ArticleCancer cell international2020
Identification of key biomarkers associated with cell adhesion in multiple myeloma by integrated bioinformatics analysis.
Article in Cancer cell international, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed, 83 citations in OpenAlex.
- The relationship between inflammation and multiple myeloma: insights into alterations and prognostic value.GeroScience · 2026Article
- Integrated single-cell transcriptomic analysis identifiesBiochemistry and biophysics reports · 2026Article
- Integrative Network Analysis of Single-Cell RNA Findings and a Priori Knowledge Highlights Gene Regulators in Multiple Myeloma Progression.International journal of molecular sciences · 2026Article
- Clinical value of emerging peripheral blood protein biomarkers in prostate cancer (Review).Oncology letters · 2026Review
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- Comprehensive Bioinformatic Analysis Reveals Survival-Associated Hub Genes and MicroRNAs in Multiple Myeloma Patients.Iranian journal of biotechnology · 2025Article
- Development and validation of a multiple myeloma diagnostic model based on systemic lupus erythematosus-associated genes and identification of specific genes.Discover oncology · 2025Article
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- Proteomic Identification of Small Extracellular Vesicle Proteins LAMB1 and Histone H4 for Prostate Cancer Diagnosis and Risk Stratification.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Development of a cell adhesion-based prognostic model for multiple myeloma: Insights into chemotherapy response and potential reversal of adhesion effects.Oncology research · 2024Article
- Diagnostic and Prognostic Value of PACAP in Multiple Myeloma.International journal of molecular sciences · 2023Article
- The molecular consequences of androgen activity in the human breast.Cell genomics · 2023Article
- Molecular mechanism of Wilms' tumor (Wt1) (+/-KTS) variants promoting proliferation and migration of ovarian epithelial cells by bioinformatics analysis.Journal of ovarian research · 2023Article
- Simulating neuronal development: exploring potential mechanisms for central nervous system metastasis in acute lymphoblastic leukemia.Frontiers in oncology · 2023Article
- Next-Generation Biomarkers in Multiple Myeloma: Understanding the Molecular Basis for Potential Use in Diagnosis and Prognosis.International journal of molecular sciences · 2021Review
- Integrative Systems Biology Approaches to Identify Potential Biomarkers and Pathways of Cervical Cancer.Journal of personalized medicine · 2021Article
- Article
- The Relationship between Hepatic Myeloid-Derived Suppressor Cells and Clinicopathological Parameters in Patients with Chronic Liver Disease.BioMed research international · 2021Article
- Identification of Therapeutic Targets and Prognostic Biomarkers Among Integrin Subunits in the Skin Cutaneous Melanoma Microenvironment.Frontiers in oncology · 2021Article
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMultiple Myeloma (MM) is a hematologic malignant disease whose underlying molecular mechanism has not yet fully understood. Generally, cell adhesion plays an important role in MM progression. In our work, we intended to identify key genes involved in cell adhesion in MM.
methodsFirst, we identified differentially expressed genes (DEGs) from the mRNA expression profiles of GSE6477 dataset using GEO2R with cut-off criterion of p < 0.05 and [logFC] ≥ 1. Then, GO and KEGG analysis were performed to explore the main function of DEGs. Moreover, we screened hub genes from the protein-protein interaction (PPI) network analysis and evaluated their prognostic and diagnostic values by the PrognoScan database and ROC curves. Additionally, a comprehensive analysis including clinical correlation analysis, GSEA and transcription factor (TF) prediction, pan-cancer analysis of candidate genes was performed using both clinical data and mRNA expression data.
resultsFirst of all, 1383 DEGs were identified. Functional and pathway enrichment analysis suggested that many DEGs were enriched in cell adhesion. 180 overlapped genes were screened out between the DEGs and genes in GO terms of cell adhesion. Furthermore, 12 genes were identified as hub genes based on a PPI network analysis. ROC curve analysis demonstrated that ITGAM, ITGB2, ITGA5, ITGB5, CDH1, IL4, ITGA9, and LAMB1 were valuable biomarkers for the diagnosis of MM. Further study demonstrated that ITGA9 and LAMB1 revealed prognostic values and clinical correlation in MM patients. GSEA and transcription factor (TF) prediction suggested that MYC may bind to ITGA9 and repress its expression and HIF-1 may bind to LAMB1 to promote its expression in MM. Additionally, pan-cancer analysis showed abnormal expression and clinical outcome associations of LAMB1 and ITGA9 in multiple cancers.
conclusionIn conclusion, ITGA9 and LAMB1 were identified as potent biomarkers associated with cell adhesion in MM.
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