Construction of an exosome-associated miRNA-mRNA regulatory network and validation of FYCO1 and miR-17-5p as potential biomarkers associated with ovarian cancer

Introduction

In 2022, an estimated of 19,880 women were newly diagnosed with ovarian cancer (OV) in United States, and it caused 12,810 deaths. For the worldwide, the death toll is more than 140,000 (1,2). As one of the three major gynaecological malignancies, it has the highest mortality rate among gynaecological malignancies, with a global average incidence of nearly 10 in 100,000 women. With the widespread use of oral contraceptives, the incidence of OV has declined, but it remains the seventh most common cancer in women worldwide (1,3,4). In women over 40 years old, OV is second only to breast cancer in terms of incidence. The 5-year survival rate for patients with early-stage (stage I) OV can be as high as 92%, while the 5-year survival rate for patients with advanced OV is only 29%, and the overall 5-year survival rate for patients at all stages is 49.7% (based on the collected data between 2012 to 2018) (2,5,6). Early clinical manifestations of OV are not obvious and there are few pre-symptoms. Unfortunately, most OVs have developed to an advanced stage by the time they are diagnosed (7,8), making radical treatment difficult. Early diagnosis of OV remains a major challenge to meet public health needs (9,10). Therefore, elucidating the molecular mechanisms of OV initiation, progression and metastasis and finding effective biomarkers are essential for early diagnosis, treatment selection, follow-up schedules and prognostic assessment to prolong patient life expectancy and clinical benefit.

Exosomes are a subset of extracellular vesicles (EVs) with a diameter of 50–150 nm and have been most extensively investigated as one of the three major subgroups (exosomes, microvesicles and apoptotic vesicles) of EVs, which are released by mammalian cells in response to stimulation by normal and pathogenic factors by a variety of cell types, including tumor cells, macrophages, mast cells, mesenchymal stem cells, lymphocytes, and fibroblasts (11-15). Some specific proteins and genetic materials in exosomes reflect their cellular origin and physiological status and can be explored as biomarkers for early clinical diagnosis, treatment and prognosis of many cancers (16). In addition, exosomes secreted by some cells, including mesenchymal stem cells and cancer cells, can be used to treat various diseases. In the last decade, the study of exosomes in cancer has progressed rapidly. As one of the components of the tumor microenvironment, exosomes have been shown to have important functions in cancer development, progression, metastasis, angiogenesis, and immunity (17,18). In addition, exosomes can mediate intercellular communication by transferring mRNAs, microRNAs (miRNAs), long non-coding RNAs, proteins and DNA (18). MiRNAs are considered to be an evolutionarily conserved family of molecules, and most miRNAs in exosomes act as tumor promoters or repressors that affect other cells in the tumor microenvironment, thereby altering their biological phenotype (19). Conversely, cancer cells receive exosomes released from stromal cells, which also affect the proliferative or invasive capacity of cancer cells. There is also an increasing number of studies on OV exosomes (16,20,21). It has been shown that MMP1 (matrix metalloproteinase 1) in exosomes from OV cells may play a role in damaging the mesothelium and promoting peritoneal dissemination of cancer cells (22). MiR-21, a cancer-associated fibroblast (CAF)-derived exosome, binds to APAF1 (apoptosis protease activating factor 1) in OV cells, conferring resistance to paclitaxel (23). A comprehensive study of the interaction of miRNAs with other regulatory factors in OV exosomes is still needed.

