Proteomic Alteration in the Progression of Multiple Myeloma: A Comprehensive Review

Abstract

Multiple myeloma (MM) is an incurable hematologic malignancy. Most MM patients are diagnosed at a late stage because the early symptoms of the disease can be uncertain and nonspecific, often resembling other, more common conditions. Additionally, MM patients are commonly associated with rapid relapse and an inevitable refractory phase. MM is characterized by the abnormal proliferation of monoclonal plasma cells in the bone marrow. During the progression of MM, massive genomic alterations occur that target multiple signaling pathways and are accompanied by a multistep process involving differentiation, proliferation, and invasion. Moreover, the transformation of healthy plasma cell biology into genetically heterogeneous MM clones is driven by a variety of post-translational protein modifications (PTMs), which has complicated the discovery of effective treatments. PTMs have been identified as the most promising candidates for biomarker detection, and further research has been recommended to develop promising surrogate markers. Proteomics research has begun in MM, and a comprehensive literature review is available. However, proteomics applications in MM have yet to make significant progress. Exploration of proteomic alterations in MM is worthwhile to improve understanding of the pathophysiology of MM and to search for new treatment targets. Proteomics studies using mass spectrometry (MS) in conjunction with robust bioinformatics tools are an excellent way to learn more about protein changes and modifications during disease progression MM. This article addresses in depth the proteomic changes associated with MM disease transformation.

Keywords: mass spectrometry; multiple myeloma; post-translational modifications; proteomics.

Figure 1

The workflow in proteomics experiments incorporates protein 2D-DIGE electrophoresis and shotgun mass spectrometry.

Figure 2

Protein–protein interaction network in active MM disease. Nodes represent proteins and edges represent protein–protein associations. The edges are composed of known and predicted interactions, among other forms of interaction, including co-expression (STRING https://string-db.org/) accessed on 3 March 2023. (a) Full STRING network. The edges indicate both functional and physical protein associations. (b) Full STRING network in cluster. Protein–protein networking in Cluster 1 (c), Cluster 2 (d), and Cluster 3 (e). The full STRING analysis report can be found at https://version-11-5.string-db.org/cgi/network?networkId=bwimO6bRnH24 accessed on 3 March 2023.

Figure 2

Protein–protein interaction network in active MM disease. Nodes represent proteins and edges represent protein–protein associations. The edges are composed of known and predicted interactions, among other forms of interaction, including co-expression (STRING https://string-db.org/) accessed on 3 March 2023. (a) Full STRING network. The edges indicate both functional and physical protein associations. (b) Full STRING network in cluster. Protein–protein networking in Cluster 1 (c), Cluster 2 (d), and Cluster 3 (e). The full STRING analysis report can be found at https://version-11-5.string-db.org/cgi/network?networkId=bwimO6bRnH24 accessed on 3 March 2023.

Figure 2

Protein–protein interaction network in active MM disease. Nodes represent proteins and edges represent protein–protein associations. The edges are composed of known and predicted interactions, among other forms of interaction, including co-expression (STRING https://string-db.org/) accessed on 3 March 2023. (a) Full STRING network. The edges indicate both functional and physical protein associations. (b) Full STRING network in cluster. Protein–protein networking in Cluster 1 (c), Cluster 2 (d), and Cluster 3 (e). The full STRING analysis report can be found at https://version-11-5.string-db.org/cgi/network?networkId=bwimO6bRnH24 accessed on 3 March 2023.