Among other biomarkers, serum CA-125, a glycoprotein of ∼200 to 5,000 kDa, has been employed routinely and is approved by the FDA for early epithelial ovarian cancer detection (Tripathi et al., 2020a). CA-125 was first defined by Bast et al. in 1981 using a monoclonal antibody (OC125) and was shown to be highly elevated when compared women with EOC to healthy controls (Bast et al., 1981). Although it has many benefits, CA-125 cannot be used to detect EOC in its early stages because of its low specificity and less adequate accuracy in women with ovarian cancer. Its levels are found to be increased in several other benign diseases, notably pelvic inflammatory disease, endometriosis, pregnancy, tuberculosis, cirrhosis of the liver and other non-gynecologic cancers such as lung and breast (Bast et al., 1998; Meden and Fattahi-Meibodi, 1998). Despite these limitations, CA-125 is regarded as one of the best ovarian cancer serum biomarkers currently accessible.

Besides CA-125, HE-4 has been proven to be a biomarker with promising results in ovarian cancer screening. When CA-125 and HE-4 diagnostic performances in women with Type-I and Type-II ovarian cysts were compared, their combination showed the best diagnostic efficiency with the area under the curve value of 0.79 and 0.93 for Type-I and Type-II EOC, respectively (Kristjansdottir et al., 2013). In another research conducted by Fujiwara et al., in 2015, when assessed serum biomarker levels of CA-125 and HE-4 in 225 Japanese women with ovarian cancer and 94 healthy controls, a sensitivity of 92.1% in type-I and 78.8% for type-II were recorded when tested together. Compared to CA-125 and HE-4 alone, ROMA’s type I and type II sensitivities (84.8% and 97.4%, respectively) performed better. The findings of this research study have led to the conclusion that a more accurate way to differentiate patients with EOC from those with benign carcinoma is to evaluate HE-4 and CA-125 together with ROMA analysis instead of measuring either factor individually (Fujiwara et al., 2015).

ROCA assay calculates the risk of occurrence of ovarian cancer based on gradual alteration in the CA-125 level over time (Gentry-Maharaj et al., 2020). This screening algorithm predicts the intermediate and elevated risk of ovarian cancer in women based on their age. Women with an average risk of occurrence are tested for the CA-125 level annually, whereas an intermediate risk calls for a test in every 3–4 months. A transvaginal scan (TVS) is referred to when there is an elevated risk of ROCA (Skates, 2012). Using data from the UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS), the Markov model of this study predicted a reduced mortality of 10% in contrast to the 11% found in the UKCTOCS trial. The screening has marginally improved ovarian cancer mortality but at great financial expense (Naumann and Brown, 2018).

Defined by Jacobs et al., in 1990, the formula used to define RMI was calculated as RMI = U x M x serum CA-125, in which M and U represent menopausal status and ultrasound results, respectively (Jacobs et al., 1990).

Later, in 2014, Javdekar and Maitra employed the Risk of Malignancy Index 2 (RMI 2) described by Tingulstad et al., value to distinguish between benign and malignant adnexal masses within a cohort of 58 women and found that RMI >200 had 70.5% sensitivity and 87.8% specificity (Tingulstad et al., 1999; Javdekar and Maitra, 2015).

Combining CA-125 and HE-4 levels, a multimarker assay was established by Moore et al. to foretell OC in women with a pelvic mass. The study included 566 women who were above the age of eighteen and had been diagnosed with an ovarian cyst or a pelvic tumor that would require surgery. Using the specified prediction probability criteria for premenopausal and postmenopausal women, this dual marker algorithm grouped individuals into high and low-risk malignancy groups (Moore et al., 2009).

The first MIA to be granted approval in the U.S. by the U.S. Food and Drug Administration (FDA) in September 2009, this serum-based screening test predicts ovarian cancer malignancy by integrating the measured levels of five different proteins: transferrin, β-2 microglobulin, apolipoprotein A1, transthyretin, and CA 125-II. Based on menopausal status, an Ova 1 score is assigned, which designates a high malignancy when more than 5 or 4.4 in premenopausal and postmenopausal women, respectively (Muller, 2010; Ueland, 2017). In addition to the protein biomarkers previously mentioned, Ova-1 comprises menopausal status and imaging data (Jordan and Bristow, 2013).

