Deciphering Aging, Genetic, and Epigenetic Heterogeneity in Cancer Evolution: Toward Personalized Precision Preventative Medicine
ABSTRACT Background Cancer's inherent ability to evolve presents significant challenges for its categorization and treatment. Cancer evolution is driven by genetic, epigenetic, and phenotypic diversity influenced by microenvironment changes. Aging plays a crucial role by altering the microenvir...
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| Format: | Article |
| Language: | English |
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Wiley
2025-03-01
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| Series: | Aging and Cancer |
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| Online Access: | https://doi.org/10.1002/aac2.12078 |
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| author | Lamis Naddaf Sheng Li |
| author_facet | Lamis Naddaf Sheng Li |
| author_sort | Lamis Naddaf |
| collection | DOAJ |
| description | ABSTRACT Background Cancer's inherent ability to evolve presents significant challenges for its categorization and treatment. Cancer evolution is driven by genetic, epigenetic, and phenotypic diversity influenced by microenvironment changes. Aging plays a crucial role by altering the microenvironment and inducing substantial genetic and epigenetic heterogeneity within an individual's somatic cells even before cancer initiation. Objectives This review highlights the clinical significance of epigenetic mechanisms in cancer evolution, focusing on hematopoietic and solid tumors. The review aims to explore opportunities for integrating evolutionary principles and data science into cancer research. Methods The review synthesizes recent advancements in omics technologies, single‐cell sequencing, and genetic barcoding to elucidate epigenetic mechanisms and aging's role in cancer evolution. Results Epigenetic mechanisms' high plasticity generates heritable phenotypic diversity, driving malignant evolution toward poor prognosis. Advances in single‐cell sequencing and genetic barcoding enable the precise detection and tracking of biomarkers, allowing early, personalized interventions. Incorporating data science into cancer research has the potential to map, predict, and prevent cancer evolution effectively. Conclusion Understanding cancer evolution through novel technologies and data analysis offers a proactive approach to cancer prevention and treatment. By predicting key evolutionary events and leveraging personalized strategies, patient outcomes can be improved, and healthcare burdens reduced, marking a transformative shift in oncology. |
| format | Article |
| id | doaj-art-c95df5b9bb2940eb95581f87d145957f |
| institution | OA Journals |
| issn | 2643-8909 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | Aging and Cancer |
| spelling | doaj-art-c95df5b9bb2940eb95581f87d145957f2025-08-20T02:29:04ZengWileyAging and Cancer2643-89092025-03-0161192910.1002/aac2.12078Deciphering Aging, Genetic, and Epigenetic Heterogeneity in Cancer Evolution: Toward Personalized Precision Preventative MedicineLamis Naddaf0Sheng Li1Department of Biochemistry and Molecular Medicine, Keck School of Medicine University of Southern California Los Angeles California USADepartment of Biochemistry and Molecular Medicine, Keck School of Medicine University of Southern California Los Angeles California USAABSTRACT Background Cancer's inherent ability to evolve presents significant challenges for its categorization and treatment. Cancer evolution is driven by genetic, epigenetic, and phenotypic diversity influenced by microenvironment changes. Aging plays a crucial role by altering the microenvironment and inducing substantial genetic and epigenetic heterogeneity within an individual's somatic cells even before cancer initiation. Objectives This review highlights the clinical significance of epigenetic mechanisms in cancer evolution, focusing on hematopoietic and solid tumors. The review aims to explore opportunities for integrating evolutionary principles and data science into cancer research. Methods The review synthesizes recent advancements in omics technologies, single‐cell sequencing, and genetic barcoding to elucidate epigenetic mechanisms and aging's role in cancer evolution. Results Epigenetic mechanisms' high plasticity generates heritable phenotypic diversity, driving malignant evolution toward poor prognosis. Advances in single‐cell sequencing and genetic barcoding enable the precise detection and tracking of biomarkers, allowing early, personalized interventions. Incorporating data science into cancer research has the potential to map, predict, and prevent cancer evolution effectively. Conclusion Understanding cancer evolution through novel technologies and data analysis offers a proactive approach to cancer prevention and treatment. By predicting key evolutionary events and leveraging personalized strategies, patient outcomes can be improved, and healthcare burdens reduced, marking a transformative shift in oncology.https://doi.org/10.1002/aac2.12078agingcancer preventionepigeneticsheterogeneity |
| spellingShingle | Lamis Naddaf Sheng Li Deciphering Aging, Genetic, and Epigenetic Heterogeneity in Cancer Evolution: Toward Personalized Precision Preventative Medicine Aging and Cancer aging cancer prevention epigenetics heterogeneity |
| title | Deciphering Aging, Genetic, and Epigenetic Heterogeneity in Cancer Evolution: Toward Personalized Precision Preventative Medicine |
| title_full | Deciphering Aging, Genetic, and Epigenetic Heterogeneity in Cancer Evolution: Toward Personalized Precision Preventative Medicine |
| title_fullStr | Deciphering Aging, Genetic, and Epigenetic Heterogeneity in Cancer Evolution: Toward Personalized Precision Preventative Medicine |
| title_full_unstemmed | Deciphering Aging, Genetic, and Epigenetic Heterogeneity in Cancer Evolution: Toward Personalized Precision Preventative Medicine |
| title_short | Deciphering Aging, Genetic, and Epigenetic Heterogeneity in Cancer Evolution: Toward Personalized Precision Preventative Medicine |
| title_sort | deciphering aging genetic and epigenetic heterogeneity in cancer evolution toward personalized precision preventative medicine |
| topic | aging cancer prevention epigenetics heterogeneity |
| url | https://doi.org/10.1002/aac2.12078 |
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