Novel methodology for the digital analysis of circulating tumor cells in ovarian cancer

Novel methodology for the digital analysis of circulating tumor cells in ovarian cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Novel methodology for the digital analysis of circulating tumor cells in ovarian cancer Abigél Mészáros, Dávid Kis, Péter Hunyadi, Szabolcs Máté, Ágnes Égető, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8106577/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Purpose Ovarian cancer (OC) is the deadliest gynecological cancer, with late-stage diagnosis and frequent relapse. Improved monitoring tools are urgently needed. Circulating tumor cells (CTCs) are promising biomarkers, but current immunostaining methods are not sensitive enough. This study aimed to develop an ultrasensitive digital PCR (dPCR) assay and define a gene expression signature to track tumor burden and recurrence. Methods We identified candidate mRNA markers using in silico analysis and literature review. Sensitivity was evaluated using spike-in experiments, where ovarian cancer cell lines (OVCAR-3, OVCAR-5, IGROV-1) were added to 3 mL of healthy donor blood at defined numbers (0, 5, 10 or 100). CTCs were isolated with the CD-Prime platform, followed by RNA extraction, reverse transcription, and dPCR quantification. A four-gene panel ( EpCAM, FOLR1, WFDC2, PPIC ) was optimized based on performance. Although SLC34A2 showed limited sensitivity in clinical samples, it was retained for technical compatibility due to co-amplification with WFDC2 . The assay was then tested in paired pre- and postoperative blood samples from five patients with high-grade serous OC and five healthy controls. Results Spike-in experiments confirmed assay sensitivity, with no markers detected in 0-cell controls and significant detection at 100-cell samples (p < 0.05). All patient samples tested positive for at least one marker at both time points, while all controls remained negative. Conclusion The RNA-based four-gene dPCR panel enables highly sensitive detection of CTCs in OC. Its ability to detect CTCs pre- and postoperatively supports its potential as a non-invasive tool for monitoring and early relapse detection. Biological sciences/Biological techniques Health sciences/Biomarkers Biological sciences/Cancer Biological sciences/Molecular biology Health sciences/Oncology Liquid biopsy Circulating tumor cells digital PCR Ovarian cancer Gene expression Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 INTRODUCTION Ovarian cancer and emerging biomarkers in diagnosis and treatment Ovarian cancer (OC) is the most lethal gynecological malignancy and the third most common cancer of the female reproductive system. It ranks as the fifth leading cause of cancer-related death among women [ 1 ]. High-grade serous ovarian carcinoma (HGSOC), the most prevalent histological subtype, accounts for approximately 70% of all OC cases. Typically diagnosed at an advanced stage, HGSOC often responds initially to standard treatment regimens consisting of primary cytoreductive surgery followed by platinum-based chemotherapy. However, despite this aggressive first-line approach, approximately 80% of patients experience recurrence, frequently accompanied by the development of chemoresistance, which significantly complicates subsequent treatment and worsens prognosis [ 2 – 4 ]. Circulating tumor cells Liquid biopsy has emerged as a powerful alternative to traditional tumor biopsies, offering several key advantages, including minimally invasive sampling, enhanced detection of tumor heterogeneity, and the ability to collect serial samples over time to monitor disease progression and treatment response [ 5 , 6 ]. By analyzing multiple blood samples from the same patient throughout the course of treatment, researchers can monitor dynamic changes in the genomic profile of circulating tumor cells (CTCs), providing insights into metastatic evolution and intratumor heterogeneity [ 7 ]. CTCs, which originate from primary or metastatic tumor sites, are shed into the bloodstream and can be broadly categorized into three phenotypic types: epithelial, mesenchymal, and hybrid. Although most CTCs are rapidly cleared by the immune system, a small subset with high metastatic potential can evade immune surveillance. These cells are capable of initiating micrometastases, contributing to disease progression and recurrence [ 8 ]. Given their close genomic resemblance to the primary tumor, CTCs serve not only as key drivers of metastasis but also as valuable, blood-accessible biomarkers that can provide critical genetic information without the need for invasive procedures [ 9 ]. However, the clinical utility of CTCs is complicated by their significant heterogeneity. They vary widely in morphology, size, molecular profile, metastatic capacity, and chemoresistance. Moreover, differences in cellular deformability and surface marker expression challenge the development of a universal method for CTC capture. The epithelial-to-mesenchymal transition (EMT), a process that alters CTC phenotype and facilitates invasion and dissemination, further complicates detection and isolation [ 10 , 11 ]. Reliable single-cell analysis of CTCs requires high-efficiency capture methods capable of reflecting this intrapatient heterogeneity. Nonetheless, in some patients, the number of CTCs in peripheral blood is extremely low or undetectable, limiting the sensitivity and robustness of current detection platforms and underscoring the need for continued technological refinement [ 12 ]. Despite recent advancements in CTC research and detection technologies, the clinical translation of CTC-based assays remains limited. The inherent rarity, fragility, and phenotypic diversity of CTCs present ongoing challenges, which have delayed their integration into routine clinical practice and hindered progress in elucidating the mechanisms underlying metastasis [ 13 , 14 ]. CTC enrichment and detection CTC enrichment is a critical step in liquid biopsy-based cancer diagnostics. Current technologies leverage either biological markers or physical properties of CTCs to separate them from blood cells. Among physical property-based methods, size-based enrichment has proven particularly promising due to the generally larger size of CTCs compared to leukocytes. Microfluidic platforms and filter-based devices utilize this difference to isolate CTCs without relying on labeling [ 15 ]. These approaches enable high-throughput, cost-effective, and label-free separation. However, technical limitations such as sample pretreatment requirements (e.g., red blood cell removal) and membrane clogging can hinder performance [ 15 , 16 ]. Density-based techniques, such as density gradient centrifugation, offer a low-cost enrichment option but have limited specificity due to overlapping densities of CTCs and white blood cells. Additionally, these methods may cause unintended activation of immune cells, negatively affecting downstream analyses [ 17 ]. Affinity-based methods, including immunoaffinity and immunomagnetic techniques targeting epithelial markers like epithelial cell adhesion molecule (EpCAM), offer high specificity but are limited by CTC heterogeneity, particularly in mesenchymal and stem-like cells with low EpCAM expression, and require extended processing times for effective antigen-antibody interactions [ 15 , 17 ]. The use of a limited number of markers during immunocapture of heterogeneous CTC populations can lead to increased false negatives. In light of this limitation, analyzing the expression of CTC-specific genes may offer a more reliable alternative, particularly in OC patients [ 15 ]. In contrast, label-free microfluidic technologies that combine multiple biophysical parameters (size, deformability, dielectric properties) offer improved sensitivity and cell viability. These systems reduce marker bias and preserve the functional integrity of isolated cells for further analysis [ 16 ]. In our study we applied the Fluid Assisted Separation Technology (FAST by the CD-Prime device (Clinomics Inc., South Korea)). The CD-Prime FAST is a compact, centrifugal microfluidic platform engineered for rapid, label-free isolation of viable CTCs directly from whole blood. Its tangential-flow-like filtration mechanism, in which centrifugal force is applied perpendicularly to membrane filtration, minimizes clogging and enhances membrane utilization by maintaining an aqueous phase beneath the filter throughout processing [ 18 ]. This high-throughput system demonstrated a CTC recovery rate of 96.2 ± 2.6% in repeated tests with spiked samples and showed reliable detection in cancer patients without distant metastasis, highlighting its promise for early-stage cancer diagnostics. Furthermore, the isolated cells remain viable and are suitable for downstream molecular and imaging analyses, including single-cell gene expression profiling, which has revealed significant inter-patient variability in CTCs [ 19 ]. The FAST disc's efficiency, simplicity, and applicability across size-based filtration methods make it a valuable tool in both research and clinical oncology [ 18 , 19 ]. Digital PCR Digital PCR (dPCR) is an advanced molecular technique that enables precise and absolute quantification of nucleic acid targets without the need for standard curves or inter-run calibrators. Unlike traditional quantitative PCR (qPCR), dPCR functions as an end-point assay, providing greater resistance to inhibitors and improved sensitivity for detecting low-abundance targets, such as gene transcripts from rare CTCs. The method employs microfluidic partitioning to divide the reaction mixture into thousands to millions of nanoliter-sized droplets, each functioning as an individual PCR microreactor. Target quantification is achieved by the end-point analysis of positive and negative partitions. The mean number of target sequences per partition is quantified by applying a Poisson correction to the fraction of positive partitions. This compensates for the possibility that multiple copies of the template may be present in certain partitions. This partition-based approach ensures high accuracy, particularly when analyzing rare genetic events or low-copy-number targets [ 20 , 21 ]. OBJECTIVE Immunostaining is currently the most widely used method for studying CTCs; however, its sensitivity is limited, and its applicability is constrained by the need for manual image verification, fluorescence thresholding, and complex instrumentation. To address these limitations, the objective of this study was to develop an ultrasensitive, RNA-based assay for the non-invasive detection and molecular characterization of CTCs in OC. The proposed approach combines size-based enrichment of unfixed CTCs using the CD-Prime system with absolute quantification of CTC-derived gene expression by dPCR. The assay was applied to blood samples collected from patients with International Federation of Gynecology and Obstetrics (FIGO) stage III–IV HGSOC at two clinical time points: prior to neoadjuvant therapy and postoperatively, before the initiation of adjuvant treatment. By profiling gene expression signatures in enriched CTCs, this study aims to establish a highly sensitive alternative to traditional immunostaining methods and to explore the dynamic molecular landscape of CTCs during OC treatment. MATERIALS AND METHODS Ovarian cancer specific marker selection To develop the ovarian CTC dPCR assay, we first analyzed publicly available databases, including The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) to identify transcripts with abundant expression in normal ovary tissue (lineage markers) or OC (OC markers). Raw count data from around 200 samples were downloaded from each of the three cohorts: OC tissue from TCGA-OV, healthy ovary and whole blood data from the corresponding GTEx V8 datasets. Differential expression analysis was performed with the edgeR package from the Bioconductor ecosystem. In order to select OC markers, we looked for transcripts with significant log-fold change between expression levels (calculated from cpm values) in cancer tissue and whole blood (cutoff: >4) as well as between cancer tissue and matched healthy tissue (cutoff: >3). To eliminate genes which would have low expression value in cancer tissue, a cutoff of > 100 median cpm in the OC tissue samples was defined. The markers that the in silico approach ( SI 1 ) and the literature review identified were further analyzed. We assessed the expression of the top candidate genes using quantitative real time PCR (RT-qPCR) in three OC cell lines, as well as in white blood cells (WBCs) (in six female healthy donor-derived WBCs) and CD-Prime FAST-Auto disc–captured cells obtained from five female healthy donors (HDs). Spike-in test and in vitro marker set verification Spiking studies (Fig. 1 ) were performed to establish the accuracy of the newly developed CD-Prime size-based CTC enrichment followed by the dPCR method of detecting cancer cells in the blood. Human OC cell lines (OVCAR-3, OVCAR-5, IGROV-1) were used to validate the efficiency and the limit of detection (LOD) of the enrichment and detection method in spike-in experiments. Cancer cell lines were selected from the NCI-60 Human Tumor Cell Lines Screen group based on the target gene expression profiles and their OC background. The OVCAR-5 cell line, though its classification as OC has been debated, was accepted as such based on the NCI-60 ovarian cancer panel list, Human Protein Atlas Ovarian Cancer Cell Lines list and literature data [ 22 – 24 ]. OC cell lines were obtained from the Developmental Therapeutics Program, National Cancer Institute at Frederick, MD. Cell lines were maintained in RPMI medium (Thermo Fisher Scientific) supplemented with 10% FBS, 1% penicillin-streptomycin, and 1% glutamine. All cell cultures were maintained at 37°C in a humidified atmosphere with 5% CO 2 and tested negative for Mycoplasma . Cells were trypsinized at about 70% confluence and counted by trypan blue exclusion. First, a cell suspension in the order of magnitude of 10 4 cells/mL was made in serum-free medium, and then a dilution of 833 cells in 5 mL, that was incubated with the Calcein-AM solution in a final concentration of 250 nM for 10 min. The diluted cell suspension was aliquoted to cell-repellent U-bottom 96-well microplates (Greiner Bio-One) in a 30 µL final volume (5 cells per well on average). Live cancer cells were identified by Calceinfluorescent signal on the GFP channel of the JULI Stage live cell imaging system, and the exact cell numbers were counted in each well (NanoEntek, Seoul, Republic of Korea) ( SI 2 ). Wells with different numbers (0, 5, 10) of cancer cells were picked and spiked manually by transferring the tumor cells with a 1% BSA-coated pipette into 3 mL blood from female HDs. In parallel, 100 cancer cells from the original serum-free cell suspension were aspirated into 3 mL of blood as well. 5, 10, and 100 cancer cell samples were tested in triplicate, and 3 mL blood without cancer cells were tested in quintuplicate. The samples were processed by the CD-Prime FAST-Auto disc CTC enrichment procedure and recovered cancer cells were detected by the expression of the tumor marker genes by dPCR assay. The absolute copy number of target genes was analyzed by QIAcuity Software Suite 2.2.0.26 (Qiagen). Clinical sample collection Human blood samples were collected at the Department of Obstetrics and Gynaecology, Semmelweis University (Budapest, Hungary), between 2022 and 2024. The histopathological confirmation was made using Formalin-Fixed Paraffin-Embedded (FFPE) tissue samples by the Department of Pathology and Experimental Cancer Research, Semmelweis University. All patients and HDs recruited in the present study signed informed consent forms approved by the Ethics Committee of the National Center for Public Health and Pharmacy (16119-8/2022/EÜIG, BM/28859-3/2023). Patients with primary tumor confirmed as HGSOC were included in the patient group. The control group consisted of healthy women, donors with a history of tumors or concurrent pregnancies were excluded. For the training set, samples were collected from 8 female HDs and 23 preoperative HGSOC patients who had not received neoadjuvant therapy prior to sample collection. These clinical samples were used for testing and fine-tuning the in vitro validated marker set. For the validation set, pre- and postoperative samples of five HGSOC patients and five age-matched controls were selected to demonstrate the potential application of the finalized marker panel. These patient cases met the following inclusion criteria: initial sampling prior to neoadjuvant therapy and availability of a postoperative sample collected 1–2 months after surgery, but before the initiation of adjuvant therapy. The first sampling was performed before surgery prior to neoadjuvant therapy. The postoperative sampling was conducted before the initiation of adjuvant therapy ranging from 29 to 55 days, with a median of 47 days after surgery (Fig. 2 ). For one patient (Patient 7) with confirmed relapse, multiple samples were enrolled throughout the course of therapy. Longitudinal sampling was conducted in association with the following clinical events: preoperative neoadjuvant therapy, surgery, and postoperative adjuvant therapy. A total of five samples were collected from the patient: three preoperative and two postoperative. The intervals between sample collections ranged from 42 to 67 days. Tumor recurrence was confirmed 4.5 months after the final sampling. Peripheral venous blood (6 + 9 mL) was collected into Vacuette® K 2 EDTA tube (Greiner Bio-One GmbH, Kremsmünster, Austria) and to avoid potential epithelial cell contamination, the first 6 mL of blood were discarded before each collection of blood samples. The blood sample was accurately inverted and rotated ten times immediately after collection and stored at 4°C until proceeding, for a maximum of 4 hours. Marker set analysis in clinical samples During the selection and in vitro verification of OC specific markers, 12 genes ( EpCAM, PPIC, FOLR1, WFDC2, SLC34A2, KLK5, KLK6, MUC16, MUC4, PRAME, SOX17, TUSC3 ) were identified as appropriate markers for dPCR based on wet lab analyses ( SI 3 ). In the training set testing, the detectability of these markers was assessed using clinical samples from 23 HGSOC patients who had not received neoadjuvant therapy prior to sample collection (specific primers listed in Table 1 ). The aim of this study was to develop a panel comprising the most sensitive molecular markers for detection of OC. Markers that were detectable in fewer than three patient samples were classified as low-sensitivity markers and excluded from further analysis. Consequently, a CTC detection panel tailored for OC patients comprising only the high-sensitivity genes was assembled. In order to demonstrate the methodological potential and validate the feasibility of the detection approach, a second set (i.e. the validation set) of pre- and postoperative OC samples and age-matched controls (5–5) was analyzed with the CTC panel. Circulating tumor cell enrichment CD-Prime enables CTC enrichment from whole blood with an antibody-independent, simple, fast and high-throughput automated protocol. The polycarbonate membrane in the device with 8 µm pores is considered to be a gold standard and cut-off pore size to isolate CTCs. Briefly, 9 mL of peripheral blood collected in VACUETTE® K2EDTA tube was centrifuged at 1150 x g for 10 min at 4°C and the plasma layer was carefully removed, after than buffy coat layer was separated in 1 mL volume from the top of the red blood cell section and transferred to a 15 mL tube. Whole blood centrifugation and buffy coat separation were carried out within 4 hours after sample collection. Due to the very low number of CTCs and to prevent cell adhesion all consumables in contact with blood were coated with 1% BSA solution (Sigma, St. Louis, MO). The 1 mL buffy coat was supplemented to 3 mL with 1x PBS (Bio-Rad Laboratories, Hercules, CA) and injected to FAST-Auto disc to the blood input compartment. 6 mL of 1x PBS was also added to the FAST-Auto disc to the wash input compartment. CD-PRIME FAST-Auto automated protocol was used for CTC enrichment with 5min/sample runtime. Captured cells on the membrane were lysed in 350 µL of buffer RLT Plus (Qiagen, Hilden, Germany) with 3.5 µL of 2-mercaptoethanol (Sigma), stored at − 80°C until RNA isolation, and subjected to dPCR detection. RNA isolation, reverse transcription, and quality check Total RNA from enriched cells was extracted using RNeasy Micro kit (Qiagen) according to the manufacturer’s manual with on-column DNase treatment. The elution step was performed twice with 30 µL of RNase-free water to enhance the eluted RNA quantity. Due to the expected ultra-low RNA yields, all the samples were directly reverse transcribed. Complementary DNA (cDNA) synthesis was performed using the qScript Ultra Supermix system (Quantabio, Beverly, MA) for RT-dPCR. Briefly, the following conditions were applied in a total volume of 37.5 µL: 30 µL of template RNA and 7.5 µL of 5x qScript Ultra Supermix (Quantabio) were mixed and incubated for 2 min at 25°C, followed 10 min at 55°C, and then 1 min at 95°C. The product of cDNA synthesis reaction was stored at − 20°C or used for RT-qPCR quality check immediately. In order to avoid false negative results of cancer-related transcripts due to low-quality RNA and/or cDNA, we have introduced a cost-effective and fast quality control step between the reverse transcription and dPCR measurements. The quality checkpoint was performed from 1/10 diluted cDNA by RT-qPCR using Perfecta SYBR Green FastMix, Low ROX (Quantabio) with initial denaturation (95°C, 30 sec) and 40 cycles of denaturation (95°C, 15 sec), annealing (59°C, 15 sec) and extension (72°C, 15 sec) on a QuantStudio 5 Real Time PCR (Applied Biosystems, Thermo Fisher Scientific, Ottawa, CA). Primer sequences were the following: ACTB (forward) AGAAAATCTGGCACCACACC and (reverse) TAGCACAGCCTGGATAGCAA [ 25 ]. The quality of cDNA was accepted in case of the β-actin gene transcript Ct value was < 35.0. Each RT-qPCR run included minimum of one positive control (10 ng PBMC) and no template control (NTC) sample. Digital PCR analysis dPCR is advantageous as it has been shown to have higher sensitivity and accuracy compared to traditional qRT-PCR and allows direct quantification of nucleic acids, multiclonal amplification, and greater resilience to inhibition from a wider range of samples. Importantly, since it does not require standard curves, it provides an absolute measure of the nucleic acid content, with significantly increased detection sensitivity. DPCR reactions were performed in QIAcuity 24-well 26k (26,000 partitions per well) nanoplates in a QIAcuity Digital PCR System (Qiagen). Ovarian CTC assay reactions included 0.4 µM final concentrations of primer pairs and 4 µL cDNA template in 3X QIAcuity EvaGreen Master Mix (Qiagen) in a 40 µl total volume. DPCR thermal conditions were used an initial heat activation 95°C for 2 min step and optimized as 50 cycles of 95°C for 15 sec (denaturation), 59°C for 15 sec (annealing), and 72°C for 15 sec (extension) following 40°C for 5 min cooling down before reading. The following settings were used for imaging: exposition: 50ms, Gain: 6. The concentration of target gene expression (copies/µL) was determined by absolute quantification by QIAcuity Software Suite version 2.2.0.26. (Qiagen). For positive controls, OVCAR-3, OVCAR-5, and IGROV-1 cell lines 5–20 ng cDNA were used depending on the expression level of the target gene in the positive control cell line. For the multiplex dPCR setups, we also used an additional cell line (A-431) with the consideration that only one of the markers would be expressed in this case, and the cell line was obtained from the American Type Culture Collection (ATCC). Non-template control (NTC) with all materials except cDNA, is also included in each digital PCR run. CTC-positive samples were defined based on dPCR analysis using the QIAcuity system. A sample was considered CTC-positive if at least two events (≥ 0.107 copies/µL) above the threshold were detected among 26,000 partitions, indicating