Epigenomic Echoes—Decoding Genomic and Epigenetic Instability to Distinguish Lung Cancer Types and Predict Relapse
Baumann A.A. Buribayev Z. Wolkenhauer O. Salybekov A.A. Wolfien M.
March 2025Multidisciplinary Digital Publishing Institute (MDPI)
Epigenomes
2025#9Issue 1
Genomic and epigenomic instability are defining features of cancer, driving tumor progression, heterogeneity, and therapeutic resistance. Central to this process are epigenetic echoes, persistent and dynamic modifications in DNA methylation, histone modifications, non-coding RNA regulation, and chromatin remodeling that mirror underlying genomic chaos and actively influence cancer cell behavior. This review delves into the complex relationship between genomic instability and these epigenetic echoes, illustrating how they collectively shape the cancer genome, affect DNA repair mechanisms, and contribute to tumor evolution. However, the dynamic, context-dependent nature of epigenetic changes presents scientific and ethical challenges, particularly concerning privacy and clinical applicability. Focusing on lung cancer, we examine how specific epigenetic patterns function as biomarkers for distinguishing cancer subtypes and monitoring disease progression and relapse.
biomarkers , disease progression , epigenetics , genomic instability , lung cancer
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Department of Systems Biology and Bioinformatics, Institute of Computer Science, University of Rostock, Rostock, 18051, Germany
Faculty of Medicine Carl Gustav Carus, Institute for Medical Informatics and Biometry, TUD Dresden University of Technology, Dresden, 01069, Germany
Department of Computer Science, Faculty of Information Technologies, Al-Farabi Kazakh National University, Almaty, 050040, Kazakhstan
Leibniz-Institute for Food Systems Biology, Technical University of Munich, Freising, 80333, Germany
Stellenbosch Institute of Advanced Study, Wallenberg Research Centre, Stellenbosch University, Stellenbosch, 7535, South Africa
Regenerative Medicine Division, Cell and Gene Therapy Department, Qazaq Institute of Innovative Medicine, Astana, 010000, Kazakhstan
Kidney Disease and Transplant Center, Shonan Kamakura General Hospital, Kamakura, 247-8533, Japan
Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI), Dresden, 01069, Germany
Department of Systems Biology and Bioinformatics
Faculty of Medicine Carl Gustav Carus
Department of Computer Science
Leibniz-Institute for Food Systems Biology
Stellenbosch Institute of Advanced Study
Regenerative Medicine Division
Kidney Disease and Transplant Center
Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI)
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