In the 1970s, the first reports of circulating tumor DNA (ctDNA) being found in cancer patients’ blood appeared by Leon et al., which is now considered the most effective biomarker for OC. ctDNAs get actively released into the blood by cancer cells or apoptotic or necrotic cells. Since it reflects the genetic constitution of the cancer cell, it can be well exploited for examining the molecular makeup of the disease (Leon et al., 1977). Hou et al. investigated the prospective applications of circulating tumor DNA (ctDNA) as a prognostic biomarker for epithelial ovarian cancer by evaluating the correlation between ctDNA and CA-125 glycoprotein, pre-and post-treatment levels. The study showed that circulating tumor DNA strongly predicted cancer disease relapse, whereas the presence of CA-125 was not that effective for the same (Hou et al., 2022). Recently, Chen et al. developed a sensitive electrochemical biosensor for detecting DNA methylation in blood. The study detected DNA methylation in ovarian cancer patient blood samples and reported a detection limit of 2 aM of 110 nucleotide methylated DNA with single-site methylation (Chen et al., 2022).

qRT-PCR is an amplification-based real-time expression analysis method for nucleic acids. On the basis of the cycle threshold (Ct) value and amplification plot, the miRNA expression is quantitated. It is a gold standard and one of the widely used methods for miRNA analysis. This method requires a smaller sample amount and is very sensitive and specific, but it is a contamination-prone method, necessitating extremely precise handling (Koshiol et al., 2010). Over the years, different forms of qRT-PCR has emerged, enabling extensive sensitivity to miRNA detection. qRT-PCR is a high throughput technique which can also be used to check the expression of precursor miRNAs (pre-miRNAs) (Schmittgen et al., 2004). The need to design individual target-specific fluorophore labelled probes makes the technique less cost effective and unsuitable for large amounts of miRNA analysis. Also, because the quality of the miRNA extract significantly impacts miRNA analysis using qRT-PCR, the results of miRNA analysis vary depending on the sampling technique used on the same sample. For example, Taylor et al. reported that a qPCR experiment could encounter errors associated with subsampling and biological variability, as well as technical errors related to the calibration of the equipment used (Taylor et al., 2019). In a recent study designed by Hu et al., graphene oxide (GO) nanoparticles were used to detect miRNA via qRT-PCR, which enhanced the specificity and sensitivity of the assay. Adding GO to the qPCR reaction assay significantly enhanced the PCR efficiency by minimizing the non-specific amplification and strengthening primer-template hybridization efficiency. Also, when compared to conventional qRT-PCR, the concentration of primers in the region where GO exists was comparatively higher due to GO-mediated adsorption of primers by π-π stacking, in turn increasing the efficiency of primer-template hybridization during annealing (Hu et al., 2021).

Northern blotting, which is based on molecular hybridization and gel electrophoresis, performs quantitative miRNA analysis. Typically, in a Northern blot, RNAs with different molecular weights and lengths are run under an electric field, separated by gel electrophoresis, and then detected using a fluorophore-tagged probe that hybridizes with the target RNA. This method of RNA estimation does not require any preliminary processing of RNA and makes the process reliable due to no change to the nucleotide bases (Ouyang et al., 2019). Northern blotting can detect the size of mature RNAs and precursor RNAs (Koscianska et al., 2011). However, it has a few constraints-the process is labor-intensive and needs expensive reagents and a lot of starting material, making it less suitable for high throughput studies. This method is complex and has low throughput, which requires the use of radiolabeled oligonucleotide probes. (Koshiol et al., 2010). Other drawbacks of the northern blotting technique includes the use of carcinogenic chemicals such as formaldehyde, ethidium bromide and radioactive probes and the sensitivity of the sample towards RNAse degradation (Kilic et al., 2018).