a high confidence that the signal reflected true amplification of cancer-related transcripts. To establish marker-specific thresholds for CTC positivity, baseline event distributions were analyzed in HD samples. As a result, the cut-off value was not uniform but varied depending on the marker, reflecting differences in background signal across markers within the HD group. Samples with 0, 1, or 2 events below the marker-specific threshold (corresponding to 0, 0.053, or 0.110 copies/µL) were classified as CTC-negative. All NTCs also tested negative. The absolute quantification and transcript-level characterization of ovarian cancer-associated CTCs was adapted for dPCR by our group, in accordance with the Minimum Information for Publication of Quantitative Digital PCR Experiments (2020 guidelines) [ 26 ]. Primers Primers (Table 1 ) were synthesized by IDT (Integrated DNA Technologies, Coralville, IA). Primers were selected from the literature along the following directives: (i) excellent gene specificity for the study reliability, (ii) PCR product as short as possible for high PCR efficiency, (iii) almost the same melting temperature of different primer pairs for multiplexing. The used primers were aligned by Primer-BLAST to confirm gene specificity and low self-complementarity. Table 1 List of PCR primer sets. Oligonucleotide sequences were used for wet lab verification of 21 potential ovarian CTC markers in the present study. The genes of the 4-marker panel were highlighted in bold style. Target gene Sequence (5’-3') Forward / Reverse Amplicon length Source BCAM AGAGATGAACCCAGAGGGCT / GATAGTGCAGGGTGAGGTGG 200 bp [ 27 ] CLDN3 GCCACCAAGGTCGTCTACTC / CCTGCGTCTGTCCCTTAGAC 101 bp [ 28 ] EPCAM CCGCAGCTCAGGAAGAATGT / TCTCCCAAGTTTTGAGCCATTC 179 bp [ 29 ] ERCC1 GCCTATGAGCAGAAACCAGC / AATGTGGTCAGGAGGGTCTG 131 bp [ 30 ] FOLR1 AGGTGCCATCTCTCCACAGT / GAGGACAAGTTGCATGAGCA 135 bp [ 31 ] KLK5 AAGGCCCAACCAGCTCTACT / CCGAGACGGACTCTGAAAAC 99 bp [ 32 ] KLK6 TGGTGCTGAGTCTGATTGCT / CGCCATGCACCAACTTATT 60 bp [ 33 ] KRT19 TGCGGGACAAGATTCTTGGT / CGTTGATGTCGGCCTCCA 144 bp [ 29 ] KRT7 CATCGAGATCGCCACCTACC / GATATTCACGGCTCCCACTCC 82 bp [ 34 ] MAL2 ACGTAGCAGCCTCAATTTTTGC / CATCTTCGTAAAGCCAGACCC 76 bp [ 35 ] MUC1 GCATCAGGCTCAGCTTCTACT / GTCTCTCTGCAGCTCTTGGTA 324 bp [ 36 ] MUC16 AGCAGACAGCAGAGACTATCC / CTGGACTTCCCAACCATTCTG 108 bp [ 37 ] MUC4 GGAGAGGTATCGCCCTGATAGATT / ACGGTAGTTGGGCCTTTCTTC 97 bp [ 38 ] PPIC AGCAAGTTTCATCGTGTCATCA / TGGAAATGTCTCACCATAGATGC 102 bp [ 39 ] PRAME ACCTGGAAGCTACCCACCTT / AGATGCATCACATCCCCTTC 299 bp [ 40 ] PRSS8 GATTACTCCGGTCGGGGAC / ACGCCTTCATAGGTGATGCT 136 bp [ 41 ] SLC34A2 CTGAGGCACCTGTAACCAAGA / TGATCCCCGAGTCCTGAAGAG 120 bp [ 42 ] SOX17 AGTGACGACCAGAGCCAGAC / CCTTAGCCCACACCATGAAA 214 bp [ 43 ] SPON1 TCTTAGACTGCTGTGCCTGC / AACTTGTTTGACGCCTTCGC 191 bp [ 44 ] TUSC3 TCGGGGGAGGACAGAAGAAA / CGGAAGATTGAGCGTCTGGA 88 bp [ 45 ] WFDC2 AGAACTGCACGCAAGAGTG / TTGAGGTTGTCGGCGCATT 52 bp [ 46 ] Multiplexing in dPCR We optimized a multiplex assay on the QIAcuity dPCR system to detect two targets ( SLC34A2 and WFDC2 ) within a single fluorescence channel using EvaGreen chemistry. Primers for both targets were added to the reactions at equal concentrations and volumes. The separation of the two targets was possible due to the difference in amplicon lengths, which resulted in distinct fluorescence amplitudes in dPCR 1D scatterplots (Table 1 ). As a positive control, we used cDNA from the OVCAR-3 cell line, which yielded well-separated fluorescence amplitudes corresponding to the two targets. Using this reference and adjusting the threshold accordingly, amplification signals from patient samples could be clearly assigned to their respective targets, allowing us to identify the presence of each target in the tested samples [ 47 ]. Prior to the multiplex setup, each primer pair was also tested in singleplex dPCR reactions using two cell lines with differential gene expression: WFDC2 was expressed exclusively in A-431 cells, while SLC34A2 was expressed only in IGROV-1 cells (Fig. 3 ). These single-target controls confirmed that the primers did not interfere with each other and that their simultaneous use did not compromise assay sensitivity. Therefore, the multiplex approach was validated as both specific and robust (Fig. 4 ). Determination of serum Cancer antigen-125 level Cancer antigen-125 (CA125) is currently the only blood-based tumor marker recommended for clinical use in the diagnosis and management of OC. CA125 was assayed in the serum of patients collected in an additional tube at the same time as the CTC isolation analysis. The immunoanalysis assay was performed on Siemens Attelica IM analyzer (Siemens Healthineers AG, Forchheim, Germany) according to standard procedures with the cut-off value of 35 U/mL. Statistical analysis All statistical analyses were performed using GraphPad Prism version 9.5.1 (GraphPad Software, San Diego, CA, USA). Group comparisons involving more than two datasets were conducted using the non-parametric Kruskal–Wallis test, followed by Dunn’s multiple comparisons post hoc test to assess pairwise differences. To assess differences in age between patients with HGSOC and healthy controls, the Shapiro–Wilk test was first used to evaluate the normality of age distribution within each group. Based on the outcome of the normality test, either a non-parametric Mann–Whitney U test (for non-normally distributed data or small sample sizes) or an unpaired t test with Welch’s correction (for normally distributed data) was applied to determine whether there was a statistically significant difference in age between the two groups. A p-value of p < 0.05 was considered statistically significant. RESULTS Ovarian cancer specific marker selection This approach identified 36 potential markers ( SI 1 ) which has been supplemented to 44 potential markers with 8 extra genes based on literature review. We initially assessed the expression of the top 21 candidate genes using RT-qPCR in three OC cell lines, as well as in WBCs (in six female healthy donor-derived WBCs) and CD-Prime FAST-Auto disc–captured cells obtained from five female HDs. Nine of these genes showed detectable expression in WBCs, indicating high background levels, and were thus excluded from further analysis. The remaining 12 genes, which were not expressed in WBCs, were subsequently evaluated in clinical samples from OC patients ( SI 3 ). Feasibility evaluation of CTC digital detection using spiked samples To evaluate the newly developed technology, we micromanipulated individual cells from the OC cell lines OVCAR-3, OVCAR-5, and IGROV-1, and introduced them into 3 mL of whole blood obtained from female HDs. These spiked samples were then processed using the CD-Prime FAST-Auto system, followed by quantification via QIAcuity dPCR (Fig. 5 ). For RNA-based dPCR detection of CTCs in OC, we selected EpCAM, WFDC2, MUC16 , and SLC34A2 as candidate marker genes in spike-in experiments ( SI 4 ). We tested EPCAM expression in all cell lines, WDFC2 in OVCAR-3 and OVCAR-5, MUC16 in OVCAR-3, and SLC34A2 in OVCAR-3 and IGROV-1 ( SI4 ). Figure 6 A and 6 B show the expression of EPCAM in OVCAR-5 cells and WFDC2 in OVCAR-3 cells, respectively. The quality of cDNA generated from these samples was confirmed to be excellent, as assessed by RT-qPCR targeting β-actin ( ACTB ). To evaluate the detection strategy, we performed spiking experiments using the OVCAR-5 cell line, examining EpCAM expression in samples containing 0, 5, 10, and 100 cells. EpCAM was undetectable in control samples (0.017 ± 0.030 copies/µL), while a significantly elevated signal was observed in samples spiked with 100 cells (4.508 ± 0.199 copies/µL; p = 0.0132) (Fig. 6 A). Similarly, the introduction of 5, 10, and 100 OVCAR-3 cells into control blood samples resulted in a progressively increasing WFDC2 signal, with significantly higher expression detected in the 100-cell samples compared to the cell-free control (0.018 ± 0.031 copies/µL). The measured WFDC2 concentrations were 0.448 ± 0.311 copies/µL, 2.01 ± 1.271 copies/µL, and 10.063 ± 3.061 copies/µL for samples spiked with 5, 10, and 100 cells, respectively (Fig. 6 B). Patient characteristics For the training set, samples were collected from 8 female HDs and 23 preoperative HGSOC patients who had not received neoadjuvant therapy prior to sample collection. At the time of preoperative sampling, the age of 23 HGSOC patients ranged from 41 to 82.23 years, with a median of 62.03 years. The age of the control group ranged from 50.26 to 58.28 years, with a median of 53.11 years ( SI 5 ). The normality of age distribution in both the OC and control groups was assessed using the Shapiro–Wilk test. As both groups followed a normal distribution (p > 0.05), an unpaired t-test with Welch’s correction was used to compare ages between the two groups. The test indicated a significant difference in age between the OC and control groups (p = 0.007). The FIGO stage distribution of the 23 HGSOC patients was as follows: stage I in 4.35% (1/23), stage III in 78.26% (18/23), stage IV in 13.04% (3/23), and in one case (4.35%) FIGO stage information was not available. Preoperative CA125 level data were available for 19 HGSOC patients. All measured values (19/23) exceeded the cut-off value of 35 U/mL. Postoperative CA125 levels were not available ( SI 5 ). In the validation set, the age of HGSOC patients ranged from 50.56 to 63.48 years, with a median of 56.29 years at the time of preoperative sampling. The age of the control group ranged from 51.82 to 59.86 years, with a median of 56.50 years. A non-parametric Mann–Whitney U test was used to compare age between OC and control groups due to the small sample size (N = 5 per group), as this test is more robust under such conditions. The analysis revealed no statistically significant difference in age between the two groups. According to FIGO classification, 60% (3/5) of HGSOC patients were diagnosed at stage III, while 40% (2/5) were at stage IV. Based on tumour, node and metastasis (TNM) classification, all patients (5/5) had a primary tumor categorized as T3. Nodal status was pN1 in 40% (2/5) of cases, and distant metastasis (pM1) was observed in 40% (2/5) of patients. All patients (5/5) had preoperative CA125 levels exceeding 35 U/mL; postoperative CA125 levels were not available (Table 2 ). Table 2 Clinicopathological characteristics of HGSOC patients, including patient ID, age, histological subtype, FIGO stage, TNM classification, CA125 level, and recurrence status. Clinicopathological characteristics of patients Patient ID Age Histological subtype FIGO stage TNM classification CA125 level at diagnosis (U/mL) Recurrence status S1OC-7 57.33 HGSOC IVB ypT3bN1bM1b 2909.4 Recurrence S1OC-55 52.64 HGSOC IIIB ypT3b 1544.4 No recurrence S1OC-59 63.48 HGSOC IIIC ypT3cN1a 10713.5 No recurrence S1OC-85 56.29 HGSOC IVB ypT3bM1b 11114.3 No recurrence S1OC-94 50.56 HGSOC IIIB ypT3b 9329.7 No recurrence Training set analysis During the selection and in vitro verification of OC specific markers, 12 genes ( EpCAM, PPIC, FOLR1, WFDC2, SLC34A2, KLK5, KLK6, MUC16, MUC4, PRAME, SOX17, TUSC3 ) were identified as appropriate markers for dPCR based on wet lab analyses (SI 3) . In the first phase of the clinical testing, the detectability of these markers was assessed using clinical samples from 23 HGSOC patients who had not received neoadjuvant therapy prior to sample collection. (specific primers listed in Table 1 ). To define CTC positivity, a threshold was established based on events detected in the HD group; therefore, the cut-off value varied for each marker depending on the event distribution within the HD group. Overall, CTC-positive samples were identified if at least two events exceeding the threshold (≥ 0.107 copies/µL) were detected among 26,000 partitions. For EpCAM the threshold was set at > 0.111 copies/µL; for FOLR1 and WFDC2 > 0.107 copies/µL; and for PPIC , > 0.056 copies/µL. In the case of SLC34A2 , no events were detected in the control group; however, as the cut-off required a minimum of two events, single events detected in patient samples for this marker were not considered positive. The aim of this study was to develop a panel comprising the most sensitive molecular markers for the detection of OC. Markers that were detectable in fewer than three patient samples were classified as low-sensitivity markers and excluded from further analysis. The excluded genes were SLC34A2 , KLK5 , KLK6 , MUC16 , MUC4 , PRAME , SOX17 , and TUSC3 . In contrast, EpCAM (epithelial, pan-cancer), WFDC2 (epithelial, OC tissue-specific), FOLR1 (epithelial, OC tissue-specific), and PPIC (epithelial/mesenchymal, pan-cancer) demonstrated higher detection rates across 23 HGSOC clinical samples − 26.09% (6/23), 13.04% (3/23), 21.43% (4/23), and 26.09% (6/23), respectively—while remaining undetectable in samples from 8 HDs. Although SLC34A2 exhibited good performance in spike-in experiments, its sensitivity in clinical samples was limited. Nevertheless, as it was multiplexed with WFDC2 in the dPCR multiplex assay, it was retained in the visualized data alongside the four high-sensitivity markers (details in the “Multiplexing in dPCR” section). Consequently, a CTC detection panel tailored for OC patients was assembled, including the four most sensitive markers and SLC34A2 (epithelial), a low-sensitivity gene retained for validation due to its co-amplification with WFDC2 in the dPCR multiplex method. Validation set analysis Finally, based on the marker sensitivity index, a four-marker panel—comprising only the high-sensitivity genes—was selected for the absolute quantification and molecular characterization of a second set of OC samples and age-matched controls (5–5) to demonstrate the methodological potential and validate the feasibility of the detection approach. The four-gene panel was validated using a small subset of clinical samples from HGSOC patients as well as healthy control samples. A patient was considered CTC-positive if at least one of the four tested markers ( EpCAM, WFDC2, FOLR1, PPIC ) was found to be positive. While SLC34A2 was not part of the finalized panel due to its limited sensitivity, it remained included in all experimental steps owing to its co-amplification with WFDC2 in the multiplex dPCR method. The HDs (N = 5) all showed CTC-negative results ( Fig. 7 ), as they did not express any of the five examined transcripts. HGSOC samples were obtained from five patients, with CTC measurements performed by dPCR at both preoperative and postoperative time points (Fig. 8 ). CTCs were detected at both time points using a five-gene assay, though only four genes formed the targeted detection panel. SLC34A2 was excluded from the panel itself but was measured due to its inclusion in the multiplex with WFDC2 (Fig. 9 ). In the preoperative samples, EPCAM was positive in three patients, while PPIC and FOLR1 were positive in two patients each, and WFDC2 was positive in one patient. In the postoperative samples, marker positivity increased: PPIC was positive in four patients, EPCAM in three, FOLR1 in two, and WFDC2 in one (Fig. 9 ). Longitudinal monitoring of CTC-associated marker profiles via multiparametric panel analysis We investigated the changes in marker expression between preoperative and postoperative samples from patients. The emergence or disappearance of individual markers may suggest phenotypic switching of CTCs, a process that can be effectively monitored through a panel-based approach during disease progression. For the EpCAM marker (Fig. 10 A), patients Pt. 7 and Pt. 55 exhibited elevated postoperative concentrations compared to their respective preoperative levels (0.210 copies/µl and 0.445 copies/µl preoperatively, increasing to 0.322 copies/µl and 2.091 copies/µl, respectively). In patient Pt. 59, EpCAM became detectable postoperatively (0.276 copies/µl), while in Pt. 85, EpCAM positivity—previously above the cut-off threshold—disappeared. For Pt. 94, EpCAM levels remained consistently below the cut-off throughout the study. The PPIC marker (Fig. 10 B) demonstrated an 80% positivity rate in postoperative samples. In patients Pt. 7 and Pt. 85, initial PPIC negativity shifted to concentrations exceeding the cut-off following surgery (10.42 copies/µl and 0.107 copies/µl, respectively). In Pt. 55, the already elevated preoperative concentration (0.422 copies/µl) further increased to 2.112 copies/µl. Pt. 94 showed a slight decrease in concentration from 0.211 copies/µl to 0.16 copies/µl, though still above the cut-off. In Pt. 59, PPIC levels remained undetectable both before and after surgery. Regarding FOLR1 (Fig. 10 C), Pt. 55 exhibited a marked increase in expression following surgery (from 0.158 copies/µl to 5.367 copies/µl). In Pt. 7, the initially negative FOLR1 status shifted to strong positivity postoperatively (1.214 copies/µl). Conversely, Pt. 59, who was initially FOLR1 -positive (0.474 copies/µl), became negative postoperatively. FOLR1 was undetectable in both pre- and postoperative samples of Pt. 85 and Pt. 94. In the case of the WFDC2 marker (Fig. 10 D), Pt. 94 presented a high preoperative concentration (2.089 copies/µl), which significantly declined postoperatively (0.374 copies/µl), although it remained above the cut-off. In patients Pt. 7, Pt. 55, Pt. 59, and Pt. 85, WFDC2 levels were below the cut-off preoperatively. Among these, only Pt. 85 showed an increase above the cut-off postoperatively (0.482 copies/µl). As expected, SLC34A2 concentrations (Fig. 10 E) remained below the cut-off threshold in both preoperative and postoperative samples for all patients analyzed. Marker sensitivity in cell-lines, preoperative and postoperative clinical samples In the 21 HGSOC cell lines — which were selected based on the article by Mei et al. (2021) [ 48 ] and the available RNA expression data of the selected genes in the Human Protein Atlas ( SI 6 ) — the expression levels of the five marker genes (Fig. 11 ), examined based on the literature, varied as follows: EpCAM showed the strongest expression, being detected in 90% of the cell lines, with 74% classified as showing high expression (≥ 100 nTPM). WFDC2 followed with an 81% expression rate, while PPIC and FOLR1 ranked next. Although PPIC was expressed in 95% of the cell lines, 85% of these cases exhibited only low expression levels. A similar pattern was observed for FOLR1 , which had an overall expression rate of 76%, but 65% of these cases showed weak expression. Lastly, SLC34A2 was the least expressed marker, detected in 57% of the cell lines, and in all cases, its expression was low. Based on the obtained results, the sensitivity of the markers decreases in the following order in preoperative samples: EpCAM , PPIC and FOLR1 , WFDC2 , and finally SLC34A2 , which, as expected, yielded negative results. (Fig. 12 ) The order of marker sensitivity in postoperative samples changed as follows: the highest positivity was observed for the PPIC marker, followed by EpCAM, FOLR1 and WFDC2 . However, SLC34A2 did not yield any positive results in the postoperative samples. (Fig. 13 ) These findings in patient samples closely mirrored those observed in cell lines (Table 3 ). Table 3 Percentages of CTC marker positivity in cell lines and in pre- and postoperative patient samples. The marker expression patterns observed in cell lines were consistent with those detected in patient samples. Markers Expressed/CTC+ (%) Cell line Preoperative Postoperative High expression Low expression EPCAM 66,67 23,81 60 60 PPIC 14,29 80,95 40 80 FOLR1 28,57 47,62 40 40 WFDC2 71,43 9,52 20 40 SLC34A2 14,29 42,86 0 0 Longitudinal analysis in a confirmed relapse case Longitudinal sampling was associated with the following events: preoperative neoadjuvant therapy, surgery, and postoperative adjuvant therapy. During dPCR analyses, the absolute concentration of various markers was determined in patient samples. The Fig. 14 shows the follow-up samples of Patient 7 (Pt. 7) who developed recurrence. In Pt. 7, CTC levels dropped significantly following neoadjuvant therapy, but this reduction was short-lived, as CTC copies reappeared in postoperative samples. Three and four out of five examined markers were already positive in the last two follow-up samples taken before recurrence, respectively. Relapse was later confirmed via imaging, and the patient died one year later (Fig. 14 ). DISCUSSION Despite initial sensitivity to platinum-based chemotherapy in HGSOC, most patients eventually relapse, and no clinically validated biomarkers currently exist to predict platinum resistance. The conventional six-month threshold for defining platinum resistance is increasingly viewed as arbitrary. Experts suggest that platinum sensitivity should be considered a continuum, influenced by time elapsed since the last chemotherapy cycle and prior use of maintenance therapies. Understanding the molecular drivers of resistance remains critical, especially as non-homologous recombination mechanisms, such as cyclin E1 ( CCNE1 ) amplification and gene breakage in key tumor suppressors, contribute to resistance in HGSOC. For monitoring relapse, CA125 remains the most practical biomarker in HGSOC, especially when interpreted alongside clinical symptoms and imaging. Its role is less clear in non-HGSOC histological subtypes and when assessing response to targeted therapies [ 49 ]. Therefore, the use of novel biomarkers with sufficient sensitivity and reliability for application in routine clinical practice is of utmost importance. By combining size-based enrichment of unfixed CTCs from whole blood with absolute quantification of CTC-derived RNA, we have developed a highly sensitive and specific assay for non-invasive sampling of OC. RNA-based identification of CTCs overcomes several limitations of imaging-based analyses, including manual verification of individual images and the need for calibration and thresholding of multiple immunofluorescence microscopy parameters. In our study, we have demonstrated the potential applications of this dPCR-based CTC assay in FIGO stage III–IV HGSOC. To evaluate the utility of our strategy for ovarian CTC detection, we first identified a panel of OC-specific transcripts that are virtually undetectable in normal peripheral nucleated blood cells (e.g., hematopoietic, endothelial, and fibroblast cells), even by using highly sensitive dPCR technology. We selected multiple markers to account for the known heterogeneity of OC cells. An initial list of 44 candidate genes was compiled based on publicly available expression databases and further supplemented with additional genes identified through literature review ( SI 1 and SI 3 ). Twelve of these transcripts were highly expressed in ovarian tissue and/or tumors, but were undetectable in normal blood cells, which may generate background noise in size-based CTC enrichment workflows ( SI 3 ). To validate our novel method for CTC detection, we spiked defined numbers of OC cells (0, 5, 10, and 100 cells) into 3 mL of peripheral blood collected from female HDs. The samples were processed using the CD-Prime device for size-based CTC isolation, followed by dPCR detection of cDNA corresponding to the selected marker genes. In the negative control samples (0 cells), none of the selected markers were detectable. In contrast, a significant difference was consistently observed between the 0-cell and 100-cell samples (p < 0.05), confirming the sensitivity of the method. As a next step, clinical blood samples from OC patients and controls were used for testing and fine-tuning the in vitro validated marker set. Markers that were detectable in fewer than three patient samples were classified as low-sensitivity markers and excluded from further analysis. Consequently, a CTC detection panel tailored for OC patients comprising only the high-sensitivity genes was assembled. The excluded genes were SLC34A2, KLK5, KLK6, MUC16, MUC4, PRAME, SOX17 , and TUSC3 . In contrast, EpCAM, WFDC2, FOLR1 , and PPIC demonstrated higher detection rates across 23 HGSOC clinical samples, while remaining undetectable in samples from 8 female HDs. This four-marker panel effectively addressed the high degree of heterogeneity observed among HGSOC cell lines and was able to detect all 21 out of 21 HGSOC cell lines based on publicly