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Identification of Omentum-derived Molecular Determinants and Pathways Mediating the Omental Metastasis of Ovarian Cancer

  • Meifeng Zhou1,
  • Li Sheng1,
  • Haixia Wang1,
  • Xufeng Zhang2,
  • Jindian Tan3,*  and
  • Lin Wang1,* 
Exploratory Research and Hypothesis in Medicine   2022;7(4):228-240

doi: 10.14218/ERHM.2021.00048

Received:

Revised:

Accepted:

Published online:

 Author information

Citation: Zhou M, Sheng L, Wang H, Zhang X, Tan J, Wang L. Identification of Omentum-derived Molecular Determinants and Pathways Mediating the Omental Metastasis of Ovarian Cancer. Explor Res Hypothesis Med. 2022;7(4):228-240. doi: 10.14218/ERHM.2021.00048.

Abstract

Background and objectives

To date, the omentum-derived determinants of ovarian cancer (OC) metastasis to the omentum have not been well elucidated. We aimed to identify the pathogenesis, potential biomarkers, and prognostic indicators underlying the omental metastasis of OC.

Methods

The expression profile GSE120196 included datasets of omental tissues from four patients with benign gynecologic diseases and cancer-infiltrated omental tissues from ten patients with high-grade serous OC. Using this dataset, we performed an analysis of differentially expressed genes (DEGs), gene ontology, Kyoto Encyclopedia of Genes and Genome, and pathway networks. The most significant module based on protein-protein interaction (PPI) network was selected, and the genes in the module were identified as hub genes. Furthermore, survival analysis and translational level of hub genes were performed to verify the results.

Results

A total of 301 upregulated DEGs and 128 downregulated DEGs were identified. Pathways in cancer, focal adhesion and Wnt signaling were found to play critical roles. Ten hub genes were identified from PPI network analysis. Expression levels of two key genes, COL1A1 and VCAN, were significantly associated with worse prognosis of patients with advanced ovarian cancer.

Conclusions

Our findings suggest that pathways in cancer, focal adhesion, Wnt signaling, and expression levels of two key genes, COL1A1 and VCAN, may become novel targets for the diagnosis and therapy of ovarian cancer with omental metastasis.

Keywords

Ovarian cancer, Omental metastasis, Differentially expressed genes, GSE120196, Bioinformatics analysis, Hub genes

Introduction

Ovarian cancer (OC) is the seventh-most common malignancy in women worldwide, with the eighth-highest rate of cancer mortality, seriously threatening women’s lives.1,2 The most common subtype of OC is serous ovarian cancer. Due to its anatomic location and the silent progression of OC, approximately 80% of patients with OC are diagnosed at an advanced stage with an extremely poor prognosis.3,4 Widespread pelvic and abdominal metastasis and planting is a major risk of death with OC.5

Ovarian cancer has a tendency to metastasize to the omentum, which is rich in adipocytes.6 Omental metastasis of OC is closely associated with the metastatic progression of the tumor.7 At present, it is not clear why ovarian cancer cells preferentially metastasize to and proliferate in the omentum. Studies have revealed that OC preferentially seeds in the omental fat band, not merely because of the anatomical structure of the omentum, but also partially because a series of changes occur at the transcriptional level in adipocytes in the omentum, induced by the cancer microenvironment, that promote homing and omental metastasis of ovarian cancer cells.8 Furthermore, Nieman et al. found that lipid could directly transfer from adipocytes to ovarian cancer cells under co-culture conditions, promoting tumor growth in vitro and in vivo.9 In addition, they demonstrated that co-culture of adipocyte-ovarian cancer cells induces fat metabolism and β-oxidation in cancer cells, indicating that adipocytes serve as energy sources for cancer cells.

