bioRxivCancer Biology
Expression Analysis, Molecular Characterization and Prognostic Evaluation on TMED4 and TMED9 Gene Expression in Glioma
M. A. Ullah, T. Tabassum, M. Farzana, A. T. Moin, U. S. Zohora, M. S. Rahman
Abstract
Here, we utilized a database mining approach to unfold the prognostic and therapeutic potentials of Transmembrane EmP24 Trafficking Protein 4 (TMED4) and 9 (TMED) coding gene expressions in glioma. Both the genes were found to be overexpressed at the mRNA and protein level in low grade glioma (LGG) and glioblastoma multiforme (GBM) tissues including different glioma cell lines. Significant increase in the expression level of these genes with advancing glioma patients age, glioma grades and histological subtypes was observed. Differential and distinct promoter and coding sequence methylation pattern of TMED4 and TMED9 was observed in LGG and GBM tissues that may aid in methylation-sensitive diagnosis of glioma patients. The presence of multiple heterozygous genetic alterations (frequency: 0.4-1.1%) in those genes unveiled their potentials in high-throughput screening of glioma patients. The overexpression of TMED4 and TMED9 genes was associated with poor overall survival (OS) of LGG and GBM patients (HR:>1.6). Association of the expression levels of these genes with different immune cell infiltration levels i.e., B cell and T cell and modulators like CD274 and IL10RB was observed providing assurance in TMED-based diagnostic measure and therapeutic intervention discovery. Furthermore, functional enrichment analysis of the neighbor genes of TMED4 and TMED9 revealed that they are involved in metal ion binding, focal adhesion of cells and protein processing, and the deregulation of these activities are associated with gliomagenesis. Altogether, this study suggests that TMED4 and TMED9 are potential prognostic and therapeutic targets for glioma. However, further laboratory research is warranted.
- organism: human
- assay: bulk RNA-seq
- research focus: differential expression
- research focus: protein structure prediction
- approach: statistical model
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