The emerging roles and potential applications of circular RNAs in ovarian cancer: a comprehensive review.

Ovarian cancer (OC) is among the most common human malignancies and the first cause of deaths among gynecologic cancers. Early diagnosis can help improving prognosis in those patients, and accordingly exploring novel molecular mechanisms may lead to find therapeutic targets. Circular RNAs (circRNAs) comprise a group of non-coding RNAs in multicellular organisms, which are identified with characteristic circular structure. CircRNAs have been found with substantial functions in regulating gene expression through interacting with RNA-binding proteins, targeting microRNAs, and transcriptional regulation. They have been found to be involved in regulating several critical processes such as cell growth, and death, organ development, signal transduction, and tumorigenesis. Accordingly, circRNAs have been implicated in a number of human diseases including malignancies. They are particularly reported to contribute to several hallmarks of cancer leading to cancer development and progression, although a number also are described with tumor-suppressor function. In OC, circRNAs are linked to regulation of cell growth, invasiveness, metastasis, angiogenesis, and chemoresistance. Notably, clinical studies also have shown potentials in diagnosis, prediction of prognosis, and therapeutic targets for OC. In this review, I have an overview to the putative mechanisms, and functions of circRNAs in regulating OC pathogenesis in addition to their clinical potentials.

Ovarian cancer, Circular RNA, circRNA, Biomarker

The genetic information flow in the biological systems was thought for more than half a century to sequentially transfer information from the DNA strands to protein/polypeptides through RNA mediators (Crick 1970). According to this theory, proteins or amino-acid chains encoded by the DNA sequences are the terminal products of genetic information responsible for substantial subcellular functions and whole organism representatives. Thus, proteins were considered characteristics of each organism discriminating individuals and species, and the complexity of organisms was thought to be dictated by proteins. The protein-encoding genome, thus, was identified as the only role player and the rest of genome was considered as “junk” DNA (Najafi et al. 2022b). This theory was later challenged by the findings indicating that the number of protein-encoding genes is similar between humans and the roundworm Caenorhabditis elegans (Taft et al. 2007), also a majority of genome in higher eukaryotes does not encode for any protein, however, transcribed to RNA transcripts described as non-coding RNAs (ncRNAs) with regulatory functions. This amount, for example, is more than 98% in humans, of them at least 75% is transcribed to non-protein coding transcripts, while 22,000 genes encoding for proteins comprise < 2% (Mattick 2001; Lander et al. 2001; Derrien et al. 2012). Andersen et al. identified the first ncRNA with regulatory role, micF RNA, in bacterium Escherichia coli in 1989 (Andersen et al. 1989). It was found to play role in thermal regulation of OmpF protein. Nowadays, the precise number of ncRNAs is unknown; however, thousands of thousands have been estimated to be expressed in a single eukaryotic cell (Statello et al. 2021). Based on the information from GENCODE project, 17,957 coding genes and 48,684 transcripts for lncRNAs are recorded in human (Frankish et al. 2020). High diversity of ncRNAs and also their roles in increasing the diversity of protein-coding RNA transcripts (messenger RNAs; mRNAs) may be the reason of high complexity in humans (Santosh et al. 2015). NcRNAs are mainly classified into long and small based on the transcript length. Long ncRNAs (lncRNAs) are longer than 200 nucleotides (nt), and short ncRNAs include several subclasses such as ribosomal RNAs (rRNAs), transfer RNAs (tRNAs), small nuclear RNAs (snRNAs), and microRNAs (miRNAs) (Najafi et al. 2022a, c). MiRNAs are the most abundant type of the second class and the most studied ncRNAs usually have 21–24 nt-length (Bushati and Cohen 2007). NcRNAs are also classified into linear and circular transcripts according to their structure. Transcription of the ncRNAs from the encoding genes in eukaryotes is conducted by either RNA polymerase I, II, or III (Goodrich and Kugel 2006; Aghaei et al. 2020). Although not completely elucidated and initially regarded as evolutionary junk (Beermann et al. 2016); however, a number of regulatory functions with critical biological impacts have been identified for ncRNAs. They are mainly known to be involved in transcriptional and post-transcriptional regulation of gene expression by controlling the transcription, stability of mRNAs, and their translation to proteins (Bartel 2009). Through their regulatory functions, ncRNAs play role in regulation of almost all of cellular functions, which indicates why expression of ncRNAs itself requires strict regulation (Bushati and Cohen 2007). These include cell hemostasis, death and apoptosis, organ development, inflammation and immunity to viral infections, and cancers (Xie and Wei 2021; Batista Pedro and Chang 2013). Thus, an increasing number of evidence have shown that deregulations in expression of ncRNAs is associated with several human diseases such as diabetes, cardiovascular diseases, genetic disorders, neurological diseases, and particularly various types of human cancers (Xie and Wei 2021; Batista Pedro and Chang 2013; Lekka and Hall 2018; Aghaei Zarch et al. 2019). In this review, I aim to outline the studies evaluating the biological functions of circRNAs in ovarian cancer cells and tissues, have an overview to their roles in progression of ovarian cancer, and eventually discuss the potentials of circRNAs as biomarkers with diagnostic and prognostic potentials in human ovarian tumors. Circular RNAs: biogenesis and functions Circular RNAs (circRNAs) comprise a group of mammalian ncRNAs, which are characterized with their loop structures unlike other classes with linear buildings. Covalent bonds between two 5′ and 3′ ends of single-stranded RNAs develop circular rings, which are resistant to RNase R digestion due to closed ends making them not available for degrading enzyme (Rahmati et al. 2021). Biogenesis of circRNAs is mainly conducted from pre-mRNAs through splicing reactions in reverse orders relative to the exons, which is called backsplicing (Wang and Wang 2015). Backsplicing is performed by joining upstream 3′ splicing site to another splicing site at the 5′ end forming 2′,5′-phosphodiester bonds (Zhang et al. 2013). CircRNAs can be transcribed from different regions of encoding sequences including exons, introns, exon–intron junctions, and intergenic sequences, thus, they are classified into exonic (ecircRNA), intronic (ciRNA), exonic–intronic (EIciRNA), and intergenic subtypes, respectively, according to the sequence they are transcribed from (Meng et al. 2017). First circRNA in higher eukaryotes was identified in 1990s, although RNA ring structures were already reported in viroids (Nigro et al. 1991; Sanger et al. 1976). Todays, with advancements in technologies facilitating discovery of RNA transcripts such as high-throughput RNA-seq, a huge number of ncRNAs including circRNAs have been identified of various types and length and several databases have been developed for storing and providing accessibility of circRNAs to the researchers and scientists (Taheri et al. 2021). CircRNAs are mainly found in low quantities within the cells, which has caused them to be initially regarded as transcription byproducts or splicing errors; however, an increasing evidenced have demonstrated that not only a number of circRNAs are expressed with higher abundance compared to their linear counterparts, but also, they show abundance and conserved among species (Jeck et al. 2013). In addition, specific expression patterns in a cell compared to another or at a developmental stage is seen for some circRNAs (Salzman et al. 2013). Although functions of circRNAs are not clear; however, this evidence have suggested substantial roles for circRNAs. Increasing evidence has reported that circRNAs show regulatory functions controlling the gene expression at the transcriptional or post-transcriptional levels and play role in substantial physiological processes (Memczak et al. 2013; Ghafouri-Fard