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Publications

Journal Article Review Epublish

PubMed: 39982247 DOI: 10.3390/epigenomes9010005

Epigenomic Echoes-Decoding Genomic and Epigenetic Instability to Distinguish Lung Cancer Types and Predict Relapse.

Epigenomes 9(1)

Authors: Alexandra A Baumann, Zholdas Buribayev, Olaf Wolkenhauer, Amankeldi A Salybekov, Markus Wolfien

Affiliations

  • Department of Systems Biology and Bioinformatics, Institute of Computer Science, University of Rostock, 18051 Rostock, Germany.
  • Faculty of Medicine Carl Gustav Carus, Institute for Medical Informatics and Biometry, TUD Dresden University of Technology, 01069 Dresden, Germany.
  • Department of Computer Science, Faculty of Information Technologies, Al-Farabi Kazakh National University, 050040 Almaty, Kazakhstan.
  • Leibniz-Institute for Food Systems Biology, Technical University of Munich, 80333 Freising, 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, 010000 Astana, Kazakhstan.
  • Kidney Disease and Transplant Center, Shonan Kamakura General Hospital, Kamakura 247-8533, Japan.
  • Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI), 01069 Dresden, Germany.

Abstract

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.

MeSH

Lung, Epigenomics, Neoplasms, Disease Progression, Biomarkers, Lung Neoplasms, Genomic Instability