Unraveling molecular signatures and prognostic biomarkers in glioblastoma: a comprehensive study on treatment resistance and personalized strategies.

BACKGROUND

Glioblastoma (GBM) is a highly aggressive primary brain tumor with limited treatment success and poor prognosis. Despite surgical resection and adjuvant therapies, GBM often recurs, and resistance to radiotherapy and temozolomide presents significant challenges. This study aimed to elucidate molecular signatures associated with treatment responses, identify potential biomarkers, and enhance personalized treatment strategies for GBM.

METHODS

We conducted a comprehensive analysis using the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. The GEO dataset (GSE206225) was used to identify differentially expressed genes (DEGs) between radiation-sensitive/resistant and temozolomide-sensitive/resistant GBM samples. TCGA data were utilized for subsequent analyses, including Lasso-Cox regression, risk score model construction, Kaplan-Meier survival analysis, and gene set enrichment analysis (GSEA). Hub genes were identified through survival analysis, and a gene prognostic nomogram was developed. Additionally, validation of the three-gene risk signature through multiple external cohorts and validation of protein expression levels were performed.

RESULTS

DEG analysis identified 111 genes associated with chemoradiotherapy resistance, providing insights into the complex landscape of GBM treatment response. The risk score model effectively stratified patients, showing significant differences in overall survival and progression-free survival. GSEA offered a deeper understanding of pathway activities, emphasizing the intricate molecular mechanisms involved. NNAT, IGFBP6, and CYGB were identified as hub genes, and a gene prognostic nomogram demonstrated predictive accuracy.

CONCLUSION

This study sheds light on the molecular intricacies governing GBM treatment response. The identified hub genes and the gene prognostic nomogram offer valuable tools for predicting patient outcomes and guiding personalized treatment strategies. These findings contribute to advancing our understanding of GBM biology and may pave the way for improved clinical management.