Elucidating the Predominant Role of AEBP1 in Different Types of Cancers with a Focus on Glioblastoma Progression – A Review

Abstract

INTRODUCTION: Glioblastoma multiforme (GBM) is a highly deleterious lesion with an increased recurrence rate even after radiotherapy and chemotherapy. In this context, additional biomarkers are needed to curb chemoresistance. Computational approaches help us process the RNA-seq and identify the Differentially Expressed Genes (DEGs) in tumors and adjacent normal regions to identify the diagnostic and therapeutic biomarkers. METHODS AND MATERIALS: In this study, we extensively reviewed the role of AEBP1 in different types of cancer, highlighting its significance as a novel target to prevent collagen deposition. Specifically, the underlying mechanisms of AEBP1 in Glioblastoma were analyzed extensively using computational approaches that include Gene Expression Omnibus (GEO), GEPIA to obtain the TCGA-GBM dataset, and Glioma-BioDP to identify the survival rate in the context of AEBP1 expression associated with patients' age. Meanwhile, Tumor Immune Single-cell Hub 2 was implemented to identify the expression of AEBP1 in immunologically lineaged, cancerous, and stromal cells. In addition to that, the miRNA regulation associated with the AEBP1 expression was predicted by implementing NetworkAnalyst, TarBase v8.0, and CancerMIRNome. We identified the DEGs by examining the GSE121723, GSE184643, and GSE14824 datasets with P-values ≤ 0.05 as statistically significant. Furthermore, we predicted and analyzed the highly expressed genes and identified the survival rate, which significantly stated that the overexpression of AEBP1 was associated with decreased survival rates in GBM patients. The Protein-Protein Interaction network was constructed to identify the correlated gene expression. RESULTS AND DISCUSSION: We identified 3695, 37001, and 8855 significantly differentially expressed genes (DEGs). The DEGs were filtered by applying a log2 fold-change cut-off of ≥2.0. Finally, 139 common genes were mapped with the identified DEGs (1338 genes) and SDEGs (500 genes) estimated from the TCGA-GBM dataset. The analysis revealed that 155 genes are commonly upregulated, and survival analyses were performed that described the AEBP1 significantly reduced the GBM patients' survival rate among other genes. The constructed PPI network and correlated expression analysis associated with the AEBP1 expression revealed that COL6A2 and THBS2 might play a significant role in the GBM stage advancements by depositing collagens in the matrix environment. Also, the miRNA analysis revealed that the hsa-miR-128-3p and hsa-miR-512-3p could be targeted as a miRNA marker gene to prevent the GBM progression associated with the AEBP1 expression. CONCLUSION: ). Targeting AEBP1 via TGFβs and its receptors could inhibit the collagen-depositing gene COL6A2 and THBS2, a key TME modulator. Further, the hsa-miR-128-3p (AUC = 0.94) could be a potential therapeutic target to prevent the expression of AEBP1. Following an extensive review and in-depth discussion, our investigation presents a potentially promising avenue to develop small drug-like molecules and monoclonal antibodies against AEBP1 expression for ameliorating patient survival rates.

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