et al. 2022). They mainly act through mechanisms such as miRNAs sponging, regulation of transcription and splicing of target genes, interaction with proteins, and epigenetic regulation (Zhao et al. 2019a; Najafi 2022). First mechanism is mostly studied, through which a circRNA binds to a target miRNA, inhibits its function, and sequentially upregulates the downstream gene. This axis is vastly reported in cancer studies as a mechanism involved in regulation of malignant behaviors of cancer cells. Although, dysregulation of circRNAs is seen in various human diseases such as diabetes, cardiovascular diseases, neurodegenerative diseases, and inflammatory disorders (Su et al. 2019; Sayad et al. 2022); however, they are frequently reported to enhance carcinogenesis in cancer experiments attracted attention in the past years. Circular RNAs in cancer An increasing number of evidence produced by RNA-seq and quantitative reverse transcription polymerase chain reaction (qRT-PCR) assays indicates that circRNAs exhibit dysregulations in the cancer cells and tissues compared to healthy conditions. These deregulations are associated with enhanced cancer growth, metastatic behavior, stemness, chemo- and radioresistance (Guarnerio et al. 2016). Six behaviors of cancer cells have been described as “cancer hallmarks”, which convert the healthy cells into malignant cells. These include limitless proliferative potential, self-sufficiency in growth signals, insensitivity to anti-growth signals, suppression of apoptosis, tissue invasion and metastasis, and sustained angiogenesis (Hanahan and Weinberg 2000). A number of circRNAs have been demonstrated with contributing effects in enhancing hallmarks of cancer (Su et al. 2019). For instance, a handful of circRNAs promote proliferation of renal cell carcinoma (RCC) through controlling the proliferative signals (Sayad et al. 2021), circZKSCAN1 evades growth suppressors in hepatocellular carcinoma (HCC) (Yao et al. 2017), circRNA-HIPK3 evades apoptosis in non-small cell lung cancer (NSCLC) (Lu et al. 2020), hsa_circ_0001178 promotes tissue invasion and metastasis in colorectal cancer (CRC) (Ren et al. 2020), and circFOXP1 enhances angiogenesis in osteosarcoma (Najafi 2022; Zhang et al. 2021a). These claims are supported by a handful of in vitro and in vivo evidence. Enhanced cell proliferation, migration, invasion, epithelial-to-mesenchymal transition (EMT), stemness and chemoresistance have been reported for upregulated circRNAs with putative oncogenic functions in cell studies. These circRNAs are shown in more than 82% of cancers with competing endogenous RNA (ceRNA) action by sponging a target miRNA (Cheng et al. 2021). This miRNA is then repressed, and sequentially target genes are upregulated. These genes are mainly known with their oncogenic function in various human cancers. Xenograft animal studies also suggest consistent results for tumor growth and metastasis in vivo. Other mechanisms of circRNA’s action such as binding to proteins, regulation of transcription, and translation into proteins are believed to potentially contribute to a small proportion of circRNAs-related carcinogenesis (Cheng et al. 2021). Ovarian cancer: circular RNAs as emerging disease players Ovarian cancer (OC) is the seventh most common type of cancer diagnosed among women worldwide, and the leading cause of mortality due to gynecological cancers with 295,414 new cases and 184,799 deaths reported for 2018 (Reid et al. 2017; Permuth-Wey et al. 2009; Bray et al. 2018). The etiology of OC is little known, although age, family history, postmenopausal hormone-replacement therapy, smoking, and alcohol drinking have been described as the main risk factors for the disease (Permuth-Wey et al. 2009). 5-year survival rate is predicted 45% for OC patients, which can be increased by early diagnosis (Siegel et al. 2014; Urban and Drescher 2008). The disease is clinically staged according to a system by the International Federation of Gynecology and Obstetrics (FIGO) Committee (Prat 2015). OC is asymptomatic at early stages, and the disease symptoms at late stages are non-specific. Over 75% of the OC cases, thus, are diagnosed with late stage (Doubeni et al. 2016). Early diagnosis is lowly conducted and routine screening is not recommended; however, application of biomarkers like cancer antigen 125 (CA-125) may be useful in real-time detection of the affected patients (Doubeni et al. 2016), however, demonstrates poor specificity, lacks sufficient power in detection of early cases, while displays elevation in 75–90% of OC patients with advanced stages (Moss et al. 2005). Human epididymis protein-4 (HE4) can provide same diagnostic power to CA-125 have received the United States Food and Drug Administration (FDA) approval for diagnosis of OC (Li et al. 2012), although shows contradicting accuracy (Rastogi et al. 2016). Development of reliable biomarkers with potentials in diagnosis of OC patients at early stages of the disease is of special importance, which consequently can suggest improved response to treatments. OC is derived from hyperproliferation of three types of cells comprising the ovaries including epithelial, stromal, and germ cells, and so, classified into epithelial–stromal, sex cord-stromal, and germ cell tumors (Chen et al. 2003). The first category is classified into five major subtypes, which are responsible for the majority of diagnosed cases accounting for 60% and 90% of benign and malignant tumors, respectively (Chen et al. 2003). Surgery is the main therapeutic strategy applied for the disease; however, recurrences and related mortality are reported for the patients. Since the etiology of OC is not well understood, exploring any aspect of the disease pathogenesis may help finding novel diagnostic and therapeutic approaches. Dysregulation of ncRNAs particularly lncRNAs has been frequently reported in OC, which they may regulate various aspects of the disease pathogenesis such as cancer development, progression and dissemination (Oncul et al. 2020). A handful of circRNA expression analyses, also, has reported aberrant expression of circRNAs in cancer cells and tissues using bioinformatics evaluations, microarray, and qRT-PCR assays. Not only resistant to degradation, but also in higher expression levels compared with coding RNAs, circRNAs have been identified in OC tissues (Bachmayr-Heyda et al. 2016). In addition, circRNAs have been found linked to other gynecological disorders such as endometriosis, and polycystic ovary syndrome (PCOS) (Xu et al. 2020a; Dong et al. 2020; Liu et al. 2020a). Particularly, interplay between circRNAs and reproductive cancers including ovarian carcinoma, ovarian epithelial tumors, and cervical carcinoma has been revealed in past studies (Liu et al. 2019b). Ahmed et al. (2016), for instance, identified 34,730 RNase R-resistant circRNAs of 67,580 candidates detected in OC tissues. They also detected 490 and 568 circRNAs with differential expression in primary OC tissues compared to peritoneal and lymph node metastatic lesions, respectively. In another study, Teng et al. (2019) found 7333 deregulated circRNAs including 2431 upregulated and 3120 downregulated transcripts. Using second-generation sequencing, Wang et al. (2020a) identified 178 circRNAs with differential expression in ribosomal RNA-depleted total RNA extracted from serum specimens of OC patients compared with healthy individuals. Of them, 175 transcripts showed upregulation, while 3 others were downregulated, and finally, 5 circRNAs were confirmed by qRT-PCR. Wang et al. (2020b) discovered 37 downregulated and 6 upregulated circRNAs in exosomes extracted from serum specimens of OC patients. Among all, circ-0001068 upregulation was validated in qRT-PCR and further confirmed in a larger cohort. Experimentally, either upregulation or downregulation has been reported to affect the cancer hallmarks in cell and animal studies, or in situ experiments also have predicted impact of circRNAs on cell functions. For instance, in the latter study, bioinformatics-based approach to evaluate the biological functions of the dysregulated circRNAs demonstrated that they are probably linked to critical cellular processes such as chemokine, ErbB, and TNF signaling pathways, phagocytosis, and angiogenesis (Wang et al. 2020a). Since upregulated circRNAs are found to potentially promote the malignant features of cancer cells