Introduction
Ovarian cancer is the most lethal gynecological cancer in the world and a general term which contains some cancers derived from various tissues [
1]. Epithelial ovarian cancer is the most common and representative histological types in primary ovarian cancer and is the primary cause of deaths in female cancer patients in North America and over 100,000 deaths every year worldwide [
2]. High-grade serous carcinoma (HGSC) is the most lethal subtype in the epithelial ovarian cancer, and most of them are diagnosed in an advanced stage [
3]. The standard treatment for ovarian cancer is maximal cytoreductive surgery and platinum-based chemotherapy [
4]. Although ovarian cancer actively responds to the initial anticancer therapy, nearly 75% of patients may relapse within two years and cannot be treated with the available chemotherapy drugs [
5,
6]. Meanwhile, metastasis within the peritoneal cavity and resistance to chemotherapy are the leading causes of the high mortality rate associated with ovarian cancer, because this cancer is often diagnosed in late clinical stages as what has been mentioned above [
5]. Thus, identifying new targets for treatment and seeking effective chemotherapy drugs are crucial for overcoming drug resistance in advanced ovarian cancer [
7].
Thus far, many genetic factors, such as
BRCA1,
BRCA2,
P53 (TP53),
KRAS,
PIK3CA,
CTNNB1, and
PTEN, have been correlated with ovarian cancer [
8]. In recent years, many studies have shown promise for gene-targeted therapies in ovarian cancer [
9‐
11]. The poly-ADP-ribose polymerase (PARP) inhibitor olaparib, a targeted therapeutic drug approved by the Food and Drug Administration, is used to treat ovarian cancer patients with BRCA1 and BRCA2 mutations. Olaparib has also been used as maintenance therapy for patients with platinum-sensitive recurrent BRCA-mutated ovarian cancer [
12]. Therefore, gene-targeted therapies provide a new possibility for the personalized treatment of ovarian cancer patients.
However, the lack of large-scale studies for the identification of differentially expressed genes (DEGs) in ovarian cancer limits the reliability of previous results and makes it difficult to screen potential targets. DNA microarray analysis is a systematic and global approach to analyze genomic expression profiles and physiological mechanisms in diseases [
13,
14]. High-throughput microarray experiments have been used to analyze gene expression patterns and identify potential target genes in ovarian cancer [
15].
To fill the gap regarding the identification of DEGs in ovarian cancer, we performed this meta-analysis to identify DEGs between ovarian cancer and healthy control tissues and aimed to provide a powerful tool to investigate microarray datasets by integrating data from multiple studies. An important advantage of this large-scale analysis lies in the reduction of discrepancies among different study conditions; additionally, this analysis combines the results from previous studies to assess an existing problem from a novel perspective [
16]. it is worth noting that although ovarian cancer is known as a series of different molecular and histological diseases [
17], we aim to uncover those common genes across different molecular subtypes of epithelial ovarian cancer. Genes discovered in meta-analyses generally overlap with genes identified in various studies, indicating increased reliability [
18]. The present meta-analysis aimed to identify DEGs between ovarian tissues and control tissues. In addition, we attempted to identify potential core genes associated with epithelial ovarian cancer and to investigate some possible related mechanisms.
Discussion
The problematic diagnosis in an early stage and recurrence and resistance to current chemotherapeutic agents are the leading causes of high mortality in ovarian cancer based on data from The Surveillance, Epidemiology, and End Results (SEER) Program of the National Cancer Institute [
21]. Therefore, the development of novel therapies for ovarian cancer is of great urgency. Previous research has proven that ovarian cancer is caused by the activation of oncogenes and the inactivation of cancer suppressor gene [
22]. With continued advancements in high-throughput technologies, some genetic alterations associated with ovarian cancer, such as specific mutations in KRAS, loss-of-function mutations in PTEN, mutations in TP53, modifications in BRCA1/2, and changes in homologous recombination genes, have been uncovered [
23]. Although a significant amount of data were produced by microarray studies, the sample sizes of most studies are small and may affect the identification of DEGs. However, meta-analysis of multiple microarray datasets makes the identification of DEGs more reliable by increasing the sample size.
In the present study, we performed a meta-analysis to determine the DEGs between ovarian cancer and normal ovarian tissues. We identified 563 DEGs, including 245 upregulated and 318 downregulated DEGs, in ovarian tissues by combining p-values (cutoff value of 5) and Z scores (cutoff value of 7). We classified the DEGs into functional categories based on their GO functions and KEGG pathways. Furthermore, we screened the following top 12 hub nodes with degree centrality more than 29 from the PPI network as hub genes: CDK1, TOP2A, CDC20, CCNB2, BIRC5, UBE2C, BUB1, NCAPG, RRM2, KIF2C, CENPA, and MELK. Additionally, we obtained two top significant modules from PPI networks of DEGs using MCODE analysis. Genes in module 1 were mainly associated with cell cycle, oocyte meiosis and the p53 signaling pathway, while genes in module 2 were primarily enriched in tight junction proteins, leukocyte transendothelial migration, hepatitis C, and CAMs.
