Recent Computational Approaches in Understanding the Links between Molecular Stress and Cancer Metastasis

Abstract

In the modern era of medicine, advancements in data science and biomedical technologies have revolutionized our understanding of diseases. Cancer, being a complex disease, has particularly benefited from the wealth of molecular data available, which can now be analyzed using cutting-edge artificial intelligence (AI) and information science methods. In this context, recent studies have increasingly recognized chronic stress as a significant factor in cancer progression. Utilizing computational methods to address this matter has demonstrated encouraging advancements, providing a hopeful outlook in our efforts to combat cancer. This review focuses on recent computational approaches in understanding the molecular links between stress and cancer metastasis. Specifically, we explore the utilization of single-cell data, an innovative technique in DNA sequencing that allows for detailed analysis. Additionally, we explore the application of AI and data mining techniques to these complex and large-scale datasets. Our findings underscore the potential of these computational pipelines to unravel the intricate relationship between stress and cancer metastasis. However, it is important to note that this field is still in its early stages, and we anticipate a proliferation of similar approaches in the near future, further advancing our understanding and treatment of cancer.