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Review

Circulating Biomarkers for Cancer Detection: Could Salivary microRNAs Be an Opportunity for Ovarian Cancer Diagnostics?

by
Marzia Robotti
1,†,
Francesca Scebba
2,† and
Debora Angeloni
2,3,*
1
Graduate School in Translational Medicine, Scuola Superiore Sant’Anna, Via G. Moruzzi, 56124 Pisa, Italy
2
Health Science Interdisciplinary Center, Scuola Superiore Sant’Anna, Via G. Moruzzi, 56124 Pisa, Italy
3
The Institute of Biorobotics, Scuola Superiore Sant’Anna, Via G. Moruzzi, 56124 Pisa, Italy
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Biomedicines 2023, 11(3), 652; https://doi.org/10.3390/biomedicines11030652
Submission received: 26 September 2022 / Revised: 2 February 2023 / Accepted: 16 February 2023 / Published: 21 February 2023
(This article belongs to the Special Issue microRNAs in Health and Disease)

Abstract

:
MicroRNAs (miRNAs) are small non-coding RNAs with the crucial regulatory functions of gene expression at post-transcriptional level, detectable in cell and tissue extracts, and body fluids. For their stability in body fluids and accessibility to sampling, circulating miRNAs and changes of their concentration may represent suitable disease biomarkers, with diagnostic and prognostic relevance. A solid literature now describes the profiling of circulating miRNA signatures for several tumor types. Among body fluids, saliva accurately reflects systemic pathophysiological conditions, representing a promising diagnostic resource for the future of low-cost screening procedures for systemic diseases, including cancer. Here, we provide a review of literature about miRNAs as potential disease biomarkers with regard to ovarian cancer (OC), with an excursus about liquid biopsies, and saliva in particular. We also report on salivary miRNAs as biomarkers in oncological conditions other than OC, as well as on OC biomarkers other than miRNAs. While the clinical need for an effective tool for OC screening remains unmet, it would be advisable to combine within a single diagnostic platform, the tools for detecting patterns of both protein and miRNA biomarkers to provide the screening robustness that single molecular species separately were not able to provide so far.

Graphical Abstract

1. Introduction

We reviewed the current literature to understand what opportunities exist in terms of circulating microRNA (miRNA), specifically salivary miRNAs, as biomarkers (BMs) for diagnostic/prognostic approaches to oncological diseases in general, and to ovarian cancer (OC) in particular. We realized that currently there are not miRNA BMs in clinical use for OC screening.
First, we provide a general introduction on the subject of OC and the state of the art in terms of screening opportunities. Section 2 introduces the subject of miRNAs, showing their recognized potential as disease biomarkers (BM). In Section 3, we analyze relevant literature on the potential of liquid biopsies for the detection of miRNAs in OC, with a specific focus on saliva (Section 4) to discuss the pros and cons of this biofluid, whose molecular profile resembles blood, with possible advantageous simplifications. For the sake of completeness, and to support the hypothesis that the search for salivary miRNAs could be useful in OC, we also reviewed the state of the art of salivary miRNAs in oncological conditions other than OC (Section 5), and, to support the validity of saliva as a useful biofluid for BMs search, we provide an excursus (Section 6) on OC candidate salivary BMs other than miRNAs. Finally, to compare saliva with other possible biofluids as a source of miRNA BMs, we reviewed the literature on this type of candidate BMs of OC in other biofluids (Section 7).
Overall, this review aims to provide an informed understanding of the need for more research on the subject of reliable BMs for OC screening, because presently, the clinical need for an early, reliable, relatively inexpensive diagnostic tool for OC screening in the population remains unmet. Specifically, we propose saliva as a convenient bioanalyte, possibly for the combined search of candidate BMs of different nature, such as miRNA and proteins.
OC, although rare, is the first cause of death for gynecological cancers and the fifth most common cause of cancer-related death for women of industrialized countries [1]. The poor outcome is mostly associated with the lack of specific symptoms and of effective screening strategies. Despite increasingly radical surgical approaches and huge efforts put into new, targeted, therapeutic agents, the prognosis for patients with OC has hardly improved in the past three decades and two thirds of women still die within ten years from diagnosis [2]. Five-year survival is less than 20% in women diagnosed with advanced-stage (stage III or IV) invasive epithelial OC, but exceeds 90% in those detected at stage I [3], suggesting that success in curing OC relies on early diagnosis. Efforts have therefore focused on diagnosing early-stage or low-volume disease through risk prediction, prevention, and screening. Worldwide, there is inconsistency in availability of and access to treatment for OC, and the outcomes are complicated because of the complexities of the disease in terms of epidemiology, genetic features, and histopathology, all contributing to the still poor understanding of OC, especially in comparison with other oncological diseases [4].
In fact, OC is heterogeneous, comprising several disease types and subtypes [5,6]. The extra-ovarian originations of epithelial OCs contribute to the intricacies of the disease. Over the past decade, a dualistic pathway of epithelial ovarian carcinogenesis has emerged: Type I and Type II, that differ in epidemiology, etiology, and treatment. Therapeutic strategies may not be equally effective for all. Therefore, success in treating OC also requires to identify new BMs, not only to detect OC early, at a time when outcomes could be improved, but also to allow a better stratification of patients with full blown disease for more efficacious, personalized therapy [7,8,9].
Years ago, the first biomarker (BM) identified and proposed as a blood test for women with OC was the carcinoembryonic antigen (CEA) [10]. However, the current literature agrees that multi-BM panels might perform better than a single BM for a personalized treatment in the context of precision medicine in general [9,11] and for early detection of OC in particular [12].
At present time, despite decades of international efforts to identify panels of blood-borne BMs suitable for screening, no one superior to serum cancer antigen 125 (CA125) has reached clinical practice, except for human epididymis protein 4 (HE4), which is still second best to CA125 [13]. Their combined analysis within the risk of ovarian malignancy (ROMA) algorithm has improved the diagnostic accuracy for OC. However, ROMA is not applicable in screening procedures for early detection [9,13,14]. In addition to ROMA, the OVA1 test, a multivariate in vitro assay for five proteomic BMs, is used as an aid to the pre-chirurgical definition of a pelvic mass, but, again, it does not possess the characteristics to be applied on large-scale, prevention screening [15].
Currently, the combination of CA125 serum analysis and transvaginal ultrasonography (TVS) is the most utilized tool for the initial evaluation of suspect cases, even though CA125 lacks specificity (as it rises due to several physiological and pathological conditions in addition to cancer [16]), and TVS can identify adnexal masses, but is less reliable in differentiating benign from malignant tumors [17].
Over time, other serum BMs have been evaluated in OC together with CA125 [18], and several strategies have been developed by combining the analysis of multiple circulating proteins [19,20].
The literature shows that proteins expressed in OC cells can be retrieved in serum and urine [11,21]. However, only a few of several reported candidate BMs were validated for clinical practice [22], this discrepancy being ascribed to different reasons [11,23].
Therefore, regardless of the enormous progress in analytical techniques, the insights on the biochemical and molecular mechanisms of OC and the description in the literature of several candidate BMs, up to now, the clinical need for an effective test able to perform early, reproducible, noninvasive, and possibly inexpensive diagnosis of OC remains completely unanswered [24].
Moreover, despite recent encouraging data on sensitivity and cost-effectiveness of multimodal analysis, screening for OC in the general population is not recommended due to the lack of a definitive mortality benefit. This has been reinforced in the latest recommendation from the U.S. Preventative Task Force (USPSTF) [25] and the U.K. National Screening Committee (UK NSC) [4].
Several recent studies aim at exploring other possible sources of BMs in addition to blood as analyte, and to proteins as BMs. For example, there are studies dealing with tumor DNA detection, also for methylation profiling, in liquid cytology samples from vagina and endocervix, including routinely collected cervical screening specimens, vaginal self-swab and tampons, and uterine lavage samples. Moreover, improvements in imaging methods are pursued to include refinements of TVS, Doppler flow, microbubble contrast-enhanced TVS, and photo-acoustic imaging, all of which allow high-resolution detection of angiogenesis with the potential to detect neovascularization in early cancers [26,27,28]. In addition, multi-omics technologies (genomics, transcriptomics, proteomics, and metabolomics) hold the possibility of discovering new informative BMs [29,30] in signature panels. Currently, important questions in this field are concerned with the type of sample source selected, the analytical technique that leads to the most valuable results, whether it is advisable to look for one specific BM type (e.g., proteins or nucleic acids) or whether to combine the results of different analytical procedures (e.g., proteins and nucleic acids in parallel).
In summary, while on one hand the state of the art in OC screening recommends against screening for fear of adverse complication in false-positive women undergoing surgery, on the other hand, personalized medicine requires an individualized approach and reliable tools for screening procedures to detect the disease at an early, curable stage, to improve patient stratification and therapeutic regimes, and to spare unnecessary, even possibly hazardous treatments and save lives and costs, as invoked by authoritative editorials [31,32].

2. MicroRNAs as Potential Disease Biomarkers

Among molecules with potential BM characteristics, miRNAs have recently emerged as powerful keys to a better understanding of molecular mechanisms controlling cancer cells and their niche. MiRNAs are small molecules (18–25 nucleotides) of single-strand, non-coding RNA (ncRNA) that are found in the transcriptome of all living beings and regulate gene expression at the transcriptional and post-transcriptional level [33] (Figure 1, lower part).
The first evidence that miRNAs may have diagnostic and therapeutic potential was presented soon after the identification of the first miRNAs in humans [34]. The study showed correlation between the loss of miR-15 and miR-16 and the occurrence of B-cell leukemia [35]. Three years later, the homozygous deletion of the gene coding for Dicer, an enzyme essential for miRNA biogenesis, showed that miRNA loss of function disrupted prenatal development of the murine embryo [36], providing the first genetic evidence of miRNAs’ vital role in development. The first indication that miRNAs may become easily accessible BMs for cancer diagnosis and prognosis came three years later when miRNAs were isolated from patient serum [37,38] and their profiling revealed specific patterns across different groups of diseases [39]. Following studies confirmed specific miRNA signatures in many types of human diseases, including different cancers [40,41,42]. MiRNA signature in blood is comparable to that of the tumor of origin [43] and specific miRNAs were associated with prostate cancer (PrC) [38], lung cancer (LC), colorectal (CRC) [44,45,46], breast cancer (BC), gastric cancer (GC) and OC [47,48,49]. Indeed, miRNA profiling revealed different patterns in different histological tumor subtypes of OC, a highly heterogeneous disease, as already mentioned [23,49,50,51,52,53,54].
Several clinical trials were initiated, based on the suggested applicability of miRNAs for cancer diagnostics and prognostics [55]. In fact, miRNAs meet most of the criteria for being ideal BMs, i.e., accessibility, specificity, and sensitivity.
Despite the lack of standardized protocols and use in current clinical practice, miRNAs hold the potential to become a routine approach in the development of personalized medicine in the future.

3. Liquid Biopsies for Detection of miRNAs in Ovarian Cancer

Tissue biopsy is the gold standard to evaluate molecular features of tumors. However, in the case of OC, tissue biopsies should be avoided because puncture can cause cancer cells dissemination into the peritoneal cavity, promoting peritoneal metastasis. On the contrary, liquid biopsy represents a safe choice, also enabling serial sampling over time [29]. Body fluids contain several types of molecules: circulating tumor cells, circulating nucleic acids, and extracellular vesicles. As miRNAs pass from tissues to blood and are stable in body fluids [56], circulating miRNAs are suitable BMs for OC detection and staging [57,58,59,60], (Figure 1, lower part).
Circulating miRNAs can be contained within extracellular vesicles (EVs), which are released by normal and tumor cells and, depending on their size, divided into three main groups: exosomes, microvesicles, and apoptotic bodies [61]. Cancer-derived exosomal miRNAs have captured the interest of many researchers for their role in promoting cancer through angiogenesis and metastasis. Cancer cells may produce more exosomes than normal cells; for this reason, exosomes could be informative regarding patients’ health [62].
However, miRNAs can also be found in the EV-free fraction, where they are complexed to RNA-binding proteins, lipids, or lipoproteins, in extracellular fluids [63].
Both types of miRNAs are resistant to RNase degradation, temperature, and pH changes [38].

