Functional assays, including in vitro experiments and in vivo experiments via a male BALB/c nude mouse orthotopic glioma model (n = 6/group), confirmed that PHF23 silencing inhibited glioma malignancy. Our results identify PHF23 as a critical oncogenic driver in glioma and support the PHF23-RPS for risk stratification. Entospletinib may offer a potential immunomodulatory option for high-risk gliomas.
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