Glioblastoma multiforme (GBM) is a highly aggressive and heterogeneous brain tumor, characterized by poor prognosis and resistance to conventional therapies. Despite advances in multimodal treatment approaches, such as surgery, radiation, and chemotherapy, the prognosis for GBM remains poor, largely due to its molecular complexity and resistance to therapies. To further understand the genetic underpinnings of GBM, this study presents a comprehensive differential gene expression analysis using RNA-sequencing data from the Gene Expression Omnibus (GEO) database. PyDESeq2, the Python implementation of DESeq2, was used to analyze transcriptional changes across glioblastoma and normal brain tissues. This study adopts a two-phase approach: (1) stratifying samples into age groups (≤65 years and ≤76 years) to examine age-related gene expression differences in both GBM and normal brain tissues, and (2) comparing global differential expression patterns between GBM and normal tissues to identify genes consistently dysregulated in GBM. In the age-stratified analysis, we identified several genes that were significantly upregulated or downregulated in younger versus middle-aged individuals, revealing age-specific transcriptional signatures. Principal Component Analysis (PCA) was applied to visualize the variance between GBM and normal brain tissue, confirming distinct transcriptional clustering between diseased and healthy states. This work contributes to the growing field of precision oncology by providing a detailed characterization of age-related and disease-specific transcriptional changes in glioblastoma. The identification of key DEGs and enriched pathways in GBM enhances our understanding of the molecular mechanisms underlying this aggressive cancer and opens new avenues for the development of age-specific, targeted therapeutic strategies.