MiRNAs are a class of small (19–25 nucleotides) non-coding RNAs that regulate gene expression primarily by targeting mRNA in a sequence-specific manner. A growing body of research indicates that miRNA dysregulation is a common feature of the tumorigenic development process, and miRNAs are closely associated with the development of a variety of cancers, including breast, lung and OVs (24-27). Mechanistic studies have shown that miRNAs are involved in a number of important cellular processes, including cell proliferation, apoptosis and vascular neogenesis (24,25,27). MiRNAs are becoming increasingly popular in the study of OV. MiR-205 promotes OV cell invasion and metastasis by suppressing the expression of the oncogene transient overexpression lysate of transcription factor 21 (TCF21), and its level in plasma exosomes has been identified as a valuable cancer biomarker that may help in the diagnosis of OV (20,28). Also, circulating exosomal miR-1290 is used as a biomarker for differential diagnosis of epithelial OV from benign ovarian neoplasm, and suppressor of cytokine signaling 4 (SOCS4) could be a potential target gene of miR-1290 (29). OV patients with high exosomal miR-1246 expression and low caveolin-1 (CAV1) expression had a significantly worse overall prognosis. Treatment with CAV1 overexpression and miR-1246 inhibition significantly increased the sensitivity of OV cells to paclitaxel stimulation. Further study showed that miR-1246 inhibits CAV1 and acts through the platelet-derived growth factor receptor β (PDGF-β) receptor on recipient cells to suppress cell proliferation (30). Exosomes with low levels of miR-543 promoted OV cell proliferation in vitro and in vivo by targeting insulin-like growth factor 2 (IGF2) (31). In addition, exosomal miR-1260a, miR-7977, miR-192-5p, miR-21-5p, miR-4732-5p and miR-429 et al. (32-35) are also promising diagnostic biomarkers or associated with chemosensitivity. Although a large number of miRNAs have been associated with OV, miRNA in serum exosomes of OV patients have not attracted enough attention. Furthermore, the function of some miRNAs carried by exosomes secreted by OV tissues is unknown. Therefore, the construction of OV-associated ceRNA regulatory network is crucial and may provide more insights into the molecular mechanisms of OV.

In this paper, we speculate that miRNAs carried by exosomes secreted from OV tissues may regulate the expression of other mRNAs in OV tissues and thus participate in the development of OV. First, we investigated the potential signaling regulatory pathway of differentially expressed miRNAs (DEMs) and genes (DEGs) from exosomes using bioinformatics strategies. Second, a miRNA-mRNA interaction network was constructed. Our study elucidated the regulatory roles and relationships of exosome-derived miRNAs in the development of OV. This study provides a new reference for the early diagnosis, subsequent treatment and prognosis of OV. We present this article in accordance with the TRIPOD reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-23-940/rc).

Methods

Data resources and preprocessing

First, we searched the public high-throughput gene expression database—Gene Expression Omnibus (GEO; https://www.ncbi.nlm.nih.gov/geoprofiles) (36) for datasets related to OV using the keywords ‘ovarian cancer’ and ‘exosomes’. Microarray data ‘MINiML formatted family file(s)’ of GSE103708, GSE76449, GSE54388, GSE17308, GSE12470 and GSE6008 were downloaded and used for analysis. Biological fluid samples were not included in this study. The data source and attributes of the datasets are summarized in Table 1. For miRNA expression profiling associated with exosomes in OV, a total of 26 exosome samples from OV cell lines and 6 exosome samples from normal human ovarian surface epithelial cell lines were included in GSE103708 (platform: GPL18402 Agilent-046064 Unrestricted_Human_miRNA_V19. 0_Microarray), while 24 exosome samples from OV cell lines and 4 exosome samples from normal ovarian cell lines were included in GSE76449 (platform: GPL19117 Affymetrix Multispecies miRNA-4 Array). For gene/mRNA expression profiling in OV, 16 OV surface epithelium samples and 6 ovarian surface epithelium samples were included in GSE54388 (platform: GPL570 Affymetrix Human Genome U133 Plus 2.0 Array), while 35 OV surface epithelium samples and 11 non-OV surface epithelium samples (4 normal and 7 benign) were included in GSE17308 (platform: GPL8926 PC Human Operon 21k v2). Forty-three OV tissues and 10 normal peritoneal samples were enrolled in GSE12470 (platform: GPL887 Agilent-012097 Human 1A Microarray), while 41 individual ovarian tumors and 4 individual normal ovarian samples were enrolled in GSE6008 (platform: GPL96 Affymetrix Human Genome U133A Array). The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013).