available gene expression data from the Human Protein Atlas ( SI 6 ). In order to demonstrate the methodological potential and validate the feasibility of the detection approach, a second set of pre- and postoperative OC samples and age-matched controls was analyzed with the CTC panel. A patient was classified as CTC-positive if at least one of the four highly sensitive markers tested ( EpCAM , WFDC2 , FOLR1 , PPIC ) was detected. SLC34A2 , a low-sensitivity marker, was not part of the panel but was included in the assay due to its co-amplification with WFDC2 . All HD samples were CTC-negative, with none of the five transcripts detected. In contrast, all five HGSOC patients were CTC-positive in both their preoperative and postoperative samples, with at least one marker detected in each case. As expected, SLC34A2 as a low-sensitivity marker remained undetectable across all samples. In agreement with our findings showing increased postoperative CTC positivity, Zhang et al. also reported a significant rise in CTC counts following surgery in both the neoadjuvant and non-neoadjuvant chemotherapy groups. In their study, monoclonal antibodies specific for the epithelial markers EpCAM, HER2, and MUC1 were used to isolate CTCs from the peripheral blood of EOC patients, and the expression of six OC–associated genes ( EpCAM, HER2, MUC1, WT1, P16 , and PAX8 ) was subsequently evaluated using multiplex RT-PCR. The authors attributed the postoperative increase in CTCs to vascular injury and the mechanical release of tumor cells during surgical manipulation, which could facilitate their entry into the bloodstream. Furthermore, they observed that the expression of EpCAM and HER2 in CTCs at the time of diagnosis was positively correlated with chemoresistance [ 50 ]. Our results demonstrated that marker sensitivity varied between pre- and postoperative samples: EpCAM was the most sensitive marker in preoperative samples, followed by PPIC and FOLR1 . In postoperative samples, this pattern was reversed, with PPIC being the most sensitive marker. In the case of patient 7, clinical relapse was confirmed during follow-up. Therefore, this patient was longitudinally monitored to assess how CTC levels changed in response to treatment. CTC levels dropped significantly following neoadjuvant therapy, but this reduction was transient, as CTCs reappeared in the postoperative samples. Notably, in the two final follow-up samples taken before clinical relapse, three and four of the five tested markers, respectively, were already positive. Relapse was later confirmed by imaging, and the patient died one year later. These findings demonstrate that dPCR-based monitoring of CTC-derived RNA could have predicted the failure to achieve a tumor-free state prior to radiological confirmation, highlighting the potential of this method for early detection of relapse. CTCs are now recognized as a heterogeneous cell population, with dynamic changes in their phenotype. Research shows that during the EMT, cancer cells can temporarily lose epithelial surface markers, allowing them to detach from the primary tumor and enter the bloodstream. Recent evidence shows that CTCs use various strategies to evade cell death, such as transitioning between epithelial and mesenchymal states, forming cell clusters, or switching between cancer stem cell and differentiated states [ 51 ]. Lewis et al. concluded that while conventional immunofluorescence is the gold standard for CTC enumeration, its limitation to only 3–4 detection channels restricts the comprehensive characterization of CTCs, including key markers associated with EMT. Advances in single-cell analysis, including genomics, transcriptomics, and proteomics, provide deeper insights into cancer biology but are often constrained by high costs. Emerging techniques like mass cytometry offer the potential for high-plex CTC characterisation, enhancing precision medicine approaches. However, challenges such as complexity, cost, and technical demands must be addressed to facilitate their clinical application [ 52 ]. Gene expression studies in CTCs are crucial for understanding tumor heterogeneity and its connection to phenotypic differences [ 51 ]. Due to the high heterogeneity of cancer cells, clinical studies have shown that EpCAM-based enrichment has low sensitivity for detecting CTCs in epithelial ovarian cancer (EOC) patients. This is primarily because EMT during metastasis results in the loss of epithelial-like CTCs [ 53 ]. Although EMT and stem cell markers have been detected in OC CTCs, qRT-PCR signals were also observed in blood samples from HDs. This suggests that qRT-PCR may not be reliable for analyzing samples where leukocytes are still present, even after the enrichment step. In general, when using qRT-PCR to detect CTCs, it is essential to set a strict cut-off threshold to accurately differentiate CTC signals from those of leukocytes [ 54 ]. In normal tissues, FRα expression is limited to polarized epithelial cells, such as those in the placenta, lungs, kidneys, and choroid plexus. This protein is overexpressed in various cancers, including breast, lung, gastrointestinal, head and neck squamous cell carcinomas, endometrial, and ovarian cancers. Notably, around 80% of primary and recurrent OCs exhibit elevated FRα expression [ 55 ]. Folate receptor alpha (FRα) has garnered considerable attention in numerous EOC clinical trials and was the first antibody-drug conjugate approved for treating advanced or recurrent EOC [ 52 ]. In a clinical study made by Li et al., 95 blood samples from 30 EOC patients and 20 samples from patients with benign ovarian disease were analyzed. The results showed that when EpCAM and FRα surface markers are used together as CTC capture targets in EOC, the sensitivity of CTC detection can be increased. When defining CTC positivity as ≥ 2 detected cells, the combined detection method had a 67.36% positive rate, compared to 48.42% with anti-EpCAM alone (χ² = 14.45, P < 0.001). In patients suspected of having EOC, the combined method achieved a sensitivity of 75.0%, which was significantly higher than using anti-EpCAM alone (χ² = 4.17, P = 0.041) [ 53 ]. Obermayr et al. used RT-qPCR to detect CTCs and identified 11 novel gene markers, including PPIC , for CTC detection. These markers effectively identified CTCs both before treatment and during follow-up. CTCs were found in 24.5% of baseline samples and 20.4% of follow-up samples, with two-thirds detected through PPIC overexpression, while only a few were identified via EpCAM overexpression. At baseline, CTC presence was associated with ascites, suboptimal debulking, and elevated CA125 and HE4 levels. During follow-up, CTCs were more frequently detected in older and platinum-resistant patients. Notably, PPIC-positive CTCs were significantly more common in platinum-resistant patients (35.7%) than in platinum-sensitive ones (10.1%, p = 0.024) and were linked to poor prognosis, independent of traditional prognostic factors [ 56 ]. Kolostova et al. in their study focused on detecting CTCs in OC patients using a size-based separation method, MetaCell®, followed by cytomorphological evaluation and gene expression analysis (GEA). The captured cells were assessed using fluorescence microscopy and further analyzed for RNA/DNA expression. The GEA panel included tumor-associated genes ( EpCAM, MUC1, MUC16, KRT18, KRT19, WT1, VEGFA, HER2 ) and chemoresistance-related genes ( MRP1-10, MDR1, ERCC1, RRM1, RRM2 ). Results showed that EPCAM expression was higher in CTC-enriched fractions than in whole blood and increased with in vitro cultivation. Similarly, KRT7, KRT18, MUC16 , and WT1 also showed elevated expression in CTC-enriched samples. These findings suggest that analyzing a combination of these genes provides greater specificity for CTC detection in OC patients compared to single-marker tests. Additionally, GEA identified two distinct patient clusters, distinguishing those with and without detectable CTCs [ 51 ]. A study by Blassl et al. introduced a multiplex gene expression profiling approach for single CTCs in EOC. Using the AdnaTest system for CTC enrichment and isolation, along with CellCelector-based micromanipulation, the authors successfully established a workflow for single-cell analysis without pre-amplification. Their panel included 19 epithelial, EMT, and stem cell-associated genes, allowing simultaneous detection of multiple transcript types. Analysis of 15 single CTCs from three patients revealed substantial inter- and intra-patient heterogeneity, including the co-expression of epithelial, mesenchymal, and stem-like markers within individual cells. These findings support the potential of transcriptional profiling of CTCs to identify therapy-resistant subpopulations and provide a non-invasive, cost-effective tool for longitudinal monitoring of molecular changes during treatment [ 57 , 58 ]. A recent review by Jou et al. systematically analyzed the diagnostic performance of various CTC detection platforms in OC and highlighted significant limitations of the AdnaTest system. Studies using this method—including those by Chebouti et al., Blassl et al., and Kuhlmann et al. - reported low detection rates between 14% and 30%. The low sensitivity is likely due to the platform’s reliance on epithelial markers such as EpCAM and MUC1, which may be downregulated during EMT [58–61]. Lemma et al. reviewed the most commonly used molecular approaches for detecting CTCs in OC patients, identifying molecular markers that have been tested in at least four independent studies using various detection methods. The key markers and their associated methods include EpCAM, detected using immunofluorescence, flow cytometry, CellSearch, AdnaTest Ovarian Cancer Detect, RT-qPCR, and RNA-ISH; CK8, CK18, CK19, and Pan-CK, analyzed with immunofluorescence, flow cytometry, CellSearch, RT-qPCR, and RNA-ISH; MUC1 and MUC16, identified using immunofluorescence, flow cytometry, AdnaTest Ovarian Cancer Detect, and RT-qPCR; FRa, examined with immunofluorescence and flow cytometry; ERCC1, assessed through RT-qPCR; N-cadherin, evaluated using immunofluorescence, flow cytometry, and RT-qPCR; HER-2, detected via immunofluorescence, flow cytometry, and RT-qPCR; Vimentin, analyzed with immunofluorescence, flow cytometry, RT-qPCR, and RNA-ISH; EGFR, identified through immunofluorescence, flow cytometry, and RT-qPCR; and PPIC, tested using RT-qPCR [ 54 ]. Thus, the use of dPCR for CTC detection in OC samples is a new approach. LIMITATIONS One limitation of the study was the uneven distribution of ages among the controls and patients in the first set of clinical samples. However, since the CTC markers analyzed are not age-dependent, this should not have affected the observations of the two groups. Another limitation was the small number of patients who met the inclusion criteria for the study, which limited the ability to demonstrate the potential of the newly developed method. Since most patients had already received neoadjuvant chemotherapy at the time of recruitment and/or could not be followed up with after two months after surgery, standardized timing of sampling was not feasible and therefore had to be excluded from the analysis. CONCLUSION CTCs have been identified as critical mediators of metastasis and have emerged as dynamic biomarkers for tumor biology, progression, and treatment response. Immunostaining is the most frequently employed technique for the detection of CTCs; however, its constrained sensitivity hinders its clinical applicability. We have developed an ultrasensitive, RNA-based four-gene dPCR panel that enables robust detection of rare tumor cells in blood, overcoming key limitations of imaging-based methods. The CTC assay’s performance in both pre- and postoperative settings supports its potential for non-invasive monitoring and early relapse detection in advanced-stage patients. Abbreviations ACTB: β-actin Bp: basepair CA125: Cancer antigen-125 CCNE1: cyclin E1 cDNA: Complementary DNA CK: Cytokeratin CTC: circulating tumor cell ddPCR: droplet digital PCR dPCR: digital PCR EMT: epithelial-to-mesenchymal transition EOC: epithelial ovarian cancer EpCAM: epithelial cell adhesion molecule FAST: Fluid Assisted Separation Technology FFPE: Formalin-Fixed Paraffin-Embedded FIGO: International Federation of Gynecology and Obstetrics FOLR1: folate receptor alpha FRα: Folate receptor alpha GEA: gene expression analysis GTEx: Genotype-Tissue Expression HD: Healthy donor HE4: Human Epididymis Protein 4 HGSOC: high-grade serous ovarian carcinoma KLK5: Kallikrein-5 KLK6: Kallikrein-6 LOD: limit of detection mRNA: messenger RNA MUC16: Mucin 16 MUC4: Mucin-4 NTC: no template control OC: ovarian cancer PPIC: Peptidylprolyl Isomerase C PRAME: Preferentially Expressed Antigen In Melanoma Pt.: patient qPCR: quantitative PCR RT-qPCR: quantitative Real Time PCR SD: standard deviation SLC34A2: solute carrier family 34 member 2 SOX17: SRY-box transcription factor 17 TCGA: The Cancer Genome Atlas TNM: Tumour, node and metastasis TUSC3: Tumour Suppressor Candidate 3 WBCs: white blood cells WFDC2: WAP four-disulfide core domain 2 Declarations Data availability The data that support the results of this research are included within the article and its accompanying supplementary information files. Acknowledgments We would like to thank the team of the Department of Obstetrics and Gynaecology, Semmelweis University, Baross Street, Budapest, Hungary, for their valuable contribution and assistance with sample collection. Ethics declarations Ethics approval and consent to participate The study was approved by the Ethics Committee of the National Center for Public Health and Pharmacy (16119-8/2022/EÜIG, BM/28859-3/2023). All patients and healthy donors recruited in the present study signed informed consent forms approved by the Ethics Committee of the National Center for Public Health and Pharmacy (16119-8/2022/EÜIG, BM/28859-3/2023). The research was conducted in accordance with the Declaration of Helsinki. Competing Interests Clinomics Europe Ltd. was a subsidiary of Clinomics Inc. (South Korea) until 2025. Funding This research received no specific grant from any funding agency in the public, commercial, or nonprofit sectors. Author contributions O.B. conceived the study. D.K. designed the analyses and performed the in vitro measurements. Sz.T., L.I.H. and A.F. prepared the cells for the in vitro measurements. P.H. performed the in silico marker selection. Sz.M., Á.É. and J.R.Jr. organized patient sample collection and obtained clinical information. Histopathological data was provided by A.R. and Cs.B. A.M. performed blood processing, CTC enrichment, dPCR analysis and data interpretation. The manuscript was written by A.M. and all authors contributed to the final version. All authors read and approved the final manuscript. References X. Tang, J. He, Q. Huang, Y. Chen, K. Chen, J. Liu, Y. Tian, H. Wang, Development and validation of a nomogram to predict recurrence in epithelial ovarian cancer using complete blood count and lipid profiles. Front Oncol (2025). doi:10.3389/fonc.2025.1525867 R.L. Hollis, M. Churchman, G.R. Grimes, A.M. Meynert, P. Gautier, L. McMahon, K. Sherwood, A.J. Oswald, I. Croy, M. Ferguson, C.W. Martin, T. McGoldrick, N. McPhail, H. Creedon, J.C. Barrett, R. March, B.A. Dougherty, P. Roxburgh, A. Ewing, C.S. Herrington, C.A. Semple, C. Gourley, Somatic BRCA1/2 mutations are associated with a similar survival advantage to their germline counterparts in tubo-ovarian high grade serous carcinoma. Eur J Cancer (2025). doi:10.1016/j.ejca.2025.115299 A. Llueca, S. Canete-Mota, A. Jaureguí, M. Barneo, M.V. Ibañez, A. Neef, E. Ochoa, S. Tomas-Perez, J. Mari-Alexandre, J. Gilabert-Estelles, A. Serra, M.T. Climent, C. Bellido, N. Ruiz, B. Segarra-Vidal, M. Llueca, The Impact of Liquid Biopsy in Advanced Ovarian Cancer Care. Diagnostics (Basel) (2024). doi:10.3390/diagnostics14171868 T. Arora, S. Mullangi, E.S. Vadakekut, M.R. Lekkala, Epithelial Ovarian Cancer, in StatPearls (StatPearls Publishing, Treasure Island (FL), 2025) L. Bergmann, A.-K. Afflerbach, T. Yuan, K. Pantel, D.J. Smit, Lessons (to be) learned from liquid biopsies: assessment of circulating cells and cell-free DNA in cancer and pregnancy-acquired microchimerism. Semin Immunopathol (2025). doi:10.1007/s00281-025-01042-z K. Pantel, C. Alix-Panabières, Liquid biopsy and minimal residual disease — latest advances and implications for cure. Nat Rev Clin Oncol (2019). doi:10.1038/s41571-019-0187-3 K. Pantel, C. Alix-Panabières, Crucial roles of circulating tumor cells in the metastatic cascade and tumor immune escape: biology and clinical translation. J Immunother Cancer (2022). doi:10.1136/jitc-2022-005615 X. Gu, S. Wei, X. Lv, Circulating tumor cells: from new biological insights to clinical practice. Sig Transduct Target Ther (2024). doi:10.1038/s41392-024-01938-6 L. Bergmann, A.-K. Afflerbach, T. Yuan, K. Pantel, D.J. Smit, Lessons (to be) learned from liquid biopsies: assessment of circulating cells and cell-free DNA in cancer and pregnancy-acquired microchimerism. Semin Immunopathol (2025). doi:10.1007/s00281-025-01042-z I. Goldstein, S. Alyas, W. Asghar, A. Ilyas, Biosensors for the Isolation and Detection of Circulating Tumor Cells (CTCs) in Point-of-Care Settings. Micromachines (2023). doi:10.3390/mi14051035 A. Szczerba, A. Śliwa, P.P. Pieta, A. Jankowska, The Role of Circulating Tumor Cells in Ovarian Cancer Dissemination. Cancers (Basel) (2022). doi:10.3390/cancers14246030 C. Alix-Panabières, K. Pantel, Characterization of single circulating tumor cells. FEBS Lett (2017). doi:10.1002/1873-3468.12662 A. Ring, B.D. Nguyen-Sträuli, A. Wicki, N. Aceto, Biology, vulnerabilities and clinical applications of circulating tumour cells. Nat Rev Cancer (2023). doi:10.1038/s41568-022-00536-4 Y. Hou, J. Lin, H. Yao, Z. Wu, Y. Lin, J.-M. Lin, Linking Metastatic Behavior and Metabolic Heterogeneity of Circulating Tumor Cells at Single-Cell Level Using an Integrative Microfluidic System. Adv Sci (Weinh) (2025). doi:10.1002/advs.202413978 A. Balla, J. Bhak, O. Biró, The application of circulating tumor cell and cell-free DNA liquid biopsies in ovarian cancer. Mol Cell Probes (2022). doi:10.1016/j.mcp.2022.101871 Y. Zhang, B. Wang, J. Cai, Y. Yang, C. Tang, X. Zheng, H. Li, F. Xu, Enrichment and separation technology for evaluation of circulating tumor cells. Talanta (2025). doi:10.1016/j.talanta.2024.127025 M.A. Mansor, C. Yang, K.L. Chong, M.A. Jamrus, K. Liu, M. Yu, M.R. Ahmad, X. Ren, Label-Free and Rapid Microfluidic Design Rules for Circulating Tumor Cell Enrichment and Isolation: A Review and Simulation Analysis. ACS Omega (2025). doi:10.1021/acsomega.4c08606 T.-H. Kim, M. Lim, J. Park, J.M. Oh, H. Kim, H. Jeong, S.J. Lee, H.C. Park, S. Jung, B.C. Kim, K. Lee, M.-H. Kim, D.Y. Park, G.H. Kim, Y.-K. Cho, FAST: Size-Selective, Clog-Free Isolation of Rare Cancer Cells from Whole Blood at a Liquid-Liquid Interface. Anal Chem (2017). doi:10.1021/acs.analchem.6b03534 M. Lim, J. Park, A.C. Lowe, H. Jeong, S. Lee, H.C. Park, K. Lee, G.H. Kim, M.-H. Kim, Y.-K. Cho, A lab-on-a-disc platform enables serial monitoring of individual CTCs associated with tumor progression during EGFR-targeted therapy for patients with NSCLC. Theranostics (2020). doi:10.7150/thno.44693 S. Divari, M. Cuccato, A. Fanelli, F.T. Cannizzo, Development of a droplet digital PCR assay to detect illicit glucocorticoid administration in bovine. PLoS One (2022). doi:10.1371/journal.pone.0271613 J.F. Huggett, C.A. Foy, V. Benes, K. Emslie, J.A. Garson, R. Haynes, J. Hellemans, M. Kubista, R.D. Mueller, T. Nolan, M.W. Pfaffl, G.L. Shipley, J. Vandesompele, C.T. Wittwer, S.A. Bustin, The digital MIQE guidelines: Minimum Information for Publication of Quantitative Digital PCR Experiments. Clin Chem (2013). doi:10.1373/clinchem.2013.206375 A.K. Mitra, D.A. Davis, S. Tomar, L. Roy, H. Gurler, J. Xie, D.D. Lantvit, H. Cardenas, F. Fang, Y. Liu, E. Loughran, J. Yang, M. Sharon Stack, R.E. Emerson, K.D. Cowden Dahl, M. V. Barbolina, K.P. Nephew, D. Matei, J.E. Burdette, In vivo tumor growth of high-grade serous ovarian cancer cell lines. Gynecologic Oncology (2015). doi:10.1016/j.ygyno.2015.05.040 Y. Fan, J. Wang, Z. Fang, S.R. Pierce, L. West, A. Staley, K. Tucker, Y. Yin, W. Sun, W. Kong, V. Prabhu, J.E. Allen, C. Zhou, V.L. Bae-Jump, Anti-Tumor and Anti-Invasive Effects of ONC201 on Ovarian Cancer Cells and a Transgenic Mouse Model of Serous Ovarian Cancer. Front Oncol (2022). doi:10.3389/fonc.2022.789450 C. Boscaro, G.E. Ramaschi, L. Trevisi, A. Cignarella, C. Bolego, MiR-206 inhibits estrogen signaling and ovarian cancer cell migration without affecting GPER. Life Sciences (2023). doi:10.1016/j.lfs.2023.122135 T. Li, H. Diao, L. Zhao, Y. Xing, J. Zhang, N. Liu, Y. Yan, X. Tian, W. Sun, B. Liu, Identification of suitable reference genes for real-time quantitative PCR analysis of hydrogen peroxide-treated human umbilical vein endothelial cells. BMC Molecular Biology (2017). doi:10.1186/s12867-017-0086-z dMIQE Group, J.F. Huggett, The Digital MIQE Guidelines Update: Minimum Information for Publication of Quantitative Digital PCR Experiments for 2020. Clin Chem (2020). doi:10.1093/clinchem/hvaa125 P.S. Godavarthy, C.B. Walter, C. Lengerke, G. Klein, The Laminin Receptors Basal Cell Adhesion Molecule/Lutheran and Integrin α7β1 on Human Hematopoietic Stem Cells. Front Cell Dev Biol (2021). doi:10.3389/fcell.2021.675240 A. Zhao, Y. Qi, K. Liu, CLDN3 expression and function in pregnancy-induced hypertension. Exp Ther Med (2020). doi:10.3892/etm.2020.9084 I. Cima, S.L. Kong, D. Sengupta, I.B. Tan, W.M. Phyo, D. Lee, M. Hu, C. Iliescu, I. Alexander, W.L. Goh, M. Rahmani, N.-A.M. Suhaimi, J.H. Vo, J.A. Tai, J.H. Tan, C. Chua, R. Ten, W.J. Lim, M.H. Chew, C.A.E. Hauser, R.M. van Dam, W.-Y. Lim, S. Prabhakar, B. Lim, P.K. Koh, P. Robson, J.Y. Ying, A.M. Hillmer, M.-H. Tan, Tumor-derived circulating endothelial cell clusters in colorectal cancer. Sci Transl Med (2016). doi:10.1126/scitranslmed.aad7369 Y.-C. Liu, P.-Y. Chang, C.C.-K. Chao, CITED2 silencing sensitizes cancer cells to cisplatin by inhibiting p53 trans-activation and chromatin relaxation on the ERCC1 DNA repair gene. Nucleic Acids Res (2015). doi:10.1093/nar/gkv934 M. Baydoun, O. Moralès, C. Frochot, C. Ludovic, B. Leroux, E. Thecua, L. Ziane, A. Grabarz, A. Kumar, C. de Schutter, P. Collinet, H. Azais, S. Mordon, N. Delhem, Photodynamic Therapy Using a New Folate Receptor-Targeted Photosensitizer on Peritoneal Ovarian Cancer Cells Induces the Release of Extracellular Vesicles with Immunoactivating Properties. J Clin Med (2020). doi:10.3390/jcm9041185 W. Gong, Y. Liu, C. Seidl, E.P. Diamandis, M. Kiechle, E. Drecoll, M. Kotzsch, V. Magdolen, J. Dorn, Quantitative assessment and clinical relevance of kallikrein-related peptidase 5 mRNA expression in advanced high-grade serous ovarian cancer. BMC Cancer (2019). doi:10.1186/s12885-019-5901-0 N. Ahmed, J. Dorn, R. Napieralski, E. Drecoll, M. Kotzsch, P. Goettig, E. Zein, S. Avril, M. Kiechle, E.P. Diamandis, M. Schmitt, V. Magdolen, Clinical relevance of kallikrein-related peptidase 6 (KLK6) and 8 (KLK8) mRNA expression in advanced serous ovarian cancer. Biol Chem (2016). doi:10.1515/hsz-2016-0177 W. Wang, J. Wang, C. Yang, J. Wang, MicroRNA-216a targets WT1 expression and regulates KRT7 transcription to mediate the progression of pancreatic cancer-A transcriptome analysis. IUBMB Life (2021). doi:10.1002/iub.2468 Y. Zhong, Z. Zhuang, P. Mo, Q. Shang, M. Lin, J. Gong, J. Huang, H. Mo, M. Huang, Overexpression of MAL2 Correlates with Immune Infiltration and Poor Prognosis in Breast Cancer. Evidence-Based Complementary and Alternative Medicine (2021). doi:10.1155/2021/5557873 B. Liu, J.R. Lague, D.P. Nunes, P. Toselli, F.G. Oppenheim, R.V. Soares, R.F. Troxler, G.D. Offner, Expression of Membrane-associated Mucins MUC1 and MUC4 in Major Human Salivary Glands. J Histochem Cytochem (2002). doi:10.1177/002215540205000607 J. Liu, L. Li, N. Luo, Q. Liu, L. Liu, D. Chen, Z. Cheng, X. Xi, Inflammatory signals induce MUC16 expression in ovarian cancer cells via NF‑κB activation. Experimental and Therapeutic Medicine (2021). doi:10.3892/etm.2020.9594 Y. Li, C. Wu, T. Chen, J. Zhang, G. Liu, Y. Pu, J. Zhu, C. Shen, Y. Zhang, N. Zeng, X. Zhang, Effects of RNAi-mediated MUC4 gene silencing on the proliferation and migration of human pancreatic carcinoma BxPC-3 cells. Oncology Reports (2016). doi:10.3892/or.2016.5152 Y.