ABSTRACT
Cancer is one of the leading causes of death in humans. Dysregulation of cell-cell or cell-extracellular matrix adhesion is the prominent feature of cancer cells during tumor formation, progression and metastasis. As regulators of tumor cell and extracellular matrix interactions, cell adhesion molecules (CAMs) play a signifi cant role in these processes. And in the case of organ-specifi c metastasis, certain primary tumor cells display frequent metastasis to specifi c organ of preference through unique interaction between particular CAMs that appear on both the surface of tumor cells and the vascular endothelium of the preferred organs. Less is known, however, about the diff erential expression of CAMs between various tumor types or between vascular endothelium preferred and non-preferred host tissues of metastasis. Th erefore, understanding the molecular diversity of CAMs associated with cell surfaces of diff erent types of tumors or host tissues becomes
important for the development of diagnostic markers and anticancer therapies. In the post-genomic era, biomarkers can be screened using whole genome approaches using high-throughput technologies and by interpreting the voluminous data generated from these technologies. However, our limited ability to interpret the voluminous data is complemented by bioinformatics approaches. We summarize recently developed bioinformatics methods that are being used to identify and predict the functional CAMs involved in various processes of tumor progression and metastasis.