4. Saliva: An Informative Analyte for miRNA Detection, with Practical Advantages

Among body fluid analytes, saliva offers some yet unexplored advantages. Saliva collection can be carried out without medical intervention and does not present risks associated with even minimally invasive procedures; the sample, properly stabilized, can be stored and shipped from even remote collection sites to high-tech testing sites with minimal costs. Saliva released by the major salivary glands consists of 99% water containing inorganic and organic species including secretion and putrefaction products, lipids, over 2400 proteins, metabolites, components of the microbiome and abundant, stable extracellular coding- and non-coding RNA species, among which are a wide variety of miRNAs [64]. Saliva represents blood very faithfully, even with possible advantages. For example, storage is simpler; saliva does not coagulate, it is stable for 24 h at room temperature and for a week at 4 °C. In addition, it may be collected at no risk several times a day to monitor therapy with repeated sampling [65]. All the above makes saliva a relatively simple, accurate, easy, safe, and economical material to be tested for clinically significant molecules.
Some of the molecules characterized in saliva are candidate BMs for cancer diagnosis, prognosis, drug monitoring, and pharmacogenetic studies, although just a few of such candidates were validated in multicenter studies, with large sample size and standardized protocols [66].
The salivary transcriptome has been entirely characterized in 2012 [67]. It includes long and small RNA species. Salivary miRNAs are more stable and discriminatory than mRNAs. They are abundant, and fit the profile of other body fluids [68,69], which makes them good BM candidates for systemic diseases. In fact, MiRNAs associated with CRC, esophageal, and pancreatic cancer (PC) were isolated from saliva [70,71], showing that profiling miRNA expression could allow for the detection of a distant neoplasia while discriminating between different cancer types [23,72]. For all these reasons, several current research efforts are focused on the detection of salivary miRNAs, although we need to better understand their biogenesis and how to recognize their tissue of origin [73].

Kits for Salivary Diagnostics

Indeed, several diagnostic kits that use saliva as the biofluid test have already been developed and are commercially available. The kits encompass different uses; SARS-CoV-2 has received emergency use authorization from the U.S. Food and Drug Administration (FDA) in the last two years, and kits for the search in oral fluids of other viruses such as HIV, HPV, HSV, or other infectious agents (e.g., Candida albicans) are also available (The ADA Science & Research Institute, LLC (ADASRI) for Oral health). Lin-Zhi International, Inc. (LZI) (https://www.lin-zhi.com/oral-fluid-eia, accessed on 20 February 2023) is a manufacturer of in vitro diagnostic reagents for oral fluid screening (in addition to urine) for the detection of drugs of abuse. Salivary miRNA diagnostic tests for ASD (autism spectrum disease) have also been validated (NIH ClinicalTrials.gov, https://www.clinicaltrials.gov/ct2/show/NCT05418023, accessed on 20 February 2023). Salimetrics, LLC (https://salimetrics.com/assay-kits/, accessed on 20 February 2023) produces salivary ELISA kits (hormones, COVID, cytochines) and Salignostics (https://www.salignostics.com/saliva-diagnostics/, accessed on 20 February 2023) produces approved salivary tests for COVID and pregnancy and is developing tests for cardiac risk, malaria, and Helicobacter pylori.

5. Salivary miRNAs in Oncological Conditions

To show the overall interest and potential of salivary miRNA in disease detection well beyond pathologies of the oral cavity [74], in this section, we provide the state of the art regarding microRNA dysregulation detected in saliva in association with systemic oncological conditions. With this objective, we reviewed articles published in the last ten years (2012 to January 2023), including case control or cohort studies for diagnostic biomarkers, review article, and meta-analysis. We did not set, as a criterion, a minimum number of patients recruited in the study. We considered only miRNAs associated to cancer types with the primary site distant from the oral cavity; therefore, studies evaluating deregulated miRNAs in patients with pharyngeal, esophageal, salivary glands, and oral cavity diseases were excluded. Similarly, studies concerning neurodegenerative diseases, microbial infections, and drug/hormone response therapy were excluded. Studies on salivary miRNAs for forensic purposes were excluded as well.
Multiple papers highlight qualitative or quantitative differences in miRNA composition between different body fluids [75,76]. At the same time, specific miRNAs are found in all body fluids analyzed. There are some inconsistencies among papers dealing with salivary miRNAs in the same oncological condition, and this can be ascribed to different reasons: the studies were performed on relatively small sample sizes, methods were often different, and subject groups were not always overlapping. Nevertheless, some miRNAs were pointed out by different authors as putative BMs for the same or for different oncological conditions [75,77,78,79].Overall, literature agrees on the promising potential of salivary miRNAs as BMs for cancer detection [80].
PC, the seventh leading cause of cancer-related deaths worldwide, is the most studied in terms of salivary miRNAs. Similar to OC, early-stage PC rarely causes any symptoms, and for this reason it remains one of the most undiagnosed and lethal malignant neoplasms [23]. Carbohydrate antigen 19-9 (CA19-9) is routinely used for prognosis and to monitor the disease [81]; however, it often fails to detect precancerous or early-stage lesions because of inadequate sensitivity and specificity. Therefore, for better prevention or treatment of PC, just as for OC, we have the urgent need to find new technologies for earlier detection. The search for miRNA biomarkers is advancing, although, to date, none has yet reached clinical applications [81]. Papers dealing with salivary miRNAs BMs for PC [75,76,77,82,83], CRC [78,84], LiC [79,85], LC [86], BC [87], GC [88,89], and PrC [90,91] are summarized in Table 1. MiRNAs were detected both as cell free or as exosomal miRNAs, both in saliva alone as well as in other parallel biofluids. Different techniques have been utilized for the discovery phase (microarrays or sequencing) and real-time quantitative PCR (QRT-PCR) was used for validation. Panels of up- or down-regulated miRNAs have been evaluated for their diagnostic ability; in some cases, sensitivity and sensibility have reached high scores (Table 1). Xie et al., 2014, profiled with a miRNome microarray 2006 mature miRNA sequences from saliva samples of 8 patients with resectable PC, and of 8 healthy subjects; they validated significant candidates with QRT-PCR on the same discovery sample set, and finally analyzed the expression level of 10 selected miRNAs on an independent cohort of 40 patients with PC, 20 with benign pancreatic tumors (BPT), and 40 healthy controls. They showed that miR-3679-5p is significantly downregulated and miR-940 significantly upregulated, in PC versus BPT, and in PC versus BPT and controls. No significant differences were observed between BPT and healthy controls [82]. Humeau et al., 2015, reported a salivary miRNA signature for PC, including miR-21, miR-23a, miR-23b, and miR-29c, that was significantly overexpressed in PC patients compared to healthy subjects. MiR-210 and let-7c were up-regulated in pancreatitis patients compared to controls; miR-216 was upregulated in PC compared to pancreatitis. The authors xenografted human-derived PC cells in athymic mice and found that miR-21 was higher in saliva from tumor-bearing mice, while the other three remained low [77]. Machida et al., 2016, analyzed the expression of miR-1246, miR-3976, miR-4306, and miR-4644 (previously reported as PC BMs in serum exosomes [83]) in salivary exosomes of pancreatobiliary tract cancer patients. MiR-1246 and miR-4644 were significantly over-expressed in salivary exosomes of pancreatobiliary tract cancer patients compared to controls [92].
The last two publications regarding salivary miRNAs in PC highlight an important discrepancy to take note of. The level of miR-1246, analyzed in serum, urine, and saliva, was significantly higher in the serum and urine of cancer patients as opposed to healthy subjects, but salivary levels did not differ significantly between the two groups [75]. Similarly, the analysis of six miRNAs (miR-21, miR-155, miR-196a, miR-200b, miR-376a, and miR-34a) in serum and saliva samples showed that, although miR-21 and miR-34a were differentially expressed in serum samples of pancreatic ductal adenocarcinoma patients, and the same miRNAs were detectable in saliva, their levels did not change significantly among cancer patients and controls [76]. These studies were performed on relatively small sample sizes and only one ethnic group was included. As already mentioned for OC studies, methods are still not adequately standardized for miRNA research in PC.
Excluding the pathologies of the oral cavity and PC, other systemic conditions were investigated for miRNA representation in saliva [74]. Sazanov et al., 2017, focused on the expression miR-21 in blood and saliva samples of patients with distal colorectal cancer (CRC) at different stages. It was found that miR-21 was higher in CRC patients than controls, with diagnostic sensitivity and specificity in saliva higher than in blood [84]. Rapado-González et al., 2019, analyzed only saliva samples of CRC and adenoma patients and of healthy subjects. Through validation phases, the authors selected five miRNAs upregulated in CRC compared to healthy controls (Table 1). Building a COX regression analysis with the clinico-pathological parameters, they found that the high level of the five miRNAs, and the concentration of CEA protein, correlated with a higher risk of progression [78]. In liver cancer (LiC), Petkevich et al., 2021 compared the exosomal with the exosomal-free fraction of blood or saliva. They included HCV-related liver cirrhosis, primary liver cancer patients, and healthy volunteers and confirmed in saliva, as already found in plasma, the differential expression of three miRNAs in liver cancer (LiC) patients compared to controls. (Table 1). The saliva exosomal fraction was found to be the best performing [79]. Mariam et al., 2022, with an RNA-seq study in saliva based on 20 hepatocellular cancer (HCC) and 19 cirrhosis patients, found that 283 salivary miRNAs were significantly downregulated in HCC. Machine learning identified a combination of 10 miRNAs as good indicators of LiC. Of interest here, mir-92b, mir-548i-2, and mir-548l were found differentially expressed both in saliva and in HCC tissue samples compared to cirrhotic liver tissue samples [85].
LC, BC, PrC, and GC are the four other oncological conditions for which salivary microRNAs have been investigated. Yang et al., 2020, profiled salivary miRNAs in four groups of lung cancer patients: 57 patients with malignant pleural effusion (MPE); 33 patients with benign pleural effusion (BPE); 50 patients with a diagnosis of malignancy without pleural effusion (MT), and 49 healthy controls (HCs). The discovery phase with microarray was performed on six salivary samples from three MPE patients and three HCs and showed 29 upregulated and 48 downregulated miRNAs in MPE compared to controls. The validation phase performed with QRT-PCR highlighted two miRNAs to be up- and down-regulated in MPE patients respectively, (Table 1) [86]. Koopaie et al., 2021, analyzed with QRT-PCR, 41 BC patients and 39 healthy subjects. MiR-21 was significantly upregulated in BC samples relative to controls, but not among disease stages [87]. Li F. et al., 2018, analyzed salivary samples of 10 early-stage GC patients and 10 non-GC controls, with TaqMan Human miRNA array. Four miRNAs (Table 1) made the best BM panel [88].
Nanographene Oxide (nGO), a novel method to detect circulating oncomiRs, was used by Hizir et al. in PrC. The authors analyzed exogenous miR-21 and miR-141 in saliva and other body fluids using the two-dimensional surface of nGO. The two miRNAs reflected the disease stage: miR-141 was higher in advanced PrC, while miR-21 was higher in early-stage cancer. The copies of circulating miRNAs in patient specimens are lower than the concentration used in this study, thus, the sensitivity of the assay needs to be improved. However, the method is fast and easy, can be performed with a spectrofluorometer, and potentially allows the reveal of PrC patients at different stages with a non-invasive approach [90].
Table 1. Salivary miRNAs in oncological conditions. PC: pancreatic cancer, BPT: benign pancreatic tumor, PBTC: pancreatobiliary tract cancer, HS: Healthy subjects, CRC: colorectal cancer, LiC: liver cancer, HCC: epatocellular carcinoma, MPE: malignant pleural effusion, BC: breast cancer, GC: gastric cancer. Each row provides information regarding the citation in parenthesis at the far left end of the row.
Table 1. Salivary miRNAs in oncological conditions. PC: pancreatic cancer, BPT: benign pancreatic tumor, PBTC: pancreatobiliary tract cancer, HS: Healthy subjects, CRC: colorectal cancer, LiC: liver cancer, HCC: epatocellular carcinoma, MPE: malignant pleural effusion, BC: breast cancer, GC: gastric cancer. Each row provides information regarding the citation in parenthesis at the far left end of the row.
ReferenceSource (Analytes)miRNAsCancer TypeN° of Subjects
(Cancer vs Control)
StatusSensitivitySpecificityAUC
[82]cell freemiR-3679-5pPC40 vs 4072.5%70.0%0.750
miR-940
[77]cell freemiR-21PC7 vs 471.4%100%-
miR-23a85.7%100%-
miR-23b85.7%100%-
miR-29c57.0%100%-
[92]exosomesmiR-1246PBTC14 vs 1383.3%92.3%0.833
miR-4644
[75]cell freemiR-1246PC41 vs 30-91.0%26.7%0.480
[84]cell freemiR-21CRC34 vs 3497.0%91.0%-
[78]cell freemiR-186-5pCRC51 vs 3772.0%66.7%0.754
miR-29a-3p
miR-29c-3p
miR-766-3p
miR-491-5p
[79]exosomes
cell free
miR-21-5pLiC24 vs 2166.0%78.0%0.770
miR-122-5p
miR-221-3p
[85]cell freemir-1262 miR-1262 mir-216a
mir-484
mir-30d
miR-216a-5p miR-30d-5p miR-484
mir-10401
miR-454-3p
HCC20 vs 19 Cirrhosis-83.0%68.0%0.780
[86]cell freemiR-4484MPE57 vs 4982.2%74.1%0.802
miR-3663-3p
[87]cell freemiR-21BC41 vs 39100%100%1.0
[88]cell freemiR-140-5pGC100 vs 100--0.700
miR-374a--0.650
miR-454--0.630
miR-15b--0.650

