The risk factors that have been commonly accepted and cited in many publications include age (usually less than 45 years), more ovulatory cycles during a lifetime [3], early menarche and late menopause, nulliparity, a lack of breastfeeding [4,5], the non-usage of oral hormonal contraceptives [6], the use of estrogen menopausal replacement therapy [7], diabetes, obesity, and cigarette smoking [3]. The observation that tubal ligation, salpingo-oophorectomy, and hysterectomy could protect against HGSOC suggests the ascending infection and the use of talc on examination gloves or for perineal hygiene as a risk factor [5,8]. A diet rich in animal proteins contains xeno-estrogens, which have carcinogenic potential [9]. High levels of serum lipids resulting from increased fat consumption stimulate leptin secretion by fatty tissue, stimulating the release of gonadotropins. This mechanism could result in disturbed re-epithelialization of ovaries and predisposes one to abnormal epithelium proliferation [10]. A lower intake of polyunsaturated omega-3 fatty acids and a high consumption of trans-fats were also associated with an increased risk of ovarian cancer [11]. However, epidemiological analysis revealed that only about 10% of ovarian cancer cases could be attributed to the presence of the above-mentioned risk factors [12].

Genetic studies have brought knowledge about other risk factors. The most discussed of them is the presence of the familiar germline BRCA1/2 mutation. It is estimated that about 3,5% of ovarian cancer patients have germline BRCA mutations, and 10–20% of ovarian cancers could be associated with these mutations [13,14]. The risk of ovarian cancer in this group of patients varies from 27% to 44% in the BRCA2-mutated and BRCA1-mutated patients, respectively [15]. For comparison, the lifetime risk of ovarian cancer in non-mutated populations does not exceed 1.5% [3]. Except for high-penetrance (high-risk) BRCA alleles, several low-penetrance (low-risk) alleles could account for the increased risk of ovarian cancer, including mutations in BRCA1-interacting helicase 1 (BRIP1), RAD51 Paralog C (RAD51C), RAD51D, and partner and localizer of BRCA2 (PALB2) genes, with the odds ratio of ovarian cancer being 14.1 for BRIP1, 5.2 for RAD1C, and 12.0 for RAD1D mutation and a relative risk of 2.9 for PALB2, respectively [16,17]. This risk ratio translates to a lifetime risk of 5–12% [18]. Both high- and low-risk mutations account for an ordinary risk of 40% for ovarian cancer [19]. The association of other low-penetrance allele mutations was also considered for ATM serine/threonine kinase (ATM), checkpoint kinase 2 (CHEK2), and BRCA1-associated RING domain 1 (BARD1) genes. According to the National Comprehensive Cancer Network (NCCN v.2.2022.), only the mutation of the ATM gene could influence the increase in the risk of ovarian cancer. It should be considered for familiar screening [20]. All these genes are connected with the homologous recombination (HR) pathway of DNA repair, which is seriously disturbed in HGSOC. Therefore, patients with the presence of one high-risk mutation or at least two low-risk mutations may benefit from therapies targeting dysfunctional genes, like poly (ADP-ribose) polymerase (PARP) inhibitors and drugs of similar mechanisms of action [21]. Lynch syndrome, also called hereditary nonpolyposis colorectal cancer syndrome (HNPCC), is associated with a disturbed DNA mismatch repair system and microsatellite instability due to the MutL protein homolog 1 (MLH1) and MutS homolog 2 (MSH2) mutations [22]. A clinical sign of the syndrome is colorectal cancer associated with endometrial and ovarian cancers. Ovarian cancer risk in women with Lynch syndrome is 6–8% and is higher in the presence of MSH2 and MSH6 mutations [23]. Women with Lynch syndrome-associated ovarian cancer are diagnosed earlier, usually have non-serous histology, and, in 22% of cases, ovarian tumors exist with synchronous endometrial cancer [24,25]. Li–Fraumeni syndrome is an autosomal dominant syndrome determined by heterozygous germline mutations in the tumor suppressor gene TP53. Less frequent tumors (15%) associated with Li–Fraumeni syndrome include ovarian cancer and occur at an earlier age compared to the onset age of sporadic ovarian cancer [26,27]. Tumors are typically characterized by worse survival, increased chemo-resistance, and a high relapse rate [28].

