همایش ملی بیوانفورماتیک ایران

صفحه اصلی / 4th international edition and 13th Iranian Conference on Bioinformatics

Investigating the Role of EMT genes in Multiple Myeloma: A Bioinformatic Approach

نویسندگان :

Seyedeh Zahra Mousavi¹ Hamid Mahdizadeh² Mehdi Totonchi³

1- Medical Genetics and Molecular Biology Research Hub, Royan TuCAGene Ltd., Tehran, Iran. 2- Medical Genetics and Molecular Biology Research Hub, Royan TuCAGene Ltd., Tehran, Iran. 3- Medical Genetics and Molecular Biology Research Hub, Royan TuCAGene Ltd., Tehran, Iran.

کلمات کلیدی :

Multiple myeloma،Epithelial-mesenchymal transition،Protein-protein interaction،Gene expression

چکیده :

Introduction: Multiple myeloma (MM) is a hematologic malignancy characterized by abnormal clonal plasma cells in the bone marrow. A critical factor in tumor invasion and metastasis is the epithelial-mesenchymal transition (EMT), which allows tumor cells to gain traits like increased motility and invasiveness. Although hematopoietic cells have a mesenchymal origin, they display various intermediate stages associated with specific EMT programs, resulting in a more invasive phenotype. This EMT-like signatures have been noted in MM. This study aims to identify differentially expressed genes (DEGs) between MM and healthy samples and explore how EMT genes affect gene expression and cancer progression. Methods: The analysis of differentially expressed genes (DEGs) was conducted using data from the GSE72213 microarray dataset available in the Gene Expression Omnibus (GEO). These DEGs were compared with a list of known human EMT genes obtained from the dbEMT database to identify overlaps, referred to as EMT-DEGs. Furthermore, the protein-protein interaction (PPI) network and co-expression modules for the EMT-DEGs were analyzed using the STRING database and Cytoscape software to clarify the regulatory mechanisms involved. Pathway enrichment analysis related to the top co-expression module was carried out using the Enricher dataset. Results: The analysis of the GSE72213 dataset compared 19 samples from the MM group to 3 samples from the control group, leading to the identification of 790 DEGs (fold change ≥ 1.0; P < 0.01). These DEGs were then cross-referenced with a list of 1,184 EMT genes, resulting in the identification of 46 EMT-related DEGs (EMT-DEGs). After constructing the PPI network, one co-expression cluster was identified with a score of 10.545, comprising 12 nodes, including EZH2, UHRF1, CCNA2, MMP9, GAPDH, MYBL2, BIRC5, ERBB2, JUN, E2F1, FOXM1, and LMNB1, along with 58 edges, as determined using MCODE. The Enricher database was used to identify enriched KEGG pathways associated with the EMT-DEGs, applying a p-adjusted value threshold of less than 0.01 for statistical significance. Two of the top pathways identified were "Pathways in Cancer" and "CellularSenescence." Conclusions: This expression study emphasized the important role of EMT-related factors in the progression of MM.