Thanks to the potent high-throughput capabilities of microarray technology in which a single experiment may monitor the expression of thousands of short non-coding RNAs simultaneously in hundreds of samples processed (Liu et al., 2008). This method is high throughput and requires much less RNA as starting material. Although it is the most widely used technique for miRNA detection, the process is costly due to fabrication costs, and it may cause cross-hybridization between homologous miRNA sequences, reducing its sensitivity (Koshiol et al., 2010; Kilic et al., 2018). Including 428 patients with ovarian tumors, Yokoi et al. designed a microarray-based diagnostic model which had a 99% sensitivity and 100% specificity to check the expression levels of ten miRNAs which were further verified using qRT-PCR to increase the accuracy as well as the usefulness of the developed model (Yokoi et al., 2018).

For miRNA analysis, high throughput sequencing or next-generation sequencing (NGS) are frequently employed to obtain genetic information. Despite advancements in sequencing techniques, this approach needed to overcome certain limitations. For instance, as having a complex workflow, the technical strategies used for library preparation can impact the detection of microRNA by NGS by skewing the relative abundance of the chosen microRNAs across various samples. Shi and group have reported that when using RNA-seq technology, biases and errors can be introduced at different stages, including sample preparation, library building, sequencing, and imaging (Shi et al., 2021). A comparative analysis and limitations of the conventional miRNA detection methods are depicted in Figure 1 (Koshiol et al., 2010; Chugh and Dittmer, 2012; Hardikar et al., 2014; Islam et al., 2017; Dave et al., 2019).

Including studies that demonstrated a relationship between miRNA expression and EOC, a systematic review was done by Ferreira et al., in 2020, which included articles published before February 2019, in which twelve miRNAs were found to have prognostic and predictive value. miRNA features, such as their expression profile in OC tissue and serum and chemosensitivity to paclitaxel or platinum-based drugs were considered for the study. By analyzing their predictive response and prognostic features, their significance in cancer management was evaluated (Ferreira et al., 2020).

A meta-analysis of miRNA microarray by Wang et al. identified the significance of miR-27a in ovarian cancer cell proliferation and migration. It was observed that miR-27a can promote cancer progression by modulating FOXO1 expression (Wang et al., 2018). Another latest work conducted by Frisk and team done a systematic review and meta-analysis to identify potential circulating miRNAs for early diagnosis of OC. A total of 22 articles were taken into consideration for the study of the quantitative meta-analysis, among which nine miRNAs were found to have dysregulated expression in OC datasets, including miR-21, miR-125, miR-141, miR-145, miR-205, miR-328, miR-200a, miR-200b and miR-200c (Frisk et al., 2023). Meta-analytical studies identifying ovarian cancer based on dysregulated miRNA expression are tabulated in Table 6.

Similar to metanalysis, White et al. worked to understand the mechanism of post-transcriptional regulation of KLK gene expression and its regulation by miRNA, for which the expression of miRNAs in OC was analyzed in silico. A total of ninety-eight miRNAs were found to carry altered expression when studied in silico among which three miRNAs were expected to target KLK10 gene, which led to reduced cell proliferation and less cancer progression. miRNAs, miR-224, let-7f and miR-516a, were shown to successfully target the gene, causing reduced expression of KLK10 protein and less cellular growth, suggesting their efficient therapeutic application in OC treatment (White et al., 2010).

Combining bioinformatics and meta-analysis, Wu and team screened potential serum miRNAs for ovarian cancer diagnosis using in silico approach. Sequencing data from the Gene Expression Omnibus (GEO) database were selected, and differentially expressed miRNAs were profiled. Five miRNAs, miR-200a, miR-200b, miR-25, miR-200c and miR-429, were deregulated in ovarian tumor patients when compared to healthy controls with an integrated sensitivity and specificity of 64% & 88%, respectively, and AUC value of 0.78. At the same time, miR-200 family produced a combined AUC value of 0.78 with a sensitivity of 64% and specificity of 88% when analyzed using meta-analysis (Wu et al., 2019). Table 7 concludes in silico studies studying the significance of aberrantly expressed miRNAs on ovarian cancer patients.

