Proteins in the spotlight of the study of lymphomas through proteomics
Keywords:
Omics, Proteomics, LymphomasAbstract
Introduction: Proteomics, understood as the scientific discipline that studies proteomes, is of vital importance in health research. It provides public health in the new millennium with scientific advances, with the goal of integrating new discoveries to provide the most up-to-date treatments.
Objective: To describe the background, emergence, and basic knowledge of mass spectrometry-based proteomic analysis, specifically for the search for biomarkers for the diagnosis and prognosis of lymphomas.
Methods: As this is a fairly controversial and recent topic, a documentary study was conducted using a cross-sectional, historical, retrospective search, supported by a review and comparative analysis of various sources.
Development: Despite significant advances, proteomics faces challenges in terms of sensitivity, specificity, and standardization of methods. Continued efforts are required to improve analytical techniques and data interpretation, as well as to validate findings from clinical studies. The future of proteomics in the context of lymphoproliferative processes promises a greater impact on personalized medicine and the development of new therapeutic strategies.
Conclusions: The integration of proteomic, genomic, and transcriptomic data has allowed a deeper understanding of lymphoproliferative processes, identifying key biomarkers for the diagnosis and prognosis of lymphomas. This multidimensional integration can help unravel the complex interactions between signaling pathways and changes in gene expression, improving our understanding of pathogenesis.
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1. Slavov N. Scaling up single-cell proteomics. Mol Cell Proteomics [Internet]. 2022 [citado 23/02/2025]; 21(1):100179. Disponible en: https://doi.org/10.1016/j.mcpro.2021.100179
2. Vistain LF, Tay S. Single-cell proteomics. Trends Biochem Sci [Internet]. 2021 [citado 23/02/2025]; 46(8):661-72. Disponible en: https://doi.org/10.1016/j.tibs.2021.01.013
3. Gatto L, Gibb S, Rainer J. MSnbase, efficient and elegant R-based processing and visualisation of raw mass spectrometry data. bioRxiv [Internet]. 2020 [citado 23/02/2025]. Disponible en: https://doi.org/10.1101/2020.04.29.067868
4. Schoof EM, Furtwängler B, Üresin N, Rapin N, Savickas S, Gentil C, et al. Quantitative single-cell proteomics as a tool to characterize cellular hierarchies. Nat Commun [Internet]. 2021 [citado 23/02/2025]; 12(1):3341. Disponible en: https://doi.org/10.1038/s41467-021-23667-y
5. Patel H, Ashton NJ, Dobson RJB, Andersson LM, Yilmaz A, Blennow K, et al. Proteomic blood profiling in mild, severe and critical COVID-19 patients. Sci Rep [Internet]. 2021 [citado 23/02/2025]; 11(1):6357. Disponible en: https://doi.org/10.1038/s41598-021-85877-0
6. Oppenheim S, Cao X, Rueppel O, Krongdang S, Phokasem P, DeSalle R, et al. Whole genome sequencing and assembly of the Asian honey bee Apis dorsata. Genome Biol Evol [Internet]. 2020 [citado 23/02/2025]; 12(12):3677-83. Disponible en: https://doi.org/10.1093/gbe/evz277
7. Bošković F, Keyser UF. Toward single-molecule proteomics. Science [Internet]. 2021 [citado 23/02/2025]; 374(6574):1443-4. Disponible en: https://doi.org/10.1126/science.abn0001
8. Xu J, Lin Y, Yang M, Zhang L. Statistics and pitfalls of trend analysis in cancer research: a review focused on statistical packages. J Cancer [Internet]. 2020 [citado 23/02/2025]; 11(10):2957-61. Disponible en: https://pubmed.ncbi.nlm.nih.gov/32226510/
