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Medical AI Inquiry: You was designed to assist in medical decision-making based on the provided user questions. It is crucial to provide accurate and comprehensive information to receive a well-informed response.  Labels: BACKGROUND, METHODS, RESULTS. Meshes: Gene Expression Profiling, Humans, Microarray Analysis, Neoplasm Metastasis, Prognosis, Sarcoma.
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Does gene expression identify heterogeneity of metastatic propensity in high-grade soft tissue sarcomas? Context: Metastatic propensity of soft tissue sarcoma (STS) is heterogeneous and may be determined by gene expression patterns that do not correlate well with morphology. The authors have reported gene expression patterns that distinguish 2 broad classes of clear cell renal carcinoma (ccRCC-gene set), and other patterns that can distinguish heterogeneity of serous ovarian carcinoma (OVCA-gene set) and aggressive fibromatosis (AF-gene set); however, clinical follow-up data were not available for these samples. In the current study, gene expression patterns in 73 samples of high-grade STS were examined using spotted cDNA microarray slides that contained ∼16,000 unique UniGene clusters. Approximately 50% of the genes present in the ccRCC-, OVCA-, and AF-gene sets were also represented in the data from this chip set, and these were combined to form a composite gene set of 278 probes. Hierarchical clustering using this composite gene set suggested the existence of subsets of the STS samples. Analysis revealed differences in the time to development of metastatic disease between the clusters defined by the first branch point of the clustering dendrogram (P = .005), and also among the 4 different clusters defined by the second branch points (P = .001).
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This approach suggests the existence of >2 subsets of high-grade pleomorphic STS, each with distinct clinical behavior. A composite gene set such as that described here may be useful to stratify STS in clinical trials, and may be of practical utility in patient management. Final Decision: yes