Khalili‐Tanha, Ghazaleh

Breast cancer is the most prevalent cancer among women and is challenging to diagnose and treat due to its diverse subtypes and stages. Precision medicine aims to improve early detection, prognosis, and treatment planning by identifying new clinical biomarkers. The review emphasizes the importance of using cutting-edge technology and artificial intelligence (AI) to identify new biomarkers associated with epithelial–mesenchymal transition (EMT). During EMT, epithelial cells transform into a mesenchymal state, a process driven by genetic and epigenetic alterations that facilitate cancer progression. The review discusses how statistical analysis and machine learning methods applied to multi-omics data facilitate the discovery of novel EMT-related biomarkers, thereby advancing therapeutic strategies. This conclusion is supported by numerous clinical and preclinical studies on breast cancer.

Lung cancer is a leading cause of cancer-related mortality worldwide, characterized by its aggressive nature and poor prognosis. Matrix metalloproteinases (MMPs), a family of zinc-dependent endopeptidases, play a pivotal role in the progression of lung cancer. They contribute to tumor invasion, metastasis, angiogenesis, and the modulation of the tumor microenvironment by degrading extracellular matrix components and regulating various cellular signaling pathways. Elevated levels of specific MMPs, such as MMP-2, MMP-9, and MMP-14, have been associated with advanced disease stages and reduced survival rates. As such, MMPs have emerged as valuable biomarkers for the diagnosis, prognosis, and prediction of treatment responses in lung cancer. This review aims to provide a comprehensive overview of the current understanding of MMPs in lung cancer, highlighting their diagnostic and prognostic significance, as well as their potential as therapeutic targets. Despite the initial setbacks in developing broad-spectrum MMP inhibitors, recent advancements have spurred interest in more selective inhibitors that minimize off-target effects and enhance therapeutic efficacy. Furthermore, combining MMP-targeted therapies with conventional treatments, such as chemotherapy and immunotherapy, holds promise for improving clinical outcomes. Future research directions include exploring novel MMP inhibitors, understanding the regulatory mechanisms of MMP activity, and integrating MMP biomarkers into personalized medicine approaches. As the field progresses, targeting MMPs may offer new therapeutic avenues and improve the prognosis for lung cancer patients, making this a promising area of investigation.