Abstract
BackgroundOvarian cancer is a leading cause of the death from gynecologic malignancies. Hypoxia is closely related to the malignant growth of cells. However, the molecular mechanism of hypoxia-regulated ovarian cancer cells remains unclear. Thus, this study was conducted to identify the key genes and pathways implicated in the regulation of hypoxia by bioinformatics analysis.MethodsUsing the datasets of GSE53012 downloaded from the Gene Expression Omnibus (GEO), the differentially expressed genes (DEGs) were screened by comparing the RNA expression from cycling hypoxia group, chronic hypoxia group, and control group. Subsequently, cluster analysis was performed followed by the construction of the protein-protein interaction (PPI) network of the overlapping DEGs between the cycling hypoxia and chronic hypoxia using ClusterONE. In addition, gene ontology (GO) functional and pathway enrichment analyses of the DEGs in the most remarkable module were performed using Database for Annotation, Visualization and Integrated Discovery (DAVID) software. Ultimately, the signaling pathways associated with hypoxia were verified by RT-PCR, WB, and MTT assays.ResultsA total of 931 overlapping DEGs were identified. Nine hub genes and seven node genes were screened by analyzing the PPI and pathway integration networks, including ESR1, MMP2, ErbB2, MYC, VIM, CYBB, EDN1, SERPINE1, and PDK. Additionally, 11 key pathways closely associated with hypoxia were identified, including focal adhesion, ErbB signaling, and proteoglycans in cancer, among which the ErbB signaling pathway was verified by RT-PCR, WB, and MTT assays. Furthermore, functional enrichment analysis revealed that these genes were mainly involved in the proliferation of ovarian cancer cells, such as regulation of cell proliferation, cell adhesion, positive regulation of cell migration, focal adhesion, and extracellular matrix binding.ConclusionThe results show that hypoxia can promote the proliferation of ovarian cancer cells by affecting the invasion and adhesion functions through the dysregulation of ErbB signaling, which may be governed by the HIF-1α-TGFA-EGFR-ErbB2-MYC axis. These findings will contribute to the identification of new targets for the diagnosis and treatment of ovarian cancer.
Highlights
Ovarian cancer is a leading cause of the death from gynecologic malignancies
protein-protein interaction (PPI) network construction and hub gene identification On the basis of the information obtained from the STRING database, the PPI framework containing a total of 3279 protein pairs and 603 nodes were generated with the threshold of combined score > 0.4 (Fig. 2), in which nodes represented proteins and edges represented interactions between proteins [32]; this is helpful for understanding the regulation of hypoxia in ovarian cancer in the aspect of proteomics
In conclusion, hub genes and key pathways closely related to the proliferation of ovarian cancer cells in hypoxia were identified by bioinformatics analysis
Summary
Ovarian cancer is a leading cause of the death from gynecologic malignancies. Hypoxia is closely related to the malignant growth of cells. Tumor cells adapt by generating energy in oxygen independent ways and minimize cellular damage by inducing the expression of genes involved in angiogenesis, glycolysis, cell survival, invasion, tumor progression, and pH regulation, which can observably influence cell metabolism by activating the hypoxia inducible factor-1 (HIF-1) signaling pathway [5]. More and more researchers have devoted themselves to exploring the potential mechanisms by which hypoxia regulates the progression of ovarian cancer cells. Hypoxia was found to induce the expression of HIF-1α and G-protein estrogen receptor (GPER) that were involved in the regulation of VEGF expression in breast cancer cells and carcinomaassociated fibroblasts (CAFs), leading to the release of angiogenic factors and the growth of new blood vessels [12]. It is of great practical significance to explore the specific action modes and pathways of hypoxia on ovarian cancer cells
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