Molecular Subtypes Based on the Stemness Index Predict Prognosis in Glioma Patients

Area of Science:

• Neuro-oncology
• Cancer Genomics
• Molecular Biology

Background:

• Glioma is a common and deadly central nervous system malignancy.
• Glioma cancer stem cells (CSCs) drive tumor recurrence and treatment resistance.
• Understanding stemness-related genes and subtypes is crucial for glioma research.

Purpose of the Study:

• To identify novel molecular subtypes of glioma based on stemness-related genes.
• To develop a prognostic model for glioma patients.
• To investigate the role of specific genes, like ETV2, in glioma progression.

Main Methods:

• Collected and analyzed RNA-sequencing (RNA-seq) data from TCGA and CGGA databases.
• Utilized differentially expressed genes (DEGs) and weighted gene correlation network analysis (WGCNA).
• Developed a risk score model and nomogram for prognosis prediction; validated ETV2 function experimentally.

Main Results:

• Identified 86 mRNA expression-based stemness index (mRNAsi)-related genes.
• Classified glioma into two distinct subtypes with differential prognoses, clinical characteristics, and immune infiltration.
• ETV2 promotes glioma invasion, migration, and epithelial-mesenchymal transition (EMT).

Conclusions:

• Two distinct molecular subtypes of glioma were identified, impacting prognosis.
• A stemness subtype-related risk score model and nomogram can predict glioma patient prognosis.
• ETV2 is a potential therapeutic target for improving glioma diagnosis and treatment.