-F. Gao, T. Zhu, C.-X. Mao, Z.-X. Liu, Z.-B. Wang, X.-Y. Mao, L. Li, J.-Y. Yin, H.-H. Zhou, Z.-Q. Liu, PPIC, EMP3 and CHI3L1 Are Novel Prognostic Markers for High Grade Glioma. International Journal of Molecular Sciences (2016). doi:10.3390/ijms17111808 W. Zhang, C.J. Barger, K.H. Eng, D. Klinkebiel, P.A. Link, A. Omilian, W. Bshara, K. Odunsi, A.R. Karpf, PRAME expression and promoter hypomethylation in epithelial ovarian cancer. Oncotarget (2016). doi:10.18632/oncotarget.9977 C. Cai, Y. Zhang, X. Peng, Knocking down Sterol regulatory element binding protein 2 (SREBF2) inhibits the Serine Protease 8 (PRSS8) /sodium channel epithelial 1alpha subunit (SCNN1A) axis to reduce the cell proliferation, migration and epithelial-mesenchymal transformation of ovarian cancer. Bioengineered (2021). doi:10.1080/21655979.2021.1978615 J. He, M. Zhou, X. Li, S. Gu, Y. Cao, T. Xing, W. Chen, C. Chu, F. Gu, J. Zhou, Y. Jin, J. Ma, D. Ma, Q. Zou, SLC34A2 simultaneously promotes papillary thyroid carcinoma growth and invasion through distinct mechanisms. Oncogene (2020). doi:10.1038/s41388-020-1181-z D.-Y. Fu, Z.-M. Wang, null Li-Chen, B.-L. Wang, Z.-Z. Shen, W. Huang, Z.-M. Shao, Sox17, the canonical Wnt antagonist, is epigenetically inactivated by promoter methylation in human breast cancer. Breast Cancer Res Treat (2010). doi:10.1007/s10549-009-0339-8 R. Tamjidifar, M. Akbari, S. Tarzi, M. Sadeghzadeh, M. Abolghasemi, E. Poursaei, N. Shomali, F. Mahdavi, Prognostic and Diagnostic Values of miR-506 and SPON 1 in Colorectal Cancer with Clinicopathological Considerations. J Gastrointest Cancer (2021). doi:10.1007/s12029-019-00356-0 H.J. Ferreira, V. Davalos, M.C. de Moura, M. Soler, M. Perez-Salvia, A. Bueno-Costa, F. Setien, S. Moran, A. Villanueva, M. Esteller, Circular RNA CpG island hypermethylation-associated silencing in human cancer. Oncotarget (2018). doi:10.18632/oncotarget.25673 Y. Xiong, L. Yuan, S. Chen, H. Xu, T. Peng, L. Ju, G. Wang, Y. Xiao, X. Wang, WFDC2 suppresses prostate cancer metastasis by modulating EGFR signaling inactivation. Cell Death Dis (2020). doi:10.1038/s41419-020-02752-y L. Miglietta, A. Moniri, I. Pennisi, K. Malpartida-Cardenas, H. Abbas, K. Hill-Cawthorne, F. Bolt, E. Jauneikaite, F. Davies, A. Holmes, P. Georgiou, J. Rodriguez-Manzano, Coupling Machine Learning and High Throughput Multiplex Digital PCR Enables Accurate Detection of Carbapenem-Resistant Genes in Clinical Isolates. Front Mol Biosci (2021). doi:10.3389/fmolb.2021.775299 J. Mei, H. Tian, H. Huang, C. Hsu, Y. Liou, N. Wu, W. Zhang, T. Chu, Cellular models of development of ovarian high‐grade serous carcinoma: A review of cell of origin and mechanisms of carcinogenesis. Cell Prolif (2021). doi:10.1111/cpr.13029 N. Colombo, C. Sessa, A. du Bois, J. Ledermann, W.G. McCluggage, I. McNeish, P. Morice, S. Pignata, I. Ray-Coquard, I. Vergote, T. Baert, I. Belaroussi, A. Dashora, S. Olbrecht, F. Planchamp, D. Querleu, ESMO-ESGO Ovarian Cancer Consensus Conference Working Group, ESMO-ESGO consensus conference recommendations on ovarian cancer: pathology and molecular biology, early and advanced stages, borderline tumours and recurrent disease†. Ann Oncol (2019). doi:10.1093/annonc/mdz062 X. Zhang, H. Li, X. Yu, S. Li, Z. Lei, C. Li, Q. Zhang, Q. Han, Y. Li, K. Zhang, Y. Wang, C. Liu, Y. Mao, X. Wang, D.M. Irwin, H. Guo, G. Niu, H. Tan, Analysis of Circulating Tumor Cells in Ovarian Cancer and Their Clinical Value as a Biomarker. Cell Physiol Biochem (2018). doi:10.1159/000492521 K. Kolostova, M. Pinkas, A. Jakabova, E. Pospisilova, P. Svobodova, J. Spicka, M. Cegan, R. Matkowski, V. Bobek, Molecular characterization of circulating tumor cells in ovarian cancer. Am J Cancer Res 6:973–980 F. Lewis, J. Beirne, B. Henderson, L. Norris, K. Cadoo, T. Kelly, C. Martin, S. Hurley, M. Kanjuga, L. O’Driscoll, K. Gately, E. Oner, V.M. Saini, D. Brooks, S. Selemidis, W. Kamran, N. Haughey, P. Maguire, C. O’Gorman, F.A. Saadeh, M.P. Ward, J.J. O’Leary, S.A. O’Toole, Unravelling the biological and clinical challenges of circulating tumour cells in epithelial ovarian carcinoma. Cancer Lett (2024). doi:10.1016/j.canlet.2024.217279 N. Li, H. Zuo, L. Chen, H. Liu, J. Zhou, Y. Yao, B. Xu, H. Gong, Y. Weng, Q. Hu, Q. Song, M. Peng, Y. Cheng, Circulating Tumor Cell Detection In Epithelial Ovarian Cancer Using Dual-Component Antibodies Targeting EpCAM And FRα. CMAR (2020). doi:10.2147/CMAR.S211455 S. Lemma, A.M. Perrone, P. De Iaco, G. Gasparre, I. Kurelac, Current methodologies to detect circulating tumor cells: a focus on ovarian cancer. Am J Cancer Res 11:4111–4126 K. Bukowski, A. Rogalska, A. Marczak, Folate Receptor Alpha—A Secret Weapon in Ovarian Cancer Treatment? Int J Mol Sci (2024). doi:10.3390/ijms252211927 E. Obermayr, D.C. Castillo-Tong, D. Pils, P. Speiser, I. Braicu, T. Van Gorp, S. Mahner, J. Sehouli, I. Vergote, R. Zeillinger, Molecular characterization of circulating tumor cells in patients with ovarian cancer improves their prognostic significance -- a study of the OVCAD consortium. Gynecol Oncol (2013). doi:10.1016/j.ygyno.2012.09.021 L. Giannopoulou, S. Kasimir-Bauer, E.S. Lianidou, Liquid biopsy in ovarian cancer: recent advances on circulating tumor cells and circulating tumor DNA. Clin Chem Lab Med (2018). doi:10.1515/cclm-2017-0019 C. Blassl, J.D. Kuhlmann, A. Webers, P. Wimberger, T. Fehm, H. Neubauer, Gene expression profiling of single circulating tumor cells in ovarian cancer - Establishment of a multi-marker gene panel. Mol Oncol (2016). doi:10.1016/j.molonc.2016.04.002 H.-J. Jou, P.-Y. Ling, H.-T. Hsu, Circulating tumor cells as a “real-time liquid biopsy”: Recent advances and the application in ovarian cancer. Taiwan J Obstet Gynecol (2022). doi:10.1016/j.tjog.2021.11.008 I. Chebouti, S. Kasimir-Bauer, P. Buderath, P. Wimberger, S. Hauch, R. Kimmig, J.D. Kuhlmann, EMT-like circulating tumor cells in ovarian cancer patients are enriched by platinum-based chemotherapy. Oncotarget (2017). doi:10.18632/oncotarget.16179 J.D. Kuhlmann, P. Wimberger, A. Bankfalvi, T. Keller, S. Schöler, B. Aktas, P. Buderath, S. Hauch, F. Otterbach, R. Kimmig, S. Kasimir-Bauer, ERCC1-positive circulating tumor cells in the blood of ovarian cancer patients as a predictive biomarker for platinum resistance. Clin Chem (2014). doi:10.1373/clinchem.2014.224808 Additional Declarations Competing interest reported. Clinomics Europe Ltd. was a subsidiary of Clinomics Inc. (South Korea) until 2025. Supplementary Files SupplementaryInformation1.36potentialOCCTCmarkersinsilicoanalysis2.xlsx SupplementaryInformation2.Livecellcounting.docx SupplementaryInformation3.Top21potentialgenes.docx SupplementaryInformation4.Spikingexperimentresults.docx SupplementaryInformation5.Trainingset.docx SupplementaryInformation6.Geneexpressionanalysisofthe4markerpanelinHGSOCcelllines.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 07 Dec, 2025 Reviews received at journal 04 Dec, 2025 Reviewers agreed at journal 25 Nov, 2025 Reviewers invited by journal 25 Nov, 2025 Editor invited by journal 19 Nov, 2025 Editor assigned by journal 14 Nov, 2025 Submission checks completed at journal 14 Nov, 2025 First submitted to journal 13 Nov, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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In these experiments, 0, 5, 10, or 100 tumor cells were added to healthy blood samples and subsequently analyzed. Samples underwent CTC enrichment using the CD-Prime system, followed by RNA isolation and reverse transcription for quality control via qPCR and absolute quantification via dPCR.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8106577/v1/aedaeb41d3f66e3497c4956d.png"},{"id":97130537,"identity":"331b28ae-4e03-454d-97d0-8267fe320acc","added_by":"auto","created_at":"2025-12-01 08:39:59","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":64444,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRepresentative timeline of sample collection.\u003c/strong\u003e Preoperative sampling was performed prior to the initiation of neoadjuvant therapy, which consisted of 3–5 cycles. This was followed by surgery. 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NTC was used as a negative control.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8106577/v1/c25acf63365c0db541c2e0d1.png"},{"id":97130490,"identity":"0cf1e32c-086e-4c8c-9094-819b74e84acc","added_by":"auto","created_at":"2025-12-01 08:39:53","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":84790,"visible":true,"origin":"","legend":"\u003cp\u003eRepresentative 1D dot plot of \u003cem\u003eWFDC2\u003c/em\u003e dPCR results from the spiking experiment, in which 0/5/10/100 tumor cells were spiked into 3 mL of blood from healthy donors. Above the threshold line are the fluorescence intensities of the positive partitions, with each dot representing a single partition, while negative partitions are displayed below the threshold. Increasing cell numbers resulted in a progressively stronger \u003cem\u003eWFDC2\u003c/em\u003e signal. The NTC served as a negative control, and no tumor cells were added to the 0-cell reaction.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8106577/v1/8bd34a108828e9d81fa16ce4.png"},{"id":97130472,"identity":"5e9008bc-6187-4d6e-b34f-5d78f54a016c","added_by":"auto","created_at":"2025-12-01 08:39:49","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":61246,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAbsolute gene expression levels in healthy female donor blood samples spiked with OVCAR-5 and OVCAR-3 tumor cells. \u003c/strong\u003eThe counted 0/5/10/100 cells were spiked into 3 mL of healthy donors’ blood. CTC enrichment protocol was operated by a fully automatic CD-Prime device and FAST-Auto disc. RNA was extracted from membrane-captured cells. Gene expression studies with (A) \u003cem\u003eEPCAM\u003c/em\u003e, and (B) \u003cem\u003eWFDC2\u003c/em\u003e were performed on nanoplate-based dPCR by absolute quantification. Bars represent mean values of absolute concentration (copies/μL), dots show individual values, error bars represent standard deviation (SD). N=3, significance tested by Kruskal-Wallis test and the post hoc Dunn's multiple comparisons test, p * \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8106577/v1/60b99ddac779933df3430cf4.png"},{"id":97130464,"identity":"2adeda49-d022-44aa-8ad7-dcdc918e68f5","added_by":"auto","created_at":"2025-12-01 08:39:46","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":27980,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap displaying results from healthy donor samples (N = 5) for each marker individually and in the overall assessment. Samples with positive findings are shown in pink, while negative findings are indicated in blue. A sample was considered CTC-positive if at least one marker yielded a positive signal, as reflected in the “Overall” category. All HDs (N = 5) were CTC-negative, as none expressed any of the five examined transcripts.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-8106577/v1/97526118a92719ba53cef794.png"},{"id":97130499,"identity":"ed208abd-c2ff-4f26-883a-91d7f45cf2ff","added_by":"auto","created_at":"2025-12-01 08:39:55","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":87253,"visible":true,"origin":"","legend":"\u003cp\u003eRepresentative 1D dot-plot of the dPCR runs. The OVCAR-3 cell line was used as a positive control for the \u003cem\u003ePPIC\u003c/em\u003e marker, and the NTC served as a negative control. The plot also includes a patient's preoperative and postoperative samples. Above the threshold line are the fluorescence intensities of the positive partitions, with each dot representing a single partition, while the negative partitions are displayed below the threshold.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-8106577/v1/e8337309877263d4c5807d64.png"},{"id":97130497,"identity":"72e1040b-f8e9-4e39-8084-6c75b50007f6","added_by":"auto","created_at":"2025-12-01 08:39:55","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":86290,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHeatmap of preoperative samples.\u003c/strong\u003e Assessment of CTC positivity in pre- and postoperative patient samples (N=5) was performed using the following molecular markers: \u003cem\u003eEPCAM, PPIC, FOLR1, WFDC2 \u003c/em\u003eand \u003cem\u003eSLC34A2\u003c/em\u003e. A sample was considered CTC-positive if at least one of the markers yielded a positive result, as reflected in the \"Overall\" category. CTC-positive samples are indicated in pink, while CTC-negative samples are shown in blue. The bottom row denotes the clinical stage of each patient: stage III is marked in yellow and stage IV in orange.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-8106577/v1/ac5fb6fac465ed49236df3b6.png"},{"id":97130544,"identity":"bc11a26c-2997-4a0b-b267-cb2796df969a","added_by":"auto","created_at":"2025-12-01 08:40:00","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":104580,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential expression of \u003cem\u003eEpCAM\u003c/em\u003e (A), \u003cem\u003ePPIC\u003c/em\u003e(B), \u003cem\u003eFOLR1\u003c/em\u003e (C), \u003cem\u003eWFDC2\u003c/em\u003e (D), and \u003cem\u003eSLC34A2\u003c/em\u003e (E) in matched pre- and postoperative samples. In the figures, concentration values corresponding to preoperative samples for each patient are indicated in blue, while those of postoperative samples are represented in orange.\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-8106577/v1/8c29f87fdc0cb1830050f64b.png"},{"id":97130406,"identity":"0d984f52-02c0-4ad9-b88c-81d62ef6dd26","added_by":"auto","created_at":"2025-12-01 08:39:41","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":25446,"visible":true,"origin":"","legend":"\u003cp\u003eMarker sensitivity in HGSOC cell lines (N=21). \u003cem\u003eEpCAM\u003c/em\u003e showed the strongest expression (90% of cell lines, 74% high), followed by \u003cem\u003eWFDC2\u003c/em\u003e (81%). \u003cem\u003ePPIC \u003c/em\u003e(95%) and \u003cem\u003eFOLR1\u003c/em\u003e (76%) were frequently expressed but mostly at low levels (85% and 65%, respectively). \u003cem\u003eSLC34A2 \u003c/em\u003eshowed the lowest expression (57%).\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-8106577/v1/5f6fe410dc8b5fe22a650a3f.png"},{"id":97130506,"identity":"668acc1e-9ae9-463a-8eed-019153ebd9a4","added_by":"auto","created_at":"2025-12-01 08:39:56","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":19387,"visible":true,"origin":"","legend":"\u003cp\u003eThe figure shows marker sensitivity in the preoperative samples (N=5). Most samples tested positive for \u003cem\u003eEpCAM \u003c/em\u003efollowed by \u003cem\u003ePPIC\u003c/em\u003e and \u003cem\u003eFOLR1, \u003c/em\u003eand \u003cem\u003eWFDC2\u003c/em\u003e. Although not part of the finalized panel, a fifth low-sensitivity gene (\u003cem\u003eSLC34A2\u003c/em\u003e) was included throughout the experimental workflow due to its co-amplification with \u003cem\u003eWFDC2\u003c/em\u003e in the dPCR multiplex method.\u003c/p\u003e","description":"","filename":"12.png","url":"https://assets-eu.researchsquare.com/files/rs-8106577/v1/f003288a0b9a9d7c2da146e1.png"},{"id":97130556,"identity":"7a04e50d-35ca-4da2-b28d-f3d745b90596","added_by":"auto","created_at":"2025-12-01 08:40:02","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":19947,"visible":true,"origin":"","legend":"\u003cp\u003eThe figure shows marker sensitivity in the postoperative samples (N=5). Most samples tested positive for \u003cem\u003ePPIC\u003c/em\u003e and \u003cem\u003eEpCAM\u003c/em\u003e, followed by \u003cem\u003eFOLR1 \u003c/em\u003eand \u003cem\u003eWFDC2\u003c/em\u003e. Although not part of the finalized panel, a fifth low-sensitivity gene (\u003cem\u003eSLC34A2\u003c/em\u003e) was included throughout the experimental workflow due to its co-amplification with \u003cem\u003eWFDC2\u003c/em\u003e in the dPCR multiplex method.\u003c/p\u003e","description":"","filename":"13.png","url":"https://assets-eu.researchsquare.com/files/rs-8106577/v1/2c9aca37b57454f6f6b5bc34.png"},{"id":97130500,"identity":"2666cc08-b66f-4c4a-b561-6cb7a31e7442","added_by":"auto","created_at":"2025-12-01 08:39:55","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":47064,"visible":true,"origin":"","legend":"\u003cp\u003eA longitudinal sampling from a patient (Patient 7). The x-axis shows sampling time points (labeled as T), along with the timing of relapse and death (exit). Yellow bars indicate the intervals of neoadjuvant and adjuvant treatments. The y-axis represents CTC counts, determined based on the expression of specific markers. In patient 7, CTC levels declined after neoadjuvant therapy but reappeared postoperatively. In the last two pre-recurrence follow-up samples, 3/5 and 4/5 markers were positive, respectively. Recurrence was later confirmed by imaging, and the patient died within one year.\u003c/p\u003e","description":"","filename":"14.png","url":"https://assets-eu.researchsquare.com/files/rs-8106577/v1/1df0862b0d83d7d3286a7336.png"},{"id":97145344,"identity":"4a3b2f0f-6749-44c8-8eb8-2d4926a55c31","added_by":"auto","created_at":"2025-12-01 10:13:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2530756,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8106577/v1/b46cdfb1-ec56-40e1-be41-ba925ff1762a.pdf"},{"id":97142759,"identity":"9a70397c-5eba-45d7-adb9-317cfdb747d4","added_by":"auto","created_at":"2025-12-01 10:07:56","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":18373,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation1.36potentialOCCTCmarkersinsilicoanalysis2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8106577/v1/12854bd20625825555f03f5b.xlsx"},{"id":97142738,"identity":"201f1a81-6217-475c-a6df-0ba056fe177d","added_by":"auto","created_at":"2025-12-01 10:07:55","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":827675,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation2.Livecellcounting.docx","url":"https://assets-eu.researchsquare.com/files/rs-8106577/v1/efa7fa49867e89237fd658bf.docx"},{"id":97142222,"identity":"38fb2438-ac3b-4670-8bc9-7d884409e3f7","added_by":"auto","created_at":"2025-12-01 10:07:26","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":27724,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation3.Top21potentialgenes.docx","url":"https://assets-eu.researchsquare.com/files/rs-8106577/v1/39cd2d71ab1517b748bf7b12.docx"},{"id":97130487,"identity":"2c62ca50-64cb-4353-9dea-2b08c386d4f6","added_by":"auto","created_at":"2025-12-01 08:39:52","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":1195438,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation4.Spikingexperimentresults.docx","url":"https://assets-eu.researchsquare.com/files/rs-8106577/v1/b7e2aedf2e28281d192e7ecd.docx"},{"id":97130477,"identity":"5079ec59-58bc-468a-96f5-7eff5229d197","added_by":"auto","created_at":"2025-12-01 08:39:50","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":32499,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation5.Trainingset.docx","url":"https://assets-eu.researchsquare.com/files/rs-8106577/v1/86f051292ae91b2be9125527.docx"},{"id":97130508,"identity":"f9afa6c4-e8e6-485f-8bde-8de608d84c11","added_by":"auto","created_at":"2025-12-01 08:39:56","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":32148,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation6.Geneexpressionanalysisofthe4markerpanelinHGSOCcelllines.docx","url":"https://assets-eu.researchsquare.com/files/rs-8106577/v1/53abb04eb79b2b3b2fdda373.docx"}],"financialInterests":"Competing interest reported. Clinomics Europe Ltd. was a subsidiary of Clinomics Inc. (South Korea) until 2025.","formattedTitle":"Novel methodology for the digital analysis of circulating tumor cells in ovarian cancer","fulltext":[{"header":"INTRODUCTION","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\u003ch2\u003eOvarian cancer and emerging biomarkers in diagnosis and treatment\u003c/h2\u003e\u003cp\u003eOvarian cancer (OC) is the most lethal gynecological malignancy and the third most common cancer of the female reproductive system. It ranks as the fifth leading cause of cancer-related death among women [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. High-grade serous ovarian carcinoma (HGSOC), the most prevalent histological subtype, accounts for approximately 70% of all OC cases. Typically diagnosed at an advanced stage, HGSOC often responds initially to standard treatment regimens consisting of primary cytoreductive surgery followed by platinum-based chemotherapy. However, despite this aggressive first-line approach, approximately 80% of patients experience recurrence, frequently accompanied by the development of chemoresistance, which significantly complicates subsequent treatment and worsens prognosis [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eCirculating tumor cells\u003c/h2\u003e\u003cp\u003eLiquid biopsy has emerged as a powerful alternative to traditional tumor biopsies, offering several key advantages, including minimally invasive sampling, enhanced detection of tumor heterogeneity, and the ability to collect serial samples over time to monitor disease progression and treatment response [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. By analyzing multiple blood samples from the same patient throughout the course of treatment, researchers can monitor dynamic changes in the genomic profile of circulating tumor cells (CTCs), providing insights into metastatic evolution and intratumor heterogeneity [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eCTCs, which originate from primary or metastatic tumor sites, are shed into the bloodstream and can be broadly categorized into three phenotypic types: epithelial, mesenchymal, and hybrid. Although most CTCs are rapidly cleared by the immune system, a small subset with high metastatic potential can evade immune surveillance. These cells are capable of initiating micrometastases, contributing to disease progression and recurrence [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Given their close genomic resemblance to the primary tumor, CTCs serve not only as key drivers of metastasis but also as valuable, blood-accessible biomarkers that can provide critical genetic information without the need for invasive procedures [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, the clinical utility of CTCs is complicated by their significant heterogeneity. They vary widely in morphology, size, molecular profile, metastatic capacity, and chemoresistance. Moreover, differences in cellular deformability and surface marker expression challenge the development of a universal method for CTC capture. The epithelial-to-mesenchymal transition (EMT), a process that alters CTC phenotype and facilitates invasion and dissemination, further complicates detection and isolation [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Reliable single-cell analysis of CTCs requires high-efficiency capture methods capable of reflecting this intrapatient heterogeneity. Nonetheless, in some patients, the number of CTCs in peripheral blood is extremely low or undetectable, limiting the sensitivity and robustness of current detection platforms and underscoring the need for continued technological refinement [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Despite recent advancements in CTC research and detection technologies, the clinical translation of CTC-based assays remains limited. The inherent rarity, fragility, and phenotypic diversity of CTCs present ongoing challenges, which have delayed their integration into routine clinical practice and hindered progress in elucidating the mechanisms underlying metastasis [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eCTC enrichment and detection\u003c/h3\u003e\n\u003cp\u003eCTC enrichment is a critical step in liquid biopsy-based cancer diagnostics. Current technologies leverage either biological markers or physical properties of CTCs to separate them from blood cells. Among physical property-based methods, size-based enrichment has proven particularly promising due to the generally larger size of CTCs compared to leukocytes. Microfluidic platforms and filter-based devices utilize this difference to isolate CTCs without relying on labeling [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. These approaches enable high-throughput, cost-effective, and label-free separation. However, technical limitations such as sample pretreatment requirements (e.g., red blood cell removal) and membrane clogging can hinder performance [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Density-based techniques, such as density gradient centrifugation, offer a low-cost enrichment option but have limited specificity due to overlapping densities of CTCs and white blood cells. Additionally, these methods may cause unintended activation of immune cells, negatively affecting downstream analyses [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Affinity-based methods, including immunoaffinity and immunomagnetic techniques targeting epithelial markers like epithelial cell adhesion molecule (EpCAM), offer high specificity but are limited by CTC heterogeneity, particularly in mesenchymal and stem-like cells with low EpCAM expression, and require extended processing times for effective antigen-antibody interactions [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The use of a limited number of markers during immunocapture of heterogeneous CTC populations can lead to increased false negatives. In light of this limitation, analyzing the expression of CTC-specific genes may offer a more reliable alternative, particularly in OC patients [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In contrast, label-free microfluidic technologies that combine multiple biophysical parameters (size, deformability, dielectric properties) offer improved sensitivity and cell viability. These systems reduce marker bias and preserve the functional integrity of isolated cells for further analysis [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn our study we applied the Fluid Assisted Separation Technology (FAST by the CD-Prime device (Clinomics Inc., South Korea)). The CD-Prime FAST is a compact, centrifugal microfluidic platform engineered for rapid, label-free isolation of viable CTCs directly from whole blood. Its tangential-flow-like filtration mechanism, in which centrifugal force is applied perpendicularly to membrane filtration, minimizes clogging and enhances membrane utilization by maintaining an aqueous phase beneath the filter throughout processing [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. This high-throughput system demonstrated a CTC recovery rate of 96.