Integrating Multi-Omics Analysis for Enhanced Diagnosis and Treatment of Glioblastoma: A Comprehensive Data-Driven Approach

Abstract

The most aggressive primary malignant brain tumor in adults is glioblastoma (GBM), which has poor overall survival (OS). There is a high relapse rate among patients with GBM despite maximally safe surgery, radiation therapy, temozolomide (TMZ), and aggressive treatment. Hence, there is an urgent and unmet clinical need for new approaches to managing GBM. The current study identified modules (MYC, EGFR, PIK3CA, SUZ12, and SPRK2) involved in GBM disease through the NeDRex plugin. Furthermore, hub genes were identified in a comprehensive interaction network containing 7560 proteins related to GBM disease and 3860 proteins associated with signaling pathways involved in GBM. By integrating the results of the analyses mentioned above and again performing centrality analysis, eleven key genes involved in GBM disease were identified. ProteomicsDB and Gliovis databases were used for determining the gene expression in normal and tumor brain tissue. The NetworkAnalyst and the mGWAS-Explorer tools identified miRNAs, SNPs, and metabolites associated with these 11 genes. Moreover, a literature review of recent studies revealed other lists of metabolites related to GBM disease. The enrichment analysis of identified genes, miRNAs, and metabolites associated with GBM disease was performed using ExpressAnalyst, miEAA, and MetaboAnalyst tools. Further investigation of metabolite roles in GBM was performed using pathway, joint pathway, and network analyses. The results of this study allowed us to identify 11 genes (UBC, HDAC1, CTNNB1, TRIM28, CSNK2A1, RBBP4, TP53, APP, DAB1, PINK1, and RELN), five miRNAs (hsa-mir-221-3p, hsa-mir-30a-5p, hsa-mir-15a-5p, hsa-mir-130a-3p, and hsa-let-7b-5p), six metabolites (HDL, N6-acetyl-L-lysine, cholesterol, formate, N, N-dimethylglycine/xylose, and X2. piperidinone) and 15 distinct signaling pathways that play an indispensable role in GBM disease development. The identified top genes, miRNAs, and metabolite signatures can be targeted to establish early diagnostic methods and plan personalized GBM treatment strategies.

Changes Induced by P2X7 Receptor Stimulation of Human Glioblastoma Stem Cells in the Proteome of Extracellular Vesicles Isolated from Their Secretome

“…Additionally, high levels of RBBP4, which appears to be related to poor prognosis of colon cancer with or without hepatic metastasis [125], could be also predictive for poor survival outcome of low-grade glioma (LGG) patients and, therefore, it could be regarded as a potential LGG biomarker and an immunotherapy target [126]. The gene codifying for this protein was also identified among other genes that can be used for early diagnosis and more specific GBM treatments [127]. Likewise, the gene codifying for MMP2 has been identified as a novel oncogene in colorectal cancer [128], while increased MMP-2 activity is involved in an essential step for the metastatic progression of most cancers [129] and also linked with a poor prognosis in multiple tumors, including glioma [130].…”

Interpreting the molecular mechanisms of RBBP4/7 and their roles in human diseases (Review)

“…RBBP4 plays an indispensable role in the disease development of GBM ( 93 ), with its mRNA expression universally upregulated in GBM tissues where it acts as an oncogene to promote GBM malignancy, countering the tumor-suppressive effects of microRNA (miR)-885-5p ( 94 ). In GBM cells, the RBBP4/p300 complex governs pro-survival genes and influences the responsiveness to TMZ ( 95 ).…”

Oral Infection, Oral Pathology and Salivary Diagnostics of Mpox Disease: Relevance in Dentistry and OMICs Perspectives

“…Research on infectious diseases and chronic diseases based on large-scale multi-omics databases permits a significant improvement in the molecular and genetic features of diseases. It is expected that the integration of multi-omics analysis can promote significant improvement in the performance of diagnostic tests [ 70 ]. The integration of salivary omics datasets still presents challenges [ 67 ] that can be potentially resolved by artificial intelligence.…”