contributing to cancer initiation and progression, thus, they are referred as putative “oncogenic” circRNAs, while downregulated transcripts demonstrate reverse effects, so can be called “onco-repressor” or “tumor suppressor” circRNAs. Oncogenic circRNAs Circular RNAs contribute to several “hallmarks of cancer” in OC CircRNAs are mainly expressed in the cytoplasm of OC cells and show significant alterations in their expression compared to normal cervical epithelium cells. Aberrantly upregulated circRNAs have been demonstrated to potentially drive the cancer cells to get malignant behaviors contributing to establishment and dissemination of OC. In a tremendous number of in vitro experiments, circRNAs are associated with proliferative and motional capacities. Enhanced cell proliferation, suppression of apoptosis, tissue invasion and metastasis, and angiogenesis are reported for upregulated circRNAs in cell and animal models (see Table 1 and Fig. 1). These behaviors potentially play role in development and progression of cancer indicating roles of circRNAs in pathogenesis of OC. For instance, circ-ABCB10 with elevated expression in OVCAR3, UWB1.289, SKOV3, and CAOV3 OC cell lines, and tissues extracted from patients is reported to enhance cell proliferation, and invasion while its knockdown reverse the malignant biological behavior (Lin et al. 2021). It potentially promotes the oncogenic potentials of cancer cells via activation of the Wnt/β-catenin signaling pathway. CircPIP5K1A exerts its positive impact on malignant phenotype of OC cells through upregulation of the insulin-like growth factor-binding protein-5 (IGFBP5) (Sun et al. 2019a). Involvement of several signaling pathways such as PI3K/AKT and Wnt/β-catenin in enhanced potency of OC cells could support self-sufficiency in growth signals as another hallmark of cancer (Lin et al. 2021; Wang et al. 2021a). IGFBP5 is already known to be upregulated and potentially play a role in development of high-grade serous ovarian carcinoma (Wang et al. 2006). CircWHSC1 contributes to similar malignant features via overexpression of the human telomerase reverse transcriptase (hTERT) gene. hTERT is upregulated in 95% of human cancers, and is known to contribute to malignancies including OC via telomere elongation and non-canonical functions (Liu et al. 2018a). Exosomal circWHSC1 can lead to dissemination of the OC to the peritoneum (Zong et al. 2019). In addition, several other circRNAs such as circFGFR3, ciRS-7, and circ_0025033 can positively affect the epithelial–mesenchymal transition (EMT) in OC contributing to facilitation of cancer cells metastasis (Zhou et al. 2020; Cheng et al. 2020). This process is known for another circRNA, circLNPEP, to be exerted through regulation of EMT-related genes E-cadherin, N-cadherin and vimentin (Wang et al. 2021b). Importantly, apoptosis assays have revealed evading this process in a number of circRNA studies in OC. For instance, flow cytometry shows that hsa_circ_0004712 downregulation stimulates apoptosis rate in OVCAR3 and SKOV3 OC cells (Zhou et al. 2021). Accordingly, qRT-PCR and western blot analyses have shown that circLNPEP affects expression of apoptosis-related genes such as caspase-3 and Bax (Wang et al. 2021b). Enhanced autophagy is also reported for several circRNAs in OC (Gan et al. 2020). CircMUC16, for instance, promotes autophagy flux of A2780 OC cells, and accordingly, this elevated autophagy can enhance invasion and metastasis in epithelial ovarian cancer (EOC). Through upregulation of Beclin1 and RUNX1, and subsequently overexpression of autophagy-related gene ATG3, circMUC16 can enhance the autophagy-mediated malignant behaviors in OC cells. CircRAB11FIP1 can positively affect the malignant phenotype of EOC via upregulation of other autophagy-related genes ATG7/14, ATG101, and ATG5/7 in independent axes, and consequently promotion of autophagy flux (Zhang et al. 2021b). Similarly, circEEF2 upregulates ATG5 and ATG7, and binds to ANXA2 to potentially play role in enhanced proliferation, and invasion of EOC (Yong et al. 2020). Through enhancing angiogenesis, oncogenic circRNAs may play role in the pathogenesis of OC, and contribute to another hallmark of cancer. Wu et al. (2021a), and Wang et al. (2021b) in two distinct studies, demonstrated angiogenesis suppression in tube formation assay, when circ-PTK2 and circLNPEP, respectively, are silenced. Moreover, another circRNA circASH2L is shown to potentially promote angiogenesis and lymphangiogenesis in mouse model of OC by upregulation of angiogenic vascular endothelial growth factor (VEGF) (Chen et al. 2020a). Furthermore, since aerobic glycolysis play essential role in the economy of cancer cell accounting for 56–63% of their ATP budget (Jiang 2017), increased glycolysis is predicted to assist the OC cells under circRNAs regulation. At least in two distinct studies, oncogenic circRNAs are reported to potentially promote aerobic glycolysis in OC cells (Hou and Zhang 2021; Xie et al. 2021). Table 1. | circRNA | In vitro study | In vivo study; concluded effects in BALB/c nude mice | Ref. | |||| |---|---|---|---|---|---|---|---| | Detection method | Deregulation | Mechanism of action/targets | OC cell lines | Effects in conclusion⁕ | ||| | circWHSC1 | qRT-PCR | Up | ↓ miR-145 and ↓miR-1182/↑TERT and ↑MUC1 | CAOV3 and OVCAR3 | ↑Cell proliferation, ↑migration, and ↑invasion, and ↓apoptosis | ↑Tumor growth | Zong et al. (2019) | | circRNA-UBAP2 | qRT-PCR | Up | ↓miR-382-5p/↑PRPF8 | SKOV3, OVCAR3, ES-2, H-ES-2, and A2780 | ↑Cell proliferation, ↑cell cycle, and ↓apoptosis | – | Xu et al. (2020c) | | qRT-PCR | Up | ↓miR-144 | A2780, HEY, OVCAR3, HO8910, and SKOV3 | ↑Cell proliferation, and ↑migration | – | Sheng et al. (2019) | | | circMUC16 | qRT-PCR | Up | ↓miR-199a-5p/↑Beclin1, and ↑RUNX1/↑ATG3 | SKOV3 and ES-2 | ↑Cell proliferation and ↓autophagy | ↑Tumor metastasis | Gan et al. (2020) | | hsa_circRNA_102958 | qRT-PCR | Up | ↓miR-1205/↑SH2D3A | SKOV3 and OVCAR3 | ↑Cell proliferation, ↑migration, and ↑invasion | – | Wang et al. (2020c) | | hsa_circ_0013958 | qRT-PCR | Up | – | A2780 and OVCAR‐3 | ↑Cell proliferation, ↑migration, and ↑invasion, and ↓apoptosis | – | Pei et al. (2020) | | circRNA_MYLK | qRT-PCR | Up | ↓miR-652 | SKOV3, OVCAR3, PEO1, 3AO, A2780, and CAOV3 | ↑Cell proliferation | – | Zhao et al. (2020) | | circFGFR3 | qRT-PCR | Up | ↓miR-29a-3p/↑E2F1 | KOV3, A2780, OV2008, and IGROV1 | ↑Cell proliferation, ↑migration, ↑invasion, and ↑EMT | ↑Tumor growth | Zhou et al. (2020) | | circCELSR1 | qRT-PCR | Up | ↓miR-598/↑BRD4 | SKOV3, A2780, IGROV1, and CAOV3 | ↑Cell proliferation, ↑migration, and ↑invasion, and ↓apoptosis | ↑Tumor growth, ↑metastasis, and ↓apoptosis | Zeng et al. (2020) | | circASH2L | qRT-PCR | Up | ↓miR-665/↑VEGFA | A2780, TOV112D, OVCAR3, and SKOV3 | ↑Cell proliferation, ↑invasion, and ↑angiogenesis | ↑Tumor growth, ↑lymphangiogenesis, and ↑angiogenesis | Chen et al. (2021) | | circ_0004390 | qRT-PCR | Up | ↓miR-198/↑MET | KOV3, HeyA-8, and OVCAR42 | ↑Cell proliferation | – | Xu et al. (2020b) | | circEPSTI1 | qRT-PCR | Up | ↓miR-942/↑EPSTI1 | A2780 and OV119 | ↑Cell proliferation, ↑invasion, and ↓apoptosis | ↑Tumor growth and ↑metastasis | Xie et al. (2019) | | circKIF4A | qRT-PCR | Up | ↓miR-127/↑JAM3 | CAOV3 and SKOV3 | ↑Cell proliferation, ↑invasion | ↑Tumor growth and ↑metastasis | Sheng et al. (2020) | | circKRT7 | qRT-PCR | Up | ↓miR-29a-3p/↑COL1A1 | SKOV3 and ES-2 | ↑Cell proliferation, ↑migration, and ↑invasion | ↑Tumor growth | An et al. (2020) | | circPUM1 | qRT-PCR | Up | ↓miR-615-5p/↑NF-κB ↓miR-6753-5p/↑MMP2 | A2780, CAOV3, and HMrSV5 | ↑Cell proliferation, ↑migration, and ↑invasion, and ↓apoptosis | ↑Tumor growth and ↑metastasis | Guan et al. (2019) | | hsa_circ_0009910 | qRT-PCR | Up | ↓miR-145/↑NF-κB and ↑notch pathways | SKOV3 | ↑Cell proliferation, ↑migration, and ↑invasion | – | Li et al. (2020b) | | circ_0015756 | qRT-PCR | Up | ↓miR-942-5p/↑CUL4B | OV90 and SKOV3 | ↑Cell proliferation, ↑migration, and ↑invasion, and ↓apoptosis | ↑Tumor growth | Du et al. (2020) | | circGFRA1 | qRT-PCR | Up | ↓miR-449/↑GFRA1 | OV119 and A2780 | ↑Cell proliferation, ↑invasion, and ↓apoptosis | ↑Tumor growth | Liu et al. (2019a) | | circ_0072995 | qRT-PCR | Up | ↓miR-147a/↑CDK6 | HO8910 and A2780 | ↑Cell proliferation, ↑invasion, and ↓apoptosis | ↑Tumor growth | Ding et al. (2020) | | circRhoC | qRT-PCR | Up | ↓miR-302e/↑VEGFA | A2780 