Screening of DEMs and DEGs

First, all data were normalized using the DESeq2 R package to ensure accuracy. They were then analysed separately. The interactive online tool GEO2R (www.ncbi.nlm.nih.gov/geo/geo2r) (37) was used to screen the DEMs between non-OV exosome samples and OV exosome samples. P value <0.05, adjusted P value <0.01 and |logFC| >1 were selected as the cut-off standard. For DEMs related to exosomes in OV, the overlapping miRNAs in GSE103708 and GSE76449 were selected as DEMs. Next, the DEGs between non-OV samples and OV samples were also screened by GEO2R. P value <0.05, adjusted P value <0.01 and |logFC| >1 were considered as cut-off criteria. For DEGs in OV, the overlapping mRNAs in GSE54388, GSE17308, GSE12470 and GSE6008 were selected as DEGs. The Venn diagram was generated using an online website (http://bioinformatics.psb.ugent.be/webtools/Venn/).

Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis

GO and KEGG pathway analyses were performed to predict the possible function of these DEGs using the “clusprofiler” R package (38). GO enrichment analysis is a common approach for functional studies of genomic or transcriptomic data (39). It has been classified into three distinct aspects of biological content: biological process (BP), cellular component (CC) and molecular function (MF). GO and KEGG analyses were performed using the DAVID (distributed access view integrated database) database (40). For GO analysis, q-value <0.05 was considered statistically significant. For KEGG analysis, P value <0.05 was set as the threshold.

Construction of exosomal miRNA-mRNA network

Here, the target genes of these miRNA signatures were obtained used the miRDB (41,42), miRTarBase (43) and TargetScan databases (44). The genes screened from the above databases were considered to be the target genes of these miRNAs. The predicted target genes were then compared with the DEGs. Construction of exosomal miRNA-mRNA networks using only the remaining overlapping genes and their interacting pairs. In our study, the mRNAs in the network were considered as hub mRNAs. Consequently, the miRNA-mRNA network was inextricably linked to exosomes in OV.

Validation of the miRNA-mRNA regulatory network

Gene Expression Profiling Interactive Analysis (GEPIA2; http://gepia2.cancer-pku.cn/) was used to verify the expression level of mRNAs in the miRNA-mRNA network based on The Cancer Genome Atlas (TCGA; https://portal.gdc.cancer.gov/). GEPIA2 is a public, open website that analyses cancer types and large amounts of genetic data. It provides free access to cancer type and gene data and allows users to validate biomarkers or target potential genes.

Validation of hub-genes on prognostic survival of OV

The Kaplan-Meier Plotter (http://kmplot.com/analysis/) (45) and GEPIA2 tools (46) were used to validate overall survival (OS) and progression-free survival (PFS) or recurrence-free survival (RFS) of hub genes in the miRNA-mRNA network. The Kaplan-Meier Plotter is a freely accessible website that provides a search service for correlations between the expression of all genes and survival in various tumor types, including breast, ovarian, lung and gastric cancer. Sources for these databases include GEO, EGA and TCGA.

Luciferase reporter assay

HEK-293T cells (5×10⁴/well) were seeded in a 24-well plate maintained in a humidified atmosphere containing 5% CO₂ at 37 ℃. After a 12 h-culture, when the cell confluency reached approximately 80%, cells were co-transfected with re-constructed plasmids (luciferase reporter containing wild type or mutant target site of miR-17-5p constructed based on Con245 plasmid vector), Renilla luciferase vector and miR-17-5p mimics (150 nM, Genecopoeia, Guangzhou, China) or mimics negative control (NC) using lipofectamine 3000 (Invitrogen, Waltham, MA, USA) for 48 h as previously described (47,48). The relative luciferase activity assay was conducted using a luciferase reporter gene assay system (Genecopoeia) equipped with a Glomax plate reader (Promega, Madison, WI, USA). The relative firefly luciferase activity was normalized with Renilla luciferase activity.

