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New Analysis Locates 2 Biomarkers for Multiple Myeloma


A new analysis of bone marrow plasma cells has helped to identify two biomarkers for multiple myeloma, which appear to play a role in the prognosis and clinical characteristics of the disease.

A new study has identified new biomarkers that appear to correlate to the clinical characteristics and prognosis of patients with multiple myeloma (MM).

The study was published in the journal Cancer Cell International. Corresponding author Aili He, MD, PhD, of the Second Affiliated Hospital of Xi’an Jiaotong University, in China, and colleagues set out to identify key genes involved in cell adhesion in MM, since cell adhesion plays an important role in the progression of the disease. Their findings could lead to important advances in the diagnosis and treatment decisions of patients with MM.

He and colleagues first set out to find differentially expressed genes (DEGs) using the mRNA expression profiles of the GSE6477 dataset of bone marrow (BM) plasma cells. The authors used GEO2R to perform the analysis, with cut-off criteria of P < 0.05 and [logFC] ≥ 1. They then performed gene ontology enrichment analysis and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis to figure out the biological pathways and functions associated with the DEGs identified. The authors also screened hub genes and analyzed their prognostic and diagnostic values, among other analyses.

In total, the team identified 1383 DEGs, many of which appeared to be enriched in cell adhesion. A dozen genes were identified as hub genes, and a receiver operating characteristic curve analysis showed that a total of 8 genes were biomarkers for the diagnosis of MM: ITGAM, ITGB2, ITGA5, ITGB5, CDH1, IL4, ITGA9, and LAMB1.

Of those, a further analysis showed the latter 2—ITGA9 and LAMB1&mdash;had prognostic values and clinical implications in patients with MM, the authors said. He and colleagues posited theories about how the 2 genes play a role in MM.

“[Gene Set Enrichment Analysis] 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,” the authors said. “Additionally, pan-cancer analysis showed abnormal expression and clinical outcome associations of LAMB1 and ITGA9 in multiple cancers.”

Specifically, abnormal expression of LAMB1 has been linked with overall survival and disease-specific survival in a number of cancers, the authors said. That may have to do with its role in cancer progression.

“LAMB1 has a high protein level in high-grade gliomas, suggesting a possible correlation with tumor progression,” they said. “What’s more, LAMB1 was identified to take part in cell attachment and have the capacity to inhibit metastasis.”

In MM, the authors said LAMB1 was shown to be significantly associated with higher β2-MG concentration and higher R-ISS stage.

Abnormal expression of ITGA9 was likewise found in many cancers. In this study, down-regulation of ITGA9 was linked with poor outcomes in patients with MM.

“Myeloma patients with low ITGA9 expression had more tendency of having the higher number of malignant [plasma cells] in BM and becoming the higher [gene expression profiling] group,” He and colleagues said.

The authors said ITGA9 appeared to be an important cell adhesion molecule, and they predicted that MYC may bind to the promoter of ITGA9 for transcriptional repression.


Peng Y, Wu D, Li F, Zhang P, Feng Y, He A. Identification of key biomarkers associated with cell adhesion in multiple myeloma by integrated bioinformatics analysis. Cancer Cell Int. 2020;20:262. Published 2020 Jun 22. doi:10.1186/s12935-020-01355-z

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