Glycosaminoglycans (GAGs) and proteoglycans (PGs) are essential components of the extracellular matrix (ECM) with pivotal roles in cellular mechanosensing pathways. GAGs, such as heparan sulfate (HS) and chondroitin sulfate (CS), interact with various cell surface receptors, including integrins and receptor tyrosine kinases, to modulate cellular responses to mechanical stimuli. PGs, comprising a core protein with covalently attached GAG chains, serve as dynamic regulators of tissue mechanics and cell behavior, thereby playing a crucial role in maintaining tissue homeostasis. Dysregulation of GAG/PG-mediated mechanosensing pathways is implicated in numerous pathological conditions, including cancer and inflammation. Understanding the intricate mechanisms by which GAGs and PGs modulate cellular responses to mechanical forces holds promise for developing novel therapeutic strategies targeting mechanotransduction pathways in disease. This comprehensive overview underscores the importance of GAGs and PGs as key mediators of mechanosensing in maintaining tissue homeostasis and their potential as therapeutic targets for mitigating mechano-driven pathologies, focusing on cancer and inflammation.

Purpose: Tumor heterogeneity may be responsible for poor response to treatment and adverse prognosis in women with HGOEC. The purpose of this study is to propose an automated classification system that allows medical experts to automatically identify intratumoral areas of different cellularity indicative of tumor heterogeneity. Methods: Twenty-two patients underwent dedicated pelvic MRI, and a database of 11,095 images was created. After image processing techniques were applied to align and assess the cancerous regions, two specific imaging series were used to extract quantitative features (radiomics). These features were employed to create, through artificial intelligence, an estimator of the highly cellular intratumoral area as defined by arbitrarily selected apparent diffusion coefficient (ADC) cut-off values (ADC < 0.85 × 10⁻³ mm²/s). Results: The average recorded accuracy of the proposed automated classification system was equal to 0.86. Conclusion: The proposed classification system for assessing highly cellular intratumoral areas, based on radiomics, may be used as a tool for assessing tumor heterogeneity.

Therapeutic peptides have emerged as promising tools in oncology due to their high specificity, favorable safety profile, and capacity to target molecular hallmarks of cancer. Their clinical translation, however, remains limited by poor stability, rapid proteolytic degradation, and inefficient biodistribution. Iron oxide nanoparticles (IONPs) offer a compelling solution to these challenges. Owing to their biocompatibility, magnetic properties, and ability to serve as both drug carriers and imaging agents, IONPs have become a versatile platform for precision nanomedicine. The integration of peptides with IONPs has generated a new class of hybrid systems that combine the biological accuracy of peptide ligands with the multifunctionality of magnetic nanomaterials. Peptide functionalization enables selective tumor targeting and deeper tissue penetration, while the IONP core supports controlled delivery, MRI-based tracking, and activation of therapeutic mechanisms such as magnetic hyperthermia. These hybrids also influence the tumor microenvironment (TME), facilitating stromal remodeling and improved drug accessibility. Importantly, the iron-driven redox chemistry inherent to IONPs can trigger regulated cell death pathways, including ferroptosis and autophagy, inhibiting opportunities to overcome resistance in aggressive or refractory tumors. As advances in peptide engineering, nanotechnology, and artificial intelligence accelerate design and optimization, peptide–IONP conjugates are poised for translational progress. Their combined targeting precision, imaging capability, and therapeutic versatility position them as promising candidates for next-generation cancer theranostics.