Title:Data Mining of Differentially Expressed Genes in Ovarian Epithelial Carcinoma: Implications in Precise Medicine
Volume: 21
Issue: 14
Author(s): Jie Zhao, Hongjie Gao and Yun He*
Affiliation:
- Key Laboratory of Biomedical Information Engineering, Ministry of Education, Mitochondrial Biomedical Research Institute, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an, 710049,China
Keywords:
Bioinformatics analysis, Epithelial ovarian cancer, Differential expression, Genes, Gene expression omnibus
(GEO), Regulatory network, GO.
Abstract:
Background: Epithelial Ovarian Carcinoma (EOC) is a ubiquitous gynecological malignancy
with complicated pathogenesis. Genetic risk factors and pathways involved in the prognosis
of this cancer are not yet understood completely. Determining genetic markers with diagnostic and
prognostic values would pave the way for efficient management of cancer.
Objective: This study aimed to investigate the genes and the regulatory networks involved in the occurrence
and prognosis of EOC through different bioinformatics analysis tools. In addition, recent
advances in using bioinformatic analysis approaches based on the genes and regulatory networks,
particularly Differentially Expressed Genes (DEGs) in improving the diagnosis and prognosis of
EOC, are discussed.
Methods: The gene expression profiles of GSE18520, GSE54388, and GSE27651 were downloaded
from the Gene Expression Omnibus (GEO) database and further analyzed with different analyses
in R language. Current literature on using bioinformatics based on DEGs and associated regulatory
networks to improve the diagnosis and prognosis of EOC was reviewed.
Results: Analyses of the gene expression levels between the malignant tissue against normal tissue
unveiled 163 DEGs. Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and
Genomes (KEGG) pathway analyses were performed on the target genes using cluster profile package
and Cytoscape package was employed to assess the protein interaction network of these genes.
The protein-protein interaction network was analyzed using the CytoHubba plug-in to identify 20
hub genes. In addition, we analyzed the prognosis of the hub genes using the Kaplan-Meier survival
analysis that revealed evident differences in the prognosis of 13 genes. The malignant tissues
exhibited a differential expression of 12 genes against healthy tissues as shown by Gene Expression
Profiling Interactive Analysis (GEPIA) analysis.
Conclusion: Findings of this study revealed 12 genes to be significantly up-regulated and the prognosis
was significantly different, which could be employed to potentially target EOC in clinical
practice.
