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Combinatorial Chemistry & High Throughput Screening


ISSN (Print): 1386-2073
ISSN (Online): 1875-5402

Research Article

Screening and Interaction Analysis of Key Genes in miR-542-3p Over- Expressed Osteosarcoma Cells by Bioinformatics

Author(s): Zhongqiu Li, Peng Zhang, Feifei Feng and Qiao Zhang*

Volume 23, Issue 5, 2020

Page: [411 - 418] Pages: 8

DOI: 10.2174/1386207323666200401103353

Price: $65


Background: Osteosarcoma is one of the most serious primary malignant bone tumors that threaten the lives of children and adolescents. However, the mechanism underlying and how to prevent or treat the disease have not been well understood.

Aims and Objective: This aim of the present study was to identify the key genes and explore novel insights into the molecular mechanism of miR-542-3p over-expressed Osteosarcoma.

Materials and Methods: Gene expression profile data GDS5367 was downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were screened using GEO2R, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed using the DAVID database. And protein-protein interaction (PPI) network was constructed by the STRING database. In addition, the most highly connected module was screened by plugin MCODE and hub genes by plugin CytoHubba. Furthermore, UALCAN and The Cancer Genome Atlas were performed for survival analysis.

Result: In total, 1421 DEGs were identified, including 598 genes were up-regulated and 823 genes were down-regulated. GO analysis showed that DEGs were classified into three groups and DEGs mainly enriched in Steroid biosynthesis, Ubiquitin mediated proteolysis and p53 signaling pathway. Six hub genes (UBA52, RNF114, UBE2H, TRIP12, HNRNPC, and PTBP1) may be key genes with the progression of osteosarcoma.

Conclusion: The results could better understand the mechanism of osteosarcoma, which may facilitate a novel insight into treatment targets.

Keywords: Osteosarcoma, miR-542-3p, module analysis, survival analysis, bioinformatics analysis, gene expression.

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