OBJECTIVE: Endometrial stromal sarcoma (ESS) and leiomyosarcoma (LMS) are the two most common uterine sarcomas, but both are rare tumors. The aim of the present study was to compare the global gene expression patterns of ESS and LMS. METHODS: Gene expression profiles of 7 ESS and 13 LMS were analyzed using the HumanRef-8 BeadChip from Illumina. Differentially expressed candidate genes were validated using quantitative real-time PCR and immunohistochemistry. RESULTS: Unsupervised hierarchical clustering using all 54,675 genes in the array separated ESS from LMS samples. We identified 549 unique probes that were significantly differentially expressed in the two malignancies by greater than 2-fold with 1% FDR cutoff using one-way ANOVA with Benjamini-Hochberg correction, of which 336 and 213 were overexpressed in ESS and LMS, respectively. Genes overexpressed in ESS included SLC7A10, EFNB3, CCND2, ECEL1, ITM2A, NPW, PLAG1 and GCGR. Genes overexpressed in LMS included CDKN2A, FABP3, TAGLN, JPH2, GEM, NAV2 and RAB23. The top 100 genes overexpressed in LMS included those coding for myosin light chain and caldesmon, but not the genes coding for desmin or actin. CD10 was not overexpressed in ESS. Results for selected genes were validated by quantitative real-time PCR and immunohistochemistry. CONCLUSIONS: We present the first study in which gene expression profiling was shown to distinguish between ESS and LMS. The molecular signatures unique to each of these malignancies may aid in expanding the diagnostic battery for their differentiation, and may provide a molecular basis for prognostic studies and therapeutic target discovery.
OBJECTIVE:Endometrial stromal sarcoma (ESS) and leiomyosarcoma (LMS) are the two most common uterine sarcomas, but both are rare tumors. The aim of the present study was to compare the global gene expression patterns of ESS and LMS. METHODS: Gene expression profiles of 7 ESS and 13 LMS were analyzed using the HumanRef-8 BeadChip from Illumina. Differentially expressed candidate genes were validated using quantitative real-time PCR and immunohistochemistry. RESULTS: Unsupervised hierarchical clustering using all 54,675 genes in the array separated ESS from LMS samples. We identified 549 unique probes that were significantly differentially expressed in the two malignancies by greater than 2-fold with 1% FDR cutoff using one-way ANOVA with Benjamini-Hochberg correction, of which 336 and 213 were overexpressed in ESS and LMS, respectively. Genes overexpressed in ESS included SLC7A10, EFNB3, CCND2, ECEL1, ITM2A, NPW, PLAG1 and GCGR. Genes overexpressed in LMS included CDKN2A, FABP3, TAGLN, JPH2, GEM, NAV2 and RAB23. The top 100 genes overexpressed in LMS included those coding for myosin light chain and caldesmon, but not the genes coding for desmin or actin. CD10 was not overexpressed in ESS. Results for selected genes were validated by quantitative real-time PCR and immunohistochemistry. CONCLUSIONS: We present the first study in which gene expression profiling was shown to distinguish between ESS and LMS. The molecular signatures unique to each of these malignancies may aid in expanding the diagnostic battery for their differentiation, and may provide a molecular basis for prognostic studies and therapeutic target discovery.
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