and CAOV3 | ↑Cell proliferation, ↑migration, and ↑invasion | ↑Tumor metastasis | Wang et al. (2019a) | | circ_0005276 | qRT-PCR | Up | ↓ADAM9 | A2780, PEO1, SKOV3, OVCAR3, 3AO, and CAOV3 | ↑Cell proliferation, and ↑migration | – | Liu et al. (2020b) | | circFoxp1 | qRT-PCR | Up | ↓miR-22 and ↓miR-150-3p/↑CEBPG and ↑FMNL3 | COC1, OVCAR3, SKOV3, and SKOV3/DDP | ↓Cell sensitivity to cisplatin | – | Luo and Gui (2020) | | ciRS-7 | qRT-PCR | Up | ↓miR-641/↑ZEB1 and ↑MDM2 | SKOV3, A2780, OV2008, IGROV1, and ES-2 | ↑Cell proliferation, and ↑EMT | ↑Tumor growth | Zhang et al. (2020 | | circ‐PGAM1 | qRT-PCR | Up | ↓miR-542-3p/↑CDC5L/PEAK1/ERK1/2 and JAK2 signaling pathways | CAOV3, SKOV3, OVCAR3, and ES‐2 | ↑Cell proliferation, ↑migration, and ↑invasion, and ↓apoptosis | ↑Tumor growth | Zhang et al. (2020) | | VPS13C-has-circ-001567 | qRT-PCR | Up | – | ES-2, SKOV3, Caov-3, and OV-1063 | ↑Cell proliferation, ↑migration, and ↑invasion, ↑cell cycle progression, and ↓apoptosis | ↑Tumor growth | Bao et al. (2019) | | circ-NOLC1 | qRT-PCR | Up | ↑ESRP1, ↑CDK1 and ↑RhoA | A2780, HO8910, ES-2, CAOV3, OVCAR3, and SKOV3 | ↑Cell proliferation, ↑colony formation, and ↓apoptosis | ↑Tumor growth | Chen et al. (2020) | | hsa_circ_0026123 | qRT-PCR | Up | ↓miR-124-3p/↑EZH2 | A2780, SKOV3, TOV112D, and OVCAR3 | ↑Cell proliferation, ↑migration, and ↑differentiation | ↑Tumor growth | Yang et al. (2021a) | | circPVT1 | qRT-PCR | Up | ↓miR-149-5p/↑FOXM1 | SKOV3 and A2780 | ↑Cell proliferation, ↑migration, and ↑invasion | – | Li et al. (2021) | | qRT-PCR | Up | ↓miR-149 | CAOV3, SKOV3, SNU119, and OVCAR3 | ↑Cell proliferation, and ↓apoptosis | – | Sun et al. (2020) | | | hsa_circ_0061140 | qRT-PCR | Up | ↓miR-370/↑FOXM1 | A2780, SKOV3, IGROV1, OV2008, and ES-2 | ↑Cell proliferation, ↑migration, and ↑invasion | ↑Tumor growth | Chen et al. (2018) | | circ-CSPP1 | qRT-PCR | Up | ↓miR-1236-3p/↑ZEB1 | A2780, CAOV3, and OVCAR3 | ↑Cell proliferation, ↑migration, and ↑invasion | – | Li et al. (2019) | | circ-FAM53B | qRT-PCR | Up | ↓miR-646/↑VAMP2 ↓miR-647/↑MDM2 | HO8910, SKOV3, OVCAR3, and A2780 | ↑Cell proliferation, ↑migration, and ↑invasion | – | Sun et al. (2019b) | | circ-ABCB10 | qRT-PCR | Up | ↓miR-1271, ↓miR-1252 and ↓miR-203 | OVCAR3, UWB1.289, SKOV3, and CAOV3 | ↑Cell proliferation, and ↓apoptosis | – | Chen et al. (2019a) | | qRT-PCR | Up | ↓miR-1271/↑Capn4/Wnt/β-catenin | OVCAR3, UWB1.289, SKOV3, and CAOV3 | ↑Cell proliferation, ↑invasion, and ↓apoptosis | – | Lin et al. (2021) | | | circ_0025033 | qRT-PCR | Up | ↓miR-184/↑LSM4 | SKOV3 and A2780 | ↑Colony formation, ↑migration, ↑invasion, and ↑glycolysis metabolism | ↑Tumor growth | Hou and Zhang (2021) | | qRT-PCR | Up | ↓miR-330-5p/↑KLK4 | A2780 and SKOV3 | ↑Cell viability, ↑migration, ↑invasion, ↑EMT, and ↓apoptosis | ↑Tumor growth | Cheng et al. (2020) | | | circNRIP1 | qRT-PCR | Up | ↓miR-211-5p/↑HOXC8 | A2780 and SKOV3 | ↑Chemoresistance to paclitaxel | ↑Sensitivity to paclitaxel | Li et al. (2020a) | | circTNPO3 | qRT-PCR | Up | ↓miR-1299/↑NEK2 | SKOV3 and HeyA-8 | ↑Cell cycle progression, ↓apoptosis, and ↑chemoresistance to paclitaxel | ↑Sensitivity to paclitaxel | Xia et al. (2020) | | hsa_circ_0051240 | qRT-PCR | Up | ↓miR-637/↑KLK4 | CAOV3, SKOV3, OVCAR3, and H8910 | ↑Cell proliferation, ↑migration, and ↑invasion | ↑Tumor formation | Zhang et al. (2019) | | circRAB11FIP1 | qRT-PCR | Up | ↓miR-129/↑ATG7/14 ↑DSC1/↑ATG101 ↑FTO/↑ATG5/7 | A2780 and SKOV3 | ↑Cell proliferation, ↑migration, ↑invasion, and ↑autophagic flux | ↑Tumor metastasis | Zhang et al. (2021) | | hsa_circ_0000714 | qRT-PCR | Up | ↓miR-370-3p/↑RAB17 | A2780 and SKOV3 | ↑cell proliferation, ↑Cell cycle progression, and ↑chemoresistance to paclitaxel | – | Guo et al. (2020) | | circ-LOPD2 | qRT-PCR | Up | ↓miR-378 | CAOV3, A2780, and OVCAR3 | ↑Cell proliferation | – | Wei et al. (2021) | | circCRIM1 | qRT-PCR | Up | ↓miR-145-5p/↑CRIM1 ↓miR-383-5p/↑ZEB2 | OVCAR3 and CAOV3 | ↑Cell proliferation, ↑migration, ↑invasion, and ↓apoptosis | ↑Tumor growth | Du et al. (2021) | | circRNA051239 | qRT-PCR | Up | ↓miR-509-5p/↑PRSS3 | A2780, SKOV3, SKOV3.ip, CAOV3, and OVCAR3 | ↑Cell proliferation, ↑migration, and ↑invasion | – | Ma et al. (2021) | | circPIP5K1A | qRT-PCR | Up | ↓miR-661/↑IGFBP5 | SKOV3, OVCAR5, A2780, and OV2008 | ↑Cell proliferation, ↑migration, and ↑invasion | ↑Tumor growth | Sun et al. (2019a) | | circ-PTK2 | qRT-PCR | Up | ↓miR-639/↑FOXC1 | SKOV3 and OVCAR3 | ↑Cell migration, ↑invasion, ↑angiogenesis, and ↑EMT | ↑Tumor growth | Wu et al. (2021a) | | circ_0007841 | qRT-PCR | Up | ↓miR-151-3p/↑MEX3C | SKOV3 and OVCAR3 | ↑Cell proliferation, ↑migration, and ↑invasion | – | Huang et al. (2021a) | | circLNPEP | qRT-PCR | Up | ↓miR-876-3p/↑WNT5A | A2780, SKOV3, SK-BR-3, OVCAR3, OV-56, and TOV-21 G | ↑Cell proliferation, ↑migration, ↑invasion, and ↑angiogenesis | ↑Tumor growth and ↓apoptosis | Wang et al. (2021b) | | circHIPK2 | qRT-PCR | Up | ↓miR-338-3p/↑CHTOP | A2780 and SKOV3 | ↑Chemoresistance to cisplatin (DDP) | ↑Tumor growth of DDP-resistant cells | Cao et al. (2021) | | circ_0072995 | qRT-PCR | Up | ↓miR-122-5p/↑SLC1A5 | SKOV3 and OVCAR3 | ↑Cell proliferation, ↑migration, ↑invasion, and ↓apoptosis | ↑Tumor growth | Huang et al. (2021b) | | circ_0002711 | qRT-PCR | Up | ↓miR-1244/↑ROCK1 | SKOV3 and OV90 | ↑Cell proliferation, and ↑aerobic glycolysis | ↑Tumor growth | Xie et al. (2021) | | circSETDB1 | qRT-PCR | Up | ↓miR-129-3p/↑MAP3K3 | A2780 and SKOV3 | ↑Cell proliferation, ↑migration, ↑invasion, and ↓apoptosis | ↑Tumor formation | Li and Zhang (2021) | | hsa_circ_0004712 | qRT-PCR | Up | ↓miR-331-3p/↑FZD4 | SKOV3 and OVCAR3 | ↑Cell proliferation, ↑migration, ↑invasion, and ↓apoptosis | ↑Tumor growth | Zhou et al. (2021) | | circNFATC3 | qRT-PCR | Up | let7-5p and miR-143-3p | PA-1, SKOV3, A2780, APOCC, and OVCAR3 | ↑Cell proliferation, ↑migration, and ↑invasion | – | Karedath et al. (2021) | | circE2F2 | qRT-PCR | Up | ↓HuR protein/↑E2F2 | A2780, SKOV3, OVCAR3, IGROV1, CAOV3, and ES-2 | ↑Cell proliferation, ↑migration, and ↑invasion | ↑Tumor growth | Zhang et al. (2021b) | | circ_0013958 | qRT-PCR | Up | ↓miR-637/↑PLXNB2 | SKOV3 and CAOV3 | ↑Cell proliferation, ↑migration, ↑invasion, and ↓apoptosis | ↑Tumor growth | Liang et al. (2021) | ↑ Upregulation or enhancement, ↓ downregulation or suppression, EMT epithelial–mesenchymal transition ⁕The corresponding results mainly are reported upon circRNA knockdown; however, to facilitate conclusion, the findings here are expressed, as the circRNA is upregulated in cancer cells Wang et al. (2020b) demonstrated that upregulated circ-0001068 in exosomes of serum samples from OC patients is delivered into T cells, sponges miR-28-5p, and consequently upregulates PD1, which is a major inhibitory receptor controlling the CD8 T cell exhaustion in some infections and cancers (Ahn et al. 2018). In the latter study, Xie et al. (2021) demonstrated that circ_0002711 knockdown causes decrease in glucose uptake, lactate level, and ATP generation along with diminished expression of several metabolism-related genes including Pyruvate Kinase M2 (PKM2), Hexokinase 2 (HK2), and Pyruvate Dehydrogenase Kinase 1 (PDK1) in OV90 and SKOV3 cells. This effect was shown to be exerted through upregulation of Rho kinase 1 (ROCK1), which is known to play role in various human cancers (Wei et al. 2016). Paclitaxel and cisplatin are among anti-cancer therapeutics used in treatment of OC. Cell studies have unveiled that oncogenic circRNAs could contribute to increased resistance of OC cells to these agents (Luo and Gui 2020; Li et al. 2020a; Xia et al. 2020; Guo et al. 2020; Cao et al. 2021). Although not completely elucidated, however, these effects are believed to pass through miRNAs, and upregulation of target genes such as FMNL3, HOXC8, NEK2, RAB17, and CHTOP contributing to enhanced chemoresistance in OC cells. HOXC8 has been found in association with enhanced drug resistance in other cancers like non-small cell lung cancer (NSCLC) (Liu et al. 2018b). NEK2 has been shown to play several carcinogenic roles such as chromosome instability, cancer development, and progression, and drug resistance in human malignancies (Kokuryo et al. 2019). CHTOP is already found in high levels in chemoresistant OC cells contributing to cisplatin-resistance (Feng et al. 2019). Therefore, increased resistance of OC cells to chemotherapy agents due to oncogenic circRNAs can be supported by previous