6. Candidate Salivary Biomarkers of Ovarian Cancer Other Than miRNAs

The urgency of finding reliable BMs for early OC detection has prompted many researchers to focus on saliva as an advantageous biofluid in terms of collection, procedure, and cost-effectiveness [93]. Several studies compared saliva with other solid or liquid biopsies for the sake of methodological cross validation, to evaluate the use of saliva as the analyte of choice.
Lee et al., 2012, explored mRNAs expression in saliva of OC patients. The study design was based on: (i) an initial profiling with microarray assay; (ii) validation with QRT-PCR of the preliminary findings obtained on a discovery cohort and on an additional, independent cohort; and (iii) logistic regression analysis of QRT-PCR results. AGPAT1, B2M, IER3, IL1B, and BASP1 mRNAs were identified as those providing the highest discriminatory power for OC diagnosis. The findings were hindered by the small sample size and the lack of benign tumor control group [93]. Yang et al., 2021, evaluated the same salivary mRNAs expression profile in a Chinese population of 140 OC patients and 140 control individuals. Concomitantly, the CEA protein concentration was measured in blood. With machine-learning methods, the authors identified a novel panel of disease predictors. The validation phase on an independent cohort of 60 OC patients and 60 healthy subjects resulted in 85.0% of sensitivity and 88.3% of specificity, while measuring CEA concentration in blood, and BASP1 and IER3 levels in saliva. The mRNAs of interest were preselected from Lee et al. [93] who collected samples from Korean rather than Chinese women [94]. Li et al., 2018, assessed the potential use of immediate early response gene X-1 (IEX-1) transcript as OC BM. In OC, IEX-1 is down-regulated, thereby acting as a tumor suppressor. Its expression was quantified in saliva and blood samples with RT-qPCR. Three groups of patients were involved in the study: 26 patients of epithelial ovarian cancer (EOC), 37 women with benign ovarian tumors (BOT), and 55 controls. IEX-1 expression in blood and saliva was lower in EOC compared to BOT and controls. No significant differences were found in IEX-1 expression between BOT and controls. Diagnostic efficacy of IEX-1 was high in blood and medium in saliva but, of interest here, the results showed that both biofluids are suitable for BM detection. Saliva might have a higher specificity but lower sensitivity in discriminating EOC from BOT [95].
Mass spectrometry (MS) proved to be a powerful method to analyze fluids without extensive chemical preparation of samples. Tajmul et al., 2018, applied it in search for an OC signature in salivary proteins. On a proteome-wide scale, they analyzed quantitative differences in saliva using two-dimensional difference gel electrophoresis (2D-DIGE) combined with matrix-assisted laser desorption/ionization time of flight (MALDI-TOF) MS. They found 44 differentially expressed proteins, among which Lipocalin-2, indoleamine-2, 3-dioxygenase1 (IDO1), and S100A8 had the highest statistical scores. The three proteins, known to be involved in pathways of cancer progression and metastasis, were validated with Western blot and Elisa. Finally, salivary proteomics was combined with tissue-based transcriptomics. Immunohistochemistry, microarray, and QRT-PCR confirmed the up-regulation of these three candidate proteins in ovarian tissue. This signature holds a good diagnostic potential even though it would require validation in a larger population set [96].
Zermeño-Nava et al., 2018, applied surface-enhanced Raman spectroscopy (SERS) to measure levels of sialic acid (SA) in saliva samples collected from 37 women with benign ovarian adnexal masses (observed by TVS), and from 15 OC patients. Salivary SA levels were statistically different among the two groups, with a threshold corresponding to SA concentration > 15.5 mg/dL. However, SA cannot be considered a specific BM for OC: it is useful in the clinical scenario for diagnosing patients with adnexal mass, but not for screening the general female population [97].
Bel’skaya et al., 2019, used infrared (IR) Fourier spectroscopy to assess changes of the lipid profile in saliva in endometrial and OC patients. Three groups of women were involved: 51 ovarian and endometrial cancer patients, 26 positive controls of non-ovarian or endometrial cancer patients, and 30 healthy women. The authors identified a significantly reduced intensity of the absorption bands 2923/2957 cm−1 in OC and endometrial cancer patients [98].
Despite the methodological limitations due to the small size of patient groups, these works suggest promising directions for developing new diagnostic methods. The idea behind them all was to reduce unnecessary biopsies using non-invasive, saliva-based protocols for the early detection of OC [93].
Currently, studies exploring saliva for detection, diagnosis, or stratification of OC are quite heterogeneous. Transcriptomics and proteomics have been applied for an unbiased approach; however, some degree of partiality was still present in the selection of study participants. In addition, often the study design missed the positive control group (benign tumor or other cancer types). Further, discrepancies exist in the methods for processing samples (e.g., supernatant of saliva or whole saliva), all of which introduces some degree of fragmentation that hampers our comprehension of results. Finally, combining the analysis of saliva with other liquid or solid biopsies is certainly advantageous to better understand the disease; however, they are not so relevant in the search for effective BMs, regardless of their biological role.

7. Candidate miRNA Biomarkers of Ovarian Cancer in Other Biofluids

A multi-marker approach could improve the diagnosis of a heterogeneous disease such as OC, and microRNA are particularly suitable to build a multi-marker model. By improving methods and standardization procedures, miRNAs could become a routine tool to profile patients and select therapeutic interventions [99].
MiRNAs were characterized in biofluids of OC patients including blood (serum or plasma), urine, ascites, and utero-tubal lavage [100]. Currently, blood-derived biofluids still remain the gold standard for liquid biopsies; they have been extensively investigated in OC and in other oncological conditions as well. Here, we provide a comparison of the most representative results from a blood-based analysis and refer to other recent review articles for a more detailed knowledge of the literature [101,102,103].

7.1. Blood-Derived Biofluids

Several articles and systematic reviews have been written about OC and microRNAs in blood-derived biofluids since the diagnostic relevance of microRNAs detected in serum, plasma, and whole blood samples was reported by Resknick and colleagues for the first time in 2008 [104]. They profiled CF circulating miRNAs from serum samples of OC patients and healthy subjects. In the same year, Taylor and Taylor [43] focused on miRNAs content in OC-derived exosomes compared to benign controls in order to validate their potential as diagnostic BMs. Since then, several research groups have tried to find the most representative miRNA signature for pathogenesis of OC [100].
Hulstaert et al. in 2021, in a meta-analysis of the literature to identify candidate RNA BMs in body fluids for early diagnosis of OC, highlighted seventy-five RNAs candidates. Only ten of them were then considered good candidates because of their differential expression in at least two studies. Five miRNAs were up-regulated in OC patients compared to healthy subjects (miR-21, the miR-200 family, miR-205, miR-10a, and miR-346), and the other five (miR-122, miR-193a, miR-223, miR-126, and miR-106b) were down-regulated. Heterogeneity among all the reviewed publications was the main limitation for the authors’ work, especially concerning the methodologies [102].
Occasionally, some inconsistencies are found in the literature; they could result from relatively small size of samples, different ethnic groups recruited, or from the type of control groups (healthy subjects or benign disease patients) [103]. Additionally, pre-analytical parameters for sample preparation (blood volume, centrifugation speed, duration) can deeply affect the recovery of miRNAs from blood samples [102]. For example, plasma and serum undergo two distinct collection processes and they are differently affected by hemolysis. In plasma samples, coagulation is prevented by adding EDTA and, through centrifugation at high speeds for a long time, platelet-depleted plasma can be easily obtained. In turn, in serum samples, clot-activation causes the release of different biological molecules (also miRNAs) from platelets [105]. Even if, for the above reason, plasma should be considered the sample of choice to detect circulating CF-miRNAs, hemolysis can in both cases affect the accuracy of serum or plasma-based tests. Shah et al., 2016, evidenced the need of quantifying hemolysis as an essential step before measuring circulating miRNAs as potential diagnostic BMs [106].
Additionally, experimental approaches differ considerably among different studies: exosomal miRNAs are usually processed with ultracentrifugation methods or with specific isolation kits. Different methods could be also used for RNA isolation (extraction solutions with or without passage in columns) with varying performance. Then, among high-throughput methods, miRNAs can be profiled with microarrays or next-generation sequencing (NGS), followed by validation with QRT-PCR [100].
The initial screening analyses and criteria of miRNAs selection for subsequent validation differ among the reviewed articles. Three key examples follow. Kim et al. studied the expression level of seven exosomal miRNAs in the serum of OC patients. The seven candidates had been previously identified with high-throughput profiling studies as the most differentially expressed in OC tissues [107].
Elias et al. applied a neural network analysis to RNA-sequencing data from 179 serum samples. In an innovative way, they analyzed miRNA-seq data with a specific algorithm for discriminating OC patients from the other groups involved in the study [108].
Kumar et al. performed a miRNA screening analyzing the methylation status of genomic DNA. The differentially methylated regions of miRNA gene promoters led to identifying three hypomethylated regions through QRT-PCR on tissues and matched serum samples [109].
Another considerable constraint is represented by the use of endogenous controls for miRNA normalization, because currently the selection of reference genes has not yet been standardized. The most used spike-in control is cel-mir-39 which is added into the lysate, and its quantification used to evaluate the success of the isolation procedure. However, endogenous controls can also be used for circulating miRNAs, for example, small nuclear and nucleolar RNA such as U6, RNU44, RNU43, and RNU48. Several authors proposed other internal controls that were more stable in the biofluids of their interest, making comparisons across different articles even more difficult [105].
Another level of complexity is due to the dual possibility of isolating miRNAs as CF or as extracellular vesicle (EV)-contained molecules. In fact, EVs are released from many different cell types, not only cancer cells. Therefore, different extraction and detection methods might influence the efficacy of discriminating among normal- or cancer-derived EVs [110], with issues related to standard BM concentrations and small sample sizes [111]. In addition, most of the published studies on exosomal miRNAs do not use high-throughput discovery approaches as a first step. Instead, they mostly select candidate exosomal miRNAs from the literature and then proceed with validation or functional assays [105].

