Originally discovered as a hypothetical protein with unknown function, CEMIP (cell migration-inducing and hyaluronan-binding protein) has been implicated in the pathogenesis of numerous diseases, including deafness, arthritis, atherosclerosis, idiopathic pulmonary fibrosis, and cancer. Although a comprehensive definition of its molecular functions is still in progress, major functions ascribed to CEMIP include the depolymerization of the extracellular matrix component hyaluronic acid (HA) and the regulation of a number of signaling pathways. CEMIP is a promising biomarker for colorectal cancer. Its expression is associated with poor prognosis for patients suffering from colorectal and other types of cancer and functionally contributes to tumor progression and metastasis. Here, we review our current understanding of how CEMIP is able to foster the process of tumor growth and metastasis, focusing particularly on colorectal cancer. Studies in cancer cells suggest that CEMIP exerts its pro-tumorigenic and pro-metastatic activities through stimulating migration and invasion, suppressing cell death and promoting survival, degrading HA, regulating pro-metastatic signaling pathways, inducing the epithelial–mesenchymal transition (EMT) program, and contributing to the metabolic reprogramming and pre-metastatic conditioning of future metastatic microenvironments. There is also increasing evidence indicating that CEMIP may be expressed in cells within the tumor microenvironment that promote tumorigenesis and metastasis formation, although this remains in an early stage of investigation. CEMIP expression and activity can be therapeutically targeted at a number of levels, and preliminary findings in animal models show encouraging results in terms of reduced tumor growth and metastasis, as well as combating therapy resistance. Taken together, CEMIP represents an exciting new player in the progression of colorectal and other types of cancer that holds promise as a therapeutic target and biomarker.

Hyaluronan (HA) levels are dynamically regulated homeostatically through biosynthesis and degradation. HA homeostasis is often perturbed under disease conditions. HA degradation products are thought to contribute to disease pathology. The hyaluronidase CEMIP requires the presence of living cells for its HA depolymerizing activity. CEMIP is overexpressed in a variety of pathological conditions, and the inhibition of its hyaluronidase activity therefore has therapeutic potential. To identify novel inhibitors of the CEMIP hyaluronidase activity, we established here a cell-compatible, medium-throughput assay for CEMIP-dependent HA depolymerization. The assay employs ultrafiltration plates to separate low- from high-molecular-weight HA, followed by quantification of HA fragments using an HA ELISA-like assay. Using this assay, we tested a range of compounds that have been reported to inhibit other hyaluronidases. Thereby, we identified several sulfated hydrocarbon polymers that inhibit CEMIP more potently than other hyaluronidases. One of these is heparin, a sulfated glycosaminoglycan produced by mast cells that constitutes the first described physiological CEMIP inhibitor. The most potent inhibitor (IC₅₀ of 1.8 nM) is dextran sulfate, a synthetic sulfated polysaccharide. Heparin and dextran sulfate are used in numerous established and experimental biomedical applications. Their ability to inhibit CEMIP needs to be taken into account in these contexts.

Osteopontin (OPN) is a phosphoprotein with diverse functions in various physiological and pathological processes. OPN expression is increased in multiple cancers, and OPN within tumour tissue has been shown to promote key stages of cancer development. OPN levels are also elevated in the circulation of cancer patients, which in some cases has been correlated with enhanced metastatic propensity and poor prognosis. However, the precise impact of circulating OPN (cOPN) on tumour growth and progression remains insufficiently understood. To examine the role of cOPN, we used a melanoma model, in which we stably increased the levels of cOPN through adeno-associated virus-mediated transduction. We found that increased cOPN promoted the growth of primary tumours, but did not significantly alter the spontaneous metastasis of melanoma cells to the lymph nodes or lungs, despite an increase in the expression of multiple factors linked to tumour progression. To assess whether cOPN has a role at later stages of metastasis formation, we employed an experimental metastasis model, but again could not detect any increase in pulmonary metastasis in animals with elevated levels of cOPN. These results demonstrate that increased levels of OPN in the circulation play distinct roles during different stages of melanoma progression.

A better understanding of the process of melanoma metastasis is required to underpin the development of novel therapies that will improve patient outcomes. The use of appropriate animal models is indispensable for investigating the mechanisms of melanoma metastasis. However, reliable and practicable quantification of metastases in experimental mice remains a challenge, particularly if the metastatic burden is low. Here, we describe a qRT-PCR-based protocol that employs the melanocytic marker Trp-1 for the sensitive quantification of melanoma metastases in the murine lung. Using this protocol, we were able to detect the presence of as few as 100 disseminated melanoma cells in lung tissue. This allowed us to quantify metastatic burden in a spontaneous syngeneic B16-F10 metastasis model, even in the absence of visible metastases, as well as in the autochthonous Tg(Grm1)/Cyld^−/− melanoma model. Importantly, we also observed an uneven distribution of disseminated melanoma cells amongst the five lobes of the murine lung, which varied considerably from animal to animal. Together, our findings demonstrate that the qRT-PCR-based detection of Trp-1 allows the quantification of low pulmonary metastatic burden in both transplantable and autochthonous murine melanoma models, and show that the analysis of lung metastasis in such models needs to take into account the stochastic distribution of metastatic lesions amongst the lung lobes.