After migrating to the omentum, ovarian cancer cells grow much faster than primary tumor cells, suggesting that the omentum can facilitate tumor cells to migrate, colonize, invade new sites, and proliferate.9 However, research has not yet elucidated specific omentum-derived molecular factors that regulate OC progression. Gene chip technology has recently undergone rapid development, and has become widely used due to its advantages of efficiency, large-scale implementation and high throughput capabilities. At present, gene chip technology has been widely adopted to assist with the discovery of biomarkers for early diagnosis and molecular typing of ovarian tumors and for establishing prognostic models.10–12 Using bioinformatic analyses to explore the mechanisms involved in the omental metastasis of OC could facilitate the clinical management of this disease.

Herein, in order to identify genes that may be involved in the omental metastasis of OC, we used the gene expression profile GSE120196 dataset from the Gene Expression Omnibus (GEO) database to search for genes that are differentially expressed in omental adipose tissues from patients with benign gynecologic disease and patients with OC. This included performing gene ontology (GO), Kyoto Encyclopedia of Genes and Genome (KEGG), and PPI-network analyses to identify hub genes and key genes that may play significant mechanistic roles in the omental metastasis of OC and form possible therapeutic targets.

Materials and methods

Gene-Cloud of Biotechnology Information (GCBI)

GCBI (https://www.gcbi.com.cn ) is an R-based online comprehensive analysis software suite developed in Shanghai, China, which integrates genetic information, sample information, research findings, data algorithms and bioinformatics. The GCBI platform includes data from 120 million copies of genomic samples, as well as approximately 90,000 copies of tumor samples and as many as 17 million copies of genetic information.13–15 In the current study, we used the GCBI platform to identify DEGs between OC and normal control (NC; the omental tissues from four patients with benign gynecologic diseases). The DEGs were then used for GO enrichment analysis, KEGG analysis, pathway network analysis, and PPI network analysis.

Gene Expression Omnibus (GEO) DataSets

The GEO DataSets (http://www.ncbi.nlm.nih.gov/gds ) at National Centre of Biotechnology Information (NCBI) form the largest and most comprehensive public functional collection of genomic datasets.16 The key words “omental adipose tissue of ovarian cancer” were used to search GEO datasets and the gene chip dataset GSE120196. The GSE120196 dataset was obtained based on the GPL570 platform [HG-U133_Plus_2], with Affymetrix Human Genome U133 Plus 2.0 Array included for the subsequent analysis. The GSE120196 dataset contains a total of 14 samples, including 10 cancer-associated omental adipose tissues obtained from high-grade serous OC (HGSOC) patients and 4 micro-dissected omental adipose tissue samples from patients with benign gynecologic diseases.

Identification of DEGs

The GCBI laboratory provides seven function modules, including sample grouping, differential gene expression analysis, GO enrichment analysis, KEGG pathway analysis, and network analysis.17 The information from the GeneChip Sample was entered into the GCBI online laboratory (https://www.gcbi.com.cn/gclib/html/index ) to identify DEGs and conduct subsequent analyses. Thresholds of Q < 0.05,p < 0.05 and log2 (fold change) > 1.2 were applied to filter and select DEGs between OC and NC samples.

GO enrichment analysis and pathway analysis

Based on the DEGs, GO enrichment analysis and pathway analysis using the GCBI online platform were performed using thresholds of p < 0.05 and false discovery rate (FDR) < 0.05 to reveal the biological functions of DEGs.18 Pathway analysis was conducted using KEGG enrichment analysis with, the p-value for identifying metabolic processes set at p < 0.05. Furthermore, pathway network analysis was constructed to identify pathway connections and core networks using the GCBI platform. By assessing the upstream and downstream relationship between pathways, a deeper and broader understanding of signaling pathways can be established.

Protein-protein interaction (PPI) network and hub gene selection

Search Tool for the Retrieval of Interacting Genes (STRING) is a database of known and predicted protein-protein interactions. In this study, protein interaction networks were constructed using STRING version 11.0 online software (www.string-db.org/ ).19 The top 100 DEGs were then mapped to STRING, and the interactions with a combined score of > 0.4 for PPI pairs were selected for construction of a PPI network using Cytoscape version 3.8.0 software. Hub genes and subnetworks were identified through network analysis using the cytoHubba plugin from Cytoscape v3.8.0.20 The betweenness centrality values of the top 10 nodes in the networks were computed and were generated. Genes in this module were regarded as hub genes.

