Pan-cancer analysis reveals molecular signatures for predicting matrix stiffness in solid tumors.

Tumor matrix stiffness plays a critical role in cancer progression, metastasis, and therapy resistance. Although traditional biophysical methods have shed light on the impact of matrix stiffness on tumor behavior, these techniques are confined to measuring the physical properties of the tumors. In this study, we leveraged RNA-seq data to predict tumor matrix stiffness, aiming to reveal mechanical properties by molecular signatures across various cancer types. To this end, we systematically analyzed RNA-seq data from tumors of varying stiffness levels to identify stiffness-associated gene signatures. With these molecular signatures, we developed a computational model for predicting tumor matrix stiffness and further applied it to The Cancer Genome Atlas (TCGA) dataset. Our analysis revealed significant differences in the tumor microenvironment as well as immune response between soft and stiff tumor samples, suggesting that tumor rigidity impacts not only cellular behavior but also characteristics of the tumor microenvironment. These findings underscore the potential of RNA-based stiffness models to enhance our comprehension of tumor mechanics and cancer biology, thereby facilitating the development of innovative targeted therapies.