studies revealing the impact of these transcripts on known chemoresistance-promoting factors. Confirming cell studies, xenograft animal experiments have shown that silencing upregulated circRNAs in OC cells injected to mouse models can inhibit tumor growth and metastasis. Decreased size and volume of the transplanted tumor in circRNA-knockdown OC cells compared to those with circRNA overexpression, while reverse effects are reported upon circRNA overexpression suggests that oncogenic circRNAs are involved in tumor growth and metastasis in vivo. Zong et al. (2019) showed more rapid rate of growth and larger tumor volume in nude mice, when 200 μg of exosomes extracted from circWHSC1-overexpressing CAOV3 OC cells was injected intraperitoneally compared to control mice with PBS-injected. Taken together, both in vitro and in vivo experiments can confirm that upregulated circRNAs in OC tissues enhance malignant phenotype contributing to development and dissemination of OC. Onco-repressor circRNAs demonstrate reverse behaviors compared to oncogenic transcripts in OC Along with oncogenic circRNAs, a handful of transcripts is known with decreased expression in OC cells compared to normal ovarian cells. Although less identified, however, this class demonstrates reverse behaviors in cell studies. Their overexpression has been shown to inhibit proliferative, migratory, and invasive potency of OC cells along with increased apoptosis, while their low expression causes enhanced malignant phenotype of cancer cell lines. Moreover, xenograft animal studies report suppression of tumor growth upon onco-repressor circRNAs overexpression. Circ-ITCH, for instance, is also reported to decrease glycolysis metabolism in A2780 and OVCAR3 cells (Lin et al. 2020a). This transcript is among the most studied downregulated circRNAs with putative onco-repressor features in OC (see Table 2). At least in 5 experiments, circ-ITCH is reported with lower expression levels in OC cells and tissues, while its overexpression hinders malignant features of OC cells (Lin et al. 2020a; Luo et al. 2018a; Yan et al. 2020; Luo et al. 2018b; Hu et al. 2018). In addition, through sponging several miRNAs including miR-106a, miR-10a, and miR-145, and upregulation of E-cadherin gene CDH1, and Ras p21 protein activator 1 (RASA1) with involvement in several human cancers including OC, circ-ITCH is also demonstrated to exert its putative biological functions via lncRNA HULC. In addition to suppression of biological functions of cancer cells, enhanced chemosensitivity to anti-cancer agents used in OC such as Paclitaxel, Cisplatin, and Docetaxel, and anti-growth effect of curcumin can further suggest onco-repressing roles of a number of circRNAs, which are downregulated in OC cells and tissues, and their overexpression suggests beneficial effects as potentials therapeutic targets against OC. Table 2. | circRNA | In vitro study | In vivo study; concluded effects in BALB/c nude mice | Ref. | |||| |---|---|---|---|---|---|---|---| | Detection method | Deregulation | Mechanism of action/targets | OC cell lines | Effects in conclusion | ||| | circRNA_100395 | qRT-PCR | Down | ↓miR-1228/↑p53 /↓EMT | A2780, OV2008, SKOV3, IGROV1 and ES-2 | ↓Cell proliferation, ↓migration, ↓invasion, and ↓EMT | ↓Tumor growth | Li et al. (2020c) | | circ-PLEKHM3 | qRT-PCR | Down | ↓miR-9/↑BRCA1, DNAJB6, and KLF4 | A2780, OV90, MCDB 105, and MDAH2274 | ↓Cell proliferation, ↓migration, and ↓EMT | ↓Tumor growth and ↓EMT | Zhang et al. (2019) | | CircEXOC6B | qRT-PCR | Down | ↓miR-376c-3p/↑FOXO3 | A2780 and SKOV3 | ↓Cell proliferation, ↓migration, ↓invasion, ↑apoptosis, and ↑sensitivity to paclitaxel | ↑Sensitivity of OC cells to paclitaxel | Zheng et al. (2020) | | hsa_circ_0078607 | qRT-PCR | Down | ↓miR-518a-5p/↑Fas | SKOV3 and A2780 | ↓Cell proliferation and ↑apoptosis | – | Zhang et al. (2020) | | circMTO1 | qRT-PCR | Down | ↓miR-182-5p/↑KLF15 | SKOV3 and OVCAR3 | ↓Cell proliferation and ↓invasion | – | Wang et al. (2020e) | | circ9119 | qRT-PCR | Down | ↓miR-21-5p/↑PTEN/↓Akt | A2780, SKOV3, HO8910, CAOV3, ES-2, and OVCAR3 | ↓Cell proliferation and ↑apoptosis | ↓Tumor growth | Gong et al. (2020) | | circEXOC6B | qRT-PCR | Down | ↓miR-421/↑RUS1 | A2870, SKOV3, and OVCAR3 | ↓Cell proliferation, ↓invasion, and ↑apoptosis | – | Wang et al. (2020d) | | circ-ITCH | qRT-PCR | Down | ↓miR-106a/↑CDH1 | A2780 and OVCAR3 | ↓Cell proliferation, ↓invasion, ↓glycolysis, and ↑apoptosis | ↓Tumor growth | Lin et al. (2020a) | | qRT-PCR | Down | ↓miR-10a | SKOV3, A2780, OVCAR3, and HO8910 | ↓Cell proliferation and ↑apoptosis | – | Luo et al. (2018a) | | | qRT-PCR | Down | ↓lncRNA HULC | UWB1.289 + BRCA1 and UWB1.289 | ↓Cell proliferation, ↓migration, and ↓invasion | – | Yan et al. (2020) | | | qRT-PCR | Down | – | SKOV3 and OVCAR3 | ↓Cell proliferation and ↑apoptosis | – | Luo et al. (2018b) | | | qRT-PCR | Down | ↓miR-145/↑RASA1 | SKOV3 and CAOV3 | ↓Cell proliferation, ↓migration, and ↓invasion | ↓Tumor growth | Hu et al. (2018) | | | circ_LARP4 | qRT-PCR | Down | ↓miR-513b-5p/↑LARP4 | A2780, SKOV3, SW626, OVCAR3, and OVCAR4 | ↓Cell proliferation, ↓migration, and ↓invasion | – | Lin et al. (2020b) | | hsa_circ_0007874 | qRT-PCR | Down | ↓miR‐760/↑SOCS3 | A2780, IGROV1, ES‐2, OV2008, and SKOV3 | ↓Cell proliferation and ↓migration | ↓Tumor growth | Li et al. (2020d) | | CDR1as | qRT-PCR | Down | ↓miR-135b-5p/↑HIF1AN | HO8910 and A2780 | ↑Cell proliferation, ↑migration, and ↑invasion | – | Chen et al. (2019b) | | qRT-PCR | Down | ↓miR-1270/↑SCAI | A2780 and SKOV3 | ↑ Cisplatin chemosensitivity | ↑ Cisplatin chemosensitivity | Zhao et al. (2019b) | | | circ_0007444 | qRT-PCR | Down | ↓miR-570-3p/↑PTEN | A2780, OV420, SKOV3, CAOV3, and OVCAR3 | ↓Cell proliferation, ↓migration, ↓invasion, and ↑apoptosis | ↓Tumor growth | Wu et al. (2021b) | | circ-PLEKHM3 | qRT-PCR | Down | ↓miR-320a/↑SMG1 | A2780 and SKOV3 | ↑Cell proliferation and ↓apoptosis ↑Anti-growth effect of curcumin | ↓Tumor growth | Sun and Fang (2021) | | hsa_circ_0006404 and hsa_circ_0000735 | qRT-PCR | Down | ↓miR346/↑DKK3/↑p-GP | SKOV3 | ↓Chemoresistance to Docetaxel | ↓Tumor response to Docetaxel | Chen and Tai (2021) | | qRT-PCR | Up | ↓miR-546b/↑DKK4/↑p-GP | ↑Chemoresistance to Docetaxel | ↑Tumor response to Docetaxel | The role of miRNAs in circRNA’s functions in OC MiRNAs are another class of ncRNAs with their characteristic short length of approximately 22 nucleotides, which are distributed widely in nature. They have been vastly studied, for them various putative biological functions have been recognized such as development, cell proliferation, apoptosis, and metabolism mainly conducted through targeting specific genes particularly those playing role in regulation of signaling pathways (Ardekani and Naeini 2010; Letafati et al. 2022). Their dysregulation, thus, independently has been associated with a wide variety of human diseases such as neurodegenerative diseases, genetic disorders, cancer, and inflammatory diseases suggested them as potential biomarkers for previously hardly diagnosed diseases (Ardekani and Naeini 2010). The interplay between circRNAs and miRNAs also is reported to likely play role in carcinogenesis of human malignancies including OC. Acting as ceRNA; circRNAs bind to, and “sponge”-specific target miRNAs responsible for regulation of sequential target genes. Therefore, circRNAs are shown to downregulate a miRNA, while overexpress the target gene, which is responsible for a function here playing role in controlling cancer development and progression. This circRNA-miRNA-mRNA network forms an axis of action, through which putative regulatory functions of circRNAs are exerted on cancer development and progression. A handful of miRNAs has been identified as sponged transcripts located downstream the circRNAs in OC (see Tables 1 and 2). Each sponged miRNA is predicted as a target of a circRNAs primarily via in situ approaches, and it can be further validated using dual luciferase assay. This interaction is facilitated through complementary seed sequence located on 5´-end of miRNA to which circRNAs bind. Expression analyses using qRT-PCR, and western blotting assays, also reveal reverse association between expression level of a circRNA and its corresponding sponged miRNA. Affected by an upstream oncogenic circRNA, a miRNA demonstrates putative tumor-suppressing functions, while a sponged miRNA downstream an onco-repressor circRNA