Statistical analysis

Kaplan-Meier survival analysis

Survival was analyzed using Kaplan-Meier survival analysis (https://kmplot.com/analysis/ ). Prognostic analysis was performed for progression free survival (PFS) or overall survival (OS). The following retrieval indexes were used: Gene symbol ‘use multiple genes’ field: enter top 10 hub gene name; split patients by: Upper Tertile; Stage: 3+4 stage; Survival: PFS or OS respectively. A p-value <0.01 was considered statistically significant.

Oncomine analysis

The Oncomine database (http://www.oncomine.org ) is a cancer gene chip database and integrated data-mining platform. It includes gene information covering 33 different cancers and can be used to analyze the differential expression of genes between cancer and adjacent normal tissue. In this study, the mRNA expression levels of COL1A1, VCAN, and MARS in ovarian cancer tissues and normal tissues were acquired. The settings were as follows: Gene: COL1A1, VCAN, and MARS, respectively; Analysis Type: Cancer vs Normal analysis; Cancer type: Ovarian cancer; Sample type: Clinical specimen; Data type: mRNA; Threshold (p-value < 0.001, fold change > 1.5, gene rank = top 10%).

Results

Identification of differentially expressed genes (DEGs)

To identify DEGs in omental adipose tissues from ovarian cancer patients, the transcription profile dataset GSE120196 was obtained from the NCBI GEO database, which includes information from 10 patients with HGSOC and 4 patients with benign gynecologic diseases as controls. Thresholds were set at Q < 0.05, p < 0.05 and log2 (fold change) > 1.2 for differential expression analysis. A total of 429 DEGs were identified, which included 301 genes that were significantly up-regulated and 128 genes that were significantly down-regulated. These results were visualized as a heat map and a volcano plot (Fig. 1). The top 12 genes displaying the most significant changes in expression were unnamed, glycoprotein M6A (GPM6A), serpin peptidase inhibitor, clade E, member 2 (SERPINE2), collagen type I alpha 1 (COL1A1), transmembrane 4L six family member 1(TM4SF1) , chromosome 2 open reading frame 40 (C2orf40), unnamed, solute carrier family 24 member 3 (SLC24A3), eukaryotic translation initiation factor 1(EIF1), glycine-N-acyltransferase (GLYAT), UDP-glucose glycoprotein glucosyltransferase 2 (UGGT2), and KN motif and ankyrin repeat domains 4 (KANK4) (Table 1).

Volcano plot and heat map of differentially expressed mRNAs.
Fig. 1  Volcano plot and heat map of differentially expressed mRNAs.

(a) Cluster heat map of the differentially expressed genes between OC and NC. The column represents tissue samples (OC blue; NC yellow) and the rows represent genes (red indicates higher gene expression; green indicates lower expression). (b) The volcano map of differential gene expression between OC and NC. OC and NC (normal control) represent cancer-associated adipose tissues from ovarian cancer patients and adipose tissues from patients with benign gynecologic diseases, respectively. Orange represents DEGs; the significantly up-regulated genes are plotted in orange on the right side and down-regulated genes plotted in orange on the left.