Cell culture and quantitative reverse transcription-polymerase chain reaction (qRT-PCR)

The SKOV3 cell line was obtained from the American Type Culture Collection (ATCC; Manassas, VA, USA). Cells were incubated in Dulbecco’s modified Eagle’s medium (DMEM; Gibco, Grand Island, NY, USA) containing 10% fetal bovine serum (Gibco), 100 IU/mL penicillin/streptomycin at 37 ℃ in a humidified atmosphere containing 5% CO₂. Total cell RNA was extracted with TRIzol according to the manufacturer’s protocol (Sigma-Aldrich, St. Louis, MO, USA). RNA (0.5 µg) was reverse transcribed into cDNA using PrimeScript RT Master Mix (TaKaRa, Otsu, Japan). The qRT-PCR was performed using SYBR Green qPCR Super Mixture (Takara, Tokyo, Japan) and a StepOnePlus Real-Time PCR System (Thermo Fisher Scientific, Inc., Waltham, MA, USA). The following primer sequences were used: GAPDH (forward: TGGTCGTATTGGGCGCCTGGT; reverse: TCGCTCCTGGAAGATGGTGA) and FYVE and coiled-coil domain containing 1 (FYCO1) (forward: GTTCGCCTCACTTGCTTGGTAG; reverse GTGTGGTAGTCCTCCTCCTTCTC). FYCO1 mRNA expression level fold change was calculated using 2^−ΔΔCt.

Statistical analysis

The differences between groups were analysed by paired Student’s t-test, and differences in multiple groups were analysed by one-way analysis of variance (ANOVA). All statistical analyses were performed using GraphPad Prism 7.0 software (GraphPad Prism 7 Software, San Diego, CA, USA). P<0.05 was considered statistically significant.

Results

Identification of DEMs and DEGs

The whole workflow of our study is shown in Figure 1. For DEMs, A total of 192 DEMs (32 up-regulated and 160 down-regulated) were screened from the GSE103708 dataset, while 201 DEMs (72 up-regulated and 129 down-regulated) were screened from GSE76449 (Figure 2A). As shown in Figure 2B, 14 overlapping miRNAs in GSE103708 and GSE76449 were selected as DEMs (3 up-regulated and 11 down-regulated). For DEGs, a total of 8,729 DEGs (5,452 up-regulated and 3,277 down-regulated), 1,526 DEGs (760 up-regulated and 766 down-regulated), 5,765 DEGs (4,324 up-regulated and 1,441 down-regulated) and 6,494 DEGs (3,328 up-regulated and 3,166 down-regulated) were screened from GSE54388, GSE17308, GSE12470 and GSE6008, respectively. The overlapping mRNAs in GSE54388, GSE17308, GSE12470 and GSE6008 were selected as DEGs. Finally, 101 DEGs (67 up-regulated and 34 down-regulated) were screened out (Figure 3A,3B). The DEMs and DEGs were then used for the following analysis.

Figure 1 Flow chart of the analysis performed in this study. The RNAs were analysed to obtain DEMs and DEGs along the OV progression. The DEMs target genes were then subjected to GO and KEGG analyses. Finally, a miRNA-mRNA network associated with OV exosomes was generated. DEMs, differentially expressed miRNAs; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; miRNA, microRNA; OV, ovarian cancer; DEGs, differentially expressed genes.

Figure 2 Volplot (A) and Venn diagram (B) of DEMs. GSE103708 and GSE76449 were screened for 3 up- and 11 down-regulated DEMs using Venn diagrams. P value <0.05, adjusted P value <0.01 and |logFC| >1 were selected as the cut-off standard. DEMs, differentially expressed miRNAs; FC, fold change; miRNA, microRNA.

Figure 3 Volplot of DEGs (A) and Venn of DEGs (B). GSE54388, GSE17308, GSE12470 and GSE6008 were screened for 67 up- and 34 down-regulated DEGs using Venn diagrams. P value <0.05, adjusted P value <0.01 and |logFC| >1 were selected as the cut-off standard. DEGs, differentially expressed genes; FC, fold change.