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.6% in repeated tests with spiked samples and showed reliable detection in cancer patients without distant metastasis, highlighting its promise for early-stage cancer diagnostics. Furthermore, the isolated cells remain viable and are suitable for downstream molecular and imaging analyses, including single-cell gene expression profiling, which has revealed significant inter-patient variability in CTCs [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The FAST disc's efficiency, simplicity, and applicability across size-based filtration methods make it a valuable tool in both research and clinical oncology [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eDigital PCR\u003c/h3\u003e\n\u003cp\u003eDigital PCR (dPCR) is an advanced molecular technique that enables precise and absolute quantification of nucleic acid targets without the need for standard curves or inter-run calibrators. Unlike traditional quantitative PCR (qPCR), dPCR functions as an end-point assay, providing greater resistance to inhibitors and improved sensitivity for detecting low-abundance targets, such as gene transcripts from rare CTCs. The method employs microfluidic partitioning to divide the reaction mixture into thousands to millions of nanoliter-sized droplets, each functioning as an individual PCR microreactor. Target quantification is achieved by the end-point analysis of positive and negative partitions. The mean number of target sequences per partition is quantified by applying a Poisson correction to the fraction of positive partitions. This compensates for the possibility that multiple copies of the template may be present in certain partitions. This partition-based approach ensures high accuracy, particularly when analyzing rare genetic events or low-copy-number targets [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e"},{"header":"OBJECTIVE","content":"\u003cp\u003eImmunostaining is currently the most widely used method for studying CTCs; however, its sensitivity is limited, and its applicability is constrained by the need for manual image verification, fluorescence thresholding, and complex instrumentation. To address these limitations, the objective of this study was to develop an ultrasensitive, RNA-based assay for the non-invasive detection and molecular characterization of CTCs in OC.\u003c/p\u003e\u003cp\u003eThe proposed approach combines size-based enrichment of unfixed CTCs using the CD-Prime system with absolute quantification of CTC-derived gene expression by dPCR. The assay was applied to blood samples collected from patients with International Federation of Gynecology and Obstetrics (FIGO) stage III\u0026ndash;IV HGSOC at two clinical time points: prior to neoadjuvant therapy and postoperatively, before the initiation of adjuvant treatment. By profiling gene expression signatures in enriched CTCs, this study aims to establish a highly sensitive alternative to traditional immunostaining methods and to explore the dynamic molecular landscape of CTCs during OC treatment.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eOvarian cancer specific marker selection\u003c/h2\u003e\u003cp\u003eTo develop the ovarian CTC dPCR assay, we first analyzed publicly available databases, including The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) to identify transcripts with abundant expression in normal ovary tissue (lineage markers) or OC (OC markers). Raw count data from around 200 samples were downloaded from each of the three cohorts: OC tissue from TCGA-OV, healthy ovary and whole blood data from the corresponding GTEx V8 datasets. Differential expression analysis was performed with the edgeR package from the Bioconductor ecosystem. In order to select OC markers, we looked for transcripts with significant log-fold change between expression levels (calculated from cpm values) in cancer tissue and whole blood (cutoff: \u0026gt;4) as well as between cancer tissue and matched healthy tissue (cutoff: \u0026gt;3). To eliminate genes which would have low expression value in cancer tissue, a cutoff of \u0026gt;\u0026thinsp;100 median cpm in the OC tissue samples was defined.\u003c/p\u003e\u003cp\u003eThe markers that the \u003cem\u003ein silico\u003c/em\u003e approach (\u003cb\u003eSI 1\u003c/b\u003e) and the literature review identified were further analyzed. We assessed the expression of the top candidate genes using quantitative real time PCR (RT-qPCR) in three OC cell lines, as well as in white blood cells (WBCs) (in six female healthy donor-derived WBCs) and CD-Prime FAST-Auto disc\u0026ndash;captured cells obtained from five female healthy donors (HDs).\u003c/p\u003e\u003cp\u003e\u003cb\u003eSpike-in test and\u003c/b\u003e \u003cb\u003ein vitro\u003c/b\u003e \u003cb\u003emarker set verification\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSpiking studies (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) were performed to establish the accuracy of the newly developed CD-Prime size-based CTC enrichment followed by the dPCR method of detecting cancer cells in the blood. Human OC cell lines (OVCAR-3, OVCAR-5, IGROV-1) were used to validate the efficiency and the limit of detection (LOD) of the enrichment and detection method in spike-in experiments. Cancer cell lines were selected from the NCI-60 Human Tumor Cell Lines Screen group based on the target gene expression profiles and their OC background. The OVCAR-5 cell line, though its classification as OC has been debated, was accepted as such based on the NCI-60 ovarian cancer panel list, Human Protein Atlas Ovarian Cancer Cell Lines list and literature data [\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. OC cell lines were obtained from the Developmental Therapeutics Program, National Cancer Institute at Frederick, MD. Cell lines were maintained in RPMI medium (Thermo Fisher Scientific) supplemented with 10% FBS, 1% penicillin-streptomycin, and 1% glutamine. All cell cultures were maintained at 37\u0026deg;C in a humidified atmosphere with 5% CO\u003csub\u003e2\u003c/sub\u003e and tested negative for \u003cem\u003eMycoplasma\u003c/em\u003e. Cells were trypsinized at about 70% confluence and counted by trypan blue exclusion. First, a cell suspension in the order of magnitude of 10\u003csup\u003e4\u003c/sup\u003e cells/mL was made in serum-free medium, and then a dilution of 833 cells in 5 mL, that was incubated with the Calcein-AM solution in a final concentration of 250 nM for 10 min. The diluted cell suspension was aliquoted to cell-repellent U-bottom 96-well microplates (Greiner Bio-One) in a 30 \u0026micro;L final volume (5 cells per well on average). Live cancer cells were identified by Calceinfluorescent signal on the GFP channel of the JULI Stage live cell imaging system, and the exact cell numbers were counted in each well (NanoEntek, Seoul, Republic of Korea) (\u003cb\u003eSI 2\u003c/b\u003e). Wells with different numbers (0, 5, 10) of cancer cells were picked and spiked manually by transferring the tumor cells with a 1% BSA-coated pipette into 3 mL blood from female HDs. In parallel, 100 cancer cells from the original serum-free cell suspension were aspirated into 3 mL of blood as well. 5, 10, and 100 cancer cell samples were tested in triplicate, and 3 mL blood without cancer cells were tested in quintuplicate. The samples were processed by the CD-Prime FAST-Auto disc CTC enrichment procedure and recovered cancer cells were detected by the expression of the tumor marker genes by dPCR assay. The absolute copy number of target genes was analyzed by QIAcuity Software Suite 2.2.0.26 (Qiagen).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eClinical sample collection\u003c/h3\u003e\n\u003cp\u003eHuman blood samples were collected at the Department of Obstetrics and Gynaecology, Semmelweis University (Budapest, Hungary), between 2022 and 2024. The histopathological confirmation was made using Formalin-Fixed Paraffin-Embedded (FFPE) tissue samples by the Department of Pathology and Experimental Cancer Research, Semmelweis University.\u003c/p\u003e\u003cp\u003eAll patients and HDs recruited in the present study signed informed consent forms approved by the Ethics Committee of the National Center for Public Health and Pharmacy (16119-8/2022/E\u0026Uuml;IG, BM/28859-3/2023). Patients with primary tumor confirmed as HGSOC were included in the patient group. The control group consisted of healthy women, donors with a history of tumors or concurrent pregnancies were excluded.\u003c/p\u003e\u003cp\u003eFor the training set, samples were collected from 8 female HDs and 23 preoperative HGSOC patients who had not received neoadjuvant therapy prior to sample collection. These clinical samples were used for testing and fine-tuning the \u003cem\u003ein vitro\u003c/em\u003e validated marker set. For the validation set, pre- and postoperative samples of five HGSOC patients and five age-matched controls were selected to demonstrate the potential application of the finalized marker panel. These patient cases met the following inclusion criteria: initial sampling prior to neoadjuvant therapy and availability of a postoperative sample collected 1\u0026ndash;2 months after surgery, but before the initiation of adjuvant therapy.\u003c/p\u003e\u003cp\u003eThe first sampling was performed before surgery prior to neoadjuvant therapy. The postoperative sampling was conducted before the initiation of adjuvant therapy ranging from 29 to 55 days, with a median of 47 days after surgery (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFor one patient (Patient 7) with confirmed relapse, multiple samples were enrolled throughout the course of therapy. Longitudinal sampling was conducted in association with the following clinical events: preoperative neoadjuvant therapy, surgery, and postoperative adjuvant therapy. A total of five samples were collected from the patient: three preoperative and two postoperative. The intervals between sample collections ranged from 42 to 67 days. Tumor recurrence was confirmed 4.5 months after the final sampling.\u003c/p\u003e\u003cp\u003ePeripheral venous blood (6\u0026thinsp;+\u0026thinsp;9 mL) was collected into Vacuette\u0026reg; K\u003csub\u003e2\u003c/sub\u003eEDTA tube (Greiner Bio-One GmbH, Kremsm\u0026uuml;nster, Austria) and to avoid potential epithelial cell contamination, the first 6 mL of blood were discarded before each collection of blood samples. The blood sample was accurately inverted and rotated ten times immediately after collection and stored at 4\u0026deg;C until proceeding, for a maximum of 4 hours.\u003c/p\u003e\n\u003ch3\u003eMarker set analysis in clinical samples\u003c/h3\u003e\n\u003cp\u003eDuring the selection and \u003cem\u003ein vitro\u003c/em\u003e verification of OC specific markers, 12 genes (\u003cem\u003eEpCAM, PPIC, FOLR1, WFDC2, SLC34A2, KLK5, KLK6, MUC16, MUC4, PRAME, SOX17, TUSC3\u003c/em\u003e) were identified as appropriate markers for dPCR based on wet lab analyses (\u003cb\u003eSI 3\u003c/b\u003e).\u003c/p\u003e\u003cp\u003eIn the training set testing, the detectability of these markers was assessed using clinical samples from 23 HGSOC patients who had not received neoadjuvant therapy prior to sample collection (specific primers listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe aim of this study was to develop a panel comprising the most sensitive molecular markers for detection of OC. Markers that were detectable in fewer than three patient samples were classified as low-sensitivity markers and excluded from further analysis. Consequently, a CTC detection panel tailored for OC patients comprising only the high-sensitivity genes was assembled. In order to demonstrate the methodological potential and validate the feasibility of the detection approach, a second set (i.e. the validation set) of pre- and postoperative OC samples and age-matched controls (5\u0026ndash;5) was analyzed with the CTC panel.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eCirculating tumor cell enrichment\u003c/h2\u003e\u003cp\u003eCD-Prime enables CTC enrichment from whole blood with an antibody-independent, simple, fast and high-throughput automated protocol. The polycarbonate membrane in the device with 8 \u0026micro;m pores is considered to be a gold standard and cut-off pore size to isolate CTCs. Briefly, 9 mL of peripheral blood collected in VACUETTE\u0026reg; K2EDTA tube was centrifuged at 1150 x g for 10 min at 4\u0026deg;C and the plasma layer was carefully removed, after than buffy coat layer was separated in 1 mL volume from the top of the red blood cell section and transferred to a 15 mL tube. Whole blood centrifugation and buffy coat separation were carried out within 4 hours after sample collection. Due to the very low number of CTCs and to prevent cell adhesion all consumables in contact with blood were coated with 1% BSA solution (Sigma, St. Louis, MO). The 1 mL buffy coat was supplemented to 3 mL with 1x PBS (Bio-Rad Laboratories, Hercules, CA) and injected to FAST-Auto disc to the blood input compartment. 6 mL of 1x PBS was also added to the FAST-Auto disc to the wash input compartment. CD-PRIME FAST-Auto automated protocol was used for CTC enrichment with 5min/sample runtime. Captured cells on the membrane were lysed in 350 \u0026micro;L of buffer RLT Plus (Qiagen, Hilden, Germany) with 3.5 \u0026micro;L of 2-mercaptoethanol (Sigma), stored at \u0026minus;\u0026thinsp;80\u0026deg;C until RNA isolation, and subjected to dPCR detection.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eRNA isolation, reverse transcription, and quality check\u003c/h2\u003e\u003cp\u003eTotal RNA from enriched cells was extracted using RNeasy Micro kit (Qiagen) according to the manufacturer\u0026rsquo;s manual with on-column DNase treatment. The elution step was performed twice with 30 \u0026micro;L of RNase-free water to enhance the eluted RNA quantity. Due to the expected ultra-low RNA yields, all the samples were directly reverse transcribed.\u003c/p\u003e\u003cp\u003eComplementary DNA (cDNA) synthesis was performed using the qScript Ultra Supermix system (Quantabio, Beverly, MA) for RT-dPCR. Briefly, the following conditions were applied in a total volume of 37.5 \u0026micro;L: 30 \u0026micro;L of template RNA and 7.5 \u0026micro;L of 5x qScript Ultra Supermix (Quantabio) were mixed and incubated for 2 min at 25\u0026deg;C, followed 10 min at 55\u0026deg;C, and then 1 min at 95\u0026deg;C. The product of cDNA synthesis reaction was stored at \u0026minus;\u0026thinsp;20\u0026deg;C or used for RT-qPCR quality check immediately.\u003c/p\u003e\u003cp\u003eIn order to avoid false negative results of cancer-related transcripts due to low-quality RNA and/or cDNA, we have introduced a cost-effective and fast quality control step between the reverse transcription and dPCR measurements. The quality checkpoint was performed from 1/10 diluted cDNA by RT-qPCR using Perfecta SYBR Green FastMix, Low ROX (Quantabio) with initial denaturation (95\u0026deg;C, 30 sec) and 40 cycles of denaturation (95\u0026deg;C, 15 sec), annealing (59\u0026deg;C, 15 sec) and extension (72\u0026deg;C, 15 sec) on a QuantStudio 5 Real Time PCR (Applied Biosystems, Thermo Fisher Scientific, Ottawa, CA). Primer sequences were the following: ACTB (forward) AGAAAATCTGGCACCACACC and (reverse) TAGCACAGCCTGGATAGCAA [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The quality of cDNA was accepted in case of the β-actin gene transcript Ct value was \u0026lt;\u0026thinsp;35.0. Each RT-qPCR run included minimum of one positive control (10 ng PBMC) and no template control (NTC) sample.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eDigital PCR analysis\u003c/h2\u003e\u003cp\u003edPCR is advantageous as it has been shown to have higher sensitivity and accuracy compared to traditional qRT-PCR and allows direct quantification of nucleic acids, multiclonal amplification, and greater resilience to inhibition from a wider range of samples. Importantly, since it does not require standard curves, it provides an absolute measure of the nucleic acid content, with significantly increased detection sensitivity. DPCR reactions were performed in QIAcuity 24-well 26k (26,000 partitions per well) nanoplates in a QIAcuity Digital PCR System (Qiagen). Ovarian CTC assay reactions included 0.4 \u0026micro;M final concentrations of primer pairs and 4 \u0026micro;L cDNA template in 3X QIAcuity EvaGreen Master Mix (Qiagen) in a 40 \u0026micro;l total volume. DPCR thermal conditions were used an initial heat activation 95\u0026deg;C for 2 min step and optimized as 50 cycles of 95\u0026deg;C for 15 sec (denaturation), 59\u0026deg;C for 15 sec (annealing), and 72\u0026deg;C for 15 sec (extension) following 40\u0026deg;C for 5 min cooling down before reading. The following settings were used for imaging: exposition: 50ms, Gain: 6. The concentration of target gene expression (copies/\u0026micro;L) was determined by absolute quantification by QIAcuity Software Suite version 2.2.0.26. (Qiagen). For positive controls, OVCAR-3, OVCAR-5, and IGROV-1 cell lines 5\u0026ndash;20 ng cDNA were used depending on the expression level of the target gene in the positive control cell line. For the multiplex dPCR setups, we also used an additional cell line (A-431) with the consideration that only one of the markers would be expressed in this case, and the cell line was obtained from the American Type Culture Collection (ATCC). Non-template control (NTC) with all materials except cDNA, is also included in each digital PCR run. CTC-positive samples were defined based on dPCR analysis using the QIAcuity system. A sample was considered CTC-positive if at least two events (\u0026ge;\u0026thinsp;0.107 copies/\u0026micro;L) above the threshold were detected among 26,000 partitions, indicating a high confidence that the signal reflected true amplification of cancer-related transcripts. To establish marker-specific thresholds for CTC positivity, baseline event distributions were analyzed in HD samples. As a result, the cut-off value was not uniform but varied depending on the marker, reflecting differences in background signal across markers within the HD group. Samples with 0, 1, or 2 events below the marker-specific threshold (corresponding to 0, 0.053, or 0.110 copies/\u0026micro;L) were classified as CTC-negative. All NTCs also tested negative. The absolute quantification and transcript-level characterization of ovarian cancer-associated CTCs was adapted for dPCR by our group, in accordance with the Minimum Information for Publication of Quantitative Digital PCR Experiments (2020 guidelines) [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003ePrimers\u003c/h2\u003e\u003cp\u003ePrimers (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) were synthesized by IDT (Integrated DNA Technologies, Coralville, IA). Primers were selected from the literature along the following directives: (i) excellent gene specificity for the study reliability, (ii) PCR product as short as possible for high PCR efficiency, (iii) almost the same melting temperature of different primer pairs for multiplexing. The used primers were aligned by Primer-BLAST to confirm gene specificity and low self-complementarity.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cb\u003eList of PCR primer sets.\u003c/b\u003e Oligonucleotide sequences were used for wet lab verification of 21 potential ovarian CTC markers in the present study. The genes of the 4-marker panel were highlighted in bold style.