Table 1

The top 12 genes with the most significant differential expression

Gene symbolGene descriptionp-valueGene featureRank
Unnamed1.90E−05down1
GPM6Aglycoprotein M6A2.00E−05down2
SERPINE2serpin peptidase inhibitor, clade E, member 22.10E−05up3
COL1A1collagen, type I, alpha 12.20E−05up4
TM4SF1transmembrane 4 L six family member 12.20E−05down5
C2orf40chromosome 2 open reading frame 402.40E−05down6
Unnamed2.40E−05down7
SLC24A3solute carrier family 24, member 32.50E−05down8
EIF1eukaryotic translation initiation factor 12.60E−05down9
GLYATglycine-N-acyltransferase2.80E−05down10
UGGT2UDP-glucose glycoprotein glucosyltransferase 22.80E−05up11
KANK4KN motif and ankyrin repeat domains 43.00E−05down12

GO enrichment analysis of DEGs

To better understand the biological functions of these DEGs, GO enrichment analysis was performed using the platform of Gene-Cloud of Biotechnology Information (GCBI) for all DEGs. GO analysis was conducted based on Fisher exact test. Using statistical difference thresholds of p < 0.05 and FDR < 0.05, we identified 145 sequences potentially involved in biological processes. The most common biological processes identified were negative regulation of cell proliferation, extracellular matrix organization, positive regulation of cell proliferation, collagen fibril organization, negative regulation of apoptotic process, collagen catabolic process, extracellular matrix disassembly, cell adhesion, skeletal system development, positive regulation of ERK1 and ERK2 cascade (Table 2 and Fig. 2a).

Table 2

The top 20 significant GO terms for biological processes. FDR indicates false discovery rate

BP nameEnrichment scorep-valueFDRRank
Negative regulation of cell proliferation7.948.82E−136.29E−101
Extracellular matrix organization10.969.14E−136.29E−102
Positive regulation of cell proliferation7.251.51E−126.90E−103
Collagen fibril organization31.252.04E−117.02E−094
Negative regulation of apoptotic process5.872.52E−106.89E−085
Collagen catabolic process18.813.01E−106.89E−086
Extracellular matrix disassembly17.147.72E−101.52E−077
Cell adhesion5.673.53E−096.07E−078
Skeletal system development11.736.26E−099.56E−079
Positive regulation of ERK1 and ERK2 cascade14.013.62E−084.98E−0610
Transforming growth factor beta receptor signaling pathway11.195.16E−086.45E−0611
Positive regulation of transcription, DNA-dependent4.967.21E−088.26E−0612
Negative regulation of cell growth10.424.81E−075.08E−0513
Negative regulation of platelet activation60.187.15E−077.02E−0514
Blood vessel development20.838.24E−077.55E−0515
Positive regulation of MAPK cascade14.819.73E−078.36E−0516
Positive regulation of protein kinase B signaling cascade13.941.48E−061.20E−0417
Chondroitin sulfate biosynthetic process27.082.01E−061.53E−0418
Cell-cell signaling6.154.40E−063.19E−0419
Platelet activation6.646.54E−064.50E−0420
GO and KEGG pathway enrichment analysis.
Fig. 2  GO and KEGG pathway enrichment analysis.

(a) The top 20 enrichment scores in GO enrichment analysis of DEGs. (b) Analysis of the top 20 enriched KEGG pathways.

KEGG pathway enrichment analysis of DEGs

KEGG is a powerful tool for the analysis of metabolic pathways and networks. The GCBI platform-based KEGG pathway enrichment analysis of DEGs showed that DEGs were enriched in 43 pathways with a statistical significance of p < 0.05. The enriched pathways were mainly in categories such as “PI3K-Akt signaling” (p = 3.17E-10), “Cytokine-cytokine receptor interaction” (p = 3.09E−08), “Focal adhesion” (p = 9.09E−07), “ECM-receptor interaction” (p = 7.92E−06), “Protein digestion and absorption” (p = 8.55E−06), “Jak-STAT signaling” (p = 5.01E−05), “Malaria” (p = 6.23E−05), “Pathways in cancer” (p = 7.38E−05), “Protein processing in endoplasmic reticulum” (p = 7.44E−05), and “Transcriptional mis-regulation in cancer” (p = 1.26E−04). The top 20 enriched pathway terms are displayed in Figure 2b.

