Integrative diagnostics of cancer's extracellular matrix: Synergizing imaging and multi-omics for precision medicine
The extracellular matrix (ECM) is a highly dynamic and biologically active network that plays a critical role in tissue homeostasis, development, and disease. In cancer, ECM remodeling—characterized by altered protein composition, increased matrix-degrading enzyme activity, and basement membrane disruption—contributes significantly to tumor progression, invasion, and metastasis. Understanding these molecular alterations is essential for improving cancer diagnostics and therapeutics. This review explores advanced methodologies for investigating ECM dynamics, with a particular focus on the integration of mass spectrometry and molecular imaging. Mass spectrometry offers high-resolution proteomic and metabolomic profiling, enabling detailed characterization of ECM composition and post-translational modifications. Concurrently, molecular imaging modalities such as PET, SPECT, and MRI provide non-invasive, in vivo visualization of ECM alterations and tumor microenvironment changes. The synergistic use of these platforms allows for comprehensive spatial and molecular insights into ECM remodeling, supporting early cancer detection, real-time monitoring of therapeutic response, and development of ECM-targeted treatments. Moreover, circulating ECM-derived proteins are emerging as promising biomarkers for liquid biopsy-based cancer diagnosis. The convergence of imaging and mass spectrometry technologies holds great potential to enhance diagnostic precision and inform personalized treatment strategies. This integrative approach represents a transformative step toward improved clinical outcomes in cancer care.
Authors
- Balasubramani Ravindran (ORCID: https://orcid.org/0000-0001-8764-7840)
- Vadanasundari Vedarethinam (ORCID: https://orcid.org/0000-0003-1129-4721)
- Soon Woong Chang (ORCID: https://orcid.org/0000-0002-6005-1796)
- M. Vijayakumar (ORCID: https://orcid.org/0000-0002-6437-1463)
- Ravishankar Ram Mani
Institutions
- Kyonggi University (KR)
- Yeungnam University (KR)
- UCSI University (MY)
- Stony Brook University (US)
- Karpagam Academy of Higher Education (IN)
- Bharath University (IN)
Publication Details
- Journal
- Journal of Radiation Research and Applied Sciences
- Published
- 2025-05-26
- DOI
- https://doi.org/10.1016/j.jrras.2025.101636
- Citations
- 1
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.36