Hamidi and team identified circulating miRNAs as biomarkers to predict the occurrence of OC utilizing a machine learning approach. In this study, Boruta, random forest-based variable selection approach in machine-learning technique, and other statistical approaches were used, which reported ten distinguishing miRNA biomarkers, namely miR-1233-5p, miR-4675, miR-1290, miR-1914-5p, miR-1469, miR-6784-5p, miR-6800-5p, miR-3184-5p, miR-5100 and miR-1228-5p, with differential expression in ovarian cancer datasets than in normal cases (Hamidi et al., 2023). Another study used the Evolutionary Biomarker Search Tool (EBST), a novel technique based on gene expression data to discover microRNAs with biomarker potential in ovarian cancer (Yaghoobi et al., 2020). After pre-processing the data and mathematical validations, the proposed model identified 11 miRNAs as biomarkers including, miR-1228-5p, miR-8073, miR-6756-5p, miR-3663-3p, miR-4697-5p, miR-6784-5p, miR-1307-3p, miR-1228-3p, miR-328-5p, miR-6821-5p and miR-1268a. This model had 100% sensitivity and 99.38% specificity with an accuracy of 99.69%.

Multi omics approach integrates various datasets based on transcriptomics, metabolomics, microarray, genomics and proteomics studies and is studied by biological scientists to extract the required information of a biological system or a cell and describe the disease profile. Analyzing miRNA data by multi omics approach based on miRNA sequencing, the Indian cohort’s HGSOC samples displayed significant expression levels of the miR-200 and miR-1269a families (Mhatre et al., 2023). Their results displayed population-specific gene expression patterns and molecular signatures based on the histotypes found in the Indian cohort. In a related work that focuses on the multi omics approach, by combining transcriptome data with genome-scale biomolecular networks, it was discovered that miR-16-5p expression and miR124-3p expression varied in ovarian cancer tissues and can act as potent biomarkers for ovarian cancer detection (Gov et al., 2017). Differential network mapping is often used to study molecules associated with disease occurrence and progression by investigating existing datasets.

The current conventional methodologies for cancer biomarker detection quickly give way to sensor-based alternatives. Biosensors are compact, quick, extremely sensitive, and selective devices. A biosensor has two basic elements: the biorecognition part, which is responsible for recognizing and interacting with the target molecule, and the transducer part, which converts this interaction into readable signals (Ivanov et al., 2022). Extensive research is ongoing to develop miRNA-based sensors for disease detection, including cancers.

For instance, in a recent work by Ivanov et al., three ovarian cancer-associated plasma miRNA biomarkers, miRNA-21, miRNA-141, and miRNA-200a were detected using silicon nanowire-based biosensors (Ivanov et al., 2022). Here, oligonucleotide probes (oDNA probes) which are complementary to target miRNA, were immobilized on a silicon nanowire-modified sensor surface which detected the miRNAs with a sensitivity of 1.1 × 10⁻¹⁶ M. In this study, it was stated that being non-amplification-based method, this strategy was less prone to contamination, allowing high specificity in contrast to qPCR, which has sensitivity but is extremely prone to sample contamination. Similarly, in another study, Moazampour and team designed an electrochemical biosensor having surface modification with L-cysteine functionalized Zinc sulfide quantum dots with a detection range between 1.0 × 10⁻¹⁴ to 1.0 × 10⁻⁶ M for miR-200a, a potential ovarian cancer biomarker (Moazampour et al., 2021). QDs functionalized with L-cysteine allowed immobilization of DNA probe on the glassy carbon electrode (GCE) surface, which differentiated target miRNA-200a strand from a single-base mismatch in miRNA strand and confirmed using electrochemical impedance spectroscopy (EIS). This label-free genosensor with the surface functionalization provided an extra active site for easy capture of the probe upon EDC/NHS activation without the requirement of sample pretreatment and led to a sensitivity of 8.4 fM in contrast to other similar works. Table 8 summarizes research reports on miRNA-based biosensors for ovarian cancer detection. Another latest study by Islam et al. included graphene oxide-loaded iron oxide (GO/IO hybrid material) having RuHex [ruthenium hexammine (III) chloride (Ru (NH₃)₆]³, reducing and electrocatalytic activity for ultrasensitive detection of miR-21 up to 1.0 fM. Here, the screen-printed carbon electrode (SPCE) was modified with the hybrid nanomaterial, responsible for the specific adsorption of target miRNA to the sensor surface and producing a chronocoulometric signal (Islam et al., 2018).

