NETWORK ANALYSIS FOR IDENTIFICATION OF DIAGNOSTIC BIOMARKERS OF GLIOMA USING NGS DATA

Abstract

Background

Gliomas are the most common primary brain tumors worldwide, about 80% of all primary brain tumors. Approximately 200,000 cases are diagnosed annually worldwide. Low-grade gliomas (LLGs) are less aggressive and have better survival outcomes, though they have a wide survival range from 1 to 15 years, depending on their subtype. There is great need to identify diagnostic biomarkers for glioma.

Objective

To investigate brain tumor glioma, through network  analyzing of deferentially expressed genes(DEGs) between normal and tumor tissues by exploring the molecular pathways dysregulated in the disease context, and integrating these findings to understand the tumor's molecular landscape holistically. This approach aims to uncover diagnostic biomarkers for early detection and therapeutic targets to improve treatment outcomes for patients with this aggressive cancer.

Methods

 In this study, two datasets obtained from the Chinese Glioma Genome Atlas (CGGA) were evaluated glioma for Glioma associated DEGs using bioinformatics analysis. On the basis of p-value<0.05 and logFC±1.5 DEGs were shortlisted Further, inputting deferentially expressed genes (DEGs) into a ring database to construct a protein-protein interaction (PPI) network 15 hub genes were discovered. Then expressions of significant genes was validated using GEPIA.

Results

Through rigorous network analysis of two independent datasets, we uncovered thousands of DEGs, with a substantial overlap between datasets. We identified total 4683 deferentially expressed genes in glioma tumors from the TC database with 810 upregulated and 3874 downregulated sets, emphasizing their pivotal roles in glioma pathogenesis. Functional enrichment and protein-protein interaction analyses elucidated the biological processes and networks disrupted in glioma, underscoring the involvement of downregulated hub genes (ATM, BCL2, BRCA1, CREBBP, EGFR, PIK3R1, EP300, IL1B, JUN, KRAS, NRAS, PTEN) by analyzing the expression levels between tumor and normal sample.

Conclusion

This retrospective study (comparison between Glioma cells and normal brain cells) has significantly advanced our understanding of the molecular landscape of low grade glioma by identifying DEGs and dysregulated molecular pathways between normal and tumor samples. Overall, our study identifies diagnostic biomarkers providing a foundation for future research aimed at improving prognosis, and treatment strategies for patients with glioma.