3 years ago

Integrated miRNA-mRNA expression profiles revealing key molecules in ovarian cancer based on bioinformatics analysis

Chao Li, Xiaodan Zhu, Linghua Zhang, Miaoling Ou, Zhantong Hong

Background: Ovarian cancer was one of the leading causes of death in gynecological malignancies, of which molecular mechanism hadn't been elucidated clearly yet. Our research aimed to reveal the potential key molecular and biological processes of ovarian cancer by means of bioinformatics.

Methods: The microarray sets of miRNA and mRNA expression profiles were downloaded from the GEO database. The target prediction was performed on the differentially expressed miRNAs identified and the overlapped differentially expressed genes (DEGs) were obtained combined with miRNA and mRNA datasets. The regulatory network of miRNA-gene was further constructed by cytoscape software. The overlapped DEGs in the network were analyzed to explore the biological processes involved by enrichment analysis. The molecular protein-protein interaction (PPI) network was used to identify key genes among the DEGs.

Results: A total of 167 overlapped DEGs were identified. The miRNA-gene network analysis found that miR-29c-3p, miR-1271-5p, and miR-133b, existed the most extensive targeting relationship with overlapped DEGs, being three key miRNAs of the regulatory network, and played the role of tumor suppressor. The GO enrichment showed that the overlapped DEGs were mainly involved in process named extracellular related organization, embryonic organ development, postsynaptic specialization, collagen trimer and DNA−binding transcription activator et al. The KEGG pathway analysis showed that these DEGs were involved in protein digestion and absorption and relaxin signaling pathway. The PPI network identified 10 key genes, playing the role in promoting tumor.

Conclusion: The methodology used and identification of key molecules in our study contributed to understanding the pathogenesis of ovarian cancer and providing new candidate biomarkers for early screening of ovarian cancer.

Open access
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