Characterization of hypoxia-related molecular clusters and prognostic riskScore for glioma.

Gliomas represent a significant burden in the realm of central nervous system (CNS) malignancies, accounting for approximately 30% of all primary brain tumors and a striking 80% of malignant cases. The incidence of gliomas is observed to escalate with advancing age, exhibiting a marginally higher prevalence in the male population. Among these tumors, high-grade gliomas, particularly glioblastoma multiforme (GBM), are characterized by their aggressive nature and dire prognosis. Conventional therapeutic approaches, including surgical intervention, radiotherapy, and chemotherapy, have demonstrated limited efficacy, underscoring an urgent need for the development of targeted therapies and enhanced mechanistic understanding to improve patient outcomes. In this study, we aimed to deepen our understanding of the role of hypoxia, a critical factor in cancer progression, within gliomas. Using comprehensive datasets from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA), we classified gliomas into two distinct subgroups based on hypoxia-related gene expression profiles: C1 and C2. This classification facilitated a comparative analysis of prognostic outcomes and tumor microenvironment characteristics between the two subgroups. Our findings revealed that patients within the C1 subgroup exhibited significantly poorer prognoses, with an upregulation of genes intricately linked to various tumor progression pathways. Moreover, the immune microenvironment within the C1 subgroup appeared more favorable for tumor survival and growth, coupled with a notable increase in genomic instability compared to the C2 subgroup. A prognostic scoring system developed from key hypoxia-related factors demonstrated substantial predictive value across multiple cohorts. Ultimately, we identified four core hub genes-SOCS3, CLCF1, PLAUR, and LIF-whose expression was validated under hypoxic conditions via Western blot analysis in glioma cell lines. This study employs bioinformatics to elucidate glioma subtypes, highlighting significant prognostic and functional disparities. The experimental validation of candidate molecules paves the way for future research aimed at unraveling their roles and underlying mechanisms in glioma pathophysiology, potentially guiding novel therapeutic strategies.