Tumor microenvironment (TME) is a complex ecosystem composed of both cellular and non-cellular components that surround tumor tissue. The extracellular matrix (ECM) is a key component of the TME, performing multiple essential functions by providing mechanical support, shaping the TME, regulating metabolism and signaling, and modulating immune responses, all of which profoundly influence cell behavior. The quantity and cross-linking status of stromal components are primary determinants of tissue stiffness. During tumor development, ECM stiffness not only serves as a barrier to hinder drug delivery but also promotes cancer progression by inducing mechanical stimulation that activates cell membrane receptors and mechanical sensors. Thus, a comprehensive understanding of how ECM stiffness regulates tumor progression is crucial for identifying potential therapeutic targets for cancer. This review examines the effects of ECM stiffness on tumor progression, encompassing proliferation, migration, metastasis, drug resistance, angiogenesis, epithelial-mesenchymal transition (EMT), immune evasion, stemness, metabolic reprogramming, and genomic stability. Finally, we explore therapeutic strategies that target ECM stiffness and their implications for tumor progression.

Background: No investigations have thoroughly explored the feasibility of combining magnetic resonance (MR) images and deep-learning methods for predicting the progression of knee osteoarthritis (KOA). We thus aimed to develop a potential deep-learning model for predicting OA progression based on MR images for the clinical setting. Methods: A longitudinal case-control study was performed using data from the Foundation for the National Institutes of Health (FNIH), composed of progressive cases [182 osteoarthritis (OA) knees with both radiographic and pain progression for 24–48 months] and matched controls (182 OA knees not meeting the case definition). DeepKOA was developed through 3-dimensional (3D) DenseNet169 to predict KOA progression over 24–48 months based on sagittal intermediate-weighted turbo-spin echo sequences with fat-suppression (SAG-IW-TSE-FS), sagittal 3D dual-echo steady-state water excitation (SAG-3D-DESS-WE) and its axial and coronal multiplanar reformation, and their combined MR images with patient-level labels at baseline, 12, and 24 months to eventually determine the probability of progression. The classification performance of the DeepKOA was evaluated using 5-fold cross-validation. An X-ray-based model and traditional models that used clinical variables via multilayer perceptron were built. Combined models were also constructed, which integrated clinical variables with DeepKOA. The area under the curve (AUC) was used as the evaluation metric. Results: The performance of SAG-IW-TSE-FS in predicting OA progression was similar or higher to that of other single and combined sequences. The DeepKOA based on SAG-IW-TSE-FS achieved an AUC of 0.664 (95% CI: 0.585–0.743) at baseline, 0.739 (95% CI: 0.703–0.775) at 12 months, and 0.775 (95% CI: 0.686–0.865) at 24 months. The X-ray-based model achieved an AUC ranging from 0.573 to 0.613 at 3 time points. However, adding clinical variables to DeepKOA did not improve performance (P>0.05). Initial visualizations from gradient-weighted class activation mapping (Grad-CAM) indicated that the frequency with which the patellofemoral joint was highlighted increased as time progressed, which contrasted the trend observed in the tibiofemoral joint. The meniscus, the infrapatellar fat pad, and muscles posterior to the knee were highlighted to varying degrees. Conclusions: This study initially demonstrated the feasibility of DeepKOA in the prediction of KOA progression and identified the potential responsible structures which may enlighten the future development of more clinically practical methods.