Deciphering Ferroptosis: From Molecular Pathways to Machine Learning-Guided Therapeutic Innovation

被引:0
|
作者
Mete, Megha [1 ]
Ojha, Amiya [1 ]
Dhar, Priyanka [2 ]
Das, Deeplina [1 ]
机构
[1] Natl Inst Technol Agartala, Dept Bioengn, Agartala 799046, Tripura, India
[2] CSIR Indian Inst Chem Biol, Kolkata 700032, India
关键词
Ferroptosis; Chronic diseases; Small molecules; Therapeutics; Machine learning; ACUTE KIDNEY INJURY; CELL-DEATH; PROMOTES FERROPTOSIS; HUB GENES; INHIBITION; MECHANISMS; STRESS; SUPPRESSOR; LIPROXSTATIN-1; IDENTIFICATION;
D O I
10.1007/s12033-024-01139-0
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
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.
引用
收藏
页码:1290 / 1309
页数:20
相关论文
共 50 条
  • [1] Machine Learning-Guided Prediction of Hydroformylation
    Shi, Haonan
    Shen, Chaoren
    Huang, Zheng
    Dong, Kaiwu
    CHEMPHYSCHEM, 2025, 26 (03)
  • [2] Machine Learning-Guided Etch Proximity Correction
    Shim, Seongbo
    Shin, Youngsoo
    IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING, 2017, 30 (01) : 1 - 7
  • [3] A review on machine learning-guided design of energy materials
    Kim, Seongmin
    Xu, Jiaxin
    Shang, Wenjie
    Xu, Zhihao
    Lee, Eungkyu
    Luo, Tengfei
    PROGRESS IN ENERGY, 2024, 6 (04):
  • [4] Machine learning-guided synthesis of nanomaterials for breast cancer therapy
    Zhou, Kun
    Tian, Baoxing
    Lu, Ji
    Dong, Bing
    Xu, Han
    SCIENTIFIC REPORTS, 2024, 14 (01):
  • [5] Machine learning-guided engineering of phosphocholine cytidylyltransferase for improved activity and halotolerance in citicoline production
    Wen, Qingshi
    Zheng, Cheng
    Miao, Rongxin
    Qu, Mingjin
    Ying, Hanjie
    Wang, Junzhi
    FOOD BIOSCIENCE, 2024, 62
  • [6] Machine learning-guided design, synthesis, and characterization of atomically dispersed electrocatalysts
    Li, Sirui
    Zhang, Hanguang
    Holby, Edward F.
    Zelenay, Piotr
    Kort-Kamp, Wilton J. M.
    CURRENT OPINION IN ELECTROCHEMISTRY, 2024, 48
  • [7] Machine learning-guided prediction and optimization of precipitation efficiency in the Bayer process
    Bakhtom, Abbas
    Bariki, Saeed Ghasemzade
    Movahedirad, Salman
    Sobati, Mohammad Amin
    CHEMICAL PAPERS, 2023, 77 (05) : 2509 - 2524
  • [8] Machine Learning-Guided Discovery of AcrB and MexB Efflux Pump Inhibitors
    Bera, Abhishek
    Roy, Rakesh Kumar
    Joshi, Pritish
    Patra, Niladri
    JOURNAL OF PHYSICAL CHEMISTRY B, 2024, 128 (03): : 648 - 663
  • [9] Machine learning-guided prediction and optimization of precipitation efficiency in the Bayer process
    Abbas Bakhtom
    Saeed Ghasemzade Bariki
    Salman Movahedirad
    Mohammad Amin Sobati
    Chemical Papers, 2023, 77 : 2509 - 2524
  • [10] Molecular mechanisms of ferroptosis and the potential therapeutic targets of ferroptosis signaling pathways for glioblastoma
    Zhang, Meng
    Lei, Qian
    Huang, Xiaobo
    Wang, Yi
    FRONTIERS IN PHARMACOLOGY, 2022, 13