BIOM-74. INVESTIGATING GLIOBLASTOMA CHEMORESISTANCE: A META-ANALYSIS OF MICRORNA SIGNATURES AND GENE NETWORKS

Abstract Chemoresistance is a significant issue in glioblastoma (GBM) treatment due to recurrence, poor survival and limited salvage options. Non-invasive biomarkers can identify early chemoresistance and thus, cohorts benefiting from early, aggressive chemotherapy or second-line drugs at an earlier timeline. Meta-analysis of identified miR through systematic review was conducted through data mining and miR-target interaction compilation in validated miR databases; we curated a list of gene targets that underwent pathway enrichment analysis using the EnrichR tool. Network analysis of pathways and protein-protein interactions was conducted using the Metascape bioinformatics tool with several ontology sources. Significantly upregulated miR validated in chemoresistant GBM were miR-221, -222, -132, and -125b. 33 miR are identified in the high expression category. 18 miR were low expression responders in TMZ-resistant GBM. MiR-21 and miR-10b are the most replicated miR for bevacizumab response assessment with a low expression in Avastin-resistant GBM. 5 miR with high expression and 11 miR with low expression were identified; two combined micro-RNA signatures predicted cohorts likely to respond well to bevacizumab therapy. MiRs upregulated in TMZ resistance showed 400 predicted mRNA targets and 411 genes in downregulated miR. For bevacizumab response, upregulated miR yielded 38 genes and 82 in downregulated miR. The current state of miR-evaluation of GBM chemotherapy response requires insights into molecular subtypes that may show specific miR response. Clinical prospective evaluation of a compiled signature’s efficacy in improving progression-free and overall survival could highlight the oncological impact and efficacy of miR in GBM response evaluation.