Among the top 12 hub genes, nine hub genes were associated with poor OS in epithelial ovarian cancer patients. Based on GO functional analysis, KEGG pathway analysis, and survival analysis, we found that CDK1, TOP2A, and UBE2C might be the core genes contributing to the development of epithelial ovarian cancer at the molecular level.
CDK1 plays a vital role in the regulation of the cell cycle by modulating the centrosome. CDK1 not only promotes G2-M transition but also regulates G1 progression and G1-S transition by binding with multiple interphase cyclins [
24,
25] In ovarian cancer, the expression of CDK1 is significantly associated with survival status, histological grade, FIGO stage, lymph node metastasis, and metastasis in epithelial ovarian cancer patients [
26].
TOP2A is a nuclear enzyme involved involved in cell division and the cell cycle. TOP2A controls topological states of DNA by transiently breaking and subsequently rejoining of DNA strands [
27]. Additionally, TOP2A is a direct molecular target of topoisomerase inhibitor, and its upregulation has been reported in several cancers including lung, nasopharyngeal, esophageal, gallbladder, hepatocellular, colorectal, breast, endometrial, pancreatic and ovarian cancer [
28‐
31].
UBE2C, an essential factor of the anaphase-promoting complex/cyclosome (APC/C), is required for the destruction of mitotic cyclins and cell cycle progression [
32]. The N-terminal extension of UBE2C contributes to the regulation of APC/C activity for substrate selection and checkpoint control [
33]. UBE2C, the exclusive partner of APC/C, participates in the degradation of the APC/C target protein family by initiating the formation of a Lys11-linked ubiquitin chain. Thus, UBE2C plays a vital role in the destruction of mitotic cyclins and other mitosis-related substrates. During early mitosis, the APC is activated through cyclin B/Cdk1-dependent phosphorylation and binding of its activator CDC20. During metaphase, UBE2C degrades securin and cyclin B by APC/C
CDC20 to promote progression to anaphase [
34]. UBE2C is significantly upregulated in several types of cancer including bladder, breast, brain, cervical, esophageal, colorectal, liver, lung, nasopharyngeal, prostate (late-stage), pancreatic, thyroid, stomach, and ovarian cancer [
33]. UBE2C is associated with tumor progression. I. van Ree et al. identified UBE2C as a prominent proto-oncogene that contributes to whole chromosome instability and tumor formation over a wide range of overexpression levels [
35].
In our study, in addition to
UBE2C upregulation,
CDC20, CDK1, and
CCNB2 are overexpressed. Combined with the above results, it is logic to assume that the interaction among UBE2C, CDC20, CDK1, and CCNB2 may play a vital role in the formation and development of ovarian cancer. Overexpression of
UBE2C was associated with poor OS for ovarian cancer patients, and thus UBE2C might be a promising prognostic molecular biomarker and therapeutic target for ovarian cancer. It is worth to emphasize that though a series of distinct molecular and histologic subtypes of ovarian cancer exists and each subtype has different tumor microenvironment. The present research mainly focuses on those common pathways of epithelial ovarian cancer. Yet we have analyzed the corresponding gene expression data acquired from GSE9891 and the results shows that 11 out of 12 hub genes (expression data of CDK1 cannot be found in the datasets GSE9891) are significantly up-regulated (Additional file
5: Figure S2, Additional file
6: Figure S3, Additional file
7: Figure S4, Additional file
8: Figure S5, Additional file
9: Figure S6, Additional file
10: Figure S7, Additional file
11: Figure S8, Additional file
12: Figure S9, Additional file
13: Figure S10, Additional file
14: Figure S11 and Additional file
15: S12, Additional file
16: Table S4) in all of the molecular subtypes (differential, immunoreacted, proliferation and mesenchymal) comparing to control group. Subsequent researches will be conducted to investigate the role each hub gene played in each subtype of ovarian cancer,
Besides, we consider the protein expression of these hub genes might be instructive to the further study. The protein expression data of hub genes is acquired from the Human Protein Atlas for evaluation. The protein expressions of 5 hub genes (CDK1, TOP2A, CDC20, NCAPG, and MELK) are significantly up-regulated in ovarian cancer compared to normal tissues (Additional file
17: Table S5.). Also, we have done a chi-square analysis to explore the relationship between the expression of 12 hub genes and the metastasis of ovarian cancer. The results show none of these genes has connections to the cancer metastasis (
P > 0.05). Overall, the present study was designed to identify DEGs through integrated bioinformatics analysis to find potential biomarkers and predict the development and prognosis of ovarian cancer. However, to obtain more accurate correlation results, we need to performed a series of validation experiments. In conclusion, this study provides robust evidence for future genomic-based individualized treatment of ovarian cancer.