9. Baxi V, Edwards R, Montalto M, Saha S. Digital pathology and artificial intelligence in translational medicine and clinical practice. Mod Pathol [Internet]. 2022 [citado 23/02/2025]; 35(1):23-32. Disponible en: https://pubmed.ncbi.nlm.nih.gov/34611303/
10. Bera K, Schalper KA, Rimm DL, Velcheti V, Madabhushi A. Artificial intelligence in digital pathology - new tools for diagnosis and precision oncology. Nat Rev Clin Oncol [Internet]. 2019 [citado 23/02/2025]; 16(11):703-15. Disponible en: https://pubmed.ncbi.nlm.nih.gov/31399699/
11. Macklin A, Khan S, Kislinger T. Recent advances in mass spectrometry based clinical proteomics: applications to cancer research. Clin Proteomics [Internet]. 2020 [citado 23/02/2025]; 17:17. Disponible en: https://pubmed.ncbi.nlm.nih.gov/32489335/
12. Quintero Sierra Y, Concepción Fernández Y, Hernández Padrón C, Romero González A, Pérez Ivis M, Lam Rosa M. Caracterización de pacientes adultos con linfoma no Hodgkin difuso de células grandes B. Rev Cubana Hematol Inmunol Hemoter [Internet]. 2020 [citado 23/02/2025]; 36(4):e1254. Disponible en: http://scielo.sld.cu/scielo.php?script=sci_arttext&pid=S0864-02892020000400006
13. Sorigue M, Sancho JM. La revisión de 2016 de la clasificación de la OMS de las neoplasias linfoides: la visión del clínico. Med Clin (Barc) [Internet]. 2018 [citado 23/02/2025]; 150(2):64-6. Disponible en: https://pubmed.ncbi.nlm.nih.gov/28545953/
14. Katz BZ, Herishanu Y. Therapeutic targeting of CD19 in hematological malignancies: past, present, future and beyond. Leuk Lymphoma [Internet]. 2014 [citado 23/02/2025]; 55(5):999-1006. Disponible en: https://pubmed.ncbi.nlm.nih.gov/23885836/
15. Moreno-Laguard Y, Laguna-Salvia L, Larquin-Comet J, León-Ramentol C, Hernandez-Soler Y, González-Basulto M. Criterios diagnósticos y nuevas opciones terapéuticas para los pacientes con diagnóstico de linfoma no Hodgkin. Arch Med Camagüey [Internet]. 2019 [citado 23/02/2025]; 23(3):398-412. Disponible en: https://revistaamc.sld.cu/index.php/amc/article/view/6340
16. Peña AS. Medicina personalizada: inevitable. Rev Esp Enferm Dig [Internet]. 2010 [citado 23/02/2025]; 102(10):573-6. Disponible en: https://scielo.isciii.es/scielo.php?pid=S1130-01082010001000001&script=sci_arttext&tlng=es
17. Hurtado C. Medicina de precisión: conceptos, aplicaciones y proyecciones. Rev Med Clin Las Condes [Internet]. 2022 [citado 23/02/2025]; 33(1):7-16. Disponible en: https://catalogo.upc.edu.pe/discovery/fulldisplay?docid=cdi_crossref_primary_10_1016_j_rmclc_2022_01_002&context=PC&vid=51UPC_INST:51UPC_INST&lang=es&search_scope=MyInst_and_CI&adaptor=Primo%20Central&tab=002Todoslosrecursos&query=sub,exact,Metabolomics,AND&mode=advanced&offset=10
18. Kramer A, Green J, Pollard J Jr, Tugendreich S. Causal analysis approaches in Ingenuity Pathway Analysis. Bioinformatics [Internet]. 2014 [citado 23/02/2025]; 30(4):523-30. Disponible en: https://pubmed.ncbi.nlm.nih.gov/24336805/
19. Schoof EM, Furtwängler B, Üresin N, Rapin N, Savickas S, Gentil C, et al. Quantitative single-cell proteomics as a tool to characterize cellular hierarchies. Nat Commun [Internet]. 2021 [citado 23/02/2025]; 12(1):3341. Disponible en: https://pubmed.ncbi.nlm.nih.gov/34099695/
20. Ching T, Himmelstein DS, Beaulieu-Jones BK, Kalinin AA, Do BT, Way GP, et al. Opportunities and obstacles for deep learning in biology and medicine. J R Soc Interface [Internet]. 2018 [citado 23/02/2025]; 15(141):20170387. Disponible en: https://pubmed.ncbi.nlm.nih.gov/29618526/
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