Functional enrichment analysis of DEGs by GO and KEGG

The GO analysis resulted in a total of 101 DEGs mapped to the GO terms. Using false discovery rate (FDR)-corrected P value <0.05 and enrichment score >1.5 as a threshold, significantly enriched functional clusters were identified in the BP, CC and MF terms (as shown in Figure 4A). In the BP group, DEGs were mainly enriched in cell cycle-related processes and nutrient metabolism, such as ‘DNA replication’, ‘DNA-dependent DNA replication’, ‘response to amino acid’. In the CC group, DEGs were mainly enriched in ‘focal adhesion’, ‘cell-substrate junction’. In the MF group, DEGs were mainly enriched in ‘glycosaminoglycan binding’, ‘cadherin binding’. For KEGG enrichment analysis, the DEGs were mainly enriched in ‘base excision repair’, ‘amino acid biosynthesis’, ‘carbon metabolism’, ‘DNA replication’ and also enriched in several signaling pathways such as ‘ras signaling pathway’ and ‘PI3K-Akt signaling pathway’ (Figure 4B).

Figure 4 GO analysis (A) and KEGG analysis (B) of DEGs. GO analysis of target genes that correspond with DEGs. BP, CC, and MF. KEGG pathway enrichment analysis on the basis of the identified DEGs (P value <0.05 was considered to be statistically significant). GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; BP, biological process; CC, cellular component; MF, molecular function; DEGs, differentially expressed genes.

Construction of miRNA-mRNA regulatory network

As the presented miRNA-mRNA network in Figure 5A, the two overlapping genes, FYCO1 and purine rich element binding protein A (PURA) were regulated by three DEMs (hsa-miR-17-5p, hsa-miR-20b-5p and hsa-miR-20a-5p). Interestingly, the three miRNAs were up-regulated while the two mRNAs were down-regulated in exosome tissues of OV disease. These results suggest that the miRNA carried by exosomes from OV tissues may regulate the expression of some crucial mRNA in OV.

Figure 5 MiRNA-mRNA network (A) and mRNA expression in TCGA database (B). The tri-angles stand for miRNAs, ellipses for mRNAs. DEGs expression levels from the network, FYCO1 and PURA, were analysed using GEPIA2 on the basis of TCGA. *, P value <0.05 was considered to be significant. miRNA, microRNA; FYCO1, FYVE and coiled-coil domain containing 1; PURA, purine rich element binding protein A; TCGA, The Cancer Genome Atlas; OV, ovarian cancer; TPM, transcripts per million; DEGs, differentially expressed genes.

Validation of the miRNAs and mRNAs in miRNA-mRNA regulatory network

Validation of FYCO1 and PURA expression level in normal tissues and OV samples was conducted through the GEPIA2 online database. However, although the expression of the two mRNAs were down-regulated in OV tissues (as shown in Figure 5B) on the basis of gene expression profiles from TCGA and the genotype-tissue expression (GTEx) projects, only the downregulation of FYCO1 expression was statistically significant. At last, the expression trends of FYCO1 and PURA in cancer tissues from OV patients and normal tissues from healthy people based on the GEPIA2 database were in accordance with that based on the GEO database.

Validation of hub-gene FYCO1 and PURA on survival of OV

The Kaplan-Meier Plotter analysis showed that high FYCO1 expression was significantly associated with decreased OS (P=0.001) (Figure 6A) and PFS (P<0.001) (Figure 6B) in OV when using a probe with affymetrix id of 1555523_a_at. However, when a different probe with id 218204_s_at was used, FYCO1 expression levels did not significantly correlate with OS (P=0.33) (Figure 6C) and PFS (P=0.071) (Figure 6D) in OV. Regardless of the probe (204020_at, 204021_s_at, 213806_at or 229167_at) selected, PURA expression level was not associated with OV survival. GEPIA2 analysis showed that both FYCO1 (P=0.33) and PURA (P=0.26) expression were not associated with OS in OV (as shown in Figure S1).

Figure 6 Kaplan-Meier Plotter analysis of the prognostic value of FYCO1 mRNA levels in OV patients. 1555523_a_at (A,B) and 218204_s_at (C,D) are two different probes of FYCO1. P value <0.05 was considered to be significant. HR, hazard ratio; OS, overall survival; FYCO1, FYVE and coiled-coil domain containing 1; PFS, progression free survival; OV, ovarian cancer.

