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Objective : The present study aims to identify the potential clinical application and molecular mechanism of plasmacytoma variant translocation 1 ( PVT1 ) in patients with sarcomas by mining an RNA sequencing dataset from The Cancer Genome Atlas (TCGA) through multiple genome-wide analysis approaches. Methods : A genome-wide RNA sequencing dataset was downloaded from TCGA, survival analysis was used to evaluate the prognostic value of PVT1 in sarcoma. The potential mechanism was investigated by multiple tools: Database for Annotation, Visualization, and Integrated Discovery v6.8, gene set enrichment analysis (GSEA), and Connectivity Map (CMap). Results : Comprehensive survival analysis indicated that overexpression of PVT1 was significantly associated with poor prognosis in patients with sarcoma, and nomogram demonstrated that PVT1 contributed more than other traditional clinical parameters in sarcoma survival prediction. Weighted gene co-expression network analysis identified ten hub differentially expressed genes (DEGs) between sarcoma tissues with low and overexpression of PVT1 , and substantiated that these DEGs have a complex co-expression network relationship. CMap analysis has identified that antipyrine, ondansetron, and econazole may be candidate targeted drugs for sarcoma patients with PVT1 overexpression. GSEA revealed that overexpression of PVT1 may be involved in the posttranscriptional regulation of gene expression, tumor invasiveness and metastasis, osteoblast differentiation and development, apoptosis, nuclear factor kappa B, Wnt, and apoptotic related signaling pathways. Conclusions : Our findings indicate that PVT1 may serve as a prognostic indicator in patients with sarcoma. Its underlying mechanism is revealed by GSEA, and CMap offers three candidate drugs for the individualized targeted therapy of sarcoma patients with overexpression of PVT1 .

journal of cancer

Comprehensive investigation of the clinical significance and molecular mechanisms of plasmacytoma variant translocation 1 in sarcoma using genome-wide RNA sequencing data