Akademik Veri Yönetim Sistemi
BIOMEDICAL MATERIALS, cilt.20, sa.6, 2025 (SCI-Expanded, Scopus)
Cancer is among the major causes of mortality, responsible for approximately 15% of all deaths worldwide. Despite remarkable progress in modern medicine, it remains a significant global health challenge. Nevertheless, conventional therapies such as chemotherapy and radiotherapy target healthy and malignant tissues, leading to adverse side effects, including hair loss, fatigue, and nausea, which significantly reduce patients' quality of life. Even more critically, the therapeutic response varies from patient to patient, which reduces the effectiveness of treatment. Therefore, cancer tissue engineering has evolved as a novel interdisciplinary field, aiming to develop structures that mimic the tumor microenvironment to elucidate cancer development mechanisms and devise effective treatment methods. However, producing a fully synthetic biosimilar matrix by assembling all individual ECM components remains unfeasible due to the heterogeneity and complex structure of tumor tissues, as well as the necessity of highly advanced micro- and nanoengineering techniques. Consequently, decellularization techniques have recently been applied to cancer tissues to produce biomimetic tumor models. In this review, we provided a comprehensive overview of the extracellular matrix (ECM) architecture and its role in tumor progression. We also discussed the structural differences between normal and malignant tissues. We briefly reviewed decellularization techniques and analytical approaches for ECM characterization. Emphasizing the cutting-edge research, we categorized developments into three groups: decellularized tumor-derived ECM (dT-ECM), hydrogels, and bioinks. Subsequently, we critically assessed the benefits, limitations, and potential future developments of dT-ECM-based strategies. Finally, we envision that tumor tissue engineering will provide preventive treatment approaches by developing patient-specific predictive and personalized cancer models through integrating advanced biomaterials with artificial intelligence and machine learning.