Data for Machine learning-based analysis of genomic and transcriptomic data unveils sarcoma clusters with superlative prognostic and predictive value

<b>Data Sources and Descriptions</b>This study integrates transcriptomic and clinical data from three main sources to classify prognostic subtypes and identify therapeutic strategies in soft tissue sarcomas (STS):<b>1. TCGA-SARC Cohort</b><b>Source:</b> Genomic Data Commons (GDC) Portal<b>Content:</b> RNA-Seq gene expression data and associated clinical information from The Cancer Genome Atlas Sarcoma Project (TCGA-SARC).<b>Purpose:</b> Serves as an external validation cohort for subtype comparisons, differential expression, and survival analyses.<b>2. CINSARC Dataset</b><b>Source:</b> Code Ocean Capsule<b>Content:</b> Gene expression and clinical data from the original CINSARC study, including risk classification.<b>Purpose:</b> Used to validate prognostic gene signatures and assess cross-cohort subtype reproducibility.<b>3. Study Cohort (22-2290 F1RNA and F1CDx)</b>This dataset includes transcriptomic and clinical data from an in-house cohort of 82 soft tissue sarcoma patients.<b>Files and Descriptions:</b><code><strong>22-2290 Roche RNA GEP-COUNTS 01MAY2023.txt</strong></code><b>Content:</b> Raw gene expression counts obtained using the <i>FoundationOne RNA (F1RNA)</i> panel.<code><strong>OSPL_n=82_F1CDXRNA-RMC-RET-22-2290_SG44174_21FEB2023145617</strong></code><b>Content: </b>Data generated using the <i>FoundationOne CDx (F1CDx)</i> kit.<code><strong>Base de dados Clinica_update.csv</strong></code><b>Content:</b> Updated clinical annotations for the patient study cohort, including histology, treatment, and outcome data.<code><strong>Sarculator_Results.xlsx</strong></code><b>Content:</b> Prognostic predictions for each sample computed using the <i>Sarculator</i> tool (https://www.sarculator.com/).