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Molecular classification of cutaneous malignant melanoma by gene expression profiling

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TLDR
Many genes underlying the classification of this subset of melanomas are differentially regulated in invasive melanomas that form primitive tubular networks in vitro, a feature of some highly aggressive metastatic melanomas.
Abstract
The most common human cancers are malignant neoplasms of the skin. Incidence of cutaneous melanoma is rising especially steeply, with minimal progress in non-surgical treatment of advanced disease. Despite significant effort to identify independent predictors of melanoma outcome, no accepted histopathological, molecular or immunohistochemical marker defines subsets of this neoplasm. Accordingly, though melanoma is thought to present with different 'taxonomic' forms, these are considered part of a continuous spectrum rather than discrete entities. Here we report the discovery of a subset of melanomas identified by mathematical analysis of gene expression in a series of samples. Remarkably, many genes underlying the classification of this subset are differentially regulated in invasive melanomas that form primitive tubular networks in vitro, a feature of some highly aggressive metastatic melanomas. Global transcript analysis can identify unrecognized subtypes of cutaneous melanoma and predict experimentally verifiable phenotypic characteristics that may be of importance to disease progression.

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Stem cells, cancer, and cancer stem cells

TL;DR: Stem cell biology has come of age: Unequivocal proof that stem cells exist in the haematopoietic system has given way to the prospective isolation of several tissue-specific stem and progenitor cells, the initial delineation of their properties and expressed genetic programmes, and the beginnings of their utility in regenerative medicine.
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Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks

TL;DR: The ability of the trained ANN models to recognize SRBCTs is demonstrated, and the potential applications of these methods for tumor diagnosis and the identification of candidate targets for therapy are demonstrated.
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WNT signalling pathways as therapeutic targets in cancer

TL;DR: This work has shown that WNTs and their downstream effectors regulate various processes that are important for cancer progression, including tumour initiation, tumour growth, cell senescence, cell death, differentiation and metastasis, and improved drug-discovery platforms and new technologies have facilitated the discovery of agents that can alter WNT signalling in preclinical models.
References
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Journal ArticleDOI

Cluster analysis and display of genome-wide expression patterns

TL;DR: A system of cluster analysis for genome-wide expression data from DNA microarray hybridization is described that uses standard statistical algorithms to arrange genes according to similarity in pattern of gene expression, finding in the budding yeast Saccharomyces cerevisiae that clustering gene expression data groups together efficiently genes of known similar function.
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Molecular classification of cancer: class discovery and class prediction by gene expression monitoring.

TL;DR: A generic approach to cancer classification based on gene expression monitoring by DNA microarrays is described and applied to human acute leukemias as a test case and suggests a general strategy for discovering and predicting cancer classes for other types of cancer, independent of previous biological knowledge.
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Use of a cDNA microarray to analyse gene expression patterns in human cancer.

TL;DR: Previously unrecognized alterations in the expression of specific genes provide leads for further investigation of the genetic basis of the tumorigenic phenotype of these cells.
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Expression profiling using cdna microarrays

TL;DR: Technical aspects of cDNA microarrays are reviewed, including the general principles, fabrication of the arrays, target labelling, image analysis and data extraction, management and mining.
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