7.2. Other Biofluids

This review’s authors selected candidates miRNAs characterized in biofluids other than blood for OC liquid biopsy.
Urine. Urinary miRNAs have been mostly explored in urological and gynecological diseases. As an advantage, urine is generated near to the OC site of origin and its collection is non-invasive [112]. Detectable miRNA level in urine is lower compared to blood. Probably most circulating miRNAs are reabsorbed by kidneys through a not yet clearly understood mechanism, or they are destroyed in the urinary tract by high levels of RNases [112]. In addition, some authors stressed that the supernatant fraction, but not the exosomal fraction, exhibit a diagnostic potential. Currently, there is not a consensus yet around this topic; it will be critical to define whether the exosomal or the supernatant fraction perform better to yield a representative signature of urinary miRNA [113,114].
Záveský et al., 2015, worked on miRNA in urine samples of pre/post-surgery endometrial and EOC patients. Analysis of the urine supernatant fraction show that miRNAs were differently expressed between pathological and healthy samples. On the contrary, while working with the exosomal RNAs, the authors did not find any difference for any miRNA under analysis [113,114].
Zhou et al., 2015, profiled miRNAs in the urine of three groups of subjects: ovarian serous adenocarcinoma patients (OSA), 26 patients with benign gynecological disease, and 30 healthy controls. They revealed a higher miR-30a-5p expression in ovarian serous adenocarcinoma patients (OSA) urines versus both controls and benign ovarian specimens. Further, comparing urine from OSA, GC, CRC, and healthy controls, they found miR-30a-5p upregulation in OSA and lower expression in the other cancer types, suggesting a diagnostic BM value for miR-30a-5p in OC. Moreover, the authors found miR-30a-5p upregulation in OSA tissue samples and cancer cell lines. Surgical removal of OSA affects urinary level of miR-30a-5p, further reinforcing the hypothesis of its ovarian origin [115].
Berner et al., 2022, detected differentially expressed miRNAs in urine, in OC cell culture, and cell culture supernatant. The authors confirmed the small total amount of miRNA that could be found in urine as one crucial issue for miRNA end-point usability [116]. Záveský et al., 2019, profiled the expression level of eight selected miRNAs, comparing tissue, ascites, and urine samples. Their work represented well the difficulties provided by urine. Some miRNAs were down-regulated in both ascitic fluid and tumor tissues in OC patients, while extracellular urine-derived miRNAs were not differentially expressed [117].
Ascites. Despite the fact that most of the studies are still based on RNA isolation from tissue, cell lines, or blood-derived biofluids, ascites is mentioned frequently in the scientific literature concerning OC, especially if compared with urine. In fact, more than other cancers, OC mostly disseminates in the peritoneal cavity, also because of the primary tumor site. The ascites, intraperitoneal liquid accumulation, is a symptom found mostly in OC patients at advanced stages, but sometimes it also occurs early on. It contains malignant tumor cells together with lymphocytes, mesothelial cells, macrophages, fibroblasts, and other cell types, that create a tumor microenvironment of soluble growth factors and cytokines. Tissue and ascites comparison for miRNAs expression level showed coherent results, confirming the close relation between the solid tumor and its dissemination in ascites [117]. Yet, the lack of systematic validationdoes not allow miRNA profiling from ascites as a novel diagnostic, prognostic, and predictive method. There are several problems related to the use of peritoneal effusions: (1) ascites (or effusion in general) is not always found in OC patients, especially at early stage; (2) effusion collection is an invasive procedure; (3) the components found in ascites are not evenly distributed, so variability between samples increases, making reproducibility even more difficult [118]; (4) there is still a gap in standardizing methods for sampling, separating extracellular fraction and isolating RNA; and (5) different studies employ different control groups (benign tumor lavages, plasma, or tissue of healthy controls) [119]. Ascites is considered an indicator of poor prognosis because it associates with tumor progression and chemoresistance [119].
Many studies have investigated miRNA expression level in ascites for the diagnosis of OC. Záveský et al., 2019, performed the first large-scale expression profiling of 754 miRNAs, working on ascitic fluid extracellular fraction (or ascites derived lavages) of high-grade serous OC patients and control plasma samples. After a screening phase with TaqMan array, seven miRNAs were validated with QRT-PCR. MiR-1290 and members of miR-200 family were found overexpressed in ascites compared to control plasma [119]. In a follow-up study, the same authors collected tissue samples and ascites, focusing on eight selected candidates (miR-203a-3p, miR-204-5p, miR-451a, miR-185-5p, miR-135b-5p, miR-182-5p, miR-200b-3p, and miR-1290) and validated six of them [117]. Yammamoto et al., 2018, examined miRNAs expression in EV from OC ascites, focusing on selected candidates. Six miRNAs were found significantly decreased in OC ascites compared to benign peritoneal fluids [120].
Vaksman et al., 2014, worked on exosomal miRNAs from effusion supernatants of OC patients. They started with a TaqMan array-based screening of miRNA from pooled exosomes derived from the effusion supernatants of OC patients, of tumor cells (positive control group), and of reactive mesothelium (negative control). 99 miRNAs showed higher expression in exosomes from OC effusion supernatants. MiR-21, miR-23a, and miR-29a were associated with poor prognosis. In vitro and in vivo assays were performed with selected miRNAs [121]. Similarly, Mitra and colleagues, 2021, characterized EVs from the ascites of OC patients to understand how EVs miRNA can influence growth, migration, and invasion in vitro and ex vivo assays. EVs from ascitic supernatants of patients with high-grade serous OC (HGSOC) or benign disease were profiled and then validated with QRT-PCR [122].
Wang and colleagues, 2022, identified an EV miRNA signature based on eight miRNAs (miR-1246, miR-1290, miR-483, miR-429, miR-34b-3p, miR-34c-5p, miR-145-5p, and miR-449a), analyzing plasma and ascites from malignant, high-grade serous OC patients and peritoneal fluids from benign gynecologic diseases patients. The authors showed how EV from malignant ascites increase the aggressive phenotypes of OC cells in 2D and 3D models. Gain and loss of function assays for the upregulated miR-1246 and miR-1290 showed their capacity of improving cell migration and invasiveness. Authors underlined the difficulties in obtaining ascites, and concomitantly to collect both plasma and ascites from the same patient [123].
Uterine cavity biofluids. The origin of high-grade serous carcinoma is still unclear. Since it could arise from the epithelium of the fallopian tube fimbriae, some authors explored the diagnostic value of utero-tubal lavage. This collection method allows to sample cells, or their secreted biological products, on the fimbriae. It is a safe and minimally invasive procedure: a saline solution is flushed in the uterine cavity and fallopian tube, then it is aspirated back from the gynecologic tract. Hulstaert et al., 2022, for the first time, sequenced the transcriptome of utero-tubal lavage and found upregulation of 300 mRNAs, mainly involved in cycle regulation and proliferation. Furthermore, they found 41 miRNAs more expressed in OC patients compared to healthy subjects. Five microRNAs were found to be associated to OC pathogenesis [124]. Skryabin et al., 2022, conducted the first exosomal miRNA profiling by small RNA-seq on uterine aspirates (UA) from EOC patients and healthy donors. The results showed significant differences for more than 57 miRNAs and three of them have been validated and confirmed for their up-/down- regulation in OC including miRNAs previously found associated with OC [125].
Proximal liquid biopsy and miRNAs detection remain insufficiently explored, although promising as a diagnostic method because they could be performed during routine gynecological visits. Moreover, the close relation with the site of origin of OC suggests the possibility to detect the disease when it is still at the initial stage. However, this is not applicable as a large-scale and rapid screening method because it is relatively time-consuming and requires specialized medical support.
To summarize the most relevant information, Table 2 reports an excursus on candidate miRNA BMs of OC in body fluids other than blood.

8. Conclusion and Future Perspectives

Current literature shows that saliva released by the major salivary glands contains various systemic BMs, thereby accurately reflecting pathophysiological conditions in humans. For this reason, salivary diagnostics were indicated by recent, authoritative editorials as a major resource for future diagnostics and for the low-cost screening procedures of systemic diseases including cancer [126].
MiRNAs present several advantages as possible BMs, over other molecules, for their accessibility, strong stability, and resistance to degradation in body fluids. More and more articles describe specific miRNA signatures in several tumor types [74]. Therefore, measuring quickly and accurately small variations of miRNAs concentration in body fluids may offer diagnostic and prognostic opportunities. International efforts aimed at a deeper understanding of circulating miRNA function and standardization of the miRNA analysis field (e.g., Extracellular RNA Communication Consortium, ERCC; https://exrna.org/ (accessed on 15 September 2022), or CANCER-ID), (www.cancerid.eu, accessed on 15 September 2022) [reviewed in Valihracha et al., 2019 [127], will help in providing more solid ground for biomedical exploitation of miRNAs.
Up to now, circulating miRNAs for OC detection have been mainly investigated in blood, a biofluid more complex than saliva in terms of collection, processing, and composition, where the high number of proteins represent a critical methodological step [128], as well as in other biofluids (Table 2). Salivary miRNAs, for their hitherto mentioned characteristics, might offer better performances compared to other fluids’ BMs [80].
In conclusion, the current lack of salivary tests for OC, whether based on miRNAs or other BMs, is a challenge to win so as to spare lives from OC and limit social costs. Current research efforts are ongoing to identify a pattern of salivary miRNAs suitable as BMs for OC diagnostics (D. Angeloni, personal communication). Auspicial results would make a possible alternative to the current options for excluding OC that still rely on gynecologist check-up, TVS, and blood test. Overall, this procedure could be expensive and difficult to manage, especially in remote areas or in disadvantaged socio-economic conditions.
For such a low-prevalence cancer type, a screening test would require a sensitivity for asimptomatic women > 75% and a specificity > 99.6% [129]. The currently used biomarkers for the blood test of OC are CA125 and HE4. They have been extensively studied and we refer to Dochez et al., 2019, for a summary of the sensitivity and specificity of the two protein biomarkers [130]. However, a pattern of good candidate miRNA BMs should reach at least the above suggested values.
With regard to the technological approach, the current on-chip technologies combined with photonic biosensors make it possible to envisage a strategy based on bringing together the molecular probes for detecting patterns of BMs within diagnostic devices that could speed up timing and reduce the costs of screening pertinent sections of the population to decrease mortality from OC [131]. Although such devices do not exist for clinical use yet, several proofs of concept exist, both for proteins [132,133] and RNA [134]. With further effort, one could imagine that combining the patterns of different BM types (e.g protein and RNA) could provide the screening robustness that single molecular types were not able to provide so far.

Author Contributions

Conceptualization, D.A.; methodology, D.A., M.R. and F.S.; resources, D.A.; writing—original draft preparation, M.R. and F.S.; writing—review and editing, D.A., M.R. and F.S.; supervision, D.A.; project administration, D.A.; funding acquisition, D.A. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Italian Ministry of University and Research under the grant “Fondo per la promozione e lo sviluppo delle politiche del Programma nazionale per la ricerca (PNR) 2021”, project title: Samarcanda, to D.A. The APC was funded under the same grant. M.R. was supported by a graduate fellowship from Scuola Sant’Anna.

Institutional Review Board Statement

Not applicable, this article reviews existing literature.

Informed Consent Statement

Not applicable, this review article does not include new human studies.

Data Availability Statement

This review article does not include original data. All statements are properly referred to citations.