Pathway network analysis of significant pathways

Pathway network analysis was constructed based on the interaction relationships provided by the KEGG database. From this network, we analyzed signal transduction of the significant upstream regulatory signaling pathways and downstream effectors in order to better understand the nature of the differences between the samples. A total of 17 pathways were identified, of which 11 were up-regulated. Six differentially regulated pathways were significantly enriched. We identified 31 relationships between these pathways (Fig. 3). As shown in Table 3, the main signaling pathways implicated in cancer-associated omental tissue were: pathways in cancer, focal adhesion, Wnt signaling, TGF-beta signaling, and cytokine-cytokine receptor interaction. Pathways in cancer was the key node in this network with the most extensive interactions with the remaining pathways.

The pathway interaction network.
Fig. 3  The pathway interaction network.

A node indicates a pathway, and the size of the node represents the value of the degree (the larger the node, the greater the degree). Expression levels are represented in different colors (red: up-regulation; yellow: both up- and down-regulation; blue: down-regulation).

Table 3

The 10 highest scoring pathway in pathway network analysis

Pathway nameOutdegreeIndegreeDegreePathway feature
Pathways in cancer14014up|down
Focal adhesion448up
Wnt signaling pathway235up|down
TGF-beta signaling pathway055up
Cytokine-cytokine receptor interaction055down|up
ECM-receptor interaction123up
Jak-STAT signaling pathway123down|up
Regulation of actin cytoskeleton123down|up
Pancreatic cancer213up
Basal cell carcinoma213up

Construction of the PPI network and identification of hub genes

PPI network analysis constructs a network of protein interactions as well as identifying hub genes. The PPI network of the top 100 DEGs obtained from the GSE120196 dataset was constructed using the STRING database and Cytoscape software. This revealed a PPI network containing 43 hub nodes and 62 edges (Fig. 4a). The top 10 of these 43 genes were screened as hub genes using the betweenness method of the cytoHubba plugin. These genes included TP53, COL1A1, HSPD1, SERPINE2, CSF3, MARS, F2R, VCAN, COL1A2, and THBD (Table 4). The expanded subnetwork of the top 10 hub genes is shown in Figure 4b.

PPI network and hub genes.
Fig. 4  PPI network and hub genes.

(a) The PPI network of top 100 DEGs with a minimum interaction score of 0.4 were visualized in Cytoscape software. Red represents up-regulated genes; green represents down-regulated genes. (b) The PPI network of the top 10 hub genes. Hub genes are presented in color depth of red to yellow, with other interaction genes shown in blue. The color depth represents the betweenness value (the redder the dot, the more important the hub gene).

Table 4

The top 10 hub genes were identified by cytoHubba

Gene symbolGene featureGene descriptionBetweenness
TP53uptumor protein p53864.2
COL1A1upcollagen, type I, alpha 1368.23
HSPD1downheat shock 60kDa protein 1 (chaperonin)186
SERPINE2upserpin peptidase inhibitor, clade E (nexin, plasminogen activator inhibitor type 1), member 2170.2
CSF3downcolony stimulating factor 3 (granulocyte)144
MARSupmethionyl-tRNA synthetase128
F2Rupcoagulation factor II (thrombin) receptor105.33
VCANupversican76.47
COL1A2upcollagen, type I, alpha 272.7
THBDdownthrombomodulin69.67

Kaplan-Meier analysis of hub genes

In the above analyses, we studied gene networks derived from OC with greater omentum metastases. To investigate the association between gene networks and OC further, we performed Kaplan-Meier survival analyses to investigate the association of ten hub genes with PFS and OS of advanced-stage ovarian cancer (FIGO Stage III–IV). We found that patients with higher expression levels of COL1A1 or VCAN had significantly lower PFS rates, and patients with higher expression levels of MARS had longer PFS rates (Fig. 5a), with these differences being statistically significant (p = 0.0022, p = 0.0025, and p = 0.00017, respectively). Moreover, survival analysis showed that only VCAN expression was negatively correlated with overall survival (Fig. 5b, p = 0.0031). The expression levels of the remaining genes were not significantly associated with survival.

