A meta‑analysis of transcriptome datasets characterizes malignant transformation from melanocytes and nevi to melanoma (original) (raw)
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Expression Profiling Reveals Novel Pathways in the Transformation of Melanocytes to Melanomas
Cancer Research, 2004
Affymetrix and spotted oligonucleotide microarrays were used to assess global differential gene expression comparing normal human melanocytes with six independent melanoma cell strains from advanced lesions. The data, validated at the protein level for selected genes, confirmed the overexpression in melanoma cells relative to normal melanocytes of several genes in the growth factor/receptor family that confer growth advantage and metastasis. In addition, novel pathways and patterns of associated expression in melanoma cells not reported before emerged, including the following:
The gene expression signatures of melanoma progression
Proceedings of the National Academy of Sciences, 2005
Because of the paucity of available tissue, little information has previously been available regarding the gene expression profiles of primary melanomas. To understand the molecular basis of melanoma progression, we compared the gene expression profiles of a series of nevi, primary melanomas, and melanoma metastases. We found that metastatic melanomas exhibit two dichotomous patterns of gene expression, which unexpectedly reflect gene expression differences already apparent in comparing laser-capture microdissected radial and vertical phases of a large primary melanoma. Unsupervised hierarchical clustering accurately separated nevi and primary melanomas. Multiclass significance analysis of microarrays comparing normal skin, nevi, primary melanomas, and the two types of metastatic melanoma identified 2,602 transcripts that significantly correlated with sample class. These results suggest that melanoma pathogenesis can be understood as a series of distinct molecular events. The gene e...
Shared Gene Expression and Immune Pathway Changes Associated with Progression from Nevi to Melanoma
Cancers
There is a need to identify molecular biomarkers of melanoma progression to assist the development of chemoprevention strategies to lower melanoma incidence. Using datasets containing gene expression for dysplastic nevi and melanoma or melanoma arising in a nevus, we performed differential gene expression analysis and regularized regression models to identify genes and pathways that were associated with progression from nevi to melanoma. A small number of genes distinguished nevi from melanoma. Differential expression of seven genes was identified between nevi and melanoma in three independent datasets. C1QB, CXCL9, CXCL10, DFNA5 (GSDME), FCGR1B, and PRAME were increased in melanoma, and SCGB1D2 was decreased in melanoma, compared to dysplastic nevi or nevi that progressed to melanoma. Further supporting an association with melanomagenesis, these genes demonstrated a linear change in expression from benign nevi to dysplastic nevi to radial growth phase melanoma to vertical growth ph...
Altered molecular pathways identify different histotypes within melanoma progression
BACKGROUND: malignant cutaneous melanoma originates in melanocytes, the pigmentproducing cells of the skin and eye. This entity is relatively rare compared to other skin cancers (< 5%), but still it is responsible for 80% of all skin cancer-related deaths. AIMS: to identify molecular signatures of melanoma progression and to understand if dysplastic nevi can be or not considered as an intermediate step within such progression. METHODS: excisional biopsies from 18 common melanocytic nevi (CMN), 8 primary radial growth phase melanomas (RGPM), 15 primary vertical growth phase melanomas (VGPM), 5 melanoma metastases (MTS) and 11 dysplastic nevi (DN) were collected and global gene expression profiling of the tissues was performed using whole genome oligomicroarrays. Differentially expressed genes for each progression step (CMN vs RGPM, RGPM vs VGPM and VGPM vs MTS) were identified, and validation of selected transcripts by qRT-PCR was performed on an independent cohort of fixed sample...
Melanoma Biomolecules: Independently Identified but Functionally Intertwined
Frontiers in Oncology, 2013
The majority of patients diagnosed with melanoma present with thin lesions and generally these patients have a good prognosis. However, 5% of patients with early melanoma (<1 mm thick) will have recurrence and die within 10 years, despite no evidence of local or metastatic spread at the time of diagnosis. Thus, there is a need for additional prognostic markers to help identify those patients that may be at risk of recurrent disease. Many studies and several meta-analyses have compared gene and protein expression in melanocytes, naevi, primary, and metastatic melanoma in an attempt to find informative prognostic markers for these patients. However, although a large number of putative biomarkers have been described, few of these molecules are informative when used in isolation. The best approach is likely to involve a combination of molecules. We believe one approach could be to analyze the expression of a group of interacting proteins that regulate different aspects of the metastatic pathway. This is because a primary lesion expressing proteins involved in multiple stages of metastasis may be more likely to lead to secondary disease than one that does not. This review focuses on five putative biomarkers -melanoma cell adhesion molecule (MCAM), galectin-3 (gal-3), matrix metalloproteinase 2 (MMP-2), chondroitin sulfate proteoglycan 4 (CSPG4), and paired box 3 (PAX3). The goal is to provide context around what is known about the contribution of these biomarkers to melanoma biology and metastasis. Although each of these molecules have been independently identified as likely biomarkers, it is clear from our analyses that each are closely linked with each other, with intertwined roles in melanoma biology.
Progression in Cutaneous Malignant Melanoma Is Associated with Distinct Expression Profiles
American Journal of Pathology, 2004
Clinical and histological variables that predict survival, such as Breslow's index, tumor size, ulceration, or vascular invasion have been identified in malignant melanoma. Nevertheless, the potential relevance of biological variables still awaits an in-depth exploration. Using tissue microarrays (TMAs), we retrospectively analyzed 165 malignant melanoma samples from 88 patients corresponding to distinct histological progression phases, radial, vertical, and metastases. A panel of 39 different antibodies for cell cycle, apoptosis, melanoma antigens, transcription factors, DNA mismatch repair, and other proteins was used. Integrating the information, the study has identified expression profiles distinguishing specific melanoma progression stages. Most of the detected alterations were linked to the control of cell cycle G1/S transition; cyclin D1 was expressed in radial cases 48% (12 of 25) with significant lost of expression in vertical cases 14% (9 of 65), P ؍ 0.002; whereas p16 INK4a (89% in vertical versus 71% in metastatic cases, P ؍ 0.009) and p27 KIP1 (76% in radial versus 45% in vertical cases, P ؍ 0.010) were diminished in advanced stages. The study also defines a combination of biological markers associated with shorter overall survival in patients with vertical growth phase melanoma, that provided a predictor model with four antibodies (Ki67, p16 INK4a , p21 CIP1 , and Bcl-6). This predictor model was validated using an independent series of 72 vertical growth phase melanoma patients.
Cancers, 2020
The identification of reliable and quantitative melanoma biomarkers may help an early diagnosis and may directly affect melanoma mortality and morbidity. The aim of the present study was to identify effective biomarkers by investigating the expression of 27 cytokines/chemokines in melanoma compared to healthy controls, both in serum and in tissue samples. Serum samples were from 232 patients recruited at the IDI-IRCCS hospital. Expression was quantified by xMAP technology, on 27 cytokines/chemokines, compared to the control sera. RNA expression data of the same 27 molecules were obtained from 511 melanoma- and healthy-tissue samples, from the GENT2 database. Statistical analysis involved a 3-step approach: analysis of the single-molecules by Mann–Whitney analysis; analysis of paired-molecules by Pearson correlation; and profile analysis by the machine learning algorithm Support Vector Machine (SVM). Single-molecule analysis of serum expression identified IL-1b, IL-6, IP-10, PDGF-BB,...