MicroRNAs and their impacts on cancer cell’s mitochondria
Mitochondria and Cancer: From Basic Science to Clinical Applications - Understanding Pathogenesis, Therapy Resistance, and Disease Relapse, Elsevier, ss.119-130, 2026
- Yayın Türü: Kitapta Bölüm / Araştırma Kitabı
- Basım Tarihi: 2026
- Doi Numarası: 10.1016/b978-0-443-40535-8.00014-4
- Yayınevi: Elsevier
- Sayfa Sayıları: ss.119-130
- Anahtar Kelimeler: Cancer therapy resistance, Deep learning, MicroRNA (miRNA), Mitochondrial metabolism, Mitophagy, Warburg effect
- Sağlık Bilimleri Üniversitesi Adresli: Evet
Özet
This chapter delves into the critical role of microRNAs (miRNAs) in regulating mitochondrial function and their implications in cancer progression and therapy resistance. miRNAs, small noncoding RNAs, serve as key posttranscriptional regulators, influencing mitochondrial processes such as metabolic reprogramming (e.g., the Warburg effect), oxidative phosphorylation, redox homeostasis, apoptosis, and mitochondrial dynamics (e.g., fission/fusion and mitophagy). By targeting nuclear-encoded mitochondrial genes or directly localizing within mitochondria as mitochondrial-associated miRNAs (mitomiRs), miRNAs modulate cancer cell survival, proliferation, and adaptation to stress. The chapter highlights specific miRNAs, such as miR-23b-3p, which suppresses PGC1α to promote glycolysis in osteosarcoma, and miR-210, a hypoxia-induced miRNA, which disrupts mitochondrial respiration. In addition, it explores how dysregulated miRNAs contribute to therapy resistance by altering mitochondrial quality control and immune evasion mechanisms, including extracellular vesicle (EV)-mediated mitochondrial transfer. The discussion extends to innovative therapeutic strategies, such as miRNA mimics and inhibitors, designed to restore mitochondrial function and sensitize cancer cells to treatment. Finally, the chapter underscores the transformative potential of deep learning in analyzing miRNA-mitochondria-cancer interactions, enabling the identification of biomarkers and personalized therapeutic targets. By integrating multiomics data, these advanced computational approaches offer new insights into cancer biology and pave the way for precision medicine.