Kaplan-Meier analysis of hub genes.
Fig. 5  Kaplan-Meier analysis of hub genes.

(a) The relationship of COL1A1, VCAN and MARS expression with PFS in FIGO stage III–IV ovarian cancer; (b) The relationship of VCAN expression with OS in FIGO stage III–IV ovarian cancer.

Validation of gene expression based on the Oncomine database

To further investigate mRNA expression levels of COL1A1, VCAN and MARS in ovarian cancer, our study used the Oncomine database. Results showed that COL1A1, VCAN and MARS mRNA expression levels were significantly higher in tumor samples than samples from non-cancerous tissues (see Figs. 6a, 7a, 8a). To further understand the expression of these genes in human ovarian cancer, their expression level in normal ovary vs. ovarian cancer tissues were compared. COL1A1, VCAN and MARS mRNA levels were significantly higher in ovarian cancer tissues than those in the adjacent normal tissues (Fig. 6b, p = 1.99E−12; Fig. 7b, p = 2.88E−4; Fig. 8b, p = 1.02E−4), and were each highly expressed in different histological types of human ovarian cancer (all p < 0.001; Fig. 6c, Fig. 7c and Fig. 8c).

Validation of <italic>COL1A1</italic> gene expression based on the Oncomine database.
Fig. 6  Validation of COL1A1 gene expression based on the Oncomine database.

(a) Summarization of COL1A1 expression in various cancer types from the Oncomine database. Red denotes significant overexpression; (b) Expression of COL1A1 mRNA levels in human ovarian cancers using the Oncomine database; (c) Expression levels of COL1A1 in different ovarian cancer datasets.

Validation of <italic>VCAN</italic> gene expression based on the Oncomine database.
Fig. 7  Validation of VCAN gene expression based on the Oncomine database.

(a) Summary of VCAN expression in various cancer types from the Oncomine database. Red denotes significant overexpression; (b) Expression of VCAN mRNA levels in human ovarian cancers using the Oncomine database; (c) Expression levels of VCAN in different ovarian cancer datasets.

Validation of <italic>MARS</italic> gene expression based on the Oncomine database.
Fig. 8  Validation of MARS gene expression based on the Oncomine database.

(a) Summary of MARS expression in various cancer types from the Oncomine database. Red denotes significant overexpression; (b) Expression of MARS mRNA levels in human ovarian cancers using the Oncomine database; (c) Expression levels of MARS in different ovarian cancer datasets.

In summary, survival analysis identified three up-regulated hub genes that were associated with survival. Increased COL1A1 and VCAN expression correlated with poorer survival, while in contrast higher expression of MARS was associated with longer PFS. Since COL1A1 and VCAN expression levels were significantly correlated with the prognosis of advanced OC patients, they may be useful as novel targets for diagnosis and therapy in OC patients with omental metastasis.

Discussion

Ovarian cancer metastasis is a complex process that involves numerous influential factors and multiple steps. The metastatic routes of OC principally consist of direct spread, intraperitoneal implantation metastasis and lymphatic metastasis. Hematogenous metastasis only occurs rarely.9,21 Exfoliated tumor cells from advanced OC are widely disseminated in the abdominal-pelvic cavity, and the greater omentum is the most common metastatic site of ovarian cancer.7 The greater omentum is an important immune organ in the abdominopelvic cavity, which covers the surface of the viscera and is richly supplied with blood vessels, lymphatic drainage, adipose tissue, and nerve fibers. It also is known to have anti-tumor properties. However, in advanced ovarian cancer the greater omentum is often contracted into a pie shape due to extensive seeding of tumor cells. Dynamic observation of animal models of ovarian tumors conducted at the University of Chicago showed that ovarian cancer cells infiltrate the greater omentum within 20 minutes of entering the abdominal cavity.22 Therefore, the study of omental metastasis in ovarian cancer has become an important focus of research in recent years. Nowicka isolated pluripotential adipose-derived stem cells from ovarian cancer patients’ greater omentum, indicating that adipose-derived stem cells could promote the proliferation and migration of tumor cells and contribute to their resistance to chemotherapy.23 Additionally, previous studies have shown that ovarian cancer cells can induce lipolysis in the omentum, which can provide energy for the invasion and metastasis of tumor cells.9 Despite progress in studying on omental metastasis of OC, the underlying mechanisms are still not fully elucidated.