The analysis of single nucleotide polymorphisms (SNPs) in many genes regulating ovarian cancer biology has been performed. Although the CHEK2 mutations were not confirmed as significant regarding the risk of ovarian cancer, other studies indicated that CHEK2 SNP rs17507066 had a significant association with the risk of HGSOC [20]. In a group of 587 studied patients with epithelial ovarian cancer, SNPs at PIK3CA rs9838117 and ERBB2 rs1058808 loci associated with cancer cell signaling were found to be correlated with the risk of cancer [29]. In the panel of genes related to ovarian cancer relapse, SNP analysis indicated that the male-specific lethal-1 homolog (MSL1) SNP rs7211770 was associated with a decreased risk of HGSOC (OR 0.81). At the same time, tumor suppressor HEXIM P-TEFb Complex Subunit 1 (HEXIM1) SNP rs1053578 was correlated with an increased risk of ovarian cancer (OR 1.4) [30]. The transcription elongation factor A (SII)-like 7 (TCEAL7) gene is epigenetically down-regulated in ovarian cancers, and SNPs rs5987515, rs5987724, and rs5945971 were significantly associated with a reduced risk of HGSOC [31]. Micro RNA (miR) miR-196a-2 locus polymorphism is observed in several malignancies, and studies have indicated that the miR-196a-2 rs11614913 CC genotype may increase the risk of ovarian cancer and enhance cell invasion [32]. The hephaestin gene (HEPH) encoding the protein responsible for iron transport regulates the proliferation and apoptosis of cancer cells and facilitates reactive oxygen species (ROS) production [33]. The HEPH SNP rs17216603 was associated with a reduced risk of HGSOC (OR 0.81) [34]. Somatic mutations in the retinoblastoma-1 (RB1) gene have been observed in several malignancies. The RB1 rs2854344 and rs4151620 polymorphism have been associated with a reduced risk of ovarian cancer [32]. The insulin-like growth factor binding protein 3 (IGFBP3) gene functions as a low-penetrance onco-suppressor gene and regulates the interaction between insulin growth factor-1 (IGF-1) and its receptor [35]. IGF-1 enhances tumor growth and inhibits cell apoptosis. The IGFBP3 SNP rs2270628 was associated with increased IGF1 plasma levels and higher ovarian cancer risk [36]. The epigenetic regulation of cancer growth is, among other things, regulated by DNA methylation provided by DNA methyltransferases encoded by DNMT genes. The DNMT1 rs2228611 and rs759920 SNPs were associated with an increased risk of ovarian cancer development (OR 1.8 and OR 1.9, respectively) [37]. The inflammatory pathways are activated in many cancers and are important in ovarian cancer. The interleukin-1α (IL1A) gene SNP rs17561 was associated with a reduced risk of endometroid, mucinous, and clear-cell ovarian cancers but not HGSOC, indicating the genetic diversity of ovarian epithelial tumors [38]. Toll-like receptors (TLRs) are engaged in the inflammatory pathways, and their activation in ovarian cancer is closely related to cancer aggressiveness, chemo-resistance, and adverse clinical outcomes [39,40]. The interaction between TLR4 and MyD88, the molecule considered to be the marker of stem cells in ovarian cancer, contributes to inflammation in the tumor environment and enhances its aggressive phenotype [41]. Recent findings suggest that the TLR4 Asp299Gly polymorphism could be a genetic risk factor for the development of ovarian cancer [42]. Class II human leukocyte antigens (HLA) regulate interactions between cancer cells and host immune responses represented by tumor-infiltrating lymphocytes (TILs). The intercellular adhesion molecule-1 (ICAM-1) also plays an essential function in immune reactions and cell–cell contact in the tumor environment. The significant association of HLA-DP rs3077 AA, HLA-DQ rs3920 GG, ICAM-1 rs1437 CC, and CT genotypes with increased risk of ovarian cancer (OR 43, 6, 25, and 2.6, respectively) was confirmed. Moreover, HLA-DQ rs3920 and ICAM-1 rs1437 alleles varied significantly among borderline and malignant types of ovarian cancer, highlighting once more the need for an individual approach to different ovarian cancer subtypes [43]. Recently, calculating polygenic risk scores for ovarian cancer has improved risk stratification and may be used in clinical prevention programs [44].

It is estimated that microbial infections account for even 20% of cancer cases, and several viruses have been involved in carcinogenesis directly and indirectly through the stimulation of chronic inflammation and immunosuppression [45,46]. Studies confirming the presence of viruses in ovarian tumors have been noticed since 1992 when the presence of human papillomavirus (HPV) 16 and 18 DNA was confirmed in ovarian cancer tissue [47]. Investigations of HGSOC confirmed the presence of HPV 16 and 33 DNA in 10.5% of samples [48], HPV 6 DNA in 25% [49], HPV 16 DNA in 5.7% [50], and cytomegalovirus (CMV) DNA in 40–50% of samples [49,51]. HPV and CMV co-infection was observed in almost two-thirds of malignant ovarian cancer tissues containing mostly HGSOC, and the presence of viruses was also noticed in fallopian tubes [52]. Generally, in advanced ovarian cancer, the predominant genotypes are high-risk HPV 16 and 18, similar to cervical cancer [53]. The integration of HPV oncogenes E6 and E7 followed by the degradation of p53 and Rb proteins triggers carcinogenesis [54] and could hypothetically account for the origin of superficial tubal intraepithelial cancer (STIC), which precedes HGSOC. Of the genes that could be affected by HPV 16 insertions, more than half were found to be associated with malignant solid tumors, and in the case of HPV 18, two genes associated with malignancy in solid tumors were affected by genomic integration [55]. Similarly, Herpes virus insertions were also noticed at the sites of genes related to epithelial cancer or different solid cancers [55]. The CMV immediate-early (IE) and late tegument (pp65) proteins were abundant in ovarian cancer tissues and correlated with worse prognosis [51]. CMV proteins can inhibit the cycle arrest functions of p53 and modulate the function of many key signaling pathways, including Wnt/β-catenin, PI3K/AKT, NF-κB, and STAT3, thus stimulating proliferation, angiogenesis, inflammation, and immune escape [56,57,58,59]. Epstein–Barr virus (EBV) is a herpes virus associated with several human malignancies [60]. The presence of EBV DNA was confirmed in 7.8% of ovarian cancer samples, but no EBV DNA presence was noticed specifically in HGSOC samples [61,62]. Compared to controls, ovarian cancer is also characterized by a different composition of viral signatures, with Anelloviridae, Astroviridae, Birnaviridae, Bornaviridae, Hepadnaviridae, Iridoviridae, Paramyxoviridae, and Rhabdoviridae found in significant levels exclusively in ovarian cancer samples [55]. Similarly, the Poxviridae family and Merkel cell Polyomavirus were also over-represented in ovarian cancer tissues [55].

In addition to viruses, a bacterial microbiome could account for ovarian cancer risk. Changes to microbiome compartments (such as vaginal, peritoneal, or tumor tissue compartments) may play an essential role in the pathogenesis of ovarian malignancy. Such a shift in the microbiome in the case of neoplastic diseases is termed oncobiosis. Oncobiosis-related interactions may influence cancer hallmarks, i.e., cellular metabolism, or induce gene expression changes, eventually leading to larger-scale events, such as epithelial-to-mesenchymal transition or angiogenesis [63]. Both bacteria and bacterial metabolites modulate the actions of the immune system. The oncobiotic transformation may cause a tolerogenic state that reduces the elimination of early cancer cells. However, immunogenic actions related to the dysbiotic microbiome may also lead to tumorigenic inflammation, the induction of oxidative stress, and a higher risk of mutations [63]. The oncobiotic microbiome in the peritoneum or tumor tissues differs from the healthy state. Several authors have suggested that the immunogenic character of the microbiome is associated with an increase in the presence of Gram-negative bacteria [64,65,66].