can positively promote cancer development and progression. Expression of a target miRNA, thus, is expected to diminish allowing an upstream circRNA to exert its biological roles. This direction of action through a circRNA, a miRNA leading to regulation of target gene (s) forms an axis involved in cancer development, and progression. A particular miRNA can be targeted by several circRNAs or specifically sponged by one circRNA. For example, miR-145 is reported with aberrant expression in OC cells compared to normal ovarian cells. It is demonstrated to be sponged and downregulated by both oncogenic and tumor-suppressor circRNAs playing role in the circRNA’s axis of action (Zong et al. 2019; Li et al. 2020b; Du et al. 2021; Hu et al. 2018). These include circWHSC1, hsa_circ_0009910, and circCRIM1 with elevated expression and oncogenic features in OC cells, and circ-ITCH downregulated exhibiting repressing effects on malignant cell phenotype. In another word, this miRNA can display both positive and negative regulatory roles on tumorigenesis. Upon sponging by an upregulated circRNA in OC cells, its putative tumor-repressing functions are targeted, and when targeted by an onco-repressor circRNA, sponged miR-145 can facilitate inhibitory effects on malignant biological functions. For the former features, repressed miR-145 allows overexpression of hTERT and MUC1, NF-κB and notch pathways, and CRIM1, while through upregulation of RASA1; miR-145 sponging facilitates repressing effects on malignant biological functions of OC cells. Analyses of biological functions at OC cells have shown that overexpression of a miRNA can cause reverse effects compared to conditions with circRNA overexpression, and same to its knockdown. For example, Zhang et al. (2020) showed that miR‐542‐3p overexpression can repress proliferation, migration, and invasion of OC cells unlike upstream circ-PGAM1, which promoted cellular malignant behavior via the miR-542-3p/CDC5L/PEAK1 axis. MiR‐542‐3p was shown to inhibit the putative tumor-promoting effect of circ-PGAM1 through suppression of Cell Division Cycle 5-Like (CDC5L) protein expression. Furthermore, circ-PGAM1 silencing in combination with miR‐542‐3p overexpression caused the maximum tumor-suppressing yield in xenograft tumor tissues. Similar results have been reported for a number of miRNAs, when a miRNA exhibits onco-repressor functions and its overexpression benefits anti-cancer potentials; however, it is sponged by a circRNA, and the final consequence favors the oncogenic circRNA (An et al. 2020; Guan et al. 2019). Conversely, putative cancer-promoting effects are seen when sponged miRNAs downstream the onco-repressor circRNAs are overexpressed such as restoration of malignant phenotype of OC cells by overexpression of miR‐760 in presence of hsa_circ_0007874 overexpression (Li et al. 2020d). Overall, miRNAs can play role in gene regulation involved in cancer development and progression, same to circRNAs and in reverse association endure dysregulation in human cancers, and in an axis of action responsible for functions of circRNAs are sponged and downregulated by upstream circRNAs, which consequently leads to carcinogenic changes. Findings on overexpression of miRNAs in axis of oncogenic circRNAs, also suggest therapeutic potentials for them. CircRNAs as potential biomarkers for OC Prognostic potentials of circRNAs in OC Dysregulated in cancer cells and tissues compared to healthy controls, and playing role in essential carcinogenesis processes affecting the survival in OC, circRNAs have been suggested with potentials in discrimination of OC patients from healthy individuals, and prediction of prognosis in affected patients. A growing body of evidence has showed that expression level of circRNAs identified by qRT-PCR on ovarian tissues isolated form OC patients is consistent with severity, grade, and stage of clinicopathological features in OC patients (Table 3). These characteristics include tumor size, FIGO stage, pathological grade, and lymph and distant metastasis and predict prognosis and survival in patients and are being used routinely for prognostic goals in OC patients. In some distinct studies, the association between expression of a circRNA in tissue, plasma, or serum of OC patients and prognosis has been evaluated. For example, Ning et al. (2018) demonstrated that deceased tissue expression of circEXOC6B, and circN4BP2L2 are associated with FIGO stage, and lymph node metastasis, while another circRNA circCELSR1 was correlated with advanced FIGO stage, higher tumor size, and massive ascites. In another study, Zhang et al. (2021c) showed this relationship between hsa_circ_0078607 expression and advanced FIGO stage and elevated CA-125 serum levels. To evaluate the association between circRNA expression and survival in OC patients, Kaplan–Meier curves have shed light on these circular transcripts as potential biomarkers with capability in prediction of prognosis. Shorter overall survival (OS), progression-free survival (PFS) or disease-free survival (DFS) is demonstrated for patients with high tissue expression of an oncogenic circRNA or low expression level of an onco-repressor circRNA (see Table 3). These findings demonstrate that circRNAs can successfully predict prognosis in OC patients, and can be employed in management of OC patients. Moreover, univariate and multivariate Cox regression analysis have validated the value of a number of circRNAs such as exosomal circFoxp1 and serum circSETDB1 as independent prognostic biomarkers in OC patients (Luo and Gui 2020; Sun et al. 2019b; Zhang et al. 2021c; Liu et al. 2018c). For instance, Luo et al. (2020) found that not only circFoxp1 promotes malignant phenotype of OC cells, its exosomal levels are associated with various clinicopathological characteristics such as FIFO stage, large tumor size, presence of lymphatic metastasis and distant metastases, and clinical response, and exosomal circFoxp1 levels act as independent factors predicting the survival of EOC patients Table 4. These findings suggest the circulating exosomal circFoxp1 not only as a prognostic biomarker but also as a potential therapeutic target for EOC. In addition, in situ studies also have helped evaluation of circRNAs as prognostic biomarkers in OC patients (Guo et al. 2019). Taken together, these results have suggested that circRNAs as emerging players in OC carcinogenesis also can be employed in prediction of prognosis and potential management of patients. Table 3. | circRNA | Patient’s specimen source and size | Detection method | Deregulation | Associated clinical features | Survival analysis | Ref. | |---|---|---|---|---|---|---| | circMUC16 | Tissue/100 | qRT-PCR | Up | Tumor stage and tumor grade | – | Gan et al. (2020) | | circRNA_100395 | Tissue/60 | qRT-PCR | Down | Lymphatic metastasis, distant metastasis, and FIGO stage | Patients with low circRNA_100395 tissue levels had lower survival rates | Li et al. (2020c) | | circ-PLEKHM3 | Tissue/12 | qRT-PCR | Down | – | Low circ-PLEKHM3 tissue level was associated with short OS and RFS in OC patients | Zhang et al. (2019) | | hsa_circRNA_102958 | Tissue/41 | qRT-PCR | Up | – | High hsa_circRNA_102958 tissue level was associated with poor OS | Wang et al. (2020c) | | circRNA_MYLK | Tissue/46 | qRT-PCR | Up | Pathological stage | High circRNA_MYLK tissue expression was associated with lower OS | Zhao et al. (2020) | | circFGFR3 | Tissue/35 | qRT-PCR | Up | Tumor stage | OC patients with high circFGFR3 tissue expression had lower survival and higher recurrence rates | Sayad et al. (2021) | | circ_0004390 | Tissue/60 | qRT-PCR | Up | – | Patients with high circFGFR3 tissue expression had shorter OS | Xu et al. (2020b) | | circPUM1 | Tissue/62 | qRT-PCR | Up | FIGO stage | – | Guan et al. (2019) | | hsa_circ_0009910 | Tissue/50 | qRT-PCR | Up | – | Patients with high hsa_circ_0009910 tissue expression had shorter OS | Li et al. (2020b) | | circ_0072995 | Tissue/40 | qRT-PCR | Up | Pathological grade | – | Ding et al. (2020) | | circRhoC | Tissue/127 | qRT-PCR | Up | FIGO stage | – | Wang et al. (2019a) | | circ_0005276 | Tissue/49 | qRT-PCR | Up | Lymphatic metastasis and distant metastasis | Patients with high circ_0005276 tissue expression had worse OS, and DFS | Liu et al. (2020b) | | circ-ITCH | Tissue/45 | qRT-PCR | Down | Tumor size and FIGO stage | Patients with high circ-ITCH tissue expression had