Network analysis of microRNAs and genes in human osteosarcoma

Wang,Tianyan; Xu,Zhiwen; Wang,Kunhao; Wang,Ning

doi:10.3892/etm.2015.2685

Network analysis of microRNAs and genes in human osteosarcoma

Authors:
- Tianyan Wang
- Zhiwen Xu
- Kunhao Wang
- Ning Wang
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Affiliations: College of Software Engineering, Jilin University, Changchun, Jilin 130012, P.R. China, Key Laboratory of Symbolic Computation and Knowledge Engineering of The Ministry of Education, Jilin University, Changchun, Jilin 130012, P.R. China
Published online on: August 17, 2015 https://doi.org/10.3892/etm.2015.2685
Pages: 1507-1514

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Abstract

To date, numerous studies have suggested that microRNAs (miRNAs) and genes play key roles in osteosarcoma (OS); however, the majority of these studies have been conducted with a specific focus on either the genes or the miRNAs, which has made the regulatory mechanisms of OS difficult to decipher. The aim of the present study was to systematically investigate the elements [genes, miRNAs and transcription factors (TFs)] associated with the morbidity of OS and to explore the associations among these elements, instead of focusing on one or several elements. The scattered data were collected from existing studies of OS, and three regulatory networks (abnormally expressed, related and global) were constructed to explore OS at a macroscopic level. The abnormally expressed network showed the numerous incorrect data linkages that are present when OS emerges, making it useful as a map of the faults in OS. In theory, the correction of these errors could lead to the prevention and even cure of the disease. Unlike studies in which cancer networks have been formed based purely on gene data, the present study focused on genes and miRNAs, as well as the associations among them, to form the regulatory networks of OS. The constructed regulatory networks were shown to contain numerous self‑adaptation associations, which may aid in the analysis of the pathogenesis of OS. By comparing and analyzing the similarities and differences, a number of important pathways were highlighted. A notable finding was the predicted TFs obtained by the P‑Match method, which could be used to further study the pathogenesis of OS. In the present study, the mechanism of OS has been systematically analyzed and a theoretical foundation for the mechanism has been provided, which may assist the development of gene therapy targeting OS.

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