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTarget gene\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSequence (5\u0026rsquo;-3')\u003c/p\u003e\u003cp\u003eForward / Reverse\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAmplicon length\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSource\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBCAM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAGAGATGAACCCAGAGGGCT / GATAGTGCAGGGTGAGGTGG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e200 bp\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCLDN3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGCCACCAAGGTCGTCTACTC / CCTGCGTCTGTCCCTTAGAC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e101 bp\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEPCAM\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eCCGCAGCTCAGGAAGAATGT / TCTCCCAAGTTTTGAGCCATTC\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e179 bp\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eERCC1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGCCTATGAGCAGAAACCAGC / AATGTGGTCAGGAGGGTCTG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e131 bp\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFOLR1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eAGGTGCCATCTCTCCACAGT / GAGGACAAGTTGCATGAGCA\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e135 bp\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKLK5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAAGGCCCAACCAGCTCTACT / CCGAGACGGACTCTGAAAAC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e99 bp\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKLK6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTGGTGCTGAGTCTGATTGCT / CGCCATGCACCAACTTATT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e60 bp\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKRT19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTGCGGGACAAGATTCTTGGT / CGTTGATGTCGGCCTCCA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e144 bp\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKRT7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCATCGAGATCGCCACCTACC / GATATTCACGGCTCCCACTCC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e82 bp\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMAL2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eACGTAGCAGCCTCAATTTTTGC / CATCTTCGTAAAGCCAGACCC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e76 bp\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMUC1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGCATCAGGCTCAGCTTCTACT / GTCTCTCTGCAGCTCTTGGTA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e324 bp\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMUC16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAGCAGACAGCAGAGACTATCC / CTGGACTTCCCAACCATTCTG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e108 bp\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMUC4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGGAGAGGTATCGCCCTGATAGATT / ACGGTAGTTGGGCCTTTCTTC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e97 bp\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePPIC\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eAGCAAGTTTCATCGTGTCATCA / TGGAAATGTCTCACCATAGATGC\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e102 bp\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePRAME\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eACCTGGAAGCTACCCACCTT / AGATGCATCACATCCCCTTC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e299 bp\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePRSS8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGATTACTCCGGTCGGGGAC / ACGCCTTCATAGGTGATGCT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e136 bp\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSLC34A2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCTGAGGCACCTGTAACCAAGA / TGATCCCCGAGTCCTGAAGAG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e120 bp\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSOX17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAGTGACGACCAGAGCCAGAC / CCTTAGCCCACACCATGAAA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e214 bp\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSPON1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTCTTAGACTGCTGTGCCTGC / AACTTGTTTGACGCCTTCGC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e191 bp\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTUSC3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTCGGGGGAGGACAGAAGAAA / CGGAAGATTGAGCGTCTGGA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e88 bp\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWFDC2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eAGAACTGCACGCAAGAGTG / TTGAGGTTGTCGGCGCATT\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e52 bp\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eMultiplexing in dPCR\u003c/h2\u003e\u003cp\u003eWe optimized a multiplex assay on the QIAcuity dPCR system to detect two targets (\u003cem\u003eSLC34A2\u003c/em\u003e and \u003cem\u003eWFDC2\u003c/em\u003e) within a single fluorescence channel using EvaGreen chemistry. Primers for both targets were added to the reactions at equal concentrations and volumes. The separation of the two targets was possible due to the difference in amplicon lengths, which resulted in distinct fluorescence amplitudes in dPCR 1D scatterplots (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). As a positive control, we used cDNA from the OVCAR-3 cell line, which yielded well-separated fluorescence amplitudes corresponding to the two targets. Using this reference and adjusting the threshold accordingly, amplification signals from patient samples could be clearly assigned to their respective targets, allowing us to identify the presence of each target in the tested samples [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e\u003cp\u003ePrior to the multiplex setup, each primer pair was also tested in singleplex dPCR reactions using two cell lines with differential gene expression: \u003cem\u003eWFDC2\u003c/em\u003e was expressed exclusively in A-431 cells, while \u003cem\u003eSLC34A2\u003c/em\u003e was expressed only in IGROV-1 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). These single-target controls confirmed that the primers did not interfere with each other and that their simultaneous use did not compromise assay sensitivity. Therefore, the multiplex approach was validated as both specific and robust (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eDetermination of serum Cancer antigen-125 level\u003c/h2\u003e\u003cp\u003eCancer antigen-125 (CA125) is currently the only blood-based tumor marker recommended for clinical use in the diagnosis and management of OC. CA125 was assayed in the serum of patients collected in an additional tube at the same time as the CTC isolation analysis. The immunoanalysis assay was performed on Siemens Attelica IM analyzer (Siemens Healthineers AG, Forchheim, Germany) according to standard procedures with the cut-off value of 35 U/mL.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eAll statistical analyses were performed using GraphPad Prism version 9.5.1 (GraphPad Software, San Diego, CA, USA). Group comparisons involving more than two datasets were conducted using the non-parametric Kruskal\u0026ndash;Wallis test, followed by Dunn\u0026rsquo;s multiple comparisons post hoc test to assess pairwise differences. To assess differences in age between patients with HGSOC and healthy controls, the Shapiro\u0026ndash;Wilk test was first used to evaluate the normality of age distribution within each group. Based on the outcome of the normality test, either a non-parametric Mann\u0026ndash;Whitney U test (for non-normally distributed data or small sample sizes) or an unpaired t test with Welch\u0026rsquo;s correction (for normally distributed data) was applied to determine whether there was a statistically significant difference in age between the two groups. A p-value of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eOvarian cancer specific marker selection\u003c/h2\u003e\u003cp\u003eThis approach identified 36 potential markers (\u003cb\u003eSI 1\u003c/b\u003e) which has been supplemented to 44 potential markers with 8 extra genes based on literature review.\u003c/p\u003e\u003cp\u003e We initially assessed the expression of the top 21 candidate genes using RT-qPCR in three OC cell lines, as well as in WBCs (in six female healthy donor-derived WBCs) and CD-Prime FAST-Auto disc\u0026ndash;captured cells obtained from five female HDs. Nine of these genes showed detectable expression in WBCs, indicating high background levels, and were thus excluded from further analysis. The remaining 12 genes, which were not expressed in WBCs, were subsequently evaluated in clinical samples from OC patients (\u003cb\u003eSI 3\u003c/b\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eFeasibility evaluation of CTC digital detection using spiked samples\u003c/h2\u003e\u003cp\u003eTo evaluate the newly developed technology, we micromanipulated individual cells from the OC cell lines OVCAR-3, OVCAR-5, and IGROV-1, and introduced them into 3 mL of whole blood obtained from female HDs. These spiked samples were then processed using the CD-Prime FAST-Auto system, followed by quantification via QIAcuity dPCR (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). For RNA-based dPCR detection of CTCs in OC, we selected \u003cem\u003eEpCAM, WFDC2, MUC16\u003c/em\u003e, and \u003cem\u003eSLC34A2\u003c/em\u003e as candidate marker genes in spike-in experiments (\u003cb\u003eSI 4\u003c/b\u003e).\u003c/p\u003e\u003cp\u003eWe tested \u003cem\u003eEPCAM\u003c/em\u003e expression in all cell lines, \u003cem\u003eWDFC2\u003c/em\u003e in OVCAR-3 and OVCAR-5, \u003cem\u003eMUC16\u003c/em\u003e in OVCAR-3, and \u003cem\u003eSLC34A2\u003c/em\u003e in OVCAR-3 and IGROV-1 (\u003cb\u003eSI4\u003c/b\u003e). Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB show the expression of \u003cem\u003eEPCAM\u003c/em\u003e in OVCAR-5 cells and \u003cem\u003eWFDC2\u003c/em\u003e in OVCAR-3 cells, respectively. The quality of cDNA generated from these samples was confirmed to be excellent, as assessed by RT-qPCR targeting β-actin (\u003cem\u003eACTB\u003c/em\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo evaluate the detection strategy, we performed spiking experiments using the OVCAR-5 cell line, examining \u003cem\u003eEpCAM\u003c/em\u003e expression in samples containing 0, 5, 10, and 100 cells. \u003cem\u003eEpCAM\u003c/em\u003e was undetectable in control samples (0.017\u0026thinsp;\u0026plusmn;\u0026thinsp;0.030 copies/\u0026micro;L), while a significantly elevated signal was observed in samples spiked with 100 cells (4.508\u0026thinsp;\u0026plusmn;\u0026thinsp;0.199 copies/\u0026micro;L; p\u0026thinsp;=\u0026thinsp;0.0132) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA).\u003c/p\u003e\u003cp\u003eSimilarly, the introduction of 5, 10, and 100 OVCAR-3 cells into control blood samples resulted in a progressively increasing \u003cem\u003eWFDC2\u003c/em\u003e signal, with significantly higher expression detected in the 100-cell samples compared to the cell-free control (0.018\u0026thinsp;\u0026plusmn;\u0026thinsp;0.031 copies/\u0026micro;L). The measured \u003cem\u003eWFDC2\u003c/em\u003e concentrations were 0.448\u0026thinsp;\u0026plusmn;\u0026thinsp;0.311 copies/\u0026micro;L, 2.01\u0026thinsp;\u0026plusmn;\u0026thinsp;1.271 copies/\u0026micro;L, and 10.063\u0026thinsp;\u0026plusmn;\u0026thinsp;3.061 copies/\u0026micro;L for samples spiked with 5, 10, and 100 cells, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003ePatient characteristics\u003c/h2\u003e\u003cp\u003eFor the training set, samples were collected from 8 female HDs and 23 preoperative HGSOC patients who had not received neoadjuvant therapy prior to sample collection. At the time of preoperative sampling, the age of 23 HGSOC patients ranged from 41 to 82.23 years, with a median of 62.03 years. The age of the control group ranged from 50.26 to 58.28 years, with a median of 53.11 years (\u003cb\u003eSI 5\u003c/b\u003e).\u003c/p\u003e\u003cp\u003eThe normality of age distribution in both the OC and control groups was assessed using the Shapiro\u0026ndash;Wilk test. As both groups followed a normal distribution (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), an unpaired t-test with Welch\u0026rsquo;s correction was used to compare ages between the two groups. The test indicated a significant difference in age between the OC and control groups (p\u0026thinsp;=\u0026thinsp;0.007).\u003c/p\u003e\u003cp\u003eThe FIGO stage distribution of the 23 HGSOC patients was as follows: stage I in 4.35% (1/23), stage III in 78.26% (18/23), stage IV in 13.04% (3/23), and in one case (4.35%) FIGO stage information was not available. Preoperative CA125 level data were available for 19 HGSOC patients. All measured values (19/23) exceeded the cut-off value of 35 U/mL. Postoperative CA125 levels were not available (\u003cb\u003eSI 5\u003c/b\u003e).\u003c/p\u003e\u003cp\u003eIn the validation set, the age of HGSOC patients ranged from 50.56 to 63.48 years, with a median of 56.29 years at the time of preoperative sampling. The age of the control group ranged from 51.82 to 59.86 years, with a median of 56.50 years.\u003c/p\u003e\u003cp\u003eA non-parametric Mann\u0026ndash;Whitney U test was used to compare age between OC and control groups due to the small sample size (N\u0026thinsp;=\u0026thinsp;5 per group), as this test is more robust under such conditions. The analysis revealed no statistically significant difference in age between the two groups.\u003c/p\u003e\u003cp\u003eAccording to FIGO classification, 60% (3/5) of HGSOC patients were diagnosed at stage III, while 40% (2/5) were at stage IV. Based on tumour, node and metastasis (TNM) classification, all patients (5/5) had a primary tumor categorized as T3. Nodal status was pN1 in 40% (2/5) of cases, and distant metastasis (pM1) was observed in 40% (2/5) of patients. All patients (5/5) had preoperative CA125 levels exceeding 35 U/mL; postoperative CA125 levels were not available (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eClinicopathological characteristics of HGSOC patients, including patient ID, age, histological subtype, FIGO stage, TNM classification, CA125 level, and recurrence status.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003eClinicopathological characteristics of patients\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePatient ID\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHistological subtype\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFIGO stage\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTNM classification\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCA125 level at diagnosis (U/mL)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eRecurrence status\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eS1OC-7\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e57.33\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHGSOC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIVB\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eypT3bN1bM1b\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2909.4\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eRecurrence\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eS1OC-55\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e52.64\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eHGSOC\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eIIIB\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eypT3b\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e1544.4\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003eNo recurrence\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eS1OC-59\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e63.48\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eHGSOC\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eIIIC\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eypT3cN1a\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e10713.5\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003eNo recurrence\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eS1OC-85\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e56.29\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eHGSOC\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eIVB\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eypT3bM1b\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e11114.3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003eNo recurrence\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eS1OC-94\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e50.56\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eHGSOC\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eIIIB\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eypT3b\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e9329.7\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003eNo recurrence\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003eTraining set analysis\u003c/h2\u003e\u003cp\u003eDuring the selection and \u003cem\u003ein vitro\u003c/em\u003e verification of OC specific markers, 12 genes (\u003cem\u003eEpCAM, PPIC, FOLR1, WFDC2, SLC34A2, KLK5, KLK6, MUC16, MUC4, PRAME, SOX17, TUSC3\u003c/em\u003e) were identified as appropriate markers for dPCR based on wet lab analyses \u003cb\u003e(SI 3)\u003c/b\u003e.\u003c/p\u003e\u003cp\u003eIn the first phase of the clinical testing, the detectability of these markers was assessed using clinical samples from 23 HGSOC patients who had not received neoadjuvant therapy prior to sample collection. (specific primers listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo define CTC positivity, a threshold was established based on events detected in the HD group; therefore, the cut-off value varied for each marker depending on the event distribution within the HD group. Overall, CTC-positive samples were identified if at least two events exceeding the threshold (\u0026ge;\u0026thinsp;0.107 copies/\u0026micro;L) were detected among 26,000 partitions. For \u003cem\u003eEpCAM\u003c/em\u003e the threshold was set at \u0026gt;\u0026thinsp;0.111 copies/\u0026micro;L; for \u003cem\u003eFOLR1\u003c/em\u003e and \u003cem\u003eWFDC2\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.107 copies/\u0026micro;L; and for \u003cem\u003ePPIC\u003c/em\u003e, \u0026gt;\u0026thinsp;0.056 copies/\u0026micro;L. In the case of \u003cem\u003eSLC34A2\u003c/em\u003e, no events were detected in the control group; however, as the cut-off required a minimum of two events, single events detected in patient samples for this marker were not considered positive.\u003c/p\u003e\u003cp\u003eThe aim of this study was to develop a panel comprising the most sensitive molecular markers for the detection of OC. Markers that were detectable in fewer than three patient samples were classified as low-sensitivity markers and excluded from further analysis. The excluded genes were \u003cem\u003eSLC34A2\u003c/em\u003e, \u003cem\u003eKLK5\u003c/em\u003e, \u003cem\u003eKLK6\u003c/em\u003e, \u003cem\u003eMUC16\u003c/em\u003e, \u003cem\u003eMUC4\u003c/em\u003e, \u003cem\u003ePRAME\u003c/em\u003e, \u003cem\u003eSOX17\u003c/em\u003e, and \u003cem\u003eTUSC3\u003c/em\u003e.\u003c/p\u003e\u003cp\u003eIn contrast, \u003cem\u003eEpCAM\u003c/em\u003e (epithelial, pan-cancer), \u003cem\u003eWFDC2\u003c/em\u003e (epithelial, OC tissue-specific), \u003cem\u003eFOLR1\u003c/em\u003e (epithelial, OC tissue-specific), and \u003cem\u003ePPIC\u003c/em\u003e (epithelial/mesenchymal, pan-cancer) demonstrated higher detection rates across 23 HGSOC clinical samples \u0026minus;\u0026thinsp;26.09% (6/23), 13.04% (3/23), 21.43% (4/23), and 26.09% (6/23), respectively\u0026mdash;while remaining undetectable in samples from 8 HDs.\u003c/p\u003e\u003cp\u003eAlthough \u003cem\u003eSLC34A2\u003c/em\u003e exhibited good performance in spike-in experiments, its sensitivity in clinical samples was limited. Nevertheless, as it was multiplexed with \u003cem\u003eWFDC2\u003c/em\u003e in the dPCR multiplex assay, it was retained in the visualized data alongside the four high-sensitivity markers (details in the \u003cb\u003e\u0026ldquo;Multiplexing in dPCR\u0026rdquo;\u003c/b\u003e section).\u003c/p\u003e\u003cp\u003eConsequently, a CTC detection panel tailored for OC patients was assembled, including the four most sensitive markers and \u003cem\u003eSLC34A2\u003c/em\u003e (epithelial), a low-sensitivity gene retained for validation due to its co-amplification with \u003cem\u003eWFDC2\u003c/em\u003e in the dPCR multiplex method.\u003c/p\u003e\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\u003ch2\u003eValidation set analysis\u003c/h2\u003e\u003cp\u003eFinally, based on the marker sensitivity index, a four-marker panel\u0026mdash;comprising only the high-sensitivity genes\u0026mdash;was selected for the absolute quantification and molecular characterization of a second set of OC samples and age-matched controls (5\u0026ndash;5) to demonstrate the methodological potential and validate the feasibility of the detection approach.\u003c/p\u003e\u003cp\u003eThe four-gene panel was validated using a small subset of clinical samples from HGSOC patients as well as healthy control samples. A patient was considered CTC-positive if at least one of the four tested markers (\u003cem\u003eEpCAM, WFDC2, FOLR1, PPIC\u003c/em\u003e) was found to be positive. While \u003cem\u003eSLC34A2\u003c/em\u003e was not part of the finalized panel due to its limited sensitivity, it remained included in all experimental steps owing to its co-amplification with \u003cem\u003eWFDC2\u003c/em\u003e in the multiplex dPCR method.\u003c/p\u003e\u003cp\u003eThe HDs (N\u0026thinsp;=\u0026thinsp;5) all showed CTC-negative results \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e), as they did not express any of the five examined transcripts.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eHGSOC samples were obtained from five patients, with CTC measurements performed by dPCR at both preoperative and postoperative time points (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). CTCs were detected at both time points using a five-gene assay, though only four genes formed the targeted detection panel. \u003cem\u003eSLC34A2\u003c/em\u003e was excluded from the panel itself but was measured due to its inclusion in the multiplex with \u003cem\u003eWFDC2\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). In the preoperative samples, \u003cem\u003eEPCAM\u003c/em\u003e was positive in three patients, while \u003cem\u003ePPIC\u003c/em\u003e and \u003cem\u003eFOLR1\u003c/em\u003e were positive in two patients each, and \u003cem\u003eWFDC2\u003c/em\u003e was positive in one patient. In the postoperative samples, marker positivity increased: \u003cem\u003ePPIC\u003c/em\u003e was positive in four patients, \u003cem\u003eEPCAM\u003c/em\u003e in three, \u003cem\u003eFOLR1\u003c/em\u003e in two, and \u003cem\u003eWFDC2\u003c/em\u003e in one (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003eLongitudinal monitoring of CTC-associated marker profiles via multiparametric panel analysis\u003c/h2\u003e\u003cp\u003eWe investigated the changes in marker expression between preoperative and postoperative samples from patients. The emergence or disappearance of individual markers may suggest phenotypic switching of CTCs, a process that can be effectively monitored through a panel-based approach during disease progression.\u003c/p\u003e\u003cp\u003eFor the \u003cem\u003eEpCAM\u003c/em\u003e marker (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eA), patients Pt. 7 and Pt. 55 exhibited elevated postoperative concentrations compared to their respective preoperative levels (0.210 copies/\u0026micro;l and 0.445 copies/\u0026micro;l preoperatively, increasing to 0.322 copies/\u0026micro;l and 2.091 copies/\u0026micro;l, respectively). In patient Pt. 59, \u003cem\u003eEpCAM\u003c/em\u003e became detectable postoperatively (0.276 copies/\u0026micro;l), while in Pt. 85, \u003cem\u003eEpCAM\u003c/em\u003e positivity\u0026mdash;previously above the cut-off threshold\u0026mdash;disappeared. For Pt. 94, \u003cem\u003eEpCAM\u003c/em\u003e levels remained consistently below the cut-off throughout the study.\u003c/p\u003e\u003cp\u003eThe \u003cem\u003ePPIC\u003c/em\u003e marker (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eB) demonstrated an 80% positivity rate in postoperative samples. In patients Pt. 7 and Pt. 85, initial \u003cem\u003ePPIC\u003c/em\u003e negativity shifted to concentrations exceeding the cut-off following surgery (10.42 copies/\u0026micro;l and 0.107 copies/\u0026micro;l, respectively). In Pt. 55, the already elevated preoperative concentration (0.422 copies/\u0026micro;l) further increased to 2.112 copies/\u0026micro;l. Pt. 94 showed a slight decrease in concentration from 0.211 copies/\u0026micro;l to 0.16 copies/\u0026micro;l, though still above the cut-off. In Pt. 59, \u003cem\u003ePPIC\u003c/em\u003e levels remained undetectable both before and after surgery.\u003c/p\u003e\u003cp\u003eRegarding \u003cem\u003eFOLR1\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eC), Pt. 55 exhibited a marked increase in expression following surgery (from 0.158 copies/\u0026micro;l to 5.367 copies/\u0026micro;l). In Pt. 7, the initially negative \u003cem\u003eFOLR1\u003c/em\u003e status shifted to strong positivity postoperatively (1.214 copies/\u0026micro;l). Conversely, Pt. 59, who was initially \u003cem\u003eFOLR1\u003c/em\u003e-positive (0.474 copies/\u0026micro;l), became negative postoperatively. \u003cem\u003eFOLR1\u003c/em\u003e was undetectable in both pre- and postoperative samples of Pt. 85 and Pt. 94.\u003c/p\u003e\u003cp\u003eIn the case of the \u003cem\u003eWFDC2\u003c/em\u003e marker (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eD), Pt. 94 presented a high preoperative concentration (2.089 copies/\u0026micro;l), which significantly declined postoperatively (0.374 copies/\u0026micro;l), although it remained above the cut-off. In patients Pt. 7, Pt. 55, Pt. 59, and Pt. 85, \u003cem\u003eWFDC2\u003c/em\u003e levels were below the cut-off preoperatively. Among these, only Pt. 85 showed an increase above the cut-off postoperatively (0.482 copies/\u0026micro;l).