longer 5-year survival | Lin et al. (2020a) | | Tissue/77 | qRT-PCR | Down | FIGO stage and pathological grade | Patients with high circ-ITCH tissue expression had longer OS | Luo et al. (2018b) | | | circFoxp1 | Tissue/49 | qRT-PCR | Up | FIGO stage, primary tumor size, lymph node metastasis, distant metastasis, residual tumor diameter, and clinical response | Patients with high circFoxp1 tissue expression had lower OS and DFS | Luo and Gui (2020) | | circHIPK3 | Tissue/69 | qRT-PCR | Up | Lymph node invasion and FIGO stage | Patients with high circHIPK3 tissue expression had lower OS and DFS | Liu et al. (2018c) | | ciRS-7 | Tissue/40 | qRT-PCR | Up | TNM stages and lymph node metastasis | High ciRS-7 levels were associated with worse prognosis | Zhang et al. (2020) | | circLARP4 | Tissue/78 | qRT-PCR | Down | FIGO stage and lymph node metastases | Patients with high circLARP4 tissue expression had lower OS and DFS | Zou et al. (2018) | | circ-FAM53B | Tissue/54 | qRT-PCR | Up | Tumor size, FIGO stages, and lymph node invasion | Patients with high circ-FAM53B tissue expression had worse OS | Sun et al. (2019b) | | hsa_circ_0051240 | Tissue/33 | qRT-PCR | Up | Lymph node metastasis, FIGO stage, and the CA-125 level | Increased hsa_circ_0051240 tissue expression was associated with poor OS in OC patients | Zhang et al. (2019) | | circ_0007444 | Tissue/87 | qRT-PCR | Down | Tumor size, stage, and grade | Patients with lower circ_0007444 tissue expression showed shorter 60-month survival | Wu et al. (2021b) | | circPIP5K1A | Tissue/25 | qRT-PCR | Up | – | Elevated circPIP5K1A tissue levels were associated with poor OS in OC patients | Sun et al. (2019a) | | circ_0007841 | Tissue/43 | qRT-PCR | Up | – | Elevated circ_0007841 tissue levels were associated with low OS in OC patients | Huang et al.( 2021a) | | circLNPEP | Tissue/40 | qRT-PCR | Up | Tumor size, FIGO stage, and residual tumor | Elevated circLNPEP tissue levels were associated with low OS, and PFS in OC patients | Wang et al. (2021b) | | circ_0078607 | Tissue/49 | qRT-PCR | Down | FIGO stage and serum CA-125 levels | Low circ_0078607 tissue expression was correlated with shorter OS and PFS in OC patients | Zhang et al. (2021c) | | hsa_circ_0004712 | Tissue/30 | qRT-PCR | Up | – | High hsa_circ_0004712 tissue expression correlated with poor OS and PFS in OC patients | Zhou et al. (2021) | Table 4. | circRNA | Patient’s specimen source and size | Detection method | Deregulation | Association with clinicopathological features | Power in differentiation of cancerous patients from healthy people | Prognostic or diagnostic potential | Ref. | || |---|---|---|---|---|---|---|---|---|---| | Sensitivity (%) | Specificity (%) | AUC value in ROC curve | ||||||| | hsa_circ_0013958 | Tissue/90 | qRT-PCR | Up | FIGO stage and lymph node metastasis | 80.0 | 91.1 | 0.912 | Prognostic and diagnostic | Pei et al. (2020) | | circUBAP2 | Tissue/24 | qRT-PCR | Up | KM curve showed a negative association between circUBAP2 and survival rate in OC patients | – | – | 0.8012 | Prognostic and diagnostic | Sheng et al. (2019) | | circ-0001068 | Serum exosomes/85 | qRT-PCR | Up | – | – | – | 0.9697 | Diagnostic | Wang et al. (2020b) | | circ-ABCB10 | Tissue/103 | qRT-PCR | Up | circ-ABCB10 tissue expression was significantly associated with tumor size, FIGO stages circ-ABCB10 tissue expression showed negative correlation with OS | – | – | 0.766, 95% CI 0.690–0.842 | Prognostic and diagnostic | Chen et al. (2019a) | | circSETDB1 | Serum/60 | qRT-PCR | Up | FIGO stage and lymph node metastasis KM curve showed that OC patients with high circSETDB1 serum levels had shorter PFS | 78.33 | 73.33 | 0.8031 | Prognostic and diagnostic | Wang et al. (2019b) | | circ_0013958 | Tissue/30 | qRT-PCR | Up | – | – | – | 0.8806 | Diagnostic | Liang et al. (2021) | Diagnostic potentials of circRNAs in OC Several biomarkers particularly CA-125 have been used for decades and along with He4 have received approval for clinical application in diagnosis of OC patients; however, CA-125 elevation is detected in advanced stages of OC, while it increases in only half of the patients at low stages of the disease (Hu et al. 2019). Furthermore, it can be detected in high levels in patients with benign and other malignant gynecological and also non-gynecological cancers, while He4 does not show increase in benign diseases; however, it lacks adequate sensitivity (Li et al. 2012; Hellström et al. 2003). In addition to association of circRNAs and their potentials in prognostic goals in OC patients, they also have demonstrated acceptable values in detection of OC patients from healthy individuals. Moreover, some circRNAs with aberrant expression in different stages of disease suggest potentials in discriminating patients at advanced stages. Chen et al. (2021) found that circ-NOLC1 expression shows consistency with CA-125 levels in OC patients, as it is higher in patients with CA-125 positive (> 35 U/ml). Hu et al. (2019) evaluated the expression of circBNC2 in blood samples of 83 EOC patients, 83 patients diagnosed with benign ovarian cysts, and 83 matched healthy controls. CircBNC2 expression was measured using qRT-PCR and its diagnostic value was compared to biomarkers CA-125 and He4 using receiver-operating characteristic (ROC) curve. The area under curve (AUC) in ROC curve demonstrated highest value for circBNC2 not only in discrimination of EOC patients from healthy individuals, but also in diagnosis of EOC patients form those with benign ovarian cysts (AUC = 0.923 and 0.879, respectively). These values were significantly lower for He4 and CA-125 (AUC in EOC vs Healthy Individuals, and EOC vs Benign Ovarian Cyst; 0.779 and 0.742, respectively, for He4, and 0.713 and 0.373, respectively, for CA-125). CircBNC2 also showed highest sensitivity, but second to CA-125 in specificity in discrimination of late stage EOC patients from those at early stages. In addition, Wang et al. (2019b) demonstrated that serum circSETDB1 with 77.78% sensitivity and 76.74% specificity, and AUC of 0.8107 ± 0.06424 can discriminate serous OC patients with primary chemoresistance to platinum-taxane-combined chemotherapy from chemosensitive patients. Overall, findings demonstrate circRNAs as potential reliable biomarkers with applications in diagnosis, and prediction of prognosis in OC patients. CircRNAs exhibit high stability in body fluids due to their characteristic structure unlike other RNA transcripts. They can be detected easily using conventional qRT-PCR, where back-splice junction is identified, and accordingly, can be introduced as good biomarkers helping early diagnosis, and management of cancerous patients including those with OC. However, it seems we have a long journey to bring circRNAs in clinical application and further investigations and more time is required same to other biomarkers, which occasionally taken several decades. Therapeutic potentials of circRNAs in OC The first-line therapy for OC patients includes surgery to remove the affected area and chemotherapy using agents like paclitaxel (Tsujioka et al. 2011). Other cytotoxic agents such as Docetaxel and cisplatin are used in treatment of OC patients. However, evidence show that chemoresistance occurs to these agents in OC patients. CircRNAs have been identified to affect chemoresistance of OC cells in several in vitro and in vivo experiments (see Tables 1 and 2). Accordingly, oncogenic circRNAs can confer chemoresistance, while onco-repressor transcripts increase chemosensitivity of OC cells to these agents. Resistance to paclitaxel, for example, is shown to increase in several cell experiments evaluating the biological functions of upregulated circRNAs (Li et al. 2020a; Xia et al. 2020; Guo et al. 2020) and in xenograft animal studies (Li et al. 2020a; Xia et al. 2020), while downregulated circEXOC6B enhances chemosensitivity to paclitaxel in both experiments (Zheng et al. 2020). These findings have gained more attention where known anti-cancer materials like curcumin with broad effects in human malignancies are reported to exert therapeutic role in OC via affecting circRNA axis (Sun and Fang 2021). In a recently published study, Sun et al. (2021) reported that curcumin suppresses cell proliferation and enhanced apoptosis in SKOV3 and A2780 OC cell lines in a dose-dependent manner (10–40 μM compared to DMSO as control), and circ-PLEKHM3 overexpression aggravated