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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Elsevier
2025-01-01
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| Series: | Computational and Structural Biotechnology Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2001037025002831 |
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| author | Grigoris Georgiou George Minadakis Nestoras Karathanasis Kyriaki Savva Efi Athieniti Marilena M. Bourdakou George M. Spyrou |
| author_facet | Grigoris Georgiou George Minadakis Nestoras Karathanasis Kyriaki Savva Efi Athieniti Marilena M. Bourdakou George M. Spyrou |
| author_sort | Grigoris Georgiou |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-6a3ebdffa3e145cb9e6f189dfd4fbdbf |
| institution | Kabale University |
| issn | 2001-0370 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computational and Structural Biotechnology Journal |
| spelling | doaj-art-6a3ebdffa3e145cb9e6f189dfd4fbdbf2025-08-20T03:27:58ZengElsevierComputational and Structural Biotechnology Journal2001-03702025-01-01273090309810.1016/j.csbj.2025.07.017Stage-specific gene pair ratios highlight genes and mechanisms related to presymptomatic and symptomatic Multiple MyelomaGrigoris Georgiou0George Minadakis1Nestoras Karathanasis2Kyriaki Savva3Efi Athieniti4Marilena M. Bourdakou5George M. Spyrou6Corresponding authors.; Bioinformatics Department, The Cyprus Institute of Neurology & Genetics, 6 International Airport Avenue, 2370, P.O.Box 23462, Nicosia 1683, CyprusBioinformatics Department, The Cyprus Institute of Neurology & Genetics, 6 International Airport Avenue, 2370, P.O.Box 23462, Nicosia 1683, CyprusBioinformatics Department, The Cyprus Institute of Neurology & Genetics, 6 International Airport Avenue, 2370, P.O.Box 23462, Nicosia 1683, CyprusBioinformatics Department, The Cyprus Institute of Neurology & Genetics, 6 International Airport Avenue, 2370, P.O.Box 23462, Nicosia 1683, CyprusBioinformatics Department, The Cyprus Institute of Neurology & Genetics, 6 International Airport Avenue, 2370, P.O.Box 23462, Nicosia 1683, CyprusBioinformatics Department, The Cyprus Institute of Neurology & Genetics, 6 International Airport Avenue, 2370, P.O.Box 23462, Nicosia 1683, CyprusCorresponding authors.; Bioinformatics Department, The Cyprus Institute of Neurology & Genetics, 6 International Airport Avenue, 2370, P.O.Box 23462, Nicosia 1683, CyprusBackground/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.http://www.sciencedirect.com/science/article/pii/S2001037025002831Multiple myelomaCancerPlasma cellsGammopathiesProgressionKey genes |
| spellingShingle | Grigoris Georgiou George Minadakis Nestoras Karathanasis Kyriaki Savva Efi Athieniti Marilena M. Bourdakou George M. Spyrou Stage-specific gene pair ratios highlight genes and mechanisms related to presymptomatic and symptomatic Multiple Myeloma Computational and Structural Biotechnology Journal Multiple myeloma Cancer Plasma cells Gammopathies Progression Key genes |
| title | Stage-specific gene pair ratios highlight genes and mechanisms related to presymptomatic and symptomatic Multiple Myeloma |
| title_full | Stage-specific gene pair ratios highlight genes and mechanisms related to presymptomatic and symptomatic Multiple Myeloma |
| title_fullStr | Stage-specific gene pair ratios highlight genes and mechanisms related to presymptomatic and symptomatic Multiple Myeloma |
| title_full_unstemmed | Stage-specific gene pair ratios highlight genes and mechanisms related to presymptomatic and symptomatic Multiple Myeloma |
| title_short | Stage-specific gene pair ratios highlight genes and mechanisms related to presymptomatic and symptomatic Multiple Myeloma |
| title_sort | stage specific gene pair ratios highlight genes and mechanisms related to presymptomatic and symptomatic multiple myeloma |
| topic | Multiple myeloma Cancer Plasma cells Gammopathies Progression Key genes |
| url | http://www.sciencedirect.com/science/article/pii/S2001037025002831 |
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