\u003c/p\u003e\u003cp\u003eAs expected, \u003cem\u003eSLC34A2\u003c/em\u003e concentrations (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eE) remained below the cut-off threshold in both preoperative and postoperative samples for all patients analyzed.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec25\" class=\"Section3\"\u003e\u003ch2\u003eMarker sensitivity in cell-lines, preoperative and postoperative clinical samples\u003c/h2\u003e\u003cp\u003eIn the 21 HGSOC cell lines \u0026mdash; which were selected based on the article by Mei et al. (2021) [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] and the available RNA expression data of the selected genes in the Human Protein Atlas (\u003cb\u003eSI 6\u003c/b\u003e) \u0026mdash; the expression levels of the five marker genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e), examined based on the literature, varied as follows: \u003cem\u003eEpCAM\u003c/em\u003e showed the strongest expression, being detected in 90% of the cell lines, with 74% classified as showing high expression (\u0026ge;\u0026thinsp;100 nTPM). \u003cem\u003eWFDC2\u003c/em\u003e followed with an 81% expression rate, while \u003cem\u003ePPIC\u003c/em\u003e and \u003cem\u003eFOLR1\u003c/em\u003e ranked next. Although \u003cem\u003ePPIC\u003c/em\u003e was expressed in 95% of the cell lines, 85% of these cases exhibited only low expression levels. A similar pattern was observed for \u003cem\u003eFOLR1\u003c/em\u003e, which had an overall expression rate of 76%, but 65% of these cases showed weak expression. Lastly, \u003cem\u003eSLC34A2\u003c/em\u003e was the least expressed marker, detected in 57% of the cell lines, and in all cases, its expression was low.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eBased on the obtained results, the sensitivity of the markers decreases in the following order in preoperative samples: \u003cem\u003eEpCAM\u003c/em\u003e, \u003cem\u003ePPIC\u003c/em\u003e and \u003cem\u003eFOLR1\u003c/em\u003e, \u003cem\u003eWFDC2\u003c/em\u003e, and finally \u003cem\u003eSLC34A2\u003c/em\u003e, which, as expected, yielded negative results. (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e)\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe order of marker sensitivity in postoperative samples changed as follows: the highest positivity was observed for the \u003cem\u003ePPIC\u003c/em\u003e marker, followed by \u003cem\u003eEpCAM, FOLR1\u003c/em\u003eand \u003cem\u003eWFDC2\u003c/em\u003e. However, \u003cem\u003eSLC34A2\u003c/em\u003e did not yield any positive results in the postoperative samples. (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003e)\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThese findings in patient samples closely mirrored those observed in cell lines (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePercentages of CTC marker positivity in cell lines and in pre- and postoperative patient samples. The marker expression patterns observed in cell lines were consistent with those detected in patient samples.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eMarkers\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003eExpressed/CTC+ (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eCell line\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ePreoperative\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ePostoperative\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh expression\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLow expression\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEPCAM\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e66,67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e23,81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePPIC\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e14,29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e80,95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFOLR1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e28,57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e47,62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e40\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWFDC2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e71,43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9,52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e40\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSLC34A2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e14,29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e42,86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec26\" class=\"Section3\"\u003e\u003ch2\u003eLongitudinal analysis in a confirmed relapse case\u003c/h2\u003e\u003cp\u003eLongitudinal sampling was associated with the following events: preoperative neoadjuvant therapy, surgery, and postoperative adjuvant therapy. During dPCR analyses, the absolute concentration of various markers was determined in patient samples.\u003c/p\u003e\u003cp\u003eThe Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e14\u003c/span\u003e shows the follow-up samples of Patient 7 (Pt. 7) who developed recurrence. In Pt. 7, CTC levels dropped significantly following neoadjuvant therapy, but this reduction was short-lived, as CTC copies reappeared in postoperative samples. Three and four out of five examined markers were already positive in the last two follow-up samples taken before recurrence, respectively. Relapse was later confirmed via imaging, and the patient died one year later (Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eDespite initial sensitivity to platinum-based chemotherapy in HGSOC, most patients eventually relapse, and no clinically validated biomarkers currently exist to predict platinum resistance. The conventional six-month threshold for defining platinum resistance is increasingly viewed as arbitrary. Experts suggest that platinum sensitivity should be considered a continuum, influenced by time elapsed since the last chemotherapy cycle and prior use of maintenance therapies. Understanding the molecular drivers of resistance remains critical, especially as non-homologous recombination mechanisms, such as cyclin E1 (\u003cem\u003eCCNE1\u003c/em\u003e) amplification and gene breakage in key tumor suppressors, contribute to resistance in HGSOC. For monitoring relapse, CA125 remains the most practical biomarker in HGSOC, especially when interpreted alongside clinical symptoms and imaging. Its role is less clear in non-HGSOC histological subtypes and when assessing response to targeted therapies [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTherefore, the use of novel biomarkers with sufficient sensitivity and reliability for application in routine clinical practice is of utmost importance.\u003c/p\u003e\u003cp\u003eBy combining size-based enrichment of unfixed CTCs from whole blood with absolute quantification of CTC-derived RNA, we have developed a highly sensitive and specific assay for non-invasive sampling of OC. RNA-based identification of CTCs overcomes several limitations of imaging-based analyses, including manual verification of individual images and the need for calibration and thresholding of multiple immunofluorescence microscopy parameters. In our study, we have demonstrated the potential applications of this dPCR-based CTC assay in FIGO stage III\u0026ndash;IV HGSOC.\u003c/p\u003e\u003cp\u003eTo evaluate the utility of our strategy for ovarian CTC detection, we first identified a panel of OC-specific transcripts that are virtually undetectable in normal peripheral nucleated blood cells (e.g., hematopoietic, endothelial, and fibroblast cells), even by using highly sensitive dPCR technology. We selected multiple markers to account for the known heterogeneity of OC cells. An initial list of 44 candidate genes was compiled based on publicly available expression databases and further supplemented with additional genes identified through literature review (\u003cb\u003eSI 1 and SI 3\u003c/b\u003e). Twelve of these transcripts were highly expressed in ovarian tissue and/or tumors, but were undetectable in normal blood cells, which may generate background noise in size-based CTC enrichment workflows (\u003cb\u003eSI 3\u003c/b\u003e).\u003c/p\u003e\u003cp\u003eTo validate our novel method for CTC detection, we spiked defined numbers of OC cells (0, 5, 10, and 100 cells) into 3 mL of peripheral blood collected from female HDs. The samples were processed using the CD-Prime device for size-based CTC isolation, followed by dPCR detection of cDNA corresponding to the selected marker genes. In the negative control samples (0 cells), none of the selected markers were detectable. In contrast, a significant difference was consistently observed between the 0-cell and 100-cell samples (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), confirming the sensitivity of the method. As a next step, clinical blood samples from OC patients and controls were used for testing and fine-tuning the \u003cem\u003ein vitro\u003c/em\u003e validated marker set. Markers that were detectable in fewer than three patient samples were classified as low-sensitivity markers and excluded from further analysis. Consequently, a CTC detection panel tailored for OC patients comprising only the high-sensitivity genes was assembled. The excluded genes were \u003cem\u003eSLC34A2, KLK5, KLK6, MUC16, MUC4, PRAME, SOX17\u003c/em\u003e, and \u003cem\u003eTUSC3\u003c/em\u003e. In contrast, \u003cem\u003eEpCAM, WFDC2, FOLR1\u003c/em\u003e, and \u003cem\u003ePPIC\u003c/em\u003e demonstrated higher detection rates across 23 HGSOC clinical samples, while remaining undetectable in samples from 8 female HDs.\u003c/p\u003e\u003cp\u003eThis four-marker panel effectively addressed the high degree of heterogeneity observed among HGSOC cell lines and was able to detect all 21 out of 21 HGSOC cell lines based on publicly available gene expression data from the Human Protein Atlas (\u003cb\u003eSI 6\u003c/b\u003e).\u003c/p\u003e\u003cp\u003eIn order to demonstrate the methodological potential and validate the feasibility of the detection approach, a second set of pre- and postoperative OC samples and age-matched controls was analyzed with the CTC panel. A patient was classified as CTC-positive if at least one of the four highly sensitive markers tested (\u003cem\u003eEpCAM\u003c/em\u003e, \u003cem\u003eWFDC2\u003c/em\u003e, \u003cem\u003eFOLR1\u003c/em\u003e, \u003cem\u003ePPIC\u003c/em\u003e) was detected. \u003cem\u003eSLC34A2\u003c/em\u003e, a low-sensitivity marker, was not part of the panel but was included in the assay due to its co-amplification with \u003cem\u003eWFDC2\u003c/em\u003e.\u003c/p\u003e\u003cp\u003eAll HD samples were CTC-negative, with none of the five transcripts detected. In contrast, all five HGSOC patients were CTC-positive in both their preoperative and postoperative samples, with at least one marker detected in each case. As expected, \u003cem\u003eSLC34A2\u003c/em\u003e as a low-sensitivity marker remained undetectable across all samples.\u003c/p\u003e\u003cp\u003eIn agreement with our findings showing increased postoperative CTC positivity, Zhang et al. also reported a significant rise in CTC counts following surgery in both the neoadjuvant and non-neoadjuvant chemotherapy groups. In their study, monoclonal antibodies specific for the epithelial markers EpCAM, HER2, and MUC1 were used to isolate CTCs from the peripheral blood of EOC patients, and the expression of six OC\u0026ndash;associated genes (\u003cem\u003eEpCAM, HER2, MUC1, WT1, P16\u003c/em\u003e, and \u003cem\u003ePAX8\u003c/em\u003e) was subsequently evaluated using multiplex RT-PCR. The authors attributed the postoperative increase in CTCs to vascular injury and the mechanical release of tumor cells during surgical manipulation, which could facilitate their entry into the bloodstream. Furthermore, they observed that the expression of EpCAM and HER2 in CTCs at the time of diagnosis was positively correlated with chemoresistance [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOur results demonstrated that marker sensitivity varied between pre- and postoperative samples: \u003cem\u003eEpCAM\u003c/em\u003e was the most sensitive marker in preoperative samples, followed by \u003cem\u003ePPIC\u003c/em\u003e and \u003cem\u003eFOLR1\u003c/em\u003e. In postoperative samples, this pattern was reversed, with \u003cem\u003ePPIC\u003c/em\u003e being the most sensitive marker.\u003c/p\u003e\u003cp\u003eIn the case of patient 7, clinical relapse was confirmed during follow-up. Therefore, this patient was longitudinally monitored to assess how CTC levels changed in response to treatment. CTC levels dropped significantly following neoadjuvant therapy, but this reduction was transient, as CTCs reappeared in the postoperative samples. Notably, in the two final follow-up samples taken before clinical relapse, three and four of the five tested markers, respectively, were already positive. Relapse was later confirmed by imaging, and the patient died one year later.\u003c/p\u003e\u003cp\u003eThese findings demonstrate that dPCR-based monitoring of CTC-derived RNA could have predicted the failure to achieve a tumor-free state prior to radiological confirmation, highlighting the potential of this method for early detection of relapse.\u003c/p\u003e\u003cp\u003eCTCs are now recognized as a heterogeneous cell population, with dynamic changes in their phenotype. Research shows that during the EMT, cancer cells can temporarily lose epithelial surface markers, allowing them to detach from the primary tumor and enter the bloodstream. Recent evidence shows that CTCs use various strategies to evade cell death, such as transitioning between epithelial and mesenchymal states, forming cell clusters, or switching between cancer stem cell and differentiated states [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eLewis et al. concluded that while conventional immunofluorescence is the gold standard for CTC enumeration, its limitation to only 3\u0026ndash;4 detection channels restricts the comprehensive characterization of CTCs, including key markers associated with EMT. Advances in single-cell analysis, including genomics, transcriptomics, and proteomics, provide deeper insights into cancer biology but are often constrained by high costs. Emerging techniques like mass cytometry offer the potential for high-plex CTC characterisation, enhancing precision medicine approaches. However, challenges such as complexity, cost, and technical demands must be addressed to facilitate their clinical application [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eGene expression studies in CTCs are crucial for understanding tumor heterogeneity and its connection to phenotypic differences [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Due to the high heterogeneity of cancer cells, clinical studies have shown that EpCAM-based enrichment has low sensitivity for detecting CTCs in epithelial ovarian cancer (EOC) patients. This is primarily because EMT during metastasis results in the loss of epithelial-like CTCs [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAlthough EMT and stem cell markers have been detected in OC CTCs, qRT-PCR signals were also observed in blood samples from HDs. This suggests that qRT-PCR may not be reliable for analyzing samples where leukocytes are still present, even after the enrichment step. In general, when using qRT-PCR to detect CTCs, it is essential to set a strict cut-off threshold to accurately differentiate CTC signals from those of leukocytes [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e54\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn normal tissues, FRα expression is limited to polarized epithelial cells, such as those in the placenta, lungs, kidneys, and choroid plexus. This protein is overexpressed in various cancers, including breast, lung, gastrointestinal, head and neck squamous cell carcinomas, endometrial, and ovarian cancers. Notably, around 80% of primary and recurrent OCs exhibit elevated FRα expression [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e55\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFolate receptor alpha (FRα) has garnered considerable attention in numerous EOC clinical trials and was the first antibody-drug conjugate approved for treating advanced or recurrent EOC [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn a clinical study made by Li et al., 95 blood samples from 30 EOC patients and 20 samples from patients with benign ovarian disease were analyzed. The results showed that when EpCAM and FRα surface markers are used together as CTC capture targets in EOC, the sensitivity of CTC detection can be increased. When defining CTC positivity as \u0026ge;\u0026thinsp;2 detected cells, the combined detection method had a 67.36% positive rate, compared to 48.42% with anti-EpCAM alone (χ\u0026sup2; = 14.45, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In patients suspected of having EOC, the combined method achieved a sensitivity of 75.0%, which was significantly higher than using anti-EpCAM alone (χ\u0026sup2; = 4.17, P\u0026thinsp;=\u0026thinsp;0.041) [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eObermayr et al. used RT-qPCR to detect CTCs and identified 11 novel gene markers, including \u003cem\u003ePPIC\u003c/em\u003e, for CTC detection. These markers effectively identified CTCs both before treatment and during follow-up. CTCs were found in 24.5% of baseline samples and 20.4% of follow-up samples, with two-thirds detected through \u003cem\u003ePPIC\u003c/em\u003e overexpression, while only a few were identified via \u003cem\u003eEpCAM\u003c/em\u003e overexpression. At baseline, CTC presence was associated with ascites, suboptimal debulking, and elevated CA125 and HE4 levels. During follow-up, CTCs were more frequently detected in older and platinum-resistant patients. Notably, PPIC-positive CTCs were significantly more common in platinum-resistant patients (35.7%) than in platinum-sensitive ones (10.1%, p\u0026thinsp;=\u0026thinsp;0.024) and were linked to poor prognosis, independent of traditional prognostic factors [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Kolostova et al. in their study focused on detecting CTCs in OC patients using a size-based separation method, MetaCell\u0026reg;, followed by cytomorphological evaluation and gene expression analysis (GEA). The captured cells were assessed using fluorescence microscopy and further analyzed for RNA/DNA expression. The GEA panel included tumor-associated genes (\u003cem\u003eEpCAM, MUC1, MUC16, KRT18, KRT19, WT1, VEGFA, HER2\u003c/em\u003e) and chemoresistance-related genes (\u003cem\u003eMRP1-10, MDR1, ERCC1, RRM1, RRM2\u003c/em\u003e). Results showed that \u003cem\u003eEPCAM\u003c/em\u003e expression was higher in CTC-enriched fractions than in whole blood and increased with \u003cem\u003ein vitro\u003c/em\u003e cultivation. Similarly, \u003cem\u003eKRT7, KRT18, MUC16\u003c/em\u003e, and \u003cem\u003eWT1\u003c/em\u003e also showed elevated expression in CTC-enriched samples. These findings suggest that analyzing a combination of these genes provides greater specificity for CTC detection in OC patients compared to single-marker tests. Additionally, GEA identified two distinct patient clusters, distinguishing those with and without detectable CTCs [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eA study by Blassl et al. introduced a multiplex gene expression profiling approach for single CTCs in EOC. Using the AdnaTest system for CTC enrichment and isolation, along with CellCelector-based micromanipulation, the authors successfully established a workflow for single-cell analysis without pre-amplification. Their panel included 19 epithelial, EMT, and stem cell-associated genes, allowing simultaneous detection of multiple transcript types. Analysis of 15 single CTCs from three patients revealed substantial inter- and intra-patient heterogeneity, including the co-expression of epithelial, mesenchymal, and stem-like markers within individual cells. These findings support the potential of transcriptional profiling of CTCs to identify therapy-resistant subpopulations and provide a non-invasive, cost-effective tool for longitudinal monitoring of molecular changes during treatment [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e58\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eA recent review by Jou et al. systematically analyzed the diagnostic performance of various CTC detection platforms in OC and highlighted significant limitations of the AdnaTest system. Studies using this method\u0026mdash;including those by Chebouti et al., Blassl et al., and Kuhlmann et al. - reported low detection rates between 14% and 30%. The low sensitivity is likely due to the platform\u0026rsquo;s reliance on epithelial markers such as EpCAM and MUC1, which may be downregulated during EMT [58\u0026ndash;61]. Lemma et al. reviewed the most commonly used molecular approaches for detecting CTCs in OC patients, identifying molecular markers that have been tested in at least four independent studies using various detection methods. The key markers and their associated methods include EpCAM, detected using immunofluorescence, flow cytometry, CellSearch, AdnaTest Ovarian Cancer Detect, RT-qPCR, and RNA-ISH; CK8, CK18, CK19, and Pan-CK, analyzed with immunofluorescence, flow cytometry, CellSearch, RT-qPCR, and RNA-ISH; MUC1 and MUC16, identified using immunofluorescence, flow cytometry, AdnaTest Ovarian Cancer Detect, and RT-qPCR; FRa, examined with immunofluorescence and flow cytometry; ERCC1, assessed through RT-qPCR; N-cadherin, evaluated using immunofluorescence, flow cytometry, and RT-qPCR; HER-2, detected via immunofluorescence, flow cytometry, and RT-qPCR; Vimentin, analyzed with immunofluorescence, flow cytometry, RT-qPCR, and RNA-ISH; EGFR, identified through immunofluorescence, flow cytometry, and RT-qPCR; and PPIC, tested using RT-qPCR [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e54\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThus, the use of dPCR for CTC detection in OC samples is a new approach.\u003c/p\u003e\u003cdiv id=\"Sec28\" class=\"Section2\"\u003e\u003ch2\u003eLIMITATIONS\u003c/h2\u003e\u003cp\u003eOne limitation of the study was the uneven distribution of ages among the controls and patients in the first set of clinical samples. However, since the CTC markers analyzed are not age-dependent, this should not have affected the observations of the two groups.\u003c/p\u003e\u003cp\u003eAnother limitation was the small number of patients who met the inclusion criteria for the study, which limited the ability to demonstrate the potential of the newly developed method. Since most patients had already received neoadjuvant chemotherapy at the time of recruitment and/or could not be followed up with after two months after surgery, standardized timing of sampling was not feasible and therefore had to be excluded from the analysis.\u003c/p\u003e\u003c/div\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eCTCs have been identified as critical mediators of metastasis and have emerged as dynamic biomarkers for tumor biology, progression, and treatment response. Immunostaining is the most frequently employed technique for the detection of CTCs; however, its constrained sensitivity hinders its clinical applicability.\u003c/p\u003e\u003cp\u003eWe have developed an ultrasensitive, RNA-based four-gene dPCR panel that enables robust detection of rare tumor cells in blood, overcoming key limitations of imaging-based methods. The CTC assay\u0026rsquo;s performance in both pre- and postoperative settings supports its potential for non-invasive monitoring and early relapse detection in advanced-stage patients.