Verification of the mRNA expression and miRNA targets

Luciferase reporter assay was conducted to confirm the potential binding relationship. Wild-type and mutant FYCO1 3'UTR were inserted into the luciferase reporter vector Con245 (Figure 7A,7B), and co-transfected with miR-17-5p mimics in HEK-293A cells. The group with wild-type FYCO1 3'UTR transfection showed a significant decrease in luciferase activity compared to the mimic NC group after transfection with miR-17-5p mimics, whereas no significant difference was observed in the mutant FYCO1 3'UTR transfection group when transfected with miR-17-5p mimics or mimics NC (Figure 7C). To further investigate whether miR-17-5p inhibits endogenous FYCO1 expression, miR-17-5p mimics were transfected into SKOV-3 cells, which resulted in significant inhibition of FYCO1 transcripts (Figure 7D). These results confirmed that miR-17-5p targets FYCO1 in OV cells.

Figure 7 Validation of the target relationship between miR-17-5p and FYCO1. (A) Prediction of the binding area between miR-17-5p and the seed site in the 3'-UTR of FYCO1. (B) Diagram of the FYCO1 3'-UTR reporter construct. (C) miR-17-5p directly represses FYCO1 trans-activation using luciferase reporter assay. (D) qRT-PCR analysis of FYCO1 mRNA expression level in SKOV3 cell line. *, P value <0.05 vs. mimics NC group. FYCO1, FYVE and coiled-coil domain containing 1; UTR, untranslated region; NC, negative control; qRT-PCR, quantitative reverse transcription-polymerase chain reaction.

Discussion

OV is more prevalent in middle-aged and older women. It poses a significant threat to the health of this demographic and is the leading cause of death among gynecological tumors (3,5,49). The inconspicuous early symptoms often result in late-stage diagnoses (49). Early diagnosis and treatment can lead to a better prognosis and longer survival. Gene-based diagnosis and treatment, a frontier in biomedical advancement, has exhibited promising results in clinical trials (49). Over the past decade, extensive research has unveiled the release of exosomes by diverse cell types, such as mesenchymal stem cells, macrophages, and tumor cells (12,50,51). Exosomes carry a multitude of nucleic acids, metabolites, proteins, and other bioactive substances. Among these, miRNAs released from cancer cell-derived exosomes play a central role in mediating intercellular communication with normal cells, thus fueling tumor progression, metastasis, and angiogenesis (17,18). Our study aims to explore the miRNA-mRNA interactions in the progression of OV, offering valuable insights for possible early diagnosis and prognosis.

In the present study, we screened a total of 14 DEMs (3 up-regulated, 11 down-regulated) and 101 DEGs (67 up-regulated, 34 down-regulated) from 6 datasets. Both GO and KEGG gene enrichment were used to analyse these DEGs. Meanwhile, miRDB (41,42), miRTarBase (43) and TargetScan databases (44) were used to predict DEMs, and then the predicted mRNAs and previously identified DEGs were intersected, resulting in a miRNA-mRNA interaction network containing three miRNAs and two mRNAs. We further confirmed that FYCO1 was the target of hsa-miR-17-5p. Interestingly, we observed that high expression of the FYCO1 gene may be associated with a lower OS in OV. This observation appears to be contradictory to the findings in Figure 5A,5B, as the expression of FYCO1 was significantly lower in OV samples compared to normal samples. However, it should be noted that the choice of different FYCO1 probes, such as 218204_s_at (as demonstrated in Figure 6C,6D), can also influence the assessment of the relationship between the expression of FYCO1 and the prognosis of OV. Therefore, FYCO1 shows potential as a diagnostic gene for predicting ovarian carcinogenesis, while its role in prognostic survival of requires further clinical investigation.