Acknowledgments

The Authors thank Giulia Robotti for preparing the figures, and Ivana Barravecchia for critical reading of the manuscript. We acknowledge Biorender (https://www.biorender.com/) as a source of three icons used for Figure 1, which for the rest is an original composition. The citations of biotechnological companies and products do not constitute any type of endorsement.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Sung, H.; Ferlay, J.; Siegel, R.L.; Laversanne, M.; Soerjomataram, I.; Jemal, A.; Bray, F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J. Clin. 2021, 71, 209–249. [Google Scholar] [CrossRef]
  2. Reid, B.M.; Permuth, J.B.; Sellers, T.A. Epidemiology of Ovarian Cancer: A Review. Cancer Biol. Med. 2017, 14, 9–32. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  3. Siegel, R.L.; Miller, K.D.; Jemal, A. Cancer Statistics, 2018. CA Cancer J. Clin. 2018, 68, 7–30. [Google Scholar] [CrossRef]
  4. Jacobs, I.J.; Menon, U.; Ryan, A.; Gentry-Maharaj, A.; Burnell, M.; Kalsi, J.K.; Amso, N.N.; Apostolidou, S.; Benjamin, E.; Cruickshank, D.; et al. Ovarian Cancer Screening and Mortality in the UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS): A Randomised Controlled Trial. Lancet 2016, 387, 945–956. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  5. Kossaï, M.; Leary, A.; Scoazec, J.Y.; Genestie, C. Ovarian Cancer: A Heterogeneous Disease. Pathobiology 2018, 85, 41–49. [Google Scholar] [CrossRef]
  6. Berek, J.S.; Crum, C.; Friedlander, M. Cancer of the Ovary, Fallopian Tube, and Peritoneum. Int. J. Gynecol. Obstet. 2012, 119, S118–S129. [Google Scholar] [CrossRef]
  7. Buys, S.S.; Partridge, E.; Black, A.; Johnson, C.C.; Lamerato, L.; Isaacs, C.; Reding, D.J.; Greenlee, R.T.; Yokochi, L.A.; Kessel, B.; et al. Effect of Screening on Ovarian Cancer Mortality: The Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Randomized Controlled Trial. JAMA J. Am. Med. Assoc. 2011, 305, 2295–2302. [Google Scholar] [CrossRef] [Green Version]
  8. Zhang, Z.; Chan, D.W. The Road from Discovery to Clinical Diagnostics: Lessons Learned from the First FDA-Cleared In Vitro Diagnostic Multivariate Index Assay of Proteomic Biomarkers. Cancer Epidemiol. Biomark. Prev. 2010, 19, 2995–2999. [Google Scholar] [CrossRef] [Green Version]
  9. Lokich, E.; Palisoul, M.; Romano, N.; Craig Miller, M.; Robison, K.; Stuckey, A.; Disilvestro, P.; Mathews, C.; Granai, C.O.; Lambert-Messerlian, G.; et al. Assessing the Risk of Ovarian Malignancy Algorithm for the Conservative Management of Women with a Pelvic Mass. Gynecol. Oncol. 2015, 139, 248–252. [Google Scholar] [CrossRef] [Green Version]
  10. Gold, P.; Freedman, S.O. Demonstration of Tumor-Specific Antigens in Human Colonic Carcinomata By Immunological Tolerance and Absorption Techniques. J. Exp. Med. 1965, 121, 439–462. [Google Scholar] [CrossRef] [Green Version]
  11. Lee, J.M.; Kohn, E.C. Proteomics as a Guiding Tool for More Effective Personalized Therapy. In Proceedings of the Annals of Oncology, Milan, Italy, 8–12 October 2010; Volume 21. [Google Scholar]
  12. Mor, G.; Visintin, I.; Lai, Y.; Zhao, H.; Schwartz, P.; Rutherford, T.; Yue, L.; Bray-Ward, P.; Ward, D.C. Serum Protein Markers for Early Detection of Ovarian Cancer. Proc. Natl. Acad. Sci. USA 2005, 102, 7677–7682. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  13. van Gorp, T.; Cadron, I.; Despierre, E.; Daemen, A.; Leunen, K.; Amant, F.; Timmerman, D.; de Moor, B.; Vergote, I. HE4 and CA125 as a Diagnostic Test in Ovarian Cancer: Prospective Validation of the Risk of Ovarian Malignancy Algorithm. Br. J. Cancer 2011, 104, 863–870. [Google Scholar] [CrossRef] [Green Version]
  14. Li, F.; Tie, R.; Chang, K.; Wang, F.; Deng, S.; Lu, W.; Yu, L.; Chen, M. Does Risk for Ovarian Malignancy Algorithm Excel Human Epididymis Protein 4 and CA125 in Predicting Epithelial Ovarian Cancer: A Meta-Analysis. BMC Cancer 2012, 12, 258. [Google Scholar] [CrossRef] [Green Version]
  15. Fung, E.T. A Recipe for Proteomics Diagnostic Test Development: The OVA1 Test, from Biomarker Discovery to FDA Clearance. Clin. Chem. 2010, 56, 327–329. [Google Scholar] [CrossRef]
  16. Zhang, M.; Cheng, S.; Jin, Y.; Zhao, Y.; Wang, Y. Roles of CA125 in Diagnosis, Prediction, and Oncogenesis of Ovarian Cancer. Biochim. Biophys. Acta Rev. Cancer 2021, 1875, 188503. [Google Scholar] [CrossRef]
  17. Kamal, R.; Hamed, S.; Mansour, S.; Mounir, Y.; Sallam, S.A. Ovarian Cancer Screening-Ultrasound; Impact on Ovarian Cancer Mortality. Br. J. Radiol. 2018, 91, 20170571. [Google Scholar] [CrossRef]
  18. Elorriaga, M.Á.; Neyro, J.L.; Mieza, J.; Cristóbal, I.; Llueca, A. Biomarkers in Ovarian Pathology: From Screening to Diagnosis. Review of the Literature. J. Pers. Med. 2021, 11, 1115. [Google Scholar] [CrossRef] [PubMed]
  19. Moore, R.G.; Blackman, A.; Miller, M.C.; Robison, K.; DiSilvestro, P.A.; Eklund, E.E.; Strongin, R.; Messerlian, G. Multiple Biomarker Algorithms to Predict Epithelial Ovarian Cancer in Women with a Pelvic Mass: Can Additional Makers Improve Performance? Gynecol. Oncol. 2019, 154, 150–155. [Google Scholar] [CrossRef] [PubMed]
  20. Russell, M.R.; Graham, C.; D’Amato, A.; Gentry-Maharaj, A.; Ryan, A.; Kalsi, J.K.; Whetton, A.D.; Menon, U.; Jacobs, I.; Graham, R.L.J. Diagnosis of Epithelial Ovarian Cancer Using a Combined Protein Biomarker Panel. Br. J. Cancer 2019, 121, 483–489. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  21. Hays, J.L.; Kim, G.; Giuroiu, I.; Kohn, E.C. Proteomics and Ovarian Cancer: Integrating Proteomics Information into Clinical Care. J. Proteom. 2010, 73, 1864–1872. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  22. Aktas, B.; Kasimir-Bauer, S.; Wimberger, P. Utility of Mesothelin, L1CAM and Afamin as Biomarkers in Primary Ovarian Cancer. Anticancer Res. 2013, 33, 329–336. [Google Scholar] [PubMed]
  23. Setti, G.; Pezzi, M.E.; Viani, M.V.; Pertinhez, T.A.; Cassi, D.; Magnoni, C.; Bellini, P.; Musolino, A.; Vescovi, P.; Meleti, M. Salivary MicroRNA for Diagnosis of Cancer and Systemic Diseases: A Systematic Review. Int. J. Mol. Sci. 2020, 21, 907. [Google Scholar] [CrossRef] [Green Version]
  24. Ueland, F. A Perspective on Ovarian Cancer Biomarkers: Past, Present and Yet-To-Come. Diagnostics 2017, 7, 14. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  25. Grossman, D.C.; Curry, S.J.; Owens, D.K.; Barry, M.J.; Davidson, K.W.; Doubeni, C.A.; Epling, J.W.; Kemper, A.R.; Krist, A.H.; Kurth, A.E.; et al. Screening for Ovarian Cancer US Preventive Services Task Force Recommendation Statement. JAMA J. Am. Med. Assoc. 2018, 319, 588–594. [Google Scholar]
  26. González-Guerrero, A.B.; Maldonado, J.; Dante, S.; Grajales, D.; Lechuga, L.M. Direct and Label-Free Detection of the Human Growth Hormone in Urine by an Ultrasensitive Bimodal Waveguide Biosensor. J. Biophotonics 2017, 10, 61–67. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  27. Jiu, L.; Hogervorst, M.A.; Vreman, R.A.; Mantel-Teeuwisse, A.K.; Goettsch, W.G. Understanding Innovation of Health Technology Assessment Methods: The IHTAM Framework. Int. J. Technol. Assess. Health Care 2022, 38, e16. [Google Scholar] [CrossRef]
  28. Barrett, J.E.; Jones, A.; Evans, I.; Reisel, D.; Herzog, C.; Chindera, K.; Kristiansen, M.; Leavy, O.C.; Manchanda, R.; Bjørge, L.; et al. The DNA Methylome of Cervical Cells Can Predict the Presence of Ovarian Cancer. Nat. Commun. 2022, 13, 448. [Google Scholar] [CrossRef]
  29. Xiao, Y.; Bi, M.; Guo, H.; Li, M. Multi-Omics Approaches for Biomarker Discovery in Early Ovarian Cancer Diagnosis-NC-ND License. EBioMedicine 2022, 79, 104001. [Google Scholar] [CrossRef]
  30. Gahlawat, A.W.; Witte, T.; Haarhuis, L.; Schott, S. A Novel Circulating MiRNA Panel for Non-Invasive Ovarian Cancer Diagnosis and Prognosis. Br. J. Cancer 2022, 127, 1550–1556. [Google Scholar] [CrossRef]
  31. Drescher, C.W.; Anderson, G.L. The yet Unrealized Promise of Ovarian Cancer Screening. JAMA Oncol. 2018, 4, 456–457. [Google Scholar] [CrossRef] [Green Version]
  32. Longo, D.L. Personalized Medicine for Primary Treatment of Serous Ovarian Cancer. N. Engl. J. Med. 2019, 381, 2471–2474. [Google Scholar] [CrossRef]
  33. Ying, S.Y.; Chang, D.C.; Lin, S.L. The MicroRNA (MiRNA): Overview of the RNA Genes That Modulate Gene Function. Mol. Biotechnol. 2008, 38, 257–268. [Google Scholar] [CrossRef] [PubMed]
  34. Pasquinelli, A.E.; Reinhart, B.J.; Slack, F.; Martindale, M.Q.; Kuroda, M.I.; Maller, B.; Hayward, D.C.; Ball, E.E.; Degnan, B.; Müller, P.; et al. Degnan Bernard Conservation of the Sequence and Temporal Expression of Let-7 Heterochronic Regulatory RNA. Nature 2000, 408, 86–89. [Google Scholar] [CrossRef] [PubMed]
  35. Calin, G.A.; Dumitru, C.D.; Shimizu, M.; Bichi, R.; Zupo, S.; Noch, E.; Aldler, H.; Rattan, S.; Keating, M.; Rai, K.; et al. Frequent Deletions and Down-Regulation of Micro-RNA Genes MiR15 and MiR16 at 13q14 in Chronic Lymphocytic Leukemia. Proc. Natl. Acad. Sci. USA 2002, 99, 15524–15529. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  36. Yang, W.J.; Yang, D.D.; Na, S.; Sandusky, G.E.; Zhang, Q.; Zhao, G. Dicer Is Required for Embryonic Angiogenesis during Mouse Development. J. Biol. Chem. 2005, 280, 9330–9335. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  37. Lawrie, C.H.; Gal, S.; Dunlop, H.M.; Pushkaran, B.; Liggins, A.P.; Pulford, K.; Banham, A.H.; Pezzella, F.; Boultwood, J.; Wainscoat, J.S.; et al. Detection of Elevated Levels of Tumour-Associated MicroRNAs in Serum of Patients with Diffuse Large B-Cell Lymphoma. Br. J. Haematol. 2008, 141, 672–675. [Google Scholar] [CrossRef]
  38. Mitchell, P.S.; Parkin, R.K.; Kroh, E.M.; Fritz, B.R.; Wyman, S.K.; Pogosova-Agadjanyan, E.L.; Peterson, A.; Noteboom, J.; O’briant, K.C.; Allen, A.; et al. Circulating MicroRNAs as Stable Blood-Based Markers for Cancer Detection. Proc. Natl. Acad. Sci. USA 2008, 105, 10513–10518. [Google Scholar] [CrossRef] [Green Version]
  39. Chen, X.; Ba, Y.; Ma, L.; Cai, X.; Yin, Y.; Wang, K.; Guo, J.; Zhang, Y.; Chen, J.; Guo, X.; et al. Characterization of MicroRNAs in Serum: A Novel Class of Biomarkers for Diagnosis of Cancer and Other Diseases. Cell Res. 2008, 18, 997–1006. [Google Scholar] [CrossRef] [Green Version]
  40. Larrea, E.; Sole, C.; Manterola, L.; Goicoechea, I.; Armesto, M.; Arestin, M.; Caffarel, M.M.; Araujo, A.M.; Araiz, M.; Fernandez-Mercado, M.; et al. New Concepts in Cancer Biomarkers: Circulating MiRNAs in Liquid Biopsies. Int. J. Mol. Sci. 2016, 17, 627. [Google Scholar] [CrossRef] [Green Version]
  41. Matsuzaki, J.; Ochiya, T. Circulating MicroRNAs and Extracellular Vesicles as Potential Cancer Biomarkers: A Systematic Review. Int. J. Clin. Oncol. 2017, 22, 413–420. [Google Scholar] [CrossRef] [PubMed]
  42. Izzotti, A.; Carozzo, S.; Pulliero, A.; Zhabayeva, D.; Ravetti, J.L.; Bersimbaev, R. Extracellular MicroRNA in Liquid Biopsy: Applicability in Cancer Diagnosis and Prevention. Am. J. Cancer Res. 2016, 7, 1461–1493. [Google Scholar]
  43. Taylor, D.D.; Gercel-Taylor, C. MicroRNA Signatures of Tumor-Derived Exosomes as Diagnostic Biomarkers of Ovarian Cancer. Gynecol. Oncol. 2008, 110, 13–21. [Google Scholar] [CrossRef] [PubMed]
  44. Lin, P.-Y.; Yu, S.-L.; Yang, P.-C. MicroRNA in Lung Cancer. Br. J. Cancer 2010, 103, 1144–1148. [Google Scholar] [CrossRef] [Green Version]
  45. Wang, Q.Z.; Xu, W.; Habib, N.; Xu, R. Potential Uses of MicroRNA in Lung Cancer Diagnosis, Prognosis, and Therapy. Curr. Cancer Drug Targets 2009, 9, 572–594. [Google Scholar] [CrossRef] [Green Version]
  46. Slaby, O.; Svoboda, M.; Michalek, J.; Vyzula, R. MicroRNAs in Colorectal Cancer: Translation of Molecular Biology into Clinical Application. Mol. Cancer 2009, 8, 102. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  47. Wang, W.; Luo, Y. ping MicroRNAs in Breast Cancer: Oncogene and Tumor Suppressors with Clinical Potential. J. Zhejiang Univ. Sci. B 2015, 16, 18–31. [Google Scholar] [CrossRef] [Green Version]
  48. Liu, H.-S.; Xiao, H.-S. MicroRNAs as Potential Biomarkers for Gastric Cancer. No. 2012AA020103. World J. Gastroenterol. 2014, 20, 12007–12017. [Google Scholar] [CrossRef] [PubMed]
  49. Kinose, Y.; Sawada, K.; Nakamura, K.; Kimura, T. The Role of MicroRNAs in Ovarian Cancer. Biomed Res. Int. 2014, 2014, 249393. [Google Scholar] [CrossRef] [Green Version]
  50. Nam, E.J.; Yoon, H.; Kim, S.W.; Kim, H.; Kim, Y.T.; Kim, J.H.; Kim, J.W.; Kim, S. MicroRNA Expression Profiles in Serous Ovarian Carcinoma. Clin. Cancer Res. 2008, 14, 2690–2695. [Google Scholar] [CrossRef] [Green Version]
  51. Dahiya, N.; Morin, P.J. MicroRNAs in Ovarian Carcinomas. Endocr. Relat. Cancer 2010, 17, F77. [Google Scholar] [CrossRef]
  52. Kö Bel, M.; Kalloger, S.E.; Boyd, N.; Mckinney, S.; Mehl, E.; Palmer, C.; Leung, S.; Bowen, N.J.; Ionescu, D.N.; Rajput, A.; et al. Ovarian Carcinoma Subtypes Are Different Diseases: Implications for Biomarker Studies. PLoS Med. 2008, 5, e232. [Google Scholar] [CrossRef]
  53. Zuberi, M.; Mir, R.; Das, J.; Ahmad, I.; Javid, J.; Yadav, P.; Masroor, M.; Ahmad, S.; Ray, P.C.; Saxena, A. Expression of Serum MiR-200a, MiR-200b, and MiR-200c as Candidate Biomarkers in Epithelial Ovarian Cancer and Their Association with Clinicopathological Features. Clin. Transl. Oncol. 2015, 17, 779–787. [Google Scholar] [CrossRef] [PubMed]
  54. Zhang, Z.; Bast, R.C.; Yu, Y.; Li, J.; Sokoll, L.J.; Rai, A.J.; Rosenzweig, J.M.; Cameron, B.; Wang, Y.Y.; Meng, X.-Y.; et al. Three Biomarkers Identified from Serum Proteomic Analysis for the Detection of Early Stage Ovarian Cancer. Cancer Res. 2004, 64, 5882–5890. [Google Scholar] [CrossRef] [Green Version]
  55. Anfossi, S.; Babayan, A.; Pantel, K.; Calin, G.A. Clinical Utility of Circulating Non-Coding RNAs—An Update. Nat. Rev. Clin. Oncol. 2018, 15, 541–563. [Google Scholar] [CrossRef] [PubMed]
  56. Ravegnini, G.; de Iaco, P.; Gorini, F.; Dondi, G.; Klooster, I.; de Crescenzo, E.; Bovicelli, A.; Hrelia, P.; Perrone, A.M.; Angelini, S. Role of Circulating Mirnas in Therapeutic Response in Epithelial Ovarian Cancer: A Systematic Revision. Biomedicines 2021, 9, 1316. [Google Scholar] [CrossRef]
  57. Zheng, H.; Liu, J.-Y.; Song, F.-J.; Chen, K.-X. Advances in Circulating MicroRNAs as Diagnostic and Prognostic Markers for Ovarian Cancer. Cancer Biol. Med. 2013, 10, 123–130. [Google Scholar] [CrossRef] [PubMed]
  58. Montagnana, M.; Benati, M.; Danese, E. Circulating Biomarkers in Epithelial Ovarian Cancer Diagnosis: From Present to Future Perspective. Ann. Transl. Med. 2017, 5, 276. [Google Scholar] [CrossRef] [Green Version]
  59. Weber, J.A.; Baxter, D.H.; Zhang, S.; Huang, D.Y.; Huang, K.H.; Lee, M.J.; Galas, D.J.; Wang, K. The MicroRNA Spectrum in 12 Body Fluids. Clin. Chem. 2010, 56, 1733–1741. [Google Scholar] [CrossRef] [Green Version]
  60. Godoy, P.M.; Bhakta, N.R.; Barczak, A.J.; Cakmak, H.; Fisher, S.; MacKenzie, T.C.; Patel, T.; Price, R.W.; Smith, J.F.; Woodruff, P.G.; et al. Large Differences in Small RNA Composition Between Human Biofluids. Cell Rep. 2018, 25, 1346–1358. [Google Scholar] [CrossRef] [Green Version]
  61. Nik Mohamed Kamal, N.N.S.B.; Shahidan, W.N.S. Non-Exosomal and Exosomal Circulatory MicroRNAs: Which Are More Valid as Biomarkers? Front. Pharmacol. 2020, 10, 1500. [Google Scholar] [CrossRef] [Green Version]
  62. Preethi, K.A.; Selvakumar, S.C.; Ross, K.; Jayaraman, S.; Tusubira, D.; Sekar, D. Liquid Biopsy: Exosomal MicroRNAs as Novel Diagnostic and Prognostic Biomarkers in Cancer. Mol. Cancer 2022, 21, 54. [Google Scholar] [CrossRef] [PubMed]
  63. Sohel, M.H. Extracellular/Circulating MicroRNAs: Release Mechanisms, Functions and Challenges. Achiev. Life Sci. 2016, 10, 175–186. [Google Scholar] [CrossRef] [Green Version]
  64. Roblegg, E.; Coughran, A.; Sirjani, D. Saliva: An all-rounder of our body. Eur J Pharm Biopharm. 2019, 142, 133–141. [Google Scholar] [CrossRef] [PubMed]
  65. Chojnowska, S.; Baran, T.; Wilińska, I.; Sienicka, P.; Cabaj-Wiater, I.; Knaś, M. Human Saliva as a Diagnostic Material. Adv. Med. Sci. 2018, 63, 185–191. [Google Scholar] [CrossRef] [PubMed]
  66. Rapado-González, Ó.; Majem, B.; Muinelo-Romay, L.; López-López, R.; Suarez-Cunqueiro, M.M. Cancer Salivary Biomarkers for Tumours Distant to the Oral Cavity. Int. J. Mol. Sci. 2016, 17, 1531. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  67. Spielmann, N.; Ilsley, D.; Gu, J.; Lea, K.; Brockman, J.; Heater, S.; Setterquist, R.; Wong, D.T.W. The Human Salivary RNA Transcriptome Revealed by Massively Parallel Sequencing. Clin. Chem. 2012, 58, 1314–1321. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  68. Bahn, J.H.; Zhang, Q.; Li, F.; Chan, T.M.; Lin, X.; Kim, Y.; Wong, D.T.W.; Xiao, X. The Landscape of MicroRNA, Piwi-Interacting RNA, and Circular RNA in Human Saliva. Clin. Chem. 2015, 61, 221–230. [Google Scholar] [CrossRef] [Green Version]
  69. Pfaffe, T.; Cooper-White, J.; Beyerlein, P.; Kostner, K.; Punyadeera, C. Diagnostic Potential of Saliva: Current State and Future Applications. Clin. Chem. 2011, 57, 675–687. [Google Scholar] [CrossRef] [Green Version]
  70. Rapado-González, Ó.; Martínez-Reglero, C.; Salgado-Barreira, Á.; Takkouche, B.; López-López, R.; Suárez-Cunqueiro, M.M.; Muinelo-Romay, L. Salivary Biomarkers for Cancer Diagnosis: A Meta-Analysis. Ann. Med. 2020, 52, 131–144. [Google Scholar] [CrossRef]
  71. Streckfus, C.F.; Guajardo-Edwards, C. The Use of Salivary as a Biometric Tool to Determine the Presence of Carcinoma of the Breast Among Women. Biometrics 2011. [CrossRef] [Green Version]
  72. Nonaka, T.; Wong, D.T.W. Saliva-Exosomics in Cancer: Molecular Characterization of Cancer-Derived Exosomes in Saliva. In Enzymes; Academic Press: Cambridge, MA, USA, 2017; Volume 42, pp. 125–151. [Google Scholar]
  73. Lin, X.; Lo, H.C.; Wong, D.T.W.; Xiao, X. Noncoding RNAs in Human Saliva as Potential Disease Biomarkers. Front. Genet. 2015, 6, 175. [Google Scholar] [CrossRef] [PubMed]
  74. Rapado-González, Ó.; Majem, B.; Muinelo-Romay, L.; álvarez-Castro, A.; Santamaría, A.; Gil-Moreno, A.; López-López, R.; Suárez-Cunqueiro, M.M. Human Salivary MicroRNAs in Cancer. J. Cancer 2018, 9, 638–649. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  75. Ishige, F.; Hoshino, I.; Iwatate, Y.; Chiba, S.; Arimitsu, H.; Yanagibashi, H.; Nagase, H.; Takayama, W. MIR1246 in Body Fluids as a Biomarker for Pancreatic Cancer. Sci. Rep. 2020, 10, 8723. [Google Scholar] [CrossRef]
  76. Alemar, B.; Izetti, P.; Gregório, C.; Macedo, G.S.; Antonio, M.; Castro, A.; Osvaldt, A.B.; Matte, U.; Ashton-Prolla, P. MiRNA-21 and MiRNA-34a Are Potential Minimally Invasive Biomarkers for the Diagnosis of Pancreatic Ductal Adenocarcinoma. Pancreas 2016, 45, 84–92. [Google Scholar] [CrossRef]
  77. Humeau, M.; Vignolle-Vidoni, A.; Sicard, F.; Martins, F.; Bournet, B.; Buscail, L.; Torrisani, J.; Cordelier, P. Salivary MicroRNA in Pancreatic Cancer Patients. PLoS ONE 2015, 10, e0130996. [Google Scholar] [CrossRef] [Green Version]
  78. Rapado-González, Ó.; Majem, B.; Álvarez-Castro, A.; Díaz-Peña, R.; Abalo, A.; Suárez-Cabrera, L.; Gil-Moreno, A.; Santamaría, A.; López-López, R.; Muinelo-Romay, L.; et al. A Novel Saliva-Based Mirna Signature for Colorectal Cancer Diagnosis. J. Clin. Med. 2019, 8, 2029. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  79. Petkevich, A.A.; Abramov, A.A.; Pospelov, V.I.; Malinina, N.A.; Kuhareva, E.I.; Mazurchik, N.V.; Tarasova, O.I. Exosomal and Non-Exosomal MiRNA Expression Levels in Patients with HCV-Related Cirrhosis and Liver Cancer. Oncotarget 2021, 12, 1697. [Google Scholar] [CrossRef]
  80. Ding, Y.; Ma, Q.; Liu, F.; Zhao, L.; Wei, W. The Potential Use of Salivary Mirnas as Promising Biomarkers for Detection of Cancer: A Meta-Analysis. PLoS ONE 2016, 11, e0166303. [Google Scholar] [CrossRef] [PubMed]
  81. Gablo, N.A.; Prochazka, V.; Kala, Z.; Slaby, O.; Kiss, I. Cell-Free MicroRNAs as Non-Invasive Diagnostic and Prognostic Biomarkers in Pancreatic Cancer. Curr. Genom. 2019, 20, 569–580. [Google Scholar] [CrossRef]
  82. Xie, Z.; Yin, X.; Gong, B.; Nie, W.; Wu, B.; Zhang, X.; Huang, J.; Zhang, P.; Zhou, Z.; Li, Z. Salivary MicroRNAs Show Potential as a Noninvasive Biomarker for Detecting Resectable Pancreatic Cancer. Cancer Prev. Res. 2015, 8, 165–173. [Google Scholar] [CrossRef] [Green Version]
  83. Madhavan, B.; Yue, S.; Galli, U.; Rana, S.; Gross, W.; Müller, M.; Giese, N.A.; Kalthoff, H.; Becker, T.; Büchler, M.W.; et al. Combined Evaluation of a Panel of Protein and MiRNA Serum-Exosome Biomarkers for Pancreatic Cancer Diagnosis Increases Sensitivity and Specificity. Int. J. Cancer 2015, 136, 2616–2627. [Google Scholar] [CrossRef] [PubMed]
  84. Sazanov, A.A.; Kiselyova, E.V.; Zakharenko, A.A.; Romanov, M.N.; Zaraysky, M.I. Plasma and Saliva MiR-21 Expression in Colorectal Cancer Patients. J. Appl. Genet. 2017, 58, 231–237. [Google Scholar] [CrossRef] [PubMed]
  85. Mariam, A.; Miller-Atkins, G.; Moro, A.; Rodarte, A.I.; Siddiqi, S.; Acevedo-Moreno, L.A.; Brown, J.M.; Allende, D.S.; Aucejo, F.; Rotroff, D.M. Salivary MiRNAs as Non-Invasive Biomarkers of Hepatocellular Carcinoma: A Pilot Study. PeerJ 2022, 9, e12715. [Google Scholar] [CrossRef] [PubMed]
  86. Yang, Y.; Ma, L.; Qiao, X.; Zhang, X.; Dong, S.F.; Wu, M.T.; Zhai, K.; Shi, H.Z. Salivary MicroRNAs Show Potential as Biomarkers for Early Diagnosis of Malignant Pleural Effusion. Transl. Lung Cancer Res. 2020, 9, 1247–1257. [Google Scholar] [CrossRef] [PubMed]
  87. Koopaie, M.; Kolahdooz, S.; Fatahzadeh, M.; Manifar, S. Salivary Biomarkers in Breast Cancer Diagnosis: A Systematic Review and Diagnostic Meta-Analysis. Cancer Med. 2022, 11, 2644–2661. [Google Scholar] [CrossRef] [PubMed]
  88. Li, F.; Yoshizawa, J.M.; Kim, K.M.; Kanjanapangka, J.; Grogan, T.R.; Wang, X.; Elashoff, D.E.; Ishikawa, S.; Chia, D.; Liao, W.; et al. Discovery and Validation of Salivary Extracellular RNA Biomarkers for Noninvasive Detection of Gastric Cancer. Clin. Chem. 2018, 64, 1513–1521. [Google Scholar] [CrossRef] [Green Version]
  89. Lopes, C.; Chaves, J.; Ortigão, R.; Dinis-Ribeiro, M.; Pereira, C. Gastric Cancer Detection by Non-Blood-Based Liquid Biopsies: A Systematic Review Looking into the Last Decade of Research. United Eur. Gastroenterol. J. 2022, 11, 114–130. [Google Scholar] [CrossRef]
  90. Hizir, M.S.; Balcioglu, M.; Rana, M.; Robertson, N.M.; Yigit, M.V. Simultaneous Detection of Circulating OncomiRs from Body Fluids for Prostate Cancer Staging Using Nanographene Oxide. ACS Appl. Mater. Interfaces 2014, 6, 14772–14778. [Google Scholar] [CrossRef]
  91. Luedemann, C.; Reinersmann, J.L.; Klinger, C.; Degener, S.; Dreger, N.M.; Roth, S.; Kaufmann, M.; Savelsbergh, A. Prostate Cancer-Associated MiRNAs in Saliva: First Steps to an Easily Accessible and Reliable Screening Tool. Biomolecules 2022, 12, 1366. [Google Scholar] [CrossRef]
  92. Machida, T.; Tomofuji, T.; Maruyama, T.; Yoneda, T.; Ekuni, D.; Azuma, T.; Miyai, H.; Mizuno, H.; Kato, H.; Tsutsumi, K.; et al. MIR 1246 and MIR-4644 in Salivary Exosome as Potential Biomarkers for Pancreatobiliary Tract Cancer. Oncol. Rep. 2016, 36, 2375–2381. [Google Scholar] [CrossRef] [Green Version]
  93. Lee, Y.H.; Kim, J.H.; Zhou, H.; Kim, B.W.; Wong, D.T. Salivary Transcriptomic Biomarkers for Detection of Ovarian Cancer: For Serous Papillary Adenocarcinoma. J. Mol. Med. 2012, 90, 427–434. [Google Scholar] [CrossRef]
  94. Yang, J.; Xiang, C.; Liu, J. Clinical Significance of Combining Salivary MRNAs and Carcinoembryonic Antigen for Ovarian Cancer Detection. Scand. J. Clin. Lab. Investig. 2021, 81, 39–45. [Google Scholar] [CrossRef]
  95. Li, Y.; Tan, C.; Liu, L.; Han, L. Significance of Blood and Salivary IEX-1 Expression in Diagnosis of Epithelial Ovarian Carcinoma. J. Obstet. Gynaecol. Res. 2018, 44, 764–771. [Google Scholar] [CrossRef] [Green Version]
  96. Tajmul, M.; Parween, F.; Singh, L.; Mathur, S.R.; Sharma, J.B.; Kumar, S.; Sharma, D.N.; Yadav, S. Identification and Validation of Salivary Proteomic Signatures for Non-Invasive Detection of Ovarian Cancer. Int. J. Biol. Macromol. 2018, 108, 503–514. [Google Scholar] [CrossRef] [PubMed]
  97. Zermeño-Nava, J.D.J.; Martínez-Martínez, M.U.; Rámirez-De-Ávila, A.L.; Hernández-Arteaga, A.C.; García-Valdivieso, M.G.; Hernández-Cedillo, A.; José-Yacamán, M.; Navarro-Contreras, H.R. Determination of Sialic Acid in Saliva by Means of Surface-Enhanced Raman Spectroscopy as a Marker in Adnexal Mass Patients: Ovarian Cancer vs Benign Cases. J. Ovarian Res. 2018, 11, 61. [Google Scholar] [CrossRef] [PubMed]
  98. Bel’skaya, L.V.; Sarf, E.A.; Solomatin, D.V.; Kosenok, V.K. Analysis of the Lipid Profile of Saliva in Ovarian and Endometrial Cancer by IR Fourier Spectroscopy. Vib. Spectrosc. 2019, 104, 102944. [Google Scholar] [CrossRef]
  99. Condrat, C.E.; Thompson, D.C.; Barbu, M.G.; Bugnar, O.L.; Boboc, A.; Cretoiu, D.; Suciu, N.; Cretoiu, S.M.; Voinea, S.C. MiRNAs as Biomarkers in Disease: Latest Findings Regarding Their Role in Diagnosis and Prognosis. Cells 2020, 9, 276. [Google Scholar] [CrossRef] [Green Version]
  100. Nakamura, K.; Sawada, K.; Yoshimura, A.; Kinose, Y.; Nakatsuka, E.; Kimura, T. Clinical Relevance of Circulating Cell-Free MicroRNAs in Ovarian Cancer. Mol. Cancer 2016, 15, 48. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  101. Yoshida, K.; Yokoi, A.; Kato, T.; Ochiya, T.; Yamamoto, Y. The Clinical Impact of Intra- and Extracellular MiRNAs in Ovarian Cancer. Cancer Sci. 2020, 111, 3435–3444. [Google Scholar] [CrossRef] [PubMed]
  102. Hulstaert, E.; Morlion, A.; Levanon, K.; Vandesompele, J.; Mestdagh, P. Candidate RNA Biomarkers in Biofluids for Early Diagnosis of Ovarian Cancer: A Systematic Review. Gynecol. Oncol. 2021, 160, 633–642. [Google Scholar] [CrossRef]
  103. Montazerian, M.; Yasari, F.; Aghaalikhani, N. Ovarian Extracellular MicroRNAs as the Potential Non-Invasive Biomarkers: An Update. Biomed. Pharmacother. 2018, 106, 1633–1640. [Google Scholar] [CrossRef]
  104. Resnick, K.E.; Alder, H.; Hagan, J.P.; Richardson, D.L.; Croce, C.M.; Cohn, D.E. The Detection of Differentially Expressed MicroRNAs from the Serum of Ovarian Cancer Patients Using a Novel Real-Time PCR Platform. Gynecol. Oncol. 2009, 112, 55–59. [Google Scholar] [CrossRef] [PubMed]
  105. Shiao, M.S.; Chang, J.M.; Lertkhachonsuk, A.A.; Rermluk, N.; Jinawath, N. Circulating Exosomal MiRNAs as Biomarkers in Epithelial Ovarian Cancer. Biomedicines 2021, 9, 1433. [Google Scholar] [CrossRef] [PubMed]
  106. Shah, J.S.; Soon, P.S.; Marsh, D.J. Comparison of Methodologies to Detect Low Levels of Hemolysis in Serum for Accurate Assessment of Serum MicroRNAs. PLoS ONE 2016, 11, e0153200. [Google Scholar] [CrossRef] [PubMed]
  107. Kim, S.; Choi, M.C.; Jeong, J.Y.; Hwang, S.; Jung, S.G.; Joo, W.D.; Park, H.; Song, S.H.; Lee, C.; Kim, T.H.; et al. Serum Exosomal MiRNA-145 and MiRNA-200c as Promising Biomarkers for Preoperative Diagnosis of Ovarian Carcinomas. J. Cancer 2019, 10, 1958–1967. [Google Scholar] [CrossRef] [Green Version]
  108. Elias, K.M.; Fendler, W.; Stawiski, K.; Fiascone, S.J.; Vitonis, A.F.; Berkowitz, R.S.; Frendl, G.; Konstantinopoulos, P.; Crum, C.P.; Kedzierska, M.; et al. Diagnostic Potential for a Serum MiRNA Neural Network for Detection of Ovarian Cancer. EIlife 2017, 6, e28932. [Google Scholar] [CrossRef]
  109. Kumar, V.; Gupta, S.; Chaurasia, A.; Sachan, M. Evaluation of Diagnostic Potential of Epigenetically Deregulated MiRNAs in Epithelial Ovarian Cancer. Front. Oncol. 2021, 11, 681872. [Google Scholar] [CrossRef]
  110. Carollo, E.; Paris, B.; Samuel, P.; Pantazi, P.; Bartelli, T.F.; Dias-Neto, E.; Brooks, S.A.; Pink, R.C.; Francisco Carter, D.R. Detecting Ovarian Cancer Using Extracellular Vesicles: Progress and Possibilities. Biochem. Soc. Trans. 2019, 47, 295–304. [Google Scholar] [CrossRef]
  111. Zheng, X.; Li, X.; Wang, X. Extracellular Vesicle-Based Liquid Biopsy Holds Great Promise for the Management of Ovarian Cancer. Biochim. Biophys. Acta Rev. Cancer 2020, 1874, 188395. [Google Scholar] [CrossRef]
  112. Gasparri, M.L.; Casorelli, A.; Bardhi, E.; Besharat, A.R.; Savone, D.; Ruscito, I.; Farooqi, A.A.; Papadia, A.; Mueller, M.D.; Ferretti, E.; et al. Beyond Circulating MicroRNA Biomarkers: Urinary MicroRNAs in Ovarian and Breast Cancer. Tumor Biol. 2017, 39, 1010428317695525. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  113. Zavesky, L.; Jandakova, E.; Turyna, R.; Langmeierova, L.; Weinberger, V.; Minar, L. Supernatant versus Exosomal Urinary MicroRNAs. Two Fractions with Different Outcomes in Gynaecological Cancers. Neoplasma 2016, 63, 121–132. [Google Scholar] [CrossRef] [Green Version]
  114. Záveský, L.; Jandáková, E.; Turyna, R.; Langmeierová, L.; Weinberger, V.; Záveská Drábková, L.; Hůlková, M.; Hořínek, A.; Dušková, D.; Feyereisl, J.; et al. Evaluation of Cell-Free Urine MicroRNAs Expression for the Use in Diagnosis of Ovarian and Endometrial Cancers. A Pilot Study. Pathol. Oncol. Res. 2015, 21, 1027–1035. [Google Scholar] [CrossRef]
  115. Zhou, J.; Gong, G.; Tan, H.; Dai, F.; Zhu, X.; Chen, Y.; Wang, J.; Liu, Y.; Chen, P.; Wu, X.; et al. Urinary MicroRNA-30a-5p Is a Potential Biomarker for Ovarian Serous Adenocarcinoma. Oncol. Rep. 2015, 33, 2915–2923. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  116. Berner, K.; Hirschfeld, M.; Weiß, D.; Rücker, G.; Asberger, J.; Ritter, A.; Nöthling, C.; Jäger, M.; Juhasz-Böss, I.; Erbes, T. Evaluation of Circulating MicroRNAs as Non-Invasive Biomarkers in the Diagnosis of Ovarian Cancer: A Case–Control Study. Arch. Gynecol. Obstet. 2022, 306, 151–163. [Google Scholar] [CrossRef] [PubMed]
  117. Záveský, L.; Jandáková, E.; Weinberger, V.; Minář, L.; Hanzíková, V.; Dušková, D.; Drábková, L.Z.; Hořínek, A. Ovarian Cancer: Differentially Expressed MicroRNAs in Tumor Tissue and Cell-Free Ascitic Fluid as Potential Novel Biomarkers. Cancer Investig. 2019, 37, 440–452. [Google Scholar] [CrossRef] [PubMed]
  118. Nicolè, L.; Cappello, F.; Cappellesso, R.; VandenBussche, C.J.; Fassina, A. MicroRNA Profiling in Serous Cavity Specimens: Diagnostic Challenges and New Opportunities. Cancer Cytopathol. 2019, 127, 493–500. [Google Scholar] [CrossRef] [PubMed]
  119. Záveský, L.; Jandáková, E.; Weinberger, V.; Minář, L.; Hanzíková, V.; Dušková, D.; Záveská Drábková, L.; Svobodová, I.; Hořínek, A. Ascites-Derived Extracellular MicroRNAs as Potential Biomarkers for Ovarian Cancer. Reprod. Sci. 2019, 26, 510–522. [Google Scholar] [CrossRef]
  120. Yamamoto, C.M.; Oakes, M.L.; Murakami, T.; Muto, M.G.; Berkowitz, R.S.; Ng, S.W. Comparison of Benign Peritoneal Fluid- and Ovarian Cancer Ascites-Derived Extracellular Vesicle RNA Biomarkers. J. Ovarian Res. 2018, 11, 20. [Google Scholar] [CrossRef] [Green Version]
  121. Vaksman, O.; Tropé, C.; Davidson, B.; Reich, R. Exosome-Derived MiRNAs and Ovarian Carcinoma Progression. Carcinogenesis 2014, 35, 2113–2120. [Google Scholar] [CrossRef] [Green Version]
  122. Mitra, A.; Yoshida-Court, K.; Solley, T.N.; Mikkelson, M.; Yeung, C.L.A.; Nick, A.; Lu, K.; Klopp, A.H. Extracellular Vesicles Derived from Ascitic Fluid Enhance Growth and Migration of Ovarian Cancer Cells. Sci. Rep. 2021, 11, 9149. [Google Scholar] [CrossRef]
  123. Wang, W.; Jo, H.; Park, S.; Kim, H.; Kim, S.I.; Han, Y.; Lee, J.; Seol, A.; Kim, J.; Lee, M.; et al. Integrated Analysis of Ascites and Plasma Extracellular Vesicles Identifies a MiRNA-Based Diagnostic Signature in Ovarian Cancer. Cancer Lett. 2022, 542, 215735. [Google Scholar] [CrossRef]
  124. Hulstaert, E.; Levanon, K.; Morlion, A.; Van Aelst, S.; Christidis, A.A.; Zamar, R.; Anckaert, J.; Verniers, K.; Bahar-Shany, K.; Sapoznik, S.; et al. RNA Biomarkers from Proximal Liquid Biopsy for Diagnosis of Ovarian Cancer. Neoplasia 2022, 24, 155–164. [Google Scholar] [CrossRef]
  125. Skryabin, G.O.; Komelkov, A.V.; Zhordania, K.I.; Bagrov, D.V.; Vinokurova, S.V.; Galetsky, S.A.; Elkina, N.V.; Denisova, D.A.; Enikeev, A.D.; Tchevkina, E.M. Extracellular Vesicles from Uterine Aspirates Represent a Promising Source for Screening Markers of Gynecologic Cancers. Cells 2022, 11, 1064. [Google Scholar] [CrossRef]
  126. Kaczor-Urbanowicz, K.E.; Wei, F.; Rao, S.L.; Kim, J.; Shin, H.; Cheng, J.; Tu, M.; Wong, D.T.W.; Kim, Y. Clinical Validity of Saliva and Novel Technology for Cancer Detection. Biochim. Biophys. Acta Rev. Cancer 2019, 1872, 49–59. [Google Scholar] [CrossRef]
  127. Valihrach, L.; Androvic, P.; Kubista, M. Circulating MiRNA Analysis for Cancer Diagnostics and Therapy. Mol. Asp. Med. 2020, 72, 100825. [Google Scholar] [CrossRef]
  128. Zhao, Y.; Song, Y.; Yao, L.; Song, G.; Teng, C. Circulating MicroRNAs: Promising Biomarkers Involved in Several Cancers and Other Diseases. DNA Cell Biol. 2017, 36, 77–94. [Google Scholar] [CrossRef] [PubMed]
  129. Nebgen, D.R.; Lu, K.H.; Bast, R.C. Novel Approaches to Ovarian Cancer Screening. Curr. Oncol. Rep. 2019, 21, 75. [Google Scholar] [CrossRef] [PubMed]
  130. Dochez, V.; Caillon, H.; Vaucel, E.; Dimet, J.; Winer, N.; Ducarme, G. Biomarkers and Algorithms for Diagnosis of Ovarian Cancer: CA125, HE4, RMI and ROMA, a Review. J. Ovarian Res. 2019, 12, 28. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  131. Soler, M.; Calvo-Lozano, O.; Estevez, M.-C.; Lechuga, L.M. Nanophotonic Biosensors Driving Personalized Medicine. Opt. Photonics News 2020, 31, 24–31. [Google Scholar] [CrossRef]
  132. Marin, Y.; Velha, P.; Faralli, S.; Di Pasquale, F.; Oton, C.J. Oton Micro-Interferometers on Chip for Sensing Applications. In Optical Sensors; Optical Sensors and Sensing Congress (ES, FTS, HISE, Sensors); Optica Publishing Group: Washington, DC, USA, 2018. [Google Scholar]
  133. Iqbal, M.; Gleeson, M.A.; Spaugh, B.; Tybor, F.; Gunn, W.G.; Hochberg, M.; Baehr-Jones, T.; Bailey, R.C.; Gunn, L.C. Label-Free Biosensor Arrays Based on Silicon Ring Resonators and High-Speed Optical Scanning Instrumentation. IEEE J. Sel. Top. Quantum Electron. 2010, 16, 654–661. [Google Scholar] [CrossRef]
  134. Scheler, O.; Kindt, J.T.; Qavi, A.J.; Kaplinski, L.; Glynn, B.; Barry, T.; Kurg, A.; Bailey, R.C. Label-Free, Multiplexed Detection of Bacterial TmRNA Using Silicon Photonic Microring Resonators. Biosens. Bioelectron. 2012, 36, 56–61. [Google Scholar] [CrossRef] [PubMed] [Green Version]
Figure 1. Anatomical and molecular visualization of miRNAs trafficking between OC tissue to bloodstream and saliva. Direction of molecular trafficking between ovarian tissue and bloodstream (upper left), and between blood and salivary glands (upper right) is indicated by arrows. There is an intense exchange of molecules from the bloodstream to the ovary and vice versa: the ovary is highly vascularized so that it is supplied with hormones and nutrients needed for its high metabolism. At the same time, small molecules, such as microRNAs, are released from the tissue, also under tumor conditions. The composition of blood therefore influences salivary composition. The salivary fluid is produced by the local vascular bed in the acinar region and through the duct system released into the oral cavity. At cell level (lower left), miRNAs generated in the cell are released within exosomes or associated with miRNA binding proteins (such AGO2) or high-density lipoprotein (HDL). They are stable and protected by RNase degradation. Intra- and extracellular mechanisms allow these molecules to be transported from blood to saliva (lower right), so they can be found in the oral cavity reflecting a distant physiological or pathological condition.
Figure 1. Anatomical and molecular visualization of miRNAs trafficking between OC tissue to bloodstream and saliva. Direction of molecular trafficking between ovarian tissue and bloodstream (upper left), and between blood and salivary glands (upper right) is indicated by arrows. There is an intense exchange of molecules from the bloodstream to the ovary and vice versa: the ovary is highly vascularized so that it is supplied with hormones and nutrients needed for its high metabolism. At the same time, small molecules, such as microRNAs, are released from the tissue, also under tumor conditions. The composition of blood therefore influences salivary composition. The salivary fluid is produced by the local vascular bed in the acinar region and through the duct system released into the oral cavity. At cell level (lower left), miRNAs generated in the cell are released within exosomes or associated with miRNA binding proteins (such AGO2) or high-density lipoprotein (HDL). They are stable and protected by RNase degradation. Intra- and extracellular mechanisms allow these molecules to be transported from blood to saliva (lower right), so they can be found in the oral cavity reflecting a distant physiological or pathological condition.
Biomedicines 11 00652 g001
Table 2. Biofluids other than blood or saliva for OC liquid biopsy and miRNAs BMs search (as selected by this review’s authors). EOC: FTC: fallopian tube cancer; HS: healthy subject; BGD: benign gynecological disease; AUC: area under the ROC curve. Each row provides information regarding the citation in parenthesis at the far left end of the row.
Table 2. Biofluids other than blood or saliva for OC liquid biopsy and miRNAs BMs search (as selected by this review’s authors). EOC: FTC: fallopian tube cancer; HS: healthy subject; BGD: benign gynecological disease; AUC: area under the ROC curve. Each row provides information regarding the citation in parenthesis at the far left end of the row.
ReferenceBiofluidSource (Analytes)miRNAsStatusCaseControlsAUC (95% CI)
[113]urinecell freemiR-92
miR-106b
miR-100
miR-200b



