Abstract
BackgroundIn addition to alterations concerning the expression of oncogenes and onco-suppressors, melanoma is characterized by the presence of distinctive gangliosides (sialic acid carrying glycosphingolipids). Gangliosides strongly control cell surface dynamics and signaling; therefore, it could be assumed that these alterations are linked to modifications of cell behavior acquired by the tumor. On these bases, this work investigated the correlations between melanoma cell ganglioside metabolism profiles and the biological features of the tumor and the survival of patients.MethodsMelanoma cell lines were established from surgical specimens of AJCC stage III and IV melanoma patients. Sphingolipid analysis was carried out on melanoma cell lines and melanocytes through cell metabolic labeling employing [3-3H]sphingosine and by FACS. N-glycolyl GM3 was identified employing the 14 F7 antibody. Gene expression was assayed by Real Time PCR. Cell invasiveness was assayed through a Matrigel invasion assay; cell proliferation was determined through the soft agar assay, MTT, and [3H] thymidine incorporation. Statistical analysis was performed using XLSTAT software for melanoma hierarchical clustering based on ganglioside profile, the Kaplan-Meier method, the log-rank (Mantel-Cox) test, and the Mantel-Haenszel test for survival analysis.ResultsBased on the ganglioside profiles, through a hierarchical clustering, we classified melanoma cells isolated from patients into three clusters: 1) cluster 1, characterized by high content of GM3, mainly in the form of N-glycolyl GM3, and GD3; 2) cluster 2, characterized by the appearance of complex gangliosides and by a low content of GM3; 3) cluster 3, which showed an intermediate phenotype between cluster 1 and cluster 3. Moreover, our data demonstrated that: a) a correlation could be traced between patients’ survival and clusters based on ganglioside profiles, with cluster 1 showing the worst survival; b) the expression of several enzymes (sialidase NEU3, GM2 and GM1 synthases) involved in ganglioside metabolism was associated with patients’ survival; c) melanoma clusters showed different malignant features such as growth in soft agar, invasiveness, expression of anti-apoptotic proteins.ConclusionsGanglioside profile and metabolism is strictly interconnected with melanoma aggressiveness. Therefore, the profiling of melanoma gangliosides and enzymes involved in their metabolism could represent a useful prognostic and diagnostic tool.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2407-14-560) contains supplementary material, which is available to authorized users.
Highlights
In addition to alterations concerning the expression of oncogenes and onco-suppressors, melanoma is characterized by the presence of distinctive gangliosides
Ganglioside profile and metabolism is strictly interconnected with melanoma aggressiveness
Our results demonstrated that: a) melanomas displayed different ganglioside patterns and three clusters of tumors could be identified; b) a correlation could be traced between patients’ survival and melanoma ganglioside profiles; c) the expression of several enzymes involved in ganglioside metabolism was associated with patients’ survival; d) melanoma clusters identified on the basis of ganglioside profile exhibited different features determining melanoma malignancy
Summary
In addition to alterations concerning the expression of oncogenes and onco-suppressors, melanoma is characterized by the presence of distinctive gangliosides (sialic acid carrying glycosphingolipids). Gangliosides strongly control cell surface dynamics and signaling; it could be assumed that these alterations are linked to modifications of cell behavior acquired by the tumor On these bases, this work investigated the correlations between melanoma cell ganglioside metabolism profiles and the biological features of the tumor and the survival of patients. Melanoma staging is based on the guidelines published by the American Joint Committee on Cancer (AJCC) in 2009 [3] and advises the employment of histopathological and clinical criteria. This system is limited in its ability to provide a precise prognosis: a large number of patients with similar or identical clinical and histopathological features has different clinical outcome, from being cured to death [4]. Many attempts have been done to identify molecular markers or gene and protein signatures predicting clinical outcome in melanoma but, so far, none of them has been sufficiently validated [6,7,8,9]
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