Only 14 DEMs were available in this study, and it was not possible to see from the GO and KEGG analysis in which pathways they were more significantly enriched. Therefore, we only show here the enrichment of 101 DEGs on GO and KEGG pathways. Uncontrolled proliferation, infiltration and metastasis, as well as energy wastage or metabolic reprogramming (Warburg effect) are common features of all cancer cells (52,53). GO analysis revealed that these DEGs were mainly enriched in these aspects of cell proliferation, metastasis and amino acid metabolism. In the present study, we used the serous OV samples, which showed that this type of OV is more prominent in proliferation and metastasis (54). Similar to GO analysis, KEGG analysis revealed that DEGs were mainly enriched in DNA replication-related regulatory pathways, in addition to amino acid and carbon metabolism. Géraldine G demonstrated for the first-time metabolic heterogeneity in high-grade serous ovarian cancer (HGSOC) by proteomics and metabolomics, where low-oxidative phosphorylation is predominantly glycolytic and high-oxidative phosphorylation is dependent on glutamine and fatty acid-supported oxidative phosphorylation (55). High levels of extracellular glutamine promote OV cell proliferation, and its important metabolic enzyme glutamine synthetase (GS) is positively correlated with poor prognosis (56). Furthermore, KEGG enrichment analysis of downregulated DEGs was also associated with renin-angiotensin system (Ras) and phosphatidylinositol 3-kinase/protein kinase B (PI3K/Akt) signaling pathways. It has been shown that PI3K/Akt is highly mutated and/or over-activated in most OV patients, it plays an important role in OV tumorigenesis, proliferation and is associated with advanced grade and poor prognosis (57,58). Itamochi et al. (59) found that 3-year OS was significantly higher in patients with activated PI3K/Akt and RTK/Ras signaling pathways, implying that both pathways are associated with OV progression. Our enrichment results suggest that the DEGs screened in the above datasets are trustworthy and that the pathways enriched by these DEGs are highly correlated with important features of selected serous OVs.

The miRNA-mRNA regulatory network was constructed by interacting the DEGs obtained from the target genes predicted by the DEMs with the other four datasets. The only three miRNAs in this network, all of which are upregulated, have been extensively reported in the literature (about 270 articles in the last 5 years), however, except for miR-17-5p, the remaining two miRNAs have not been reported in ovarian-related studies. The combined studies suggest that miR-17-5p may play a role as a pro-oncogenic factor in a variety of cancers (60-62). By targeting heparan sulfate proteoglycan 2 (HSP2) and cell adhesion molecule 2 (CADM2), two tumor suppressor genes, it inhibits apoptosis and promotes proliferation, migration and invasion of colorectal cancer cells (63,64). Another study showed that hsa-miR-17-5p acts on multiple targets of the transforming growth factor-β (TGF-β) signaling pathway, and maintains long-term downregulation before and after conventional or combination chemotherapy, making hsa-miR-17-5p a potential biomarker for effective combination chemotherapy in colorectal cancer (65). Similarly, miR-17-5p was expressed 2-fold higher in peripheral lymphocytes of OV patients compared to healthy controls. Also, miR-17-5p expression levels were higher in OV patients with a history of smoking compared to non-smoking patients (66). The remaining two miRNAs belonging to the miR-20-5p family have not been studied in OV and their effect on tumor progression is controversial. In the studies of bladder cancer and prostate cancer, miR-20a-5p was considered as a negative factor for cancer progression (67,68). By promoting epithelial-mesenchymal transition (EMT), miR-20a-5p overexpression promotes the proliferation, invasion and migration of bladder cancer cells (67). In contrast, miR-20a-5p has been implicated a tumor suppressor in endometrial cancer, inhibiting cancer cell proliferation, invasion and adhesion of cancer cells by decreasing janus kinase 1 (JAK1) expression (69). Like hsa-miR-20a-5p, hsa-miR-20b-5p expression is upregulated in prostate cancer (70). High expression of hsa-miR-20b-5p in leukemic cells reduces apoptosis and promotes proliferation. This may be related to the fact that hsa-miR-20b-5p inhibits the expression of phosphatase and tensin homolog deleted on chromosome ten (PTEN) and Bcl-2 interacting mediator (BIM) of cell death, two important tumor suppressor genes (71). However, this miRNA is a tumor suppressor in colon cancer, and through the cyclin D1 (CCND1)/cyclin-dependent kinase 4 (CDK4)/forkhead box protein M1 (FOXM1) axis it achieves regulation of cell cycle, migration and invasion (72). We tried to explore the differences in the expression of these miRNAs in normal and OV samples using a web-based database (http://bioinfo.jialab-ucr.org/CancerMIRNome), which unexpectedly contains only cancer samples and lacks corresponding normal samples. Due to the limited clinical resources, we could not collect enough clinical OV samples to verify whether the above miRNAs are associated with tumorigenesis, progression and prognosis, and we will explore the relationship between these miRNAs and OV in more detail in future studies.

