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Identification of key genes and molecular mechanisms associated with dedifferentiated liposarcoma based on bioinformatic methods

Authors Yu H, Pei D, Chen L, Zhou X, Zhu H

Received 13 January 2017

Accepted for publication 20 April 2017

Published 16 June 2017 Volume 2017:10 Pages 3017—3027

DOI https://doi.org/10.2147/OTT.S132071

Checked for plagiarism Yes

Review by Single-blind

Peer reviewers approved by Dr Colin Mak

Peer reviewer comments 4

Editor who approved publication: Dr Chiung-Kuei Huang

Hongliang Yu,1 Dong Pei,2 Longyun Chen,2 Xiaoxiang Zhou,2 Haiwen Zhu2

1Department of Radiation Oncology, Jiangsu Cancer Hospital and Jiangsu Institute of Cancer Research, The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, 2Department of Radiation Oncology, Yancheng Third People’s Hospital, Yancheng, Jiangsu, People’s Republic of China


Background: Dedifferentiated liposarcoma (DDLPS) is one of the most deadly types of soft tissue sarcoma. To date, there have been few studies dedicated to elucidating the molecular mechanisms behind the disease; therefore, the molecular mechanisms behind this malignancy remain largely unknown.
Materials and methods: Microarray profiles of 46 DDLPS samples and nine normal fat controls were extracted from Gene Expression Omnibus (GEO). Quality control for these microarray profiles was performed before analysis. Hierarchical clustering and principal component analysis were used to distinguish the general differences in gene expression between DDLPS samples and the normal fat controls. Differentially expressed genes (DEGs) were identified using the Limma package in R. Next, the enriched Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were obtained using the online tool DAVID (http://david.abcc.ncifcrf.gov/). A protein–protein interaction (PPI) network was constructed using the STRING database and Cytoscape software. Furthermore, the hub genes within the PPI network were identified.
Results: All 55 microarray profiles were confirmed to be of high quality. The gene expression pattern of DDLPS samples was significantly different from that of normal fat controls. In total, 700 DEGs were identified, and 83 enriched GO terms and three KEGG pathways were obtained. Specifically, within the DEGs of DDLPS samples, several pathways were identified as being significantly enriched, including the PPAR signaling pathway, cell cycle pathway, and pyruvate metabolism pathway. Furthermore, the dysregulated PPI network of DDLPS was constructed, and 14 hub genes were identified. Characteristic of DDLPS, the genes CDK4 and MDM2 were universally found to be up-regulated and amplified in gene copy number.
Conclusion: This study used bioinformatics to comprehensively mine DDLPS microarray data in order to obtain a deeper understanding of the molecular mechanism of DDLPS.

Keywords: dedifferentiated liposarcoma, molecular mechanisms, microarray, bioinformatic methods

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