Artikel ; Online: Deciphering Ferroptosis: From Molecular Pathways to Machine Learning-Guided Therapeutic Innovation.
2024
Abstract: Ferroptosis is a unique form of cell death reliant on iron and lipid peroxidation. It disrupts redox balance, causing cell death by damaging the plasma membrane, with inducers acting through enzymatic pathways or transport systems. In cancer treatment, ... ...
| Abstract | Ferroptosis is a unique form of cell death reliant on iron and lipid peroxidation. It disrupts redox balance, causing cell death by damaging the plasma membrane, with inducers acting through enzymatic pathways or transport systems. In cancer treatment, suppressing ferroptosis or circumventing it holds significant promise. Beyond cancer, ferroptosis affects aging, organs, metabolism, and nervous system. Understanding ferroptosis mechanisms holds promise for uncovering novel therapeutic strategies across a spectrum of diseases. However, detection and regulation of this regulated cell death are still mired with challenges. The dearth of cell, tissue, or organ-specific biomarkers muted the pharmacological use of ferroptosis. This review covers recent studies on ferroptosis, detailing its properties, key genes, metabolic pathways, and regulatory networks, emphasizing the interaction between cellular signaling and ferroptotic cell death. It also summarizes recent findings on ferroptosis inducers, inhibitors, and regulators, highlighting their potential therapeutic applications across diseases. The review addresses challenges in utilizing ferroptosis therapeutically and explores the use of machine learning to uncover complex patterns in ferroptosis-related data, aiding in the discovery of biomarkers, predictive models, and therapeutic targets. Finally, it discusses emerging research areas and the importance of continued investigation to harness the full therapeutic potential of targeting ferroptosis. |
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| Sprache | Englisch |
| Erscheinungsdatum | 2024-04-13 |
| Erscheinungsland | Switzerland |
| Dokumenttyp | Journal Article ; Review |
| ZDB-ID | 1193057-3 |
| ISSN | 1559-0305 ; 1073-6085 |
| ISSN (online) | 1559-0305 |
| ISSN | 1073-6085 |
| DOI | 10.1007/s12033-024-01139-0 |
| Datenquelle | MEDical Literature Analysis and Retrieval System OnLINE |
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