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Molecular Profiling Defines Three Subtypes of Synovial Sarcoma

GSE271517 Homo sapiens Expression profiling by high throughput sequencing 91 samples 2024/08/19 GPL24676
Summary
Synovial Sarcomas (SS) are characterized by the presence of the SS18::SSX fusion gene, which protein product induce chromatin changes through remodeling of the BAF complex (BRG1/BRM-associated factor complex), which leads to widespread alterations in gene expression that drive tumor development and progression. However, there is no established molecular model which explain the variation in clinical behavior of SS. To elucidate the genomic events that drive phenotypic diversity in SS, we performed RNA and targeted DNA sequencing on 91 tumors from 55 patients. Our results were verified by proteomic analysis, public gene expression cohorts and single-cell RNA sequencing. Transcriptome profiling identified three distinct SS subtypes resembling the known histological subtypes: SS subtype I and was characterized by hyperproliferation, evasion of immune detection and a poor prognosis. SS subtype II and was dominated by a vascular-stromal component and had a significantly better outcome. SS Subtype III was characterized by biphasic differentiation, increased genomic complexity and immune suppression mediated by checkpoint inhibition, and poor prognosis despite good responses to neoadjuvant therapy. Chromosomal abnormalities were an independent significant risk factor for metastasis. KRT8 was identified as a key component for epithelial differentiation in biphasic tumors, potentially controlled by OVOL1 regulation. Our multi-omics analysis revealed that the biological and genetic variability between these subtypes, including key transcriptome patterns and secondary genomic events such as copy number alterations, were associated with long-term outcomes. Our findings explain the histological grounds for SS classification and indicate that a significantly larger proportion of patients have high risk tumors (corresponding to SS subtype I) than previously believed.
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NCBI GEO page ↗ Paper (PMID 39257029) ↗ {# Names what the click gives you. "Open in finder" meant nothing to a visitor who arrived from a search engine and has never seen the tool. #} Find more human RNA-seq datasets →
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