## 3 Results
### GPRC5A Is Significantly Correlated with Mesenchymal Stem-like Phenotype  
To further investigate the functions of GPRC5A in GBM, we performed gene set enrichment analysis (GSEA) using differentially expressed genes (DEGs) from the TCGA GBM dataset available on Xena. Our analysis revealed that, among the top 10 signatures, hypoxia pathways were significantly enriched in the GPRC5A high-expression group (\(p<0.0001\)), along with cytokine response pathways, including TNF\(\alpha\) signaling (\(p<0.0001\)) and IL6-STAT3 signaling (\(p=0.003\)). Additionally, the high-expression group was associated with oncogenes, such as KRAS (\(p=0.001\)) and mTORC1 (\(p=0.0003\)) signaling (Figure 3A). Notably, epithelial-mesenchymal transition (EMT) was the third most represented hallmark (\(p<0.0001\)) in the GPRC5A high-expression group, correlating with the mesenchymal subtypes of GBM (Figure 3B). Additional enrichment was observed, such as apical junction (\(p=0.0005\)), glycolysis (\(p=0.0009\)), and complement pathways (\(p=0.002\)). Figure 3: GPRC5A is strongly associated with the mesenchymal stem-like phenotype. (**A**) Hallmark gene sets from GSEA analysis for high GPRC5A expression versus low expression. The top 10 significantsignatures are represented as the normalized enrichment score (NES), with the p-values shown in the code color. (**B**) Enrichment plots of epithelial-mesenchymal transition pathway from GSEA analysis. (**C**) Differential gene expression of the mesenchymal marker CD44 in low and high GPRC5A group (n = 86) (left panel) using unpaired Student's f-tests (***** \(p<\) 0.001) and Spearman correlation test between CD44 and GPRC5A expression across GBM cohort (right panel). (**D**) Differential gene expression of CD133 in low and high GPRC5A groups (n = 86) (left panel) using unpaired Student's \(t\)-tests (NS = non-significant) and Spearman correlation between CD133 and GPRC5A expression across GBM cohort (right panel). (**E**) Top 10 canonical gene sets from GSEA for high GPRC5A expression versus low expression, with the p-values shown in the code color. (**F**) Differential correlation between GPRC5A expression with multiple GBM stem cell markers (n = 172) by plotting log2 (norm count + 1) for each marker. \(\alpha\)\(\beta\)3 (integrin alpha 5 beta 3); VEGFA (vascular endothelial growth factor A); NRF2 (nuclear factor erythroid 2-related factor 2); uPAR (urokinase plasminogen activator surface receptor), PDGFRB (platelet-derived growth factor receptor beta), CXCR4 (CXC chemokine receptor 4). All statistical correlations between GPRC5A and stem cell markers were analyzed using the Spearman correlation test. The mesenchymal phenotype associated with GPRC5A was further evaluated through correlation analysis with established mesenchymal markers, such as CD44, as described in prior research [48]. The samples were divided into two groups based on the median GPRC5A expression level, and CD44 expression was assessed in each group. We found that CD44 was significantly upregulated in the GPRC5A high-expression group and strongly correlated with GPRC5A (Figure 3C). We also assessed the correlation between GPRC5A and other brain lineage markers, including oligodendrocyte markers (_OLIG1_ and _OLIG2_) and glial/astrocytic lineage genes (_SOX9_, _HOPX_, and _HEPACAM_). GPRC5a negatively correlated with the markers of oligodendrocyte, while no significance was found with the glial/astrocytes (Table 1). These data suggest that GPRC5A is strongly associated with mesenchymal features in GBM. CD44, a key mesenchymal marker in GBM, has been extensively linked to the GBM stem cell phenotype and is used in cancer stem cell sorting [49]. To confirm GPRC5A's association with the stem cell phenotype, we examined the correlation with CD133 (PROM1), a putative stem cell marker in GBM [50], and found no differences in CD133 expression among high and low GPRC5A or significant correlations between CD133 and GPRC5A (Figure 3D).