SERPINH1 and CTSZ are Key Markers of Glioma Angiogenesis

• Published in: Journal of molecular neuroscience Vol. 75; no. 2; p. 51
• Main Authors: Wei, Haotian, Li, Xinlong, Feng, Peng, He, Zhaohui
• Format: Journal Article
• Language: English
• Published: New York Springer US 21.04.2025; Springer Nature B.V
• Subjects: Algorithms; Angiogenesis; Annotations; Biomarkers; Biomarkers, Tumor - genetics; Biomarkers, Tumor - metabolism; Biomedical and Life Sciences; Biomedicine; Brain Neoplasms - blood supply; Brain Neoplasms - genetics; Brain Neoplasms - metabolism; Brain tumors; Cell Biology; Central nervous system; Decision making; Decision trees; Diagnosis; Gene set enrichment analysis; Genes; Glioma; Glioma - blood supply; Glioma - genetics; Glioma - metabolism; Glioma - pathology; Heterogeneity; Humans; Learning algorithms; Machine learning; Neovascularization, Pathologic - genetics; Neovascularization, Pathologic - metabolism; Network analysis; Neurochemistry; Neurology; Neurosciences; Neurosurgery; Prognosis; Protein Interaction Maps; Proteins; Proteomics; Serpins - genetics; Serpins - metabolism; Support vector machines; Angiogenesis; Glioma; Machine Learning; NMF; WCGNA
• ISSN: 1559-1166; 0895-8696; 1559-1166
• DOI: 10.1007/s12031-025-02349-0

Glioma, as one of the most complex and prognostically variable malignant tumors of the central nervous system, poses a significant challenge to clinical decision-making due to its molecular heterogeneity. This study aims to deepen our understanding of glioma molecular subtypes and explore key gene markers with prognostic and diagnostic value. We utilized an angiogenesis-related gene set and employed the Non-negative Matrix Factorization (NMF) algorithm to successfully identify two distinct prognostic subtypes, with subtype one exhibiting more unfavorable prognostic characteristics. To further elucidate the biological functional differences between these two subtypes, we conducted Gene Ontology (GO) functional annotation, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and Gene Set Enrichment Analysis (GSEA). Building on this, we integrated differentially expressed genes between subtypes with core genes revealed by Weighted Gene Co-expression Network Analysis (WGCNA) through intersection analysis to pinpoint a series of key candidate genes. Subsequently, we constructed a Protein–Protein Interaction (PPI) network to identify genes occupying central nodes within the network. To screen markers with high specificity and sensitivity for prognosis and diagnosis, we adopted a dual-track strategy: on the one hand, we utilized machine learning algorithms such as Lasso regression, Support Vector Machine (SVM), and Random Forest (RF) to select core genes, identifying markers that can accurately predict the subtype with a poor prognosis; on the other hand, we employed a comprehensive evaluation system incorporating 101 machine learning ensemble algorithms to further validate and screen prognosis-related genes. Through cross-validation using these two strategies, we ultimately determined SERPINH1 and CTSZ as dual prognostic and diagnostic markers for glioma. This study not only provides a new perspective and tool for the molecular subclassification of glioma but also, through a rigorous multi-algorithm, multi-dimensional screening process, uncovers SERPINH1 and CTSZ as markers with potential clinical translational value. These findings are expected to offer more precise biomarker support for the early diagnosis and prognostic assessment of glioma, potentially paving new avenues for the development of personalized treatment strategies and improving patient outcomes. This has far-reaching implications for the clinical management of glioma in the field of neurosurgery.