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eACTB: \u0026beta;-actin\u003c/p\u003e\n\u003cp\u003eBp: basepair\u003c/p\u003e\n\u003cp\u003eCA125: Cancer antigen-125\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCCNE1: cyclin E1\u003c/p\u003e\n\u003cp\u003ecDNA: Complementary DNA\u003c/p\u003e\n\u003cp\u003eCK: Cytokeratin\u003c/p\u003e\n\u003cp\u003eCTC: circulating tumor cell\u003c/p\u003e\n\u003cp\u003eddPCR: droplet digital PCR\u003c/p\u003e\n\u003cp\u003edPCR: digital PCR\u003c/p\u003e\n\u003cp\u003eEMT: epithelial-to-mesenchymal transition\u003c/p\u003e\n\u003cp\u003eEOC: epithelial ovarian cancer\u003c/p\u003e\n\u003cp\u003eEpCAM: epithelial cell adhesion molecule\u003c/p\u003e\n\u003cp\u003eFAST: Fluid Assisted Separation Technology\u003c/p\u003e\n\u003cp\u003eFFPE: Formalin-Fixed Paraffin-Embedded\u003c/p\u003e\n\u003cp\u003eFIGO: International Federation of Gynecology and Obstetrics\u003c/p\u003e\n\u003cp\u003eFOLR1: folate receptor alpha\u003c/p\u003e\n\u003cp\u003eFR\u0026alpha;: Folate receptor alpha\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGEA: gene expression analysis\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGTEx: Genotype-Tissue Expression\u003c/p\u003e\n\u003cp\u003eHD: Healthy donor\u003c/p\u003e\n\u003cp\u003eHE4: Human Epididymis Protein 4\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHGSOC: high-grade serous ovarian carcinoma\u003c/p\u003e\n\u003cp\u003eKLK5: Kallikrein-5\u003c/p\u003e\n\u003cp\u003eKLK6: Kallikrein-6\u003c/p\u003e\n\u003cp\u003eLOD: limit of detection\u003c/p\u003e\n\u003cp\u003emRNA: messenger RNA\u003c/p\u003e\n\u003cp\u003eMUC16: Mucin 16\u003c/p\u003e\n\u003cp\u003eMUC4: Mucin-4\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNTC: no template control\u003c/p\u003e\n\u003cp\u003eOC: ovarian cancer\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePPIC: Peptidylprolyl Isomerase C\u003c/p\u003e\n\u003cp\u003ePRAME: Preferentially Expressed Antigen In Melanoma\u003c/p\u003e\n\u003cp\u003ePt.: patient\u003c/p\u003e\n\u003cp\u003eqPCR: quantitative PCR\u003c/p\u003e\n\u003cp\u003eRT-qPCR: quantitative Real Time PCR\u003c/p\u003e\n\u003cp\u003eSD: standard deviation\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSLC34A2: solute carrier family 34 member 2\u003c/p\u003e\n\u003cp\u003eSOX17: SRY-box transcription factor 17\u003c/p\u003e\n\u003cp\u003eTCGA: The Cancer Genome Atlas\u003c/p\u003e\n\u003cp\u003eTNM: Tumour, node and metastasis\u003c/p\u003e\n\u003cp\u003eTUSC3: Tumour Suppressor Candidate 3\u003c/p\u003e\n\u003cp\u003eWBCs: white blood cells\u003c/p\u003e\n\u003cp\u003eWFDC2: WAP four-disulfide core domain 2\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the results of this research are included within the article and its accompanying supplementary information files.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank the team of the Department of Obstetrics and Gynaecology, Semmelweis University, Baross Street, Budapest, Hungary, for their valuable contribution and assistance with sample collection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Ethics Committee of the National Center for Public Health and Pharmacy (16119-8/2022/EÜIG, BM/28859-3/2023). All patients and healthy donors recruited in the present study signed informed consent forms approved by the Ethics Committee of the National Center for Public Health and Pharmacy (16119-8/2022/EÜIG, BM/28859-3/2023). The research was conducted in accordance with the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eClinomics Europe Ltd. was a subsidiary of Clinomics Inc. (South Korea) until 2025.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or nonprofit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eO.B. conceived the study. D.K. designed the analyses and performed the in vitro measurements. Sz.T., L.I.H. and A.F. prepared the cells for the in vitro measurements. P.H. performed the \u003cem\u003ein silico\u003c/em\u003e marker selection. Sz.M., Á.É. and J.R.Jr. organized patient sample collection and obtained clinical information. Histopathological data was provided by A.R. and Cs.B. A.M. performed blood processing, CTC enrichment, dPCR analysis and data interpretation. The manuscript was written by A.M. and all authors contributed to the final version. All authors read and approved the final manuscript.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eX. Tang, J. He, Q. Huang, Y. Chen, K. Chen, J. Liu, Y. Tian, H. Wang, Development and validation of a nomogram to predict recurrence in epithelial ovarian cancer using complete blood count and lipid profiles. Front Oncol (2025). doi:10.3389/fonc.2025.1525867\u003c/li\u003e\n\u003cli\u003eR.L. Hollis, M. Churchman, G.R. Grimes, A.M. Meynert, P. Gautier, L. McMahon, K. Sherwood, A.J. Oswald, I. Croy, M. Ferguson, C.W. Martin, T. McGoldrick, N. McPhail, H. Creedon, J.C. Barrett, R. March, B.A. Dougherty, P. Roxburgh, A. Ewing, C.S. Herrington, C.A. Semple, C. Gourley, Somatic BRCA1/2 mutations are associated with a similar survival advantage to their germline counterparts in tubo-ovarian high grade serous carcinoma. Eur J Cancer (2025). doi:10.1016/j.ejca.2025.115299\u003c/li\u003e\n\u003cli\u003eA. Llueca, S. Canete-Mota, A. Jauregu\u0026iacute;, M. Barneo, M.V. Iba\u0026ntilde;ez, A. Neef, E. Ochoa, S. Tomas-Perez, J. Mari-Alexandre, J. Gilabert-Estelles, A. Serra, M.T. Climent, C. Bellido, N. Ruiz, B. Segarra-Vidal, M. Llueca, The Impact of Liquid Biopsy in Advanced Ovarian Cancer Care. Diagnostics (Basel) (2024). doi:10.3390/diagnostics14171868\u003c/li\u003e\n\u003cli\u003eT. Arora, S. Mullangi, E.S. Vadakekut, M.R. Lekkala, Epithelial Ovarian Cancer, in StatPearls (StatPearls Publishing, Treasure Island (FL), 2025)\u003c/li\u003e\n\u003cli\u003eL. Bergmann, A.-K. Afflerbach, T. Yuan, K. Pantel, D.J. Smit, Lessons (to be) learned from liquid biopsies: assessment of circulating cells and cell-free DNA in cancer and pregnancy-acquired microchimerism. Semin Immunopathol (2025). doi:10.1007/s00281-025-01042-z\u003c/li\u003e\n\u003cli\u003eK. Pantel, C. Alix-Panabi\u0026egrave;res, Liquid biopsy and minimal residual disease \u0026mdash; latest advances and implications for cure. Nat Rev Clin Oncol (2019). doi:10.1038/s41571-019-0187-3\u003c/li\u003e\n\u003cli\u003eK. Pantel, C. Alix-Panabi\u0026egrave;res, Crucial roles of circulating tumor cells in the metastatic cascade and tumor immune escape: biology and clinical translation. J Immunother Cancer (2022). doi:10.1136/jitc-2022-005615\u003c/li\u003e\n\u003cli\u003eX. Gu, S. Wei, X. Lv, Circulating tumor cells: from new biological insights to clinical practice. Sig Transduct Target Ther (2024). doi:10.1038/s41392-024-01938-6\u003c/li\u003e\n\u003cli\u003eL. Bergmann, A.-K. Afflerbach, T. Yuan, K. Pantel, D.J. Smit, Lessons (to be) learned from liquid biopsies: assessment of circulating cells and cell-free DNA in cancer and pregnancy-acquired microchimerism. Semin Immunopathol (2025). doi:10.1007/s00281-025-01042-z\u003c/li\u003e\n\u003cli\u003eI. Goldstein, S. Alyas, W. Asghar, A. Ilyas, Biosensors for the Isolation and Detection of Circulating Tumor Cells (CTCs) in Point-of-Care Settings. Micromachines (2023). doi:10.3390/mi14051035\u003c/li\u003e\n\u003cli\u003eA. Szczerba, A. Śliwa, P.P. Pieta, A. Jankowska, The Role of Circulating Tumor Cells in Ovarian Cancer Dissemination. Cancers (Basel) (2022). doi:10.3390/cancers14246030\u003c/li\u003e\n\u003cli\u003eC. Alix-Panabi\u0026egrave;res, K. Pantel, Characterization of single circulating tumor cells. FEBS Lett (2017). doi:10.1002/1873-3468.12662\u003c/li\u003e\n\u003cli\u003eA. Ring, B.D. Nguyen-Str\u0026auml;uli, A. Wicki, N. Aceto, Biology, vulnerabilities and clinical applications of circulating tumour cells. Nat Rev Cancer (2023). doi:10.1038/s41568-022-00536-4\u003c/li\u003e\n\u003cli\u003eY. Hou, J. Lin, H. Yao, Z. Wu, Y. Lin, J.-M. Lin, Linking Metastatic Behavior and Metabolic Heterogeneity of Circulating Tumor Cells at Single-Cell Level Using an Integrative Microfluidic System. Adv Sci (Weinh) (2025). doi:10.1002/advs.202413978\u003c/li\u003e\n\u003cli\u003eA. Balla, J. Bhak, O. Bir\u0026oacute;, The application of circulating tumor cell and cell-free DNA liquid biopsies in ovarian cancer. Mol Cell Probes (2022). doi:10.1016/j.mcp.2022.101871\u003c/li\u003e\n\u003cli\u003eY. Zhang, B. Wang, J. Cai, Y. Yang, C. Tang, X. Zheng, H. Li, F. Xu, Enrichment and separation technology for evaluation of circulating tumor cells. Talanta (2025). doi:10.1016/j.talanta.2024.127025\u003c/li\u003e\n\u003cli\u003eM.A. Mansor, C. Yang, K.L. Chong, M.A. Jamrus, K. Liu, M. Yu, M.R. Ahmad, X. Ren, Label-Free and Rapid Microfluidic Design Rules for Circulating Tumor Cell Enrichment and Isolation: A Review and Simulation Analysis. ACS Omega (2025). doi:10.1021/acsomega.4c08606\u003c/li\u003e\n\u003cli\u003eT.-H. Kim, M. Lim, J. Park, J.M. Oh, H. Kim, H. Jeong, S.J. Lee, H.C. Park, S. Jung, B.C. Kim, K. Lee, M.-H. Kim, D.Y. Park, G.H. Kim, Y.-K. Cho, FAST: Size-Selective, Clog-Free Isolation of Rare Cancer Cells from Whole Blood at a Liquid-Liquid Interface. Anal Chem (2017). doi:10.1021/acs.analchem.6b03534\u003c/li\u003e\n\u003cli\u003eM. Lim, J. Park, A.C. Lowe, H. Jeong, S. Lee, H.C. Park, K. Lee, G.H. Kim, M.-H. Kim, Y.-K. Cho, A lab-on-a-disc platform enables serial monitoring of individual CTCs associated with tumor progression during EGFR-targeted therapy for patients with NSCLC. Theranostics (2020). doi:10.7150/thno.44693\u003c/li\u003e\n\u003cli\u003eS. Divari, M. Cuccato, A. Fanelli, F.T. Cannizzo, Development of a droplet digital PCR assay to detect illicit glucocorticoid administration in bovine. PLoS One (2022). doi:10.1371/journal.pone.0271613\u003c/li\u003e\n\u003cli\u003eJ.F. Huggett, C.A. Foy, V. Benes, K. Emslie, J.A. Garson, R. Haynes, J. Hellemans, M. Kubista, R.D. Mueller, T. Nolan, M.W. Pfaffl, G.L. Shipley, J. Vandesompele, C.T. Wittwer, S.A. Bustin, The digital MIQE guidelines: Minimum Information for Publication of Quantitative Digital PCR Experiments. Clin Chem (2013). doi:10.1373/clinchem.2013.206375\u003c/li\u003e\n\u003cli\u003eA.K. Mitra, D.A. Davis, S. Tomar, L. Roy, H. Gurler, J. Xie, D.D. Lantvit, H. Cardenas, F. Fang, Y. Liu, E. Loughran, J. Yang, M. Sharon Stack, R.E. Emerson, K.D. Cowden Dahl, M. V. Barbolina, K.P. Nephew, D. Matei, J.E. Burdette, In vivo tumor growth of high-grade serous ovarian cancer cell lines. Gynecologic Oncology (2015). doi:10.1016/j.ygyno.2015.05.040\u003c/li\u003e\n\u003cli\u003eY. Fan, J. Wang, Z. Fang, S.R. Pierce, L. West, A. Staley, K. Tucker, Y. Yin, W. Sun, W. Kong, V. Prabhu, J.E. Allen, C. Zhou, V.L. Bae-Jump, Anti-Tumor and Anti-Invasive Effects of ONC201 on Ovarian Cancer Cells and a Transgenic Mouse Model of Serous Ovarian Cancer. Front Oncol (2022). doi:10.3389/fonc.2022.789450\u003c/li\u003e\n\u003cli\u003eC. Boscaro, G.E. Ramaschi, L. Trevisi, A. Cignarella, C. Bolego, MiR-206 inhibits estrogen signaling and ovarian cancer cell migration without affecting GPER. Life Sciences (2023). doi:10.1016/j.lfs.2023.122135\u003c/li\u003e\n\u003cli\u003eT. Li, H. Diao, L. Zhao, Y. Xing, J. Zhang, N. Liu, Y. Yan, X. Tian, W. Sun, B. Liu, Identification of suitable reference genes for real-time quantitative PCR analysis of hydrogen peroxide-treated human umbilical vein endothelial cells. BMC Molecular Biology (2017). doi:10.1186/s12867-017-0086-z\u003c/li\u003e\n\u003cli\u003edMIQE Group, J.F. Huggett, The Digital MIQE Guidelines Update: Minimum Information for Publication of Quantitative Digital PCR Experiments for 2020. Clin Chem (2020). doi:10.1093/clinchem/hvaa125\u003c/li\u003e\n\u003cli\u003eP.S. Godavarthy, C.B. Walter, C. Lengerke, G. Klein, The Laminin Receptors Basal Cell Adhesion Molecule/Lutheran and Integrin \u0026alpha;7\u0026beta;1 on Human Hematopoietic Stem Cells. Front Cell Dev Biol (2021). doi:10.3389/fcell.2021.675240\u003c/li\u003e\n\u003cli\u003eA. Zhao, Y. Qi, K. Liu, CLDN3 expression and function in pregnancy-induced hypertension. Exp Ther Med (2020). doi:10.3892/etm.2020.9084\u003c/li\u003e\n\u003cli\u003eI. Cima, S.L. Kong, D. Sengupta, I.B. Tan, W.M. Phyo, D. Lee, M. Hu, C. Iliescu, I. Alexander, W.L. Goh, M. Rahmani, N.-A.M. Suhaimi, J.H. Vo, J.A. Tai, J.H. Tan, C. Chua, R. Ten, W.J. Lim, M.H. Chew, C.A.E. Hauser, R.M. van Dam, W.-Y. Lim, S. Prabhakar, B. Lim, P.K. Koh, P. Robson, J.Y. Ying, A.M. Hillmer, M.-H. Tan, Tumor-derived circulating endothelial cell clusters in colorectal cancer. Sci Transl Med (2016). doi:10.1126/scitranslmed.aad7369\u003c/li\u003e\n\u003cli\u003eY.-C. Liu, P.-Y. Chang, C.C.-K. Chao, CITED2 silencing sensitizes cancer cells to cisplatin by inhibiting p53 trans-activation and chromatin relaxation on the ERCC1 DNA repair gene. Nucleic Acids Res (2015). doi:10.1093/nar/gkv934\u003c/li\u003e\n\u003cli\u003eM. Baydoun, O. Moral\u0026egrave;s, C. Frochot, C. Ludovic, B. Leroux, E. Thecua, L. Ziane, A. Grabarz, A. Kumar, C. de Schutter, P. Collinet, H. Azais, S. Mordon, N. Delhem, Photodynamic Therapy Using a New Folate Receptor-Targeted Photosensitizer on Peritoneal Ovarian Cancer Cells Induces the Release of Extracellular Vesicles with Immunoactivating Properties. J Clin Med (2020). doi:10.3390/jcm9041185\u003c/li\u003e\n\u003cli\u003eW. Gong, Y. Liu, C. Seidl, E.P. Diamandis, M. Kiechle, E. Drecoll, M. Kotzsch, V. Magdolen, J. Dorn, Quantitative assessment and clinical relevance of kallikrein-related peptidase 5 mRNA expression in advanced high-grade serous ovarian cancer. BMC Cancer (2019). doi:10.1186/s12885-019-5901-0\u003c/li\u003e\n\u003cli\u003eN. Ahmed, J. Dorn, R. Napieralski, E. Drecoll, M. Kotzsch, P. Goettig, E. Zein, S. Avril, M. Kiechle, E.P. Diamandis, M. Schmitt, V. Magdolen, Clinical relevance of kallikrein-related peptidase 6 (KLK6) and 8 (KLK8) mRNA expression in advanced serous ovarian cancer. Biol Chem (2016). doi:10.1515/hsz-2016-0177\u003c/li\u003e\n\u003cli\u003eW. Wang, J. Wang, C. Yang, J. Wang, MicroRNA-216a targets WT1 expression and regulates KRT7 transcription to mediate the progression of pancreatic cancer-A transcriptome analysis. IUBMB Life (2021). doi:10.1002/iub.2468\u003c/li\u003e\n\u003cli\u003eY. Zhong, Z. Zhuang, P. Mo, Q. Shang, M. Lin, J. Gong, J. Huang, H. Mo, M. Huang, Overexpression of MAL2 Correlates with Immune Infiltration and Poor Prognosis in Breast Cancer. Evidence-Based Complementary and Alternative Medicine (2021). doi:10.1155/2021/5557873\u003c/li\u003e\n\u003cli\u003eB. Liu, J.R. Lague, D.P. Nunes, P. Toselli, F.G. Oppenheim, R.V. Soares, R.F. Troxler, G.D. Offner, Expression of Membrane-associated Mucins MUC1 and MUC4 in Major Human Salivary Glands. J Histochem Cytochem (2002). doi:10.1177/002215540205000607\u003c/li\u003e\n\u003cli\u003eJ. Liu, L. Li, N. Luo, Q. Liu, L. Liu, D. Chen, Z. Cheng, X. Xi, Inflammatory signals induce MUC16 expression in ovarian cancer cells via NF‑\u0026kappa;B activation. Experimental and Therapeutic Medicine (2021). doi:10.3892/etm.2020.9594\u003c/li\u003e\n\u003cli\u003eY. Li, C. Wu, T. Chen, J. Zhang, G. Liu, Y. Pu, J. Zhu, C. Shen, Y. Zhang, N. Zeng, X. Zhang, Effects of RNAi-mediated MUC4 gene silencing on the proliferation and migration of human pancreatic carcinoma BxPC-3 cells. Oncology Reports (2016). doi:10.3892/or.2016.5152\u003c/li\u003e\n\u003cli\u003eY.-F. Gao, T. Zhu, C.-X. Mao, Z.-X. Liu, Z.-B. Wang, X.-Y. Mao, L. Li, J.-Y. Yin, H.-H. Zhou, Z.-Q. Liu, PPIC, EMP3 and CHI3L1 Are Novel Prognostic Markers for High Grade Glioma. International Journal of Molecular Sciences (2016). doi:10.3390/ijms17111808\u003c/li\u003e\n\u003cli\u003eW. Zhang, C.J. Barger, K.H. Eng, D. Klinkebiel, P.A. Link, A. Omilian, W. Bshara, K. Odunsi, A.R. Karpf, PRAME expression and promoter hypomethylation in epithelial ovarian cancer. Oncotarget (2016). doi:10.18632/oncotarget.9977\u003c/li\u003e\n\u003cli\u003eC. Cai, Y. Zhang, X. Peng, Knocking down Sterol regulatory element binding protein 2 (SREBF2) inhibits the Serine Protease 8 (PRSS8) /sodium channel epithelial 1alpha subunit (SCNN1A) axis to reduce the cell proliferation, migration and epithelial-mesenchymal transformation of ovarian cancer. Bioengineered (2021). doi:10.1080/21655979.2021.1978615\u003c/li\u003e\n\u003cli\u003eJ. He, M. Zhou, X. Li, S. Gu, Y. Cao, T. Xing, W. Chen, C. Chu, F. Gu, J. Zhou, Y. Jin, J. Ma, D. Ma, Q. Zou, SLC34A2 simultaneously promotes papillary thyroid carcinoma growth and invasion through distinct mechanisms. Oncogene (2020). doi:10.1038/s41388-020-1181-z\u003c/li\u003e\n\u003cli\u003eD.-Y. Fu, Z.-M. Wang, null Li-Chen, B.-L. Wang, Z.-Z. Shen, W. Huang, Z.-M. Shao, Sox17, the canonical Wnt antagonist, is epigenetically inactivated by promoter methylation in human breast cancer. Breast Cancer Res Treat (2010). doi:10.1007/s10549-009-0339-8\u003c/li\u003e\n\u003cli\u003eR. Tamjidifar, M. Akbari, S. Tarzi, M. Sadeghzadeh, M. Abolghasemi, E. Poursaei, N. Shomali, F. Mahdavi, Prognostic and Diagnostic Values of miR-506 and SPON 1 in Colorectal Cancer with Clinicopathological Considerations. J Gastrointest Cancer (2021). doi:10.1007/s12029-019-00356-0\u003c/li\u003e\n\u003cli\u003eH.J. Ferreira, V. Davalos, M.C. de Moura, M. Soler, M. Perez-Salvia, A. Bueno-Costa, F. Setien, S. Moran, A. Villanueva, M. Esteller, Circular RNA CpG island hypermethylation-associated silencing in human cancer. Oncotarget (2018). doi:10.18632/oncotarget.25673\u003c/li\u003e\n\u003cli\u003eY. Xiong, L. Yuan, S. Chen, H. Xu, T. Peng, L. Ju, G. Wang, Y. Xiao, X. Wang, WFDC2 suppresses prostate cancer metastasis by modulating EGFR signaling inactivation. Cell Death Dis (2020). doi:10.1038/s41419-020-02752-y\u003c/li\u003e\n\u003cli\u003eL. Miglietta, A. Moniri, I. Pennisi, K. Malpartida-Cardenas, H. Abbas, K. Hill-Cawthorne, F. Bolt, E. Jauneikaite, F. Davies, A. Holmes, P. Georgiou, J. Rodriguez-Manzano, Coupling Machine Learning and High Throughput Multiplex Digital PCR Enables Accurate Detection of Carbapenem-Resistant Genes in Clinical Isolates. Front Mol Biosci (2021). doi:10.3389/fmolb.2021.775299\u003c/li\u003e\n\u003cli\u003eJ. Mei, H. Tian, H. Huang, C. Hsu, Y. Liou, N. Wu, W. Zhang, T. Chu, Cellular models of development of ovarian high‐grade serous carcinoma: A review of cell of origin and mechanisms of carcinogenesis. Cell Prolif (2021). doi:10.1111/cpr.13029\u003c/li\u003e\n\u003cli\u003eN. Colombo, C. Sessa, A. du Bois, J. Ledermann, W.G. McCluggage, I. McNeish, P. Morice, S. Pignata, I. Ray-Coquard, I. Vergote, T. Baert, I. Belaroussi, A. Dashora, S. Olbrecht, F. Planchamp, D. Querleu, ESMO-ESGO Ovarian Cancer Consensus Conference Working Group, ESMO-ESGO consensus conference recommendations on ovarian cancer: pathology and molecular biology, early and advanced stages, borderline tumours and recurrent disease\u0026dagger;. Ann Oncol (2019). doi:10.1093/annonc/mdz062\u003c/li\u003e\n\u003cli\u003eX. Zhang, H. Li, X. Yu, S. Li, Z. Lei, C. Li, Q. Zhang, Q. Han, Y. Li, K. Zhang, Y. Wang, C. Liu, Y. Mao, X. Wang, D.M. Irwin, H. Guo, G. Niu, H. Tan, Analysis of Circulating Tumor Cells in Ovarian Cancer and Their Clinical Value as a Biomarker. Cell Physiol Biochem (2018). doi:10.1159/000492521\u003c/li\u003e\n\u003cli\u003eK. Kolostova, M. Pinkas, A. Jakabova, E. Pospisilova, P. Svobodova, J. Spicka, M. Cegan, R. Matkowski, V. Bobek, Molecular characterization of circulating tumor cells in ovarian cancer. Am J Cancer Res 6:973\u0026ndash;980\u003c/li\u003e\n\u003cli\u003eF. Lewis, J. Beirne, B. Henderson, L. Norris, K. Cadoo, T. Kelly, C. Martin, S. Hurley, M. Kanjuga, L. O\u0026rsquo;Driscoll, K. Gately, E. Oner, V.M. Saini, D. Brooks, S. Selemidis, W. Kamran, N. Haughey, P. Maguire, C. O\u0026rsquo;Gorman, F.A. Saadeh, M.P. Ward, J.J. O\u0026rsquo;Leary, S.A. O\u0026rsquo;Toole, Unravelling the biological and clinical challenges of circulating tumour cells in epithelial ovarian carcinoma. Cancer Lett (2024). doi:10.1016/j.canlet.2024.217279\u003c/li\u003e\n\u003cli\u003eN. Li, H. Zuo, L. Chen, H. Liu, J. Zhou, Y. Yao, B. Xu, H. Gong, Y. Weng, Q. Hu, Q. Song, M. Peng, Y. Cheng, \u0026lt;p\u0026gt;Circulating Tumor Cell Detection In Epithelial Ovarian Cancer Using Dual-Component Antibodies Targeting EpCAM And FR\u0026amp;alpha;\u0026lt;/p\u0026gt;. CMAR (2020). doi:10.2147/CMAR.S211455\u003c/li\u003e\n\u003cli\u003eS. Lemma, A.M. Perrone, P. De Iaco, G. Gasparre, I. Kurelac, Current methodologies to detect circulating tumor cells: a focus on ovarian cancer. Am J Cancer Res 11:4111\u0026ndash;4126\u003c/li\u003e\n\u003cli\u003eK. Bukowski, A. Rogalska, A. Marczak, Folate Receptor Alpha\u0026mdash;A Secret Weapon in Ovarian Cancer Treatment? Int J Mol Sci (2024). doi:10.3390/ijms252211927\u003c/li\u003e\n\u003cli\u003eE. Obermayr, D.C. Castillo-Tong, D. Pils, P. Speiser, I. Braicu, T. Van Gorp, S. Mahner, J. Sehouli, I. Vergote, R. Zeillinger, Molecular characterization of circulating tumor cells in patients with ovarian cancer improves their prognostic significance -- a study of the OVCAD consortium. Gynecol Oncol (2013). doi:10.1016/j.ygyno.2012.09.021\u003c/li\u003e\n\u003cli\u003eL. Giannopoulou, S. Kasimir-Bauer, E.S. Lianidou, Liquid biopsy in ovarian cancer: recent advances on circulating tumor cells and circulating tumor DNA. Clin Chem Lab Med (2018). doi:10.1515/cclm-2017-0019\u003c/li\u003e\n\u003cli\u003eC. Blassl, J.D. Kuhlmann, A. Webers, P. Wimberger, T. Fehm, H. Neubauer, Gene expression profiling of single circulating tumor cells in ovarian cancer - Establishment of a multi-marker gene panel. Mol Oncol (2016). doi:10.1016/j.molonc.2016.04.002\u003c/li\u003e\n\u003cli\u003eH.-J. Jou, P.-Y. Ling, H.-T. Hsu, Circulating tumor cells as a \u0026ldquo;real-time liquid biopsy\u0026rdquo;: Recent advances and the application in ovarian cancer. Taiwan J Obstet Gynecol (2022). doi:10.1016/j.tjog.2021.11.008\u003c/li\u003e\n\u003cli\u003eI. Chebouti, S. Kasimir-Bauer, P. Buderath, P. Wimberger, S. Hauch, R. Kimmig, J.D. Kuhlmann, EMT-like circulating tumor cells in ovarian cancer patients are enriched by platinum-based chemotherapy. Oncotarget (2017). doi:10.18632/oncotarget.16179\u003c/li\u003e\n\u003cli\u003eJ.D. Kuhlmann, P. Wimberger, A. Bankfalvi, T. Keller, S. Sch\u0026ouml;ler, B. Aktas, P. Buderath, S. Hauch, F. Otterbach, R. Kimmig, S. Kasimir-Bauer, ERCC1-positive circulating tumor cells in the blood of ovarian cancer patients as a predictive biomarker for platinum resistance. Clin Chem (2014). doi:10.1373/clinchem.2014.224808\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Liquid biopsy, Circulating tumor cells, digital PCR, Ovarian cancer, Gene expression","lastPublishedDoi":"10.21203/rs.3.rs-8106577/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8106577/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e\u003cp\u003eOvarian cancer (OC) is the deadliest gynecological cancer, with late-stage diagnosis and frequent relapse. Improved monitoring tools are urgently needed. Circulating tumor cells (CTCs) are promising biomarkers, but current immunostaining methods are not sensitive enough. This study aimed to develop an ultrasensitive digital PCR (dPCR) assay and define a gene expression signature to track tumor burden and recurrence.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe identified candidate mRNA markers using \u003cem\u003ein silico\u003c/em\u003e analysis and literature review. Sensitivity was evaluated using spike-in experiments, where ovarian cancer cell lines (OVCAR-3, OVCAR-5, IGROV-1) were added to 3 mL of healthy donor blood at defined numbers (0, 5, 10 or 100). CTCs were isolated with the CD-Prime platform, followed by RNA extraction, reverse transcription, and dPCR quantification. A four-gene panel (\u003cem\u003eEpCAM, FOLR1, WFDC2, PPIC\u003c/em\u003e) was optimized based on performance. Although \u003cem\u003eSLC34A2\u003c/em\u003e showed limited sensitivity in clinical samples, it was retained for technical compatibility due to co-amplification with \u003cem\u003eWFDC2\u003c/em\u003e. The assay was then tested in paired pre- and postoperative blood samples from five patients with high-grade serous OC and five healthy controls.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eSpike-in experiments confirmed assay sensitivity, with no markers detected in 0-cell controls and significant detection at 100-cell samples (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). All patient samples tested positive for at least one marker at both time points, while all controls remained negative.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThe RNA-based four-gene dPCR panel enables highly sensitive detection of CTCs in OC. Its ability to detect CTCs pre- and postoperatively supports its potential as a non-invasive tool for monