Oncotarget

Research Papers:

Identification of differentially expressed genes in the development of osteosarcoma using RNA-seq

Yihao Yang, Ya Zhang, Xin Qu, Junfeng Xia, Dongqi Li, Xiaojuan Li, Yu Wang, Zewei He, Su Li, Yonghong Zhou, Lin Xie and Zuozhang Yang _

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Oncotarget. 2016; 7:87194-87205. https://doi.org/10.18632/oncotarget.13554

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Abstract

Yihao Yang1,*, Ya Zhang1,*, Xin Qu1,*, Junfeng Xia1, Dongqi Li1, Xiaojuan Li1, Yu Wang1, Zewei He1, Su Li1, Yonghong Zhou1, Lin Xie1,*, Zuozhang Yang1,*

1Bone and Soft Tissue Tumors Research Center of Yunnan Province, Department of Orthopaedics, The Third Affiliated Hospital of Kunming Medical University (Tumor Hospital of Yunnan Province), Kunming, Yunnan 650118, China

*These authors contributed equally to this work and co-first authors

Correspondence to:

Zuozhang Yang, email: [email protected], [email protected]

Lin Xie, email: [email protected]

Keywords: differentially expressed genes, network, primary osteosarcoma, metastatic osteosarcoma, target

Received: August 15, 2016     Accepted: November 07, 2016     Published: November 24, 2016

ABSTRACT

Objective: Osteosarcoma (OS) is a malignant bone tumor with high morbidity in young adults and adolescents. This study aimed to discover potential early diagnosis biomarkers in OS.

Results: In total, 111 differentially expressed genes (DEGs) were identified in primary OS compared with normal controls and 235 DEGs were identified in metastatic OS compared with primary OS. AURKB and PPP2R2B were the significantly up-regulated and down-regulated hub proteins, respectively, in the PPI protein-protein network (PPI) network of primary OS. ISG15 and BTRC were the significantly up-regulated and down-regulated hub proteins, respectively, in the network of metastatic OS. The DEGs in metastatic OS compared with primary OS were significantly enriched in the arachidonic acid metabolism, malaria, and chemokine signaling pathways. Finally, we employed quantitative real-time polymerase chain reaction (qRT-PCR) to validate the expression levels of candidate DEGs and the results indicated that our bioinformatics approach was acceptable.

Materials and Methods: The mRNA expression profiling of 20 subjects was obtained through high-throughput RNA-sequencing. DEGs were identified between primary OS and normal Control, and between primary OS and metastatic OS, respectively. Functional annotation and PPI networks were used to obtain insights into the functions of DEGs. qRT-PCR was performed to detect the expression levels of dysregulated genes in OS.

Conclusions: Our work might provide groundwork for the further exploration of tumorigenesis and metastasis mechanisms of OS.


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