6 EOC
FTC
13 HS1.00 (0.815–1.000)
0.97 (0.764–1.000)
0.85 (0.601–0.970)
1.00 (0.782–1.000)
[115]urineexosomesmiR-30a-5p39 OSA26 BGD 30 HS0.86 (0.709–1.016)
[116]urinecell freemiR-15a
let-7a

13 OC17 HS
[117]Ascite
vs
Plasma
exosomesmiR-203-3p
miR-204-5p
miR-135b-5p
miR-182-5p
miR-451a




12 OC12 HS1.000 (0.782–1.000)
1.000 (0.782–1.000)
1.000 (0.782–1.000)
0.964 (0.725–1.000)
0.964 (0.725–1.000)
[120]Ascite
vs
Benign peritoneal fluids
exosomeslet-7b.
miR-23b
miR-29a
miR-30d
miR-205
miR-720





8 OC10 HS
[122]Ascite
and
plasma
exosomesmiR-200c-3p miR-18a-5p
miR-1246
miR-1290
miR-100- 5p
miR125b-3p





5 OC2 BGD
[123]Ascite
vs
Benign peritoneal fluids
exosomesmiR-1246
miR-1290

78 OC72 BGD
[124]Uterine cavity fluidsCell freelet-7d-5p
miR-203a
miR-200b
miR-200c
miR-191




26 OC48 BGD
[125]Uterine cavity fluidsexosomesmiR-451a
miR-199a-3p
miR-375-3p


5 EOC5 HS
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Robotti, M.; Scebba, F.; Angeloni, D. Circulating Biomarkers for Cancer Detection: Could Salivary microRNAs Be an Opportunity for Ovarian Cancer Diagnostics? Biomedicines 2023, 11, 652. https://doi.org/10.3390/biomedicines11030652

AMA Style

Robotti M, Scebba F, Angeloni D. Circulating Biomarkers for Cancer Detection: Could Salivary microRNAs Be an Opportunity for Ovarian Cancer Diagnostics? Biomedicines. 2023; 11(3):652. https://doi.org/10.3390/biomedicines11030652

Chicago/Turabian Style

Robotti, Marzia, Francesca Scebba, and Debora Angeloni. 2023. "Circulating Biomarkers for Cancer Detection: Could Salivary microRNAs Be an Opportunity for Ovarian Cancer Diagnostics?" Biomedicines 11, no. 3: 652. https://doi.org/10.3390/biomedicines11030652

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