Among the elements of the regulatory networks we constructed, two down-regulated mRNAs, FYCO1 (73,74) and PURA (47,75), correspond to miRNAs. Interestingly, these genes have rarely been reported in tumor studies. Nevertheless, FYCO1 has been increasingly studied in recent years. There are 21 articles on FYCO1 research in the last 10 years, including 16 in the last 5 years and 12 in 2021 to date. FYCO1 is an adaptor protein that binds to autophagy-associated proteins, such as microtubule-associated protein 1 light chain3 (LC3), and functionally assists in the transport of autophagic vesicles (76,77). Mutations in the FYCO1 gene can affect lens development and transparency, ultimately leading to cataract development (78). During starvation or stress overload stage, FYCO1 rescues impaired cardiac function by inducing autophagy (79). Importantly, FYCO1 reduces post-mitotic midbody (MB) accumulation by regulating MB degradation, thereby reducing anchorage-independent growth and invasiveness of HeLa and squamous cell carcinoma cells (80). In our previous enrichment analysis, we also found that OV exosomal DEGs were mainly enriched in pathways associated with cancer cell proliferation and invasion; therefore, our study is consistent with literature reports. Most of the research on the functions of PURA is related to developmental disorders of the central nervous system (81). However, one article reported that PURA is a potential downstream target of miR-144. Overexpression of miR-144 promotes esophageal cancer cell proliferation and migration accompanied with significantly decreased PURA mRNA expression (47). Although the expression level of PURA between normal sample and OV sample was not significant, as a regulatory network member, PURA showed the common effects among the different types of cancer.

Unexpectedly, both FYCO1 and PURA were found to have no significant relationship with prognostic survival of OV using GEPIA2 survival analysis (Figure S1). Interestingly, the higher but not lower expression of FYCO1 was associated with lower OS and PFS as verified by Kaplan-Meier Plotter analysis (probe: 1555523_a_at) (45), other probe of FYCO1 showed no significant relation with survival of OV. In fact, the lower expression level of FYCO1 was a negative factor for survival in most cancers (82), such as breast cancer (OS, P<0.01, probe: 218204_s_at; RFS, P<0.001, all probes, Kaplan-Meier Plotter), gastric cancer (OS, P<0.001, 218204_s_at, Kaplan-Meier Plotter) and lung cancer (OS, P<0.001, probe: 218204_s_at, Kaplan-Meier Plotter; OS, P<0.05, GEPIA2) (Figure S2). Although no study has been reported on the relationship between FYCO1 expression and tumor survival. Due to the limited number of OV samples collected and the observation time, a statistically significant survival analysis could not be performed.

Conclusions

In summary, we constructed a miRNA-mRNA regulatory network. By target prediction and interaction, we identified three miRNAs (hsa-miR-17-5p, hsa-miR-20b-5p and hsa-miR-20a-5p) and two mRNAs (FYCO1 and PURA) that were associated with ovarian carcinogenesis. By functional validation, our study reported for the first time that FYCO1 was significantly downregulated in OV, and this result was validated in clinical samples tested by qPCR. However, the impact of FYCO1 expression level on the prognostic survival of OV patients remains controversial, which may be mainly related to our use of cancer cell exosomes as the study subject. The present study provides new ideas and new opportunities for OV diagnosis and prognostic survival. As more standardized OV exosome data are developed in the future, the relationship between FYCO1 and OV prognosis will become clearer.

Acknowledgments

Funding: This research was supported by the Doctoral Workstation Project of Guangdong Second Provincial General Hospital (No. 2019BSGZ017) and the Science and Technology Planning Project of Guangzhou (No. 202102020799).

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-23-940/rc

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-23-940/prf

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-23-940/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013).

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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