Identification of potential prognostic small nucleolar RNA biomarkers for predicting overall survival in patients with sarcoma.

OBJECTIVE

The main purpose of the present study is to screen prognostic small nucleolar RNA (snoRNA) markers using the RNA-sequencing (RNA-seq) dataset of The Cancer Genome Atlas (TCGA) sarcoma cohort.

METHODS

The sarcoma RNA-seq dataset comes from the TCGA cohort. A total of 257 sarcoma patients were included into the prognostic analysis. Multiple bioinformatics analysis methods for functional annotation of snoRNAs and screening of targeted drugs, including biological network gene ontology tool, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Set Enrichment Analysis (GSEA), and connectivity map (CMap) are used.

RESULTS

We had identified 15 snoRNAs that were significantly related to the prognosis of sarcoma and constructed a prognostic signature based on four prognostic snoRNA (U3, SNORA73B, SNORD46, and SNORA26) expression values. Functional annotation of these four snoRNAs by their co-expression genes suggests that some of them were closely related to cell cycle-related biological processes and tumor-related signaling pathways, such as Wnt, mitogen-activated protein kinase, target of rapamycin, and nuclear factor-kappa B signaling pathway. GSEA of the risk score suggests that high risk score phenotype was significantly enriched in cell cycle-related biological processes, protein SUMOylation, DNA replication, p53 binding, regulation of DNA repair, and DNA methylation, as well as Myc, Wnt, RB1, E2F, and TEL pathways. Then we also used the CMap online tool to screen five targeted drugs (rilmenidine, pizotifen, amiprilose, quipazine, and cinchonidine) for this risk score model in sarcoma.

CONCLUSION

Our study have identified 15 snoRNAs that may be serve as novel prognostic biomarkers for sarcoma, and constructed a prognostic signature based on four prognostic snoRNA expression values.