Gene chip technology makes it possible to explore specific mechanisms of ovarian cancer occurrence at the genomic level and then focus more precisely on the identified gene targets. In this study, we sought to explore and analyze the gene chip detection data of RNA samples from cancer-associated omental tissues derived from ten patients with OC and benign control tissues derived from four patients in order to identify differentially expressed genes related to the omental metastasis of OC. Our findings show significant differences in the gene expression profiles of the two sets of tissues. Among more than 50,000 genes detected, a total of 429 genes are differentially expressed, including 301 up-regulated genes and 128 down-regulated genes, potentially identifying genes affecting tumor development. We further screened the top 12 genes identified by these analyses (unnamed, GPM6A, SERPINE2, COL1A1, TM4SF1, C2orf40, unnamed, SLC24A3, EIF1, GLYAT, UGGT2, and KANK4). Of special note, expression of GPM6A was the lowest.

GO analysis revealed that differentially expressed genes have functions that are mainly related to cell proliferation, extracellular matrix, collagen, and cell adhesion. The PI3K-Akt signaling pathway, cytokine-cytokine receptor interaction, focal adhesion, ECM-receptor interaction, protein digestion and absorption, Jak-STAT signaling, and other pathways in cancer were also identified by KEGG pathway enrichment analysis. Prior research has shown that the progression of malignant tumors is often accompanied by changes in the construction of the extracellular matrix and expression of cell surface receptors,24,25 consistent with our findings in cancer-associated omental tissues from the patients with advanced stage, high-grade serous ovarian cancer (HGSOC) in the current study. In addition, changes in collagen content within tumor cells and the tumor microenvironment play an essential role in the invasion and metastasis of tumor cells by affecting tumor metabolism, gene expression, angiogenesis, and epithelial-mesenchymal transition.26–30

In the current study, we found that genes that were significantly differentially expressed were involved in 43 pathways, including PI3K-Akt signaling, cytokine-cytokine receptor interaction, focal adhesion, and ECM-receptor interaction. The activation of PI3K-AKT pathway is important for cancer cell proliferation, migration, invasion, and chemoresistance, including in ovarian cancer cells.31,32 In the PPI network analysis, a total of ten hub genes formed the center of the network: TP53, COL1A1, HSPD1, SERPINE2, CSF3, MARS, F2R, VCAN, COL1A2 and THBD. With the exception of HSPD1, CSF3 and THBD, each of these genes were up-regulated in metastatic ovarian cancer-associated samples compared to samples from patients with benign gynecologic diseases.

The present study implicated several DEGs in the development and progression of ovarian cancer, including COL1A1, VCAN, and MARS. In addition, examination of the association between expression levels of ten hub genes and patients’ PFS and OS demonstrated that higher expression levels of VCAN were associated with worse PFS and OS, higher expression levels of COL1A1 were associated with reduced PFS, and that higher expression of MARS correlated with increased PFS. These findings on MARS are not consistent with previous studies in which MARS expression is upregulated and positively correlated with poor prognosis in various cancers, such as breast cancer and lung cancer. Taken together, we conclude that MARS may not be a regulator for advanced ovarian cancer. COL1A1 and VCAN, which were identified as key genes associated with OC survival in the current study, could be useful as diagnostic and prognostic biomarkers.