The observation that a history of pelvic inflammatory disease (PID) increased the risk of ovarian borderline tumors and HGSOC suggested a link between genital tract infection and ovarian cancer [67,68]. Another study showed that a history of more than five episodes of PID was correlated with a 2.5-times more significant risk of ovarian cancer in women aged over 35 years [69]. One of the considerable causes of PID is Chlamydia trachomatis infection [70]. The DNA of Chlamydia was found in 17% of ovarian cancer samples, and chlamydial heat shock protein-60 (HSP-60) was detected more frequently in HGSOC samples than in benign ovarian tumors [71]. The presence of chlamydial plasmid-encoded Pgp3 (virulence factor in genital tract) antibody was associated with a doubled risk of ovarian cancer [72]. The mechanistic explanation for the relation between Chlamydia infection and ovarian cancer is based on the observations that infection provokes oxidative DNA damage, the down-regulation of p53 function, and induces DNA double-strand breaks [73,74]. Moreover, chlamydial infection causes an epithelial-to-mesenchymal transition (EMT) in host cells and increases the stemness and CpG methylation of genes. All of these actions may contribute to promoting HGSOC cancer [75,76]. Menopausal status is one of the factors influencing the composition of cervicovaginal microbiota. In healthy premenopausal women, a Lactobacillus-dominated microbiota was observed. At the same time, after menopause, it shifted to a highly diverse bacterial species dominated by Propionibacterium, Corynebacterium, and Staphylococcus, similar to skin bacterial composition [77,78]. In ovarian cancer, a Lactobacillus-poor and highly diversified microbiota mimicking the post-menopausal environment was described, regardless of the menopausal status [77]. Surprisingly, it was discovered that women with the BRCA1 mutation had Lactobacillus-depleted cervicovaginal microbiota even before the onset of ovarian cancer [79]. In the study performed by Banerjee et al. [55], two predominant bacterial phyla were detected, mainly Proteobacteria and Firmicutes, present in 52% and 22% of ovarian cancer samples, respectively. Similarly, the Proteobacteria/Firmicutes ratio was significantly increased in ovarian cancer samples [80]. This study confirmed that ovarian cancer samples had much more diversified microbiota than controls.

The upper genital tract was in the past thought to be a sterile environment; however, recent studies contradict these notions. The ovarian follicular fluid of patients subjected to in vitro fertilization contained bacteria such as Actinomyces, Staphylococcus, and Bifidobacterium [81]. The endometrial cavity also indicates the presence of bacteria, with Bacteroides being a dominant resident of the non-pregnant endometrium [82]. A microbiome was also confirmed in the fallopian tubes, fimbriae, and ovarian surface epithelium. Firmicutes, Proteobacteria, Actinobacteria, and Bacteroidetes phyla were identified in the upper genital tract, with Proteobacteria being the most abundant while Firmicutes were the least common microbiota, respectively. There were also differences in microbiota distribution between tubes, fimbriae, and ovaries [83]. In ovarian cancer, the most abundant microbiota were Ralstonia, Mycobacterium, and Variovorax, and the abundances of several taxa were reduced in cancer compared to control samples, indicating the occurrence of bacterial dysbiosis in ovarian cancer [83].

The probable influence of the gut microbiome on polycystic ovary syndrome and endometriosis or chronic pelvic pain has been postulated [84,85]. The impact of the gut microbiome on the formation and development of ovarian cancer is much more hypothetical; however, the connections between gut microbiome and intestinal estrobolome could explain this dependency. Firstly, the gut microbiome disturbs the entero-hepatic circulation of estrogen [86]. Secondly, the microbiome interferes with the secretion of β-glucuronidase, an enzyme responsible for the degradation of the active forms of estrogen [87]. Through both mechanisms, the gut microbiome up-regulates the estrogen level and induces carcinogenesis with β-glucuronidase [88]. A recent study on a mouse model of HGSOC could confirm the role of the gut microbiome in ovarian cancer. Control and antibiotic-treated mice were stimulated by tamoxifen to build up malignant ovarian tumors. After one year of observation, it was found that antibiotic-treated mice had significantly fewer and less advanced tumors. The changes in gut microbiota following antibiotic therapy included the increase in Bacteroidales species, which are negatively correlated with cancer, accompanied by the reduction or elimination of cancer-associated Prevotella. It seems that Bacteroidales in the fecal microbiota might prevent the recolonization of cancer-associated Prevotella and further inhibit HGSOC development [29].

The fungal and parasitic signatures have also been found to differentiate between ovarian cancer patients and control groups. The signatures of Cladosporium, Pneumocystis, Acremonium, Malassezia, and Pleistophora were detected in all ovarian cancer samples, whereas signatures of Rhizomucor, Rhodotorula, Alternaria, and Geotrichum were detected in 95% of cancer samples [55]. Parasitic signatures in ovarian cancer samples were significantly more diversified compared to controls. They revealed the presence of genetic material from Dipylidium, Trichuris, Leishmania, and Babesia in all samples, as well as signatures of Trichinella, Ascaris, and Trichomonas in more than 95% of cancer samples [55].

The classic and most widely used tumor marker for HGSOC is CA125, a glycoprotein encoded by the MUC16 gene [89]. CA125 is detected in serum samples of more than 80% of ovarian cancer patients; however, its accuracy for detecting early-stage ovarian cancer is limited [90,91]. The low sensitivity of CA125 (50–60%) for detecting early-stage ovarian cancer arises because about 50% of such patients do not have elevated CA125 levels [92]. The specificity of CA125 is also relatively low (70–80%), and about 6/10 patients with increased CA125 levels do not have ovarian cancer but have different causes, like other malignancies and benign conditions (menstruation, pregnancy, benign ovarian tumors, uterine fibroids, endometriosis, adenomyosis, pelvic inflammatory disease, and liver diseases) [93]. To improve CA125 accuracy, different approaches have been developed. The detection of CA125-containing exosomes was shown to improve specificity, sensitivity, and AUC [94]. Detecting the glycoforms of CA125 is another strategy to enhance the efficacy of CA125 testing. CA125 is a highly glycosylated protein containing many Thomsen-nouveau (Tn) antigens, which are significantly up-regulated in ovarian cancer cells but have low expression in normal tissue. The combined use of CA125-Tn improved the diagnosis specificity in patients over 45 years [95]. Another form of Tn antigen, the sialyl-Tn antigen (STn), is exclusively abundant in cancer cells but has limited concentration in normal cells. The STn serum levels were increased by around 50%, 10%, and 4% in ovarian cancer patients, endometriosis, and healthy controls, respectively [96].

