Comparative quantitative proteomics of tumoral and peritumoral tissues for distinguishing human glioblastoma from low-grade astrocytoma.

Glioblastoma multiforme (GBM) and low-grade astrocytoma (LGA) are diffuse gliomas with distinct biological features and prognoses. However, the molecular mechanisms driving their differences are not fully understood. In this study, we performed a multi-dataset analysis integrating in-house quantitative proteomics data with high-quality external datasets to identify GBM-specific proteomic alterations between tumoral and peritumoral tissues relative to LGA. Our analysis revealed more pronounced proteomic differences between intra- and peritumoral tissues in GBM than in LGA. Proteins specifically dysregulated in GBM were predominantly linked to upregulated RNA splicing and spliceosome signaling. Through multi-dataset integration, we identified 73 GBM-specific upregulated proteins enriched in processes such as transcriptional regulation, hypoxia and necrosis, cytoskeleton organization, extracellular matrix remodeling, and immune response. Conversely, the 129 GBM-specific downregulated proteins were mainly involved in tumor suppression, G-protein signaling, calcium signaling, and neuronal gap junctions. Using these signature proteins, we developed a risk model based on CDK2, HDAC1, IGFBP2, SLC6A1, and TNR, which significantly predicted overall survival in glioma patients. This study delineates the proteomic landscape of GBM in comparison to LGA and offers a valuable resource for future mechanistic and clinical investigations. SIGNIFICANCE: Diffuse gliomas are a heterogeneous brain tumor that include both low-grade and high-grade variants, each characterized by distinct morphological and biological features. Glioblastoma multiforme (GBM) is the most common primary high-grade and malignant brain tumor, with a median survival of less than 15 months despite aggressive treatment, including surgery, radiotherapy, and chemotherapy. In contrast, low-grade astrocytoma (LGA) exhibits indolent growth and a less aggressive clinical course, resulting in significantly longer patient survival compared to GBM. The molecular mechanisms underlying the biological differences between LGA and GBM are incompletely understood, and there is an urgent need for new molecular biomarkers to enhance diagnosis and therapeutic options. In this study, we conducted a comprehensive multi-dataset analysis by integrating our in-house quantitative proteomics data with several high-quality external datasets. We analyzed tumor and peritumoral tissue samples from human GBM and LGA, and further identified the GBM-specifically regulated proteomic signatures and signaling pathways, which is crucial for understanding the molecular mechanisms driving the aggressive behavior of GBM and could serve as a foundation for developing novel diagnostic biomarkers and therapeutic targets. Future research should focus on validating these GBM-specific proteins in larger cohorts and exploring their functional roles in GBM progression. Additionally, integrating multi-omics data (e.g., genomics, transcriptomics, and metabolomics) with proteomics could provide a more comprehensive understanding of the molecular mechanisms underlying GBM.