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BACKGROUND: Bone and soft tissue tumors represent a diverse group of neoplasms thought to derive from cells of the mesenchyme or neural crest. Histological diagnosis is challenging due to the poor or heterogenous differentiation of many tumors, resulting in uncertainty over prognosis and appropriate therapy. RESULTS: We have undertaken a broad and comprehensive study of the gene expression profile of 96 tumors with representatives of all mesenchymal tissues, including several problem diagnostic groups. Using machine learning methods adapted to this problem we identify molecular fingerprints for most tumors, which are pathognomonic (decisive) and biologically revealing. CONCLUSION: We demonstrate the utility of gene expression profiles and machine learning for a complex clinical problem, and identify putative origins for certain mesenchymal tumors.

Original publication

DOI

10.1186/gb-2005-6-9-r76

Type

Journal

Genome Biol

Publication Date

2005

Volume

6

Keywords

Gene Expression Profiling, Gene Expression Regulation, Neoplastic, Genes, Neoplasm, Humans, Mesoderm, Models, Biological, Neoplasms, Connective and Soft Tissue