Background: Multiple Myeloma (MM) is a hematologic malignancy whose underlying molecular mechanism has not yet fully understood. Cell adhesion plays a pivotal role in regulating MM progression. In this work, we aim to identify key genes involved in cell adhesion.
Methods: Differentially expressed genes (DEGs) with p<0.05 and [logFC]≥1 were identified from the mRNA expression profiles of GSE6477 using GEO2R. Functional analysis and the protein-protein interaction (PPI) network analysis was performed to explore the biological function of the identified DEGs. Interactive networks of selected genes were built by STRING and Cytoscape software. Then,the prognostic and diagnostic values of hub genes were performed by the PrognoScan online tool and ROC curves. Moreover, a comprehensive analysis of candidate genes was performed using both clinical data and mRNA expression data. A retrospective clinical study was performed to evaluate the correlation between the expression of candidate proteins and the clinical characteristics of patients. Additionally, GSEA and transcription factor (TF) prediction were performed to identify biological progression that might be involved.
Results: In total, 1383 DEGs were identified. GO enrichment analysis showed that these DEGs were enriched in the extracellular matrix (ECM) organization and cell adhesion. KEGG enrichment analysis showed that the DEGs were significantly associated with cell adhesion molecules (CAMs), and ECM-receptor interaction. To further identify key regulators involved in the cell adhesion process, we screened 180 overlapped genes between the DEGs and genes in GO terms of cell adhesion. Moreover, we built a PPI network to analyze filtered 123 proteins by STRING and Cytoscape software. 12 hub genes were identified as hub genes. Based on the PrognoScan database, ITGA9 and LAMB1 revealed the prognostic and diagnostic values of MM patients. They were both down-regulated in MM and had a correlation with some clinical characteristics of MM patients. Finally, GSEA analysis showed LAMB1 has a strong correlation with hypoxia, AKT1/mTOR signaling pathway, and oxidative phosphorylation. ITGA9 may be a target gene of MYC and has a strong correlation with oxidative phosphorylation. Moreover, based on TF prediction and the correlation analysis, we speculated that MYC may repress transcription of ITGA9 through binding to the promoter, which needs further verification.
Conclusion: ITGA9 and LAMB1 were identified as key cell adhesion genes, serving as biomarkers and potential therapeutic targets for MM.