tumor-suppressive effects of curcumin. In addition, curcumin effects were shown to be exerted through miR-320a/SMG1 axis regulated by circ-PLEKHM3. An anesthetic drug, propofol, which is used conventionally before surgeries, already has shown with anti-cancer potentials, suppressed malignant features, and cell cycle progression through upregulation of miR-145, while suppressed circVPS13C in OC cells. The circVPS13C/miR-145/MEK/ERK signaling axis was identified as pathway through which propofol affected malignant behaviors of cancer cells. In a same study, Yang et al. (2021b) showed that circ_MUC16 knockdown increases inhibitory effects of propofol on tumor growth in xenograft animal experiment through miR-1182/S100B signaling pathway. Furthermore, silencing oncogenic circRNAs or overexpression of onco-repressor circular transcripts in OC cells have produced good results in attenuation of malignant behaviors of cancer cells and dissemination of tumors in vivo. These findings, overall, makes RNA interference technology, oligonucleotides, and CRISPR-Cas gene editing technology to target the oncogenic circRNAs or augmentation of onco-repressor circRNAs as potential therapeutics in fighting against OC. Exosomal circRNAs in OC Exosomes comprise a group of extracellular vesicles with an average size of 30–100 nm released from some cell types including most cancer cells. These nanostructures contain various biological macromolecules such as proteins, lipids, and nucleic acids including circRNAs, which are able to transfer them to recipient cells considered as an exchange method between cells (Valadi et al. 2007; Asemani et al. 2022). Evidence have reported involvement of tumor-derived exosomes in several biological processes such as cell proliferation, chemoresistance, angiogenesis, and metastasis (Wang et al. 2020b). Thus, exosomes are expected to affect cancer hallmarks. Exosomes not only has been reported to transfer circRNAs between cancer cells, but they are known to contain higher concentrations compared with intracellular environment (Li et al. 2015). This mechanism can affect cancer development and progression in OC. Several studies have reported that tumor cells-derived exosomes containing circRNAs contribute to enhanced OC tumorigenesis and progression. Guan et al. (2019) detected circPUM1 in exosomes isolated from tissues of OC patients, its overexpression facilitated OC progression via upregulation of NF-κB and MMP2 in vitro and in vivo. Enhanced chemoresistance to cisplatin in OC cell via circulating exosomes containing circFoxp1 was found in another study by Luo et al. (2020). Furthermore, Wang et al. (2020b) identified six upregulated circRNAs in exosomes isolated from sera of OC patients. They also demonstrated that elevated exosomal circ-0001068 can serve as a biomarker for diagnosis of OC patients.

CircRNAs play role in regulation of cellular processes, and their dysregulations are associated with diverse human diseases including cancer. Both upregulation and downregulation of circRNAs are frequently reported in OC cells and tissues affecting malignant behavior of cancer cells. Upregulated circRNAs exhibit enhancing impact on malignant behavior of cancer cells, promote cell proliferation, migration, and invasion, and suppress apoptosis in vitro and in vivo. This group, thus, can be regarded as oncogenic circRNAs, while downregulated circRNAs demonstrate onco-repressor behaviors. Mechanistically, circRNAs exert their putative role through several ways such as sponging miRNAs, binding to RNA-binding proteins, protein scaffolding, and transcriptional regulation (Najafi 2022). Binding to and repressing a target miRNA is the main mechanism of action described for circRNAs. Via this axis of action, a circRNA modulate the expression of a target gene, which eventually can affect malignant behavior of cancer cells. CircRNAs in clinical studies have showed association with clinicopathological features and prognosis in OC patients, while they also can discriminate the patients from healthy individuals with high diagnostic values. Due to lack of good biomarkers for early detection of OC patients, they are mainly diagnosed when the clinical manifestations have appeared, and the malignancy has progressed to the advanced stages. Current biomarkers like CA-125 show deficiencies in early detection of OC, and application of alternative biomarkers like miRNAs face challenges like degradation in the blood/serum samples due to circulating RNases; additionally, they have no adequate accuracy in detection of preoperative cases (Wang et al. 2020a). Employment of circRNAs as novel biomarkers with diagnostic power in early detection, thus, may be helpful and promising since they show high stability in the circulation. Rather than prognostic and diagnostic values of circRNAs, they have been suggested as therapeutic targets as targeting oncogenic circRNAs or augmentation of onco-repressor transcripts have produced potent impact against cancer development and progression in cell and animal studies. Therefore, circRNAs have been introduced as potential therapeutic targets to find novel anti-cancer treatments. This strategy has been suggested for targeting miRNAs using double-stranded miRNA mimics to restore the functions of onco-suppressor miRNAs or blockade of oncogenic by employment of single-stranded antisense oligonucleotides (Thorsen et al. 2012). Targeting circRNAs have suggested advantages to those used for miRNAs such as low off-target rates which is due to their structure and half-life since miRNAs have short length and shorter half-life (Wang et al. 2021). Several strategies to manipulate the circRNA levels are under evaluation, which include inhibiting circRNA using RNA interference, complete knockdown using the CRISPR-Cas gene editing technology, and employment of antisense oligonucleotides (ASOs) (Rajappa et al. 2020; Najafi et al. 2022d). Accordingly, some achievements have been reported in cardiac hypertrophy (Lavenniah et al. 2020), gastric carcinoma (Liu et al. 2018d), osteosarcoma (Wan et al. 2020), renal cell carcinoma (Chen et al. 2020b), and OC (see above). Future perspectives Although suggesting as good potentials, however, clinical application of circRNAs in prediction of prognosis and diagnosis of OC patients has faced challenges and limitations. Main restrictions include methodological identification of circRNAs, which requires already identified back-splice junctions, and also low concentration of circRNAs compared to their linear counterparts. RNA-seq is the main technology used for identification of circRNAs with acceptable accuracy and approximately unbiased. However, novel technologies such as long-read third-generation RNA sequencing, which offer sequencing of whole exon rather than only the back-splice junction in short-read sequencing can be the solution (Artemaki et al. 2020). Long-read techniques such as the Oxford Nanopore Technologies (ONT) and the Pacific Biosciences (PacBio) have the power to sequence several thousands of nucleotides corresponding to the full-length of circRNAs revealing its complete composition in a single read and, therefore, evaluate where a full-length sequence of a circRNA which has been read by short reads is correct and complete (Zhang et al. 2022). Four main approaches for circRNAs long-read sequencing are available, which include circRNA nicking and long-read sequencing (CircNick-LRS), circRNA panel long-read sequencing (CircPanel-LRS), isoCirc, and CIRI-long (Rahimi et al. 2021). Despite the numerous advantages of these technologies like portable setup making them, however, they have several limitations and challenges for routine application such as higher rates of errors than those in short-read sequencing and higher expenses (Rahimi et al. 2021). Therapeutic applications also require improvements in technologies and extensive clinical investigations for efficient targeting of circRNAs particularly in solid tumors. Author contributions SN conducted all stages of the study. Funding This study received no funding. Data availability The data are available upon request to the author. Declarations Conflict of interest Not applicable. Ethical approval Not applicable. Consent for participation Not applicable. Consent for publication Not applicable. Footnotes Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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