Stage-specific gene pair ratios highlight genes and mechanisms related to presymptomatic and symptomatic Multiple Myeloma
Background/aim: Multiple Myeloma (MM) is the second most common blood cancer, characterised by the accumulation of malignant plasma cells and the production of large amounts of a monoclonal immunoglobulin protein, in the bone marrow. The identification and progression/behaviour of molecular markers...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-01-01
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| Series: | Computational and Structural Biotechnology Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2001037025002831 |
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| Summary: | Background/aim: Multiple Myeloma (MM) is the second most common blood cancer, characterised by the accumulation of malignant plasma cells and the production of large amounts of a monoclonal immunoglobulin protein, in the bone marrow. The identification and progression/behaviour of molecular markers across stages remains a scientific challenge. This work aims to provide a holistic approach in the understanding of the disease progression through a computational approach, capable of characterising and distinguishing disease stages. Materials and methods: Stage-stratified MM transcriptomic datasets were used to mine stage-specific information and calculate key entities at the gene level by means of: (a) differentially expressed genes (DEGs), (b) monotonically expressed genes (MEGs), (c) ratios of MEG-related pairs of genes (RMEGs). The performance of the RMEGs across samples has been investigated regarding their ability to characterize and discriminate the MM stages. The genes participating in the top-ranked RMEGs were further used for pathway enrichment analysis to further enlighten the functional understanding per MM stage. Results: We show that the proposed computational methodology by means of RMEGs reveals short lists of key genes across stages, which in turn highlight significant groups of pathways associated with regulation and cell cycle. Conclusion: We integrate the traditional analysis of DEGs with the concepts of MEGs and RMEGs, creating a novel computational approach capable of identifying key genes and pathways that can serve as a highly-filtered pool of candidate MM stage identification markers for further experimental investigation. |
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| ISSN: | 2001-0370 |