Despite the availability of multiple miRNA detection techniques, a quicker and more precise method for diagnosis is still required and herein lies the application of microfluidic systems. Microfluidic devices have a controlled fluid flow comprised of small parts, now among the highly prominent and accepted platforms for cancer diagnosis, prognosis and research. A basic representation of microfluic system is shown in Figure 2. miRNA-based integrated microfluidic devices are among the most sensitive and user-friendly devices developed so far in modern medicine. Apart from being highly sensitive, these offer other advantages such as the requirement of less amount of sample, miniaturization, real-time output with less reaction time, and ease of use. To detect miRNA levels, optical or electrochemical biosensors are frequently coupled with microfluidic technologies.

By integrating a microfluidic platform with digital PCR (dPCR), Sung et al., quantified the amount of plasma miRNA-21 with a detection limit of 1.4 attomolar for ovarian cancer detection. This design showed an increased PCR efficiency with absolute miRNA quantification with no sample pretreatment and detection of the target at an extremely low concentration (Sung et al., 2021). A similar design by the same team was again reported in 2022, which depicted the quantification and detection of extracellular vesicle (EV) derived miR-21 and miR-200a by integrating multiplex digital PCR and microfluidic system for early screening and prognosis of ovarian cancer. Here, the entire process from EV extraction to multiplex dPCR was integrated on the microfluidic chip. The target miRNA was quantified in a brief amount of time—120 minutes—and the PCR calibration curve displayed a high linearity rate with no false positive signal for the simultaneous detection of both miRNAs. This design also had an EV capture rate that was 1.8 times higher. (Huang et al., 2022). Another study discussed various aptamer-based microfluidics systems for detecting ovarian cancer via targeting biomarkers such as CA-125 and CEA and ovarian cancer cells (Vandghanooni et al., 2021).

A non-PCR based miRNA detection employing fluorescence has been widely used via a toehold-mediated strand displacement (TMSD) reaction for disease diagnosis, including cancers. It is driven by Gibb’s free energy, involving three steps, including target toehold binding with the invader strand, which is followed by branch migration and chain dissociation (Mayer et al., 2022). Recently, a TMSD-based method employing fluorescence has been developed by Sun et al., in 2023 to measure miRNA-21 and diagnose ovarian cancer early. The study developed an enzyme-free, rapid, non-PCR dependent, isothermal signal amplification assay with 6.05 pM as the lower detection limit. Furthermore, a strong linear correlation between the concentration of target microRNA and the strength of the fluorescence signal was found using quantitative analysis based on the change in fluorescence intensity at miR-21 concentrations ranging from 10 to 100 nM (Sun et al., 2023).

Another recent technological progress in miRNA detection includes the CRISPR/Cas system, which is an amplification-free approach which employs liposome-encapsulated sensing components to deliver the components inside an extracellular vesicle (EV) through EV-liposome fusion, allowing for the detection and quantification of target miRNA. For instance, Hong et al. developed a liposome fusion-based CRISPR/Cas13a system by incorporating CRISPR components (Leptotrichia wadei/LwaCas13a, crRNA, and FQ probes) into a liposome to detect miR‐21‐5p present inside the tumor-derived extracellular vesicles of ovarian cancer cell lines. This approach, which required neither RNA nor EV extraction achieved a high degree of specificity in identifying intact EVs containing target miRNA and a liposome fusion efficiency over 80% (Hong et al., 2023).