Human Epididymis Secretory Protein 4 (HE4) has been introduced to ovarian cancer diagnosis. Compared with CA125, HE4 is less frequently affected by benign gynecological conditions, especially endometriosis [97]. HE4 provides a specificity of 96% and a sensitivity of 67% for the detection of ovarian cancer [98]. HE4 enabled the diagnosis of more than half of ovarian tumors not expressing CA125. In early-stage ovarian cancer, the sensitivity and specificity of HE4 were 0.64 and 0.87, respectively, and HE4 performed better than CA125 concerning the specificity (97% vs. 67%) [99,100]. However, similarly to CA125, HE4 is not specific to ovarian cancer and may be increased in other malignancies (endometrial, lung, and breast cancers) and adenomyosis [97,99]. The diagnostic accuracy of HE4 is modified by both the age and menopausal status of the patients. Therefore, HE4 performed better in postmenopausal women than in premenopausal women but had decreased specificity and sensitivity in older women [101,102]. As with CA125, impaired renal function could be a source of significant mistakes in HE4 interpretation [102].

Glycoprotein CA72-4 is elevated in ovarian cancer, but contrary to CA125, it is not affected by the menstrual cycle, pregnancy, or endometriosis [103,104]. Its increased levels were detected in mucinous and clear-cell cancers, where HE4 usually was not changed. Therefore, CA72-4 is a valuable additional marker in the cases missed by standard diagnostic markers [105].

Folate receptor-α (FOLR1) is over-expressed in several malignant cancers, including 76% of patients with HGSOC [106,107]. Its serum concentration significantly increases in malignant compared to benign ovarian tumors and healthy controls [108]. The concentration of FOLR1 depends on the clinical stage of the cancer and its histology, grade, and size; therefore, FOLR1 serum levels are much higher in the case of aggressive and advanced tumors of the HGSOC type while being lower in mucinous and early-stage tumors [109].

Transthyretin (TTR) is a protein that transports thyroxine and retinol to the liver. Contrary to the previously discussed markers, its serum concentration is decreased in ovarian cancer. TTR performed better than CA125 and HE4 in detecting early-stage ovarian cancer; however, because of low sensitivity, it proved more efficient when combined with other biomarkers [110].

Further need to improve diagnostic accuracy brought about the implementation of multivariate index assays. One of the first such approaches was the introduction of serial CA125 measurements and their use in calculating the ROCA (Risk of Ovarian Cancer Algorithm). The ROCA used with transvaginal ultrasound (TVUS) in patients with increased serial CA125 levels improved sensitivity to 85% for earlier detection [111]. The UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS) revealed that longitudinal CA125 testing combined with annual TVUS screening improved the recognition capability for early-stage ovarian cancer by 47%. Still, it was not accompanied by a significant decrease in mortality [112].

The Risk of Ovarian Malignancy Index (ROMI) is a multivariate index assay based on serum levels of CA125, HE4, and thymidine kinase-1 (TK1). It showed higher specificity and sensitivity than ROMA in premenopausal and postmenopausal women [113].

The subsequent multivariate index assay is OVA1, which combines serum biomarkers CA125, transthyretin, transferrin, beta-2 microglobulin, and apolipoprotein A-1 to calculate a risk score for ovarian cancer. Compared to CA125 alone, OVA1 performs better concerning the sensitivity and identification of 60–80% of early-stage ovarian cancer cases missed by CA125 serum measurements [114,115]. OVERA was created as the second-generation test for OVA1 and combined CA125, HE4, apolipoprotein A1, transferrin, and follicle-stimulating hormone for screening patients with a pelvic mass. Compared to OVA1, OVERA had improved sensitivity and specificity of 91% and 69%, respectively [116].

Combining transthyretin (TTR) with CA125, hemoglobin, apolipoprotein AI, and transferrin improved the detection of early-stage ovarian cancer compared to CA125 [117]. Another combination of CA125, HE4, leptin, osteopontin, and prolactin showed improved AUC compared to CA125 alone [118]. CA125, in combination with annexin A2 (ANXA2), showed 100% sensitivity and improved accuracy in distinguishing stage IA ovarian cancer from benign tumors [119]. The following two multivariate models not only performed better but also anticipated the diagnosis of ovarian cancer by 5 months to 2 years compared to CA125 alone. The first model used insulin-like growth factor-binding protein-2 (IGFBP-2), lecithin cholesterol acyltransferase (LCAT), and CA125. In contrast, the second one was based on a combination of CA125, HE4, arginase 2 (AGR2), phosphatidylethanolamine binding protein 4 (PEBP4), and chitinase-3-like protein 1 (CHI3L1) [120,121]. A combination of ROMA, HE4, CA125, the neutrophil-to-lymphocyte ratio (NLR), and lactate dehydrogenase (LD) with machine-learning algorithms could improve the prediction of ovarian cancer with AUC from 0.62 to 0.76. Both LD and NLR were correlated with survival in ovarian cancer patients [122]. In the European Prospective Investigation into Cancer and Nutrition cohort of patients and ovarian cancer-free women, nine possible markers were chosen from 92 preselected proteins. These markers included CA125, HE4, FOLR1, kallikrein-related peptidase 11 (KLK11), WNT1-inducible-signaling pathway protein 1 (WISP1), midline or neurite growth-promoting factor 2 (MDK), C-X-C motif chemokine ligand 13 (CXCL13), mesothelin (MSLN), and ADAM metallopeptidase domain 8 (ADAM8). These markers showed good discrimination between groups for a lag time of 0–9 months; however, none of the new markers improved the prediction when added to CA125 [123]. The panel of 26 proteins in plasma associated with the dysregulation of Hippo, Hippo-Merlin, Focal-Adhesion-PI3K/AKT/mTOR, PI3K/AKT, Ras, Ebola virus, and NOTCH signaling pathways was able to identify patients with HGSOC risk at a false discovery rate of <0.05 [124].

The evaluation of urinary biomarkers for the detection of ovarian cancer is a new and dynamic diagnostic possibility. Similar to serum-based studies, urine component searches are based on single or multiple markers. The marking of CA125 in urine samples gave a sensitivity of 89% and a specificity of 67% [125]. The subsequent urine marker tested was HE4, which provided a sensitivity of 51–89% and a specificity of 75–100%, depending on the study, and showed comparable accuracy to the serum testing [126,127,128]. The mesothelin urine assay showed that a more significant fraction of patients with both early-stage and advanced-stage ovarian cancer was detected with a urine assay (42% and 75%, respectively) compared to a serum assay (12% and 48%, respectively) [129]. Elevated levels of the anti-apoptotic protein Bcl-2 were present in urine from patients with ovarian cancer, independently from tumor stage, grade, and size, and differentiated between healthy and ovarian cancer patients with an AUC of 0.9 [130]. Urine high mobility group AT-hook 1 (HMGA1) protein was significantly elevated in serous epithelial ovarian cancer, and a higher AUC (0.88) was noted for urine HMGA1 than for serum CA125 [131]. The AUC of 0.68 for differentiating ovarian cancer from benign disease was pointed out for the urine minichromosome maintenance complex component 5 (MCM5) protein concentration [132].

Similarly to the serum, the multiple markers were also marked in urine. The combination of HE4, creatinine, carcinoembryonic antigen (CEA), and transthyretin (TTR) had 93.7% sensitivity and 70.6% specificity in predicting ovarian cancer compared to benign tumors [133]. The different combinations of the urine levels of two metalloproteinases (MMP-2 and MMP-9) and MMP-9/neutrophil gelatinase-associated lipocalin (NGAL) complex and age were evaluated in ovarian cancer compared to benign tumors. The best prediction with an AUC of 0.88 was obtained for MMP-2, MMP-9, and age [134]. Combining urinary eosinophil-derived neurotoxin (EDN) and osteopontin resulted in a sensitivity of 72% and specificity of 95% to distinguish ovarian cancer from normal controls and elevation in early-stage ovarian cancer [135]. MiR-30a-5p was shown to be up-regulated in HGSOC, while urinary miR-30a-5p was notably reduced following the surgical removal of ovarian tumors. The miR-30a-5p was concentrated within the exosomes from ovarian cancer or urine from HGSOC patients. MiR-30a-5p showed good discrimination between HGSOC patients and healthy controls with an AUC of 0.86 [136]. Several proteins, including LY6/PLAUR domain containing 1 (LYPD1), lymphatic vessel endothelial hyaluronan receptor 1 (LYVE1), prothymosin alpha (PTMA), and secretoglobin family 1A member 1 (SCGB1A1) were confirmed to be increased in the urine of ovarian cancer patients compared to benign ovarian tumors, giving an AUC of 0.92 [137]. The combination of fibrinogen α fragment, collagen α 1 fragment, and fibrinogen β NT fragment showed an AUC of 0.88 for discrimination between ovarian cancer and benign ovarian tumors. The AUC was enhanced to 0.96 by an additional combination with CA125 [138].

Micro RNAs belonging to non-coding regulatory RNAs play an essential role in changing the expression of many target genes and intercellular communication. The levels of several miRNAs were investigated in serum, plasma, and circulating peripheral blood exosomes. The panel of eight miRNAs was both up-regulated (miR-21, miR29a, miR92, miR-93, and miR-126) and down-regulated (miR-99, miR-127, miR-155) in the serum of ovarian cancer patients compared to normal controls [139]. In the next studied panel of plasma 11 miRNAs, the up-regulation of miR-191-5p, miR-206, miR-34c-5p, miR-548a-3p, miR-320a, miR-574-3p, miR-590-5p, and miR-106b-5p and the down-regulation of miR-19a-3p, miR-30a-5p, miR-645, and miR-150-5p was noticed [140]. Similarly, serum expression of miR-145 and miR-133b was significantly decreased in ovarian cancer patients [141]. Moreover, the combined detection of miR-193-5p with CA125 and HE4 improved the accuracy of the diagnosis of ovarian cancer with an AUC of 0.996 [142]. MiR-200a, miR-200b, and miR-200c were significantly increased in the serum of HGSOC patients, with miR0-200b + miR-200c having the power to discriminate between ovarian cancer and healthy patients with an AUC of 0.78 [143]. A set of ten different miRNAs (miR-4687-3p, miR-939-5p, miR-5739, miR-211-3p, miR-1273g-3p, miR-3663-3p, miR-4726-5p, miR-4745-5p, miR-1268b, and miR-658) provided a prediction of ovarian cancer with an AUC of 0.97. However, every individual miRNA from the set was insufficient to discriminate cancer patients from healthy subjects when used alone [144]. The different sets of ten serum miRNAs enabled the identification of patients at risk of HGSOC. The set was constructed from miR-5100, miR-6800-5p, miR-1233-5p, miR-4532, miR-4783-3p, miR-4787-3p, miR-1228-5p, miR-1290, miR-3184-5p, and miR-320b and identified at-risk patients with an AUC of 1.00 [145]. A serum miRNA neural network of seven miRNAs (miR-29a-3p, miR-92a-3p, miR-200c-3p, miR- 320c, miR-335–5p, miR-450b-5p, and miR-1307–5 p) tested on an independent external sample of ovarian cancer showed a diagnostic AUC of 0.85. Moreover, the in situ hybridization of three miRNAs from the network (mir-200c-3p, mir-335–5 p, and mir-92a-3p) showed a complete overlap between the serum miRNAs and the miRNAs expressed in serous tubal intraepithelial cancer (STIC) and early stage I HGSOC [146]. The combination of miR-320b and miR-141-3p alone or integrated with CA125 and HE4 showed the accurate discrimination capability of early-stage ovarian cancer and healthy controls (AUC 0.79 and 1.00, respectively) [147]. Finally, miR-204, CA125, and CA19.9 panels showed an AUC of 0.94, 1.00, and 0.99 for benign, early-stage, and advanced ovarian cancer tumors, respectively [148]. Besides miRNA, another group of non-coding RNA called circular RNA has been tested for diagnostic accuracy. Circ _0003972 and circ_0007288 were down-regulated in the plasma of ovarian cancer patients and acted as diagnostic markers with an AUC of 0.78 and an AUC of 0.92 when combined with CA125. Both circ_RNAs function as tumor suppressors by sponging oncogenic miRNAs [149]. Tumor-associated molecules could be transferred to platelets, which are then educated to induce the specific splicing of pre-messenger RNA. These are called tumor-educated platelets (TEPs) and were tested as diagnostic tools in differentiating between early-stage ovarian cancer and benign tumors with 80% accuracy [150]. The usefulness of TEPs is evaluated in clinical trials (ClinicalTrials.gov identifier: NCT04022863).

An exciting possibility comes from the study demonstrating the use of a DNA-methylation signature of cervical cells to identify both ovarian and endometrial cancer. The signature Women’s Risk Identification for Ovarian Cancer Index (WID-OC index) enabled the identification of women with ovarian cancer with an AUC of 0.76. Interestingly, the WID-OC index was not connected with the presence of ovarian tumor DNA in cervical samples but rather with cervical epigenetic profiles originating from disturbed Müllerian duct differentiation [151]. Testing the DNA methylation according to meta-analysis showed better performance for the prediction of ovarian cancer than cell-free tumor DNA with an AUC of 0.93 [152]. The homeobox A9 (HOXA9) and hypermethylated cancer-1 (HIC1) promoter methylation in serum cell-free DNA samples showed an AUC of 0.95 in discrimination between ovarian cancer and healthy controls [153,154]. The plasma cell-free DNA (cfDNA) methylation pattern has been shown to have diagnostic potential. The detection model based on cfDNA methylation achieved a sensitivity of 95% and a specificity of 89%, outperforming the CA125 [155].

The most common mutation in HGSOC is the TP53 mutation, which is present in over 95% of cancer cases [156]. The mutation is observed even in the early stages of HGSOC, and the lead time to the diagnosis using anti-TP53 serum AAbs is about 8–9 months, enabling the earlier prediction of cancer than with the use of CA125 or ROCA [157]. However, the anti-TP53 indicated minimal sensitivity as a single marker. Moreover, the presence of anti-TP53 AAbs is observed in only 40% of HGSOC patients and correlates with tumor burden. The combination of anti-TP53 AAbs with CA125 improved the accuracy of CA125 alone. The panel of three anti-tumor-associated antibodies, anti-G-protein alpha subunit Gs-α (GNAS), anti-TP53, and anti-nucleophosmin 1 (NMP1), could identify 51% of ovarian cancer patients negative for CA125 [158]. Paraneoplastic tripartite motif-containing protein 21 (TRIM21) AAbs in combination with anti-New York esophageal squamous cell carcinoma 1 (NY-ESO-1), anti-TP53, and anti-paired box 8 (PAX8, marker of the Fallopian tube secretory cell lineage) AAbs tested in the group of patients with increased risk of ovarian cancer (positive family history, Lynch syndrome, or BRCA germinal mutations) showed 46% sensitivity and 98% specificity. It could complement CA125 screening in this group of women [159]. Another panel of nine AAbs (anti-TP53, C-myc, protein kinase p90, ubiquitin-binding p62, alpha 2-HS glycoprotein AHSG, ubiquitously expressed 14-3-3zeta, Ras-related protein RalA, KH domain-containing protein over-expressed in cancer Koc, and tumor suppressor p16) showed an AUC of 0.91 and identified 79% of ovarian cancer patients presenting with normal CA125 levels [160]. Autoantibodies against leucine repeat death domain-containing protein (LRDD), stanniocalcin-1 (STC1), and forkhead box a1 (FOXA1) were over-expressed in ovarian cancer patients compared to normal controls and showed an AUC of 0.91, 0.88 and 0.82, respectively. The best discriminating combination was anti-LRDD + anti-FOXA1 AAbs, and the accuracy of this set improved when further combined with CA125 [161]. This promising combination was based on anti-BRCA1-associated RING domain 1 (BARD1) and CA125 levels. It showed high specificity and sensitivity (over 90%) for distinguishing ovarian cancer patients from healthy controls both for average-risk and high-risk women with hereditary breast and ovarian cancer syndrome (HBOC) [162]. Anti-PDZ and LIM domain-1 (PDLIM1) AAbs were able to differentiate both ovarian cancer patients from healthy controls (AUC 0.76) and ovarian benign tumors (AUC 0.76), as well as to identify 41% of early-stage and 39% of late-stage ovarian cancer, respectively. The anti-PDLIM1 AAbs could also find 15% of ovarian cancer patients negative on the base of CA125 marking alone. The combination with CA125 improved the AUC to 0.85 [163]. The panel of serum C-C motif chemokine ligand 18 (CCL18) and C-X-C motif chemokine ligand 1 (CXCL1) antigens combined with nucleic acid-binding protein (C1D), fragile X mental retardation syndrome-related protein 1 (FXR1), zinc finger protein 573 (ZNF573) and transmembrane 4 L six family member 1 (TM4SF1) AAbs showed an AUC of 0.99 for ovarian cancer diagnosis, an AUC of 0.95 for early-stage ovarian cancer, and an AUC of 0.75–0.96 for discriminating patients with ovarian cancer from patients with other malignancies [164]. The systematic review from Heidelberg [165] showed that the highest individual sensitivity was obtained with anti-Rho GDP-dissociation inhibitor (RhoGDI) and anti-tubulin alpha 1c (TUBA1C) AAbs but with concomitant low specificity. Better accuracy was obtained for anti-homeobox A7 (HOXA7) and anti-interleukin-8 (IL8) AAbs for detecting moderately differentiated and early-stage tumors, respectively. The panel of eleven AAbs (intercellular adhesion molecule 3-ICAM3, cancer/testis antigen 2-CTAG2, transcription factor p53, serine/threonine/tyrosine interacting like 1-STYXL1, PVR cell adhesion molecule-PVR, proopiomelanocortin-POMC, Nudix hydrolase 11-NUDT11, tripartite motif containing 39-TRIM39, serine/threonine protein kinase Kist-UHMK1, kinase suppressor of ras 1-KSR1, and nuclear RNA export factor 3-NXF3) provided 45% sensitivity at 98% specificity for HGSOC. Generally, limited data suggest that AAbs could improve diagnostic discrimination when combined with CA125 and HE4 [165].

The IOTA Simple Rules were devised by the International Ovarian Tumor Analysis (IOTA) group to predict malignancy of the recognized adnexal mass [166]. The model is based on the ultrasound estimation of B (benign) and M (malignant) features of the adnexal tumor, which helps to classify the patient into gynecologic oncology assessment. IOTA-SR predicts ovarian malignancy with a 92% sensitivity and 96% specificity and is more accurate than RMI or ROMA alone [167]. Therefore, the IOTA should be the first method to assess a pelvic mass, and its results should be confirmed using an expert ultrasound. We believe the IOTA system could be used for screening in postmenopausal women.

The Risk of Malignancy Index (RMI) combines TVUS, CA125 level, and menopausal status and expresses the result as a calculated product of these components. The cut-off value of 200 has better sensitivity and specificity than the CA125 level alone [168,169]. Using different values for TVUS and menopausal status, four RMI algorithms were created; however, their clinical efficacy illustrated by the AUC did not prove to differ between them [170].

The multivariate index Risk of Malignancy Algorithm (ROMA) has incorporated the CA125 serum level, HE4, and menopausal status [111]. Compared to other assays, ROMA showed satisfactory specificity (91%) and sensitivity (90%) in the diagnosis of ovarian cancer [171]. However, the ROMA index showed a lower specificity in postmenopausal compared to premenopausal women [172]. Compared to CA125, ROMA showed improved specificity, especially in premenopausal women, as well as higher sensitivity compared with the RMI [173].

The problem of screening for ovarian cancer has not been solved so far, although several attempts at screening in the average-risk postmenopausal women population have been undertaken. The prevalence of pathology in the population determines the demands for effective screening, which, in the case of ovarian cancer, taking into account the incidence of 40 cases per 100,000 persons/year, means that the test accuracy required to achieve a 10% positive predictive value in the whole population demands a sensitivity of 60–100% and a specificity of 99.6–99.8% [174]. In postmenopausal women, the test should have a sensitivity more significant than 75% and a specificity greater than 99.6% to reach a positive predictive value of 10% for the detection of early cancer. That means that based on the screening test itself, it would be nine false positive cases per one case of true ovarian cancer, which is an acceptable result. Using a single test is insufficient to attain this screening efficacy level. Therefore, the solution is to use either multimodal testing or to combine it with serial testing [175]. Several attempts at screening in the average-risk postmenopausal women population have been undertaken. In the Kentucky Study, women over 50 with no risk and women over 25 from risk families were included and received annual transvaginal ultrasounds, followed by detailed diagnostic procedures in suspected cases. The 5-year survival rate for women with ovarian cancer detected by screening was 75% compared with 54% for unscreened women with ovarian cancer treated in the same way [176]. The Shizuoka Cohort Study of Ovarian Cancer Screening (SCSOCS) recruited asymptomatic postmenopausal women for annual transvaginal ultrasound and CA125 (intervention group n = 41,688 vs. control group n = 40,799). Eight more cancers were diagnosed outside the screening program. The proportion of early-stage ovarian cancer was higher in the screened group (63%) than in the control group (38%); however, this did not reach statistical significance [177]. Extended follow-up of the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial indicated no mortality benefit from screening for ovarian cancer with annual CA125 and transvaginal ultrasound [178]. However, the UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS) showed an overall mortality reduction of 20% in favor of multimodal screening (ROCA algorithm + transvaginal ultrasound in the group of patients with raised ROCA) [168]. In the subsequent trial, called the Normal Risk of Ovarian Cancer screening study (NROSS), postmenopausal women were tested using the ROCA algorithm and then selected for consecutive annual CA125 tests (low risk), repeat CA125 test in 3 months (intermediate risk), or transvaginal ultrasound and gynecologic oncologist consultation (high risk). The authors concluded that ROCA followed by transvaginal ultrasound demonstrated 99.9% specificity in a population of women at average risk for ovarian cancer [179]. Due to these discrepancies, screening programs have yet to be implemented commonly in clinics, and the efficacy of such proceedings still needs confirmation in randomized trials. An interesting problem is the question of how early the increase in biomarkers precedes the diagnosis of ovarian cancer (pre-diagnostic efficacy). Generally, for CA125, the lead time (the time to diagnosis) is shorter than one year and, in most studies, does not overrun six months. It has a higher sensitivity for advanced cancer (FIGO III-IV). The same is true for other biomarkers (HE4, CA72.4); however, panels of biomarkers outperform single biomarkers like CA125, having a longer lead time [120,180].

Circulating tumor cells (CTCs) are the cells shed from the tumor, intravasated, and found in the peripheral blood. The isolation of CTCs is challenging due to the relative scarcity of tumor cells in all circulating cells, mainly of hematological lineage [201]. Most CTCs undergo both apoptosis and necrosis due to the hostile environment in the blood, including starvation, stress, and immunologically mediated elimination [202]. The most popular and approved system to isolate CTCs is a CellSearch detection system based on isolating cells with positive epithelial cell adhesion molecule (EpCAM) expression [203]. However, its efficacy is limited due to varied numbers of EpCAM(+) CTCs [204]. Novel approaches capable of omitting this limitation use CTC enrichment methods with multiple identification markers or the utilization of CTC’s ability to invade the artificial matrix [205,206,207].

One of the first publications devoted to CTCs identified them in the blood of 12% of ovarian cancer patients, using an immuno-microbead that recognized an EpCAM, regularly expressed on ovarian cancer cells [208]. Another early paper confirmed the evidence of CTCs in the peripheral blood of 19% of ovarian cancer patients, especially with most malignant grade 3 tumors. The CTCs were identified using cytokeratins CK7, CK8 CK18, and CK20, transcription factor-2 (TFS-2), and epithelial growth factor receptor (EGFR) [209]. The study detecting CTCs identified as cells positive for EpCAM, CK 4, 5, 6, 8, 10, 13, and 18 showed CTC positivity in 10% of early-stage cancer patients and 73% of late-stage patients. Patients with advanced cancer showed higher numbers of peripheral blood CTCs [199]. In another study, CTCs were detected before surgery in 19% of patients expressing EpCAM (31%), MUC-1 (50%), HER-2 (31%), and CA 125 (50%), respectively [210]. In another study, CTCs were isolated on the base of EpCAM/CK positivity combined with physical cellular features like cell size and deformability in patients with benign and malignant ovarian tumors. A total of 56% of cases were positive for CTCs, including 100% early-stage and 68% advanced-stage cancer. However, 44% of patients with benign tumors also showed the presence of cells interpreted as CTCs. The CTC detection had a sensitivity of 77%, 100%, and 100% for benign vs. all-stage cancer, benign vs. stage I-II cancer, and benign vs. stage I cancer, respectively [195]. In the next study, the CTC count established by a microfluidic method differed between healthy and ovarian cancer patients. CTCs defined as Hoechst+, CK+, and CD45- cells showed counts of more than eight in 87% of ovarian cancer patients but in none of the healthy volunteers, and the total CTC counts were found to be significantly elevated in the ovarian cancer group (55 vs. 0.5) even though CTC-like cells were identified in low numbers in healthy controls [211]. The immunomagnetic targeting of combined mesenchymal N-cadherin and epithelial EpCAM-positive cells enriched the CTC population approximately three times more efficiently than targeting EpCAM alone. According to previous studies, in some blood samples of healthy individuals, the presence of cells expressing markers common to CTCs was also observed. Analysis showed that these “false positives” were identified as circulating endothelial cells (CECs) by vascular endothelial-cadherin co-staining and that their count could be highly variable in patients and healthy controls [212]. This is a reliable explanation for the identification of CTC-like cells in benign tumors and healthy individuals. The CTCs marked as CD45-/HE4+EpCAM+cytokeratin+vimentin+ cells were increased in ovarian cancer patients and showed positivity in 49% of patients with a sensitivity of 73%, higher than that for CA125 [190]. Comparison of the gene expression level in the group of CTC-negative and -positive peripheral blood samples confirmed a statistically significant difference for the expression of the Wilms tumor WT1, EPCAM, MUC16, MUC1, keratin KRT7, KRT18, and KRT19 genes [213]. Another study of the genetic pattern of epithelial EpCAM+ CTCs showed the expression of MUC1 and cytokeratin 19 (CK19) but also of genes associated with mesenchymal and more malignant features such as tissue inhibitor of metallopeptidase 1 (TIMP1), C-X-C chemokine receptor type 4 (CXCR4), and the stem markers CD24 and CD44 [214].

The evolution of isolation techniques improved the CTC detection rate to almost 90% of ovarian cancer patients and increased the number of CTCs correlated with the clinical stage of the tumor. Moreover, patients with early-stage disease had a CTC-positive rate of 93% compared to CA125, which was positive at merely 64% [215]. The combination of anti-EpCAM-moAbs and anti-FRα-moAbs showed a significantly increased positive rate of CTC detection in ovarian cancer patients compared with anti-EpCAM-moAbs alone [216]. Recently, the detection model of CTCs based on the expression of EpCAM, MUC1, and Wilms tumor protein WT1 showed significantly higher specificity than CA125 (92% vs. 82%), especially in early-stage ovarian cancer (74% vs. 58%). The sensitivity of the detection, as mentioned in the above model, ranged up to 79.4% [196].

Cell-free DNA (cfDNA) is a DNA released from apoptotic or necrotic cells and contains a subpopulation of circulating tumor DNA (ctDNA) in the case of cancer. The ctDNA is a more achievable target than CTCs due to its relative abundance [217]. The amount of cfDNA varies in plasma in wide borders (3–93%) and depends on the cancer’s presence and the tumor size [218]. The ctDNA fragments’ length is usually shorter than cfDNA; therefore, searching for shorter fragments could enrich the sample in ctDNA [219]. Moreover, cfDNA Integrity (cfDI) indicates the ratio of long necrosis-derived DNA fragments to short apoptosis-derived fragments. Cancer patients have a higher cfDI compared to healthy controls and benign tumors [220]. The stability of cfDNA in the blood depends on the amount of DNA released and the clearance of DNA, which, in the case of cancer, is significantly decreased [221]. The estimated time of the half-life of cfDNA was around 4–30 min [222]. The ctDNA detection is based on several methods, including quantitative polymerase chain reaction (PCR), digital droplet PCR, next-generation sequencing (NGS), and whole-genome sequencing (WGS) [223]. Combining NGS with targeted error correction sequencing (TEC-Seq) or duplex sequencing can improve further detection accuracy [224,225].

The main challenge in ovarian cancer is to recognize tumors in their early stages when surgery is much easier and the prognosis is better than in the advanced stage. The early phenomenon in ovarian cancer is promoter methylation of suppressor genes, which could be used as a diagnostic tool [226,227]. The methylation of opioid-binding protein/cell adhesion molecules like OPCML, Runt-related transcription factor 3 (RUNX3), and tissue factor pathway inhibitor 2 (TFPI2) genes was studied in ovarian cancer patients using methylation-specific NGS. The methylation of OPCML showed a significant difference between early-stage ovarian cancer patients and healthy controls, even when CA125 did not differ between them. Ras-association domain family 2A (RASSF2A) is a suppressor gene whose down-regulated expression has been described in several malignancies. The epigenetic inactivation of RASSF2A through aberrant promoter methylation was found in 36% of ovarian cancer plasma samples [228]. The multiplex assay of aberrantly methylated seven genes (RASSF1A, RUNX3, TFPI2, OPCML, secreted frizzled-related protein 5-SFRP5, cadherin 1-CDH1, and APC regulator of WNT signaling pathway-APC) achieved a sensitivity of 85% and a specificity of 90% in early-stage ovarian cancer, which was surprisingly better than the result obtained for CA125 [200]. The following suppressor gene, the slit homolog 2 (SLIT2), was shown to be aberrantly methylated. Among the cases with hypermethylation in ovarian tissue, 93% of the case-matched serum DNA samples also showed hypermethylation, including cases of early-stage ovarian cancer [229].