1046P - Identification of a RNA-Seq based signature to improve prognostic for uterine sarcoma

Abstract

BackgroundUterine sarcoma (US) is a highly malignant cancer with poor prognosis and high mortality. This study focused on identification of RNA-Seqexpressionsignature for prognosis prediction of Uterine Sarcoma. MethodsWe obtained RNA-Seqexpression profiles from The Cancer Genome Atlas (TCGA) database and differential expressed genes (DEGs) were identified between US tissues and normal tissues. Univariate Cox proportional hazards regression analysis was performed to identify Prognosis associated DEGscorrelated with survival of US patients.The RNA-Seqbased prognostic signature was identified by least absolute shrinkage and selection operator (LASSO) Cox model. The cohort was randomly divided into training and testing groups. Thebiological pathway and processof putative RNA targets was also analyzed by bioinformatics. ResultsThis study identified a RNA-Seq signature based on 11 genes, AP000320.1, HNRNPA1P33, RPL21P10, AC067773.1, AC011933.3, STX19, AC091133.4, HIST1H3A, AC004988.1, AL356585.1 and HNRNPA3P1. In the training group, the median OS in the high-risk and low-risk groups were 13.7 vs 88.1 months (HR, 0.204, 95% CI, 0.08589 - 0.4846; P<0.0001), respectively. In the testing group, the median OS in the high-risk and low-risk groups were 11.9 vs 67.2 months (HR, 0.04315, 95% CI, 0.004845 - 0.3842; P<0.0001) respectively. Genes in the model were put into gene ontology biological process enrichment and Kyoto Encyclopedia of Genes and Genomes signaling pathways analysis, which suggested that these genes might contribute to cancer-associated processes such as the nuclear nucleosome and DNA packaging complex. ConclusionsThis 11-genebased prognostic signature may improve prognosis prediction of US. Legal entity responsible for the studyThe authors. FundingHas not received any funding. DisclosureAll authors have declared no conflicts of interest.Linguistic correction