Studies have suggested that aberrant expression of COL1A1 is closely related to tumorigenesis in several malignancies.33–36COL1A1 expression can induce the epithelial-mesenchymal transition, thus promoting invasion and metastasis of tumor cells.37 Liu et al. confirmed that COL1A1 is a potential therapeutic target for breast cancer as it mediates the metastatic process.38 Recent studies have suggested that COL1A1 may be a biomarker for early detection of gastric cancer.39 Moreover, up-regulation of COL1A1 may contribute to cisplatin resistance in ovarian cancer cells.40 Our results show that COL1A1 expression was significantly up-regulated in OC-associated omental tissues. In addition, patients with higher COL1A1 expression had worse PFS.

VCAN belongs to the family of large aggregating chondroitin sulfate proteoglycans and is mainly found in the extracellular matrix.41VCAN expression levels have been shown to correlate with tumor progression, and are a poor prognostic indicator in stage II–III colon cancer patients.42 Kulbe et al. have suggested that VCAN has the potential to be used as a biomarker for ovarian cancer,43 as stromal VCAN is a biomarker with a poor prognosis.44 Our study showed that high VCAN expression is associated with worse PFS and OS in patients with advanced OC, suggesting that it may be implicated in the omental metastasis of OC.

Future directions

Herein, we identified two key genes, COL1A1 and VCAN, which may play a key role in the omental metastasis of OC. These genes may be useful as new prognostic biomarkers for advanced OC. Further studies are needed to validate the roles played by COL1A1 and VCAN genes in advanced OC and their role in the progression of early stages of OC.

Conclusions

This study used comprehensive bioinformatics to identify two genes (COL1A1 and VCAN), which may serve key roles in the tumorigenesis of OC with omental metastasis (Fig. 9). These key genes could serve as novel prognostic biomarkers of advanced OC. However, a limitation of the current study is the lack of in vivo and in vitro experiments. Further experiments are required to confirm the present findings, and explore the specific mechanisms and the exact functions of these genes in advanced-stage OC.

Schematic summary of the present study.
Fig. 9  Schematic summary of the present study.

Abbreviations

OC: 

ovarian cancer

HGSOC: 

high-grade serous ovarian cancer

DEGs: 

differentially expressed genes

GO: 

gene ontology

KEGG: 

Kyoto Encyclopedia of Genes and Genomes

PPI network: 

protein-protein interaction network

GCBI: 

Gene-Cloud of Biotechnology Information

GEO: 

Gene Expression Omnibus

NCBI: 

National Centre of Biotechnology Information

GPM6A

glycoprotein M6A

SERPINE2

serpin peptidase inhibitor, clade E, member 2

COL1A1

collagen type I alpha 1

TM4SF1

transmembrane 4L six family member 1

TP53

tumor protein p53

HSPD1

heat shock protein family D (Hsp60) member 1

CSF3

colony stimulating factor 3

MARS

methionyl-tRNA synthetase

F2R

coagulation factor II (thrombin) receptor

VCAN

versican

C2orf40

chromosome 2 open reading frame 40

SLC24A3

solute carrier family 24 member 3

EIF1

eukaryotic translation initiation factor 1

GLYAT

glycine-N-acyltransferase

UGGT2

UDP-glucose glycoprotein glucosyltransferase 2

KANK4

KN motif and ankyrin repeat domains 4

STRING: 

Search Tool for the Retrieval of Interacting Genes

Declarations

Acknowledgement

We are grateful to researchers who have deposited their data in the GEO database. The authors would like to acknowledge the technical assistance of the GCBI online analysis platform.

Data sharing statement

The datasets generated and/or analyzed in the present study are available in the GEO (http://www.ncbi.nlm.nih.gov/gds) and the Oncomine (http://www.oncomine.org) repositories.

Funding

This work was supported by grants from Hainan health science and education project [Item No. 21A200020] and Hainan health science and education project [Item No. 20A200415].

Conflict of interest

The authors declare no competing interests.

Authors’ contributions

MZ and LW conceived and designed the project; MZ and JT performed the analysis and constructed the model; LS, HW and XZ checked the associated database and classified raw data; MZ and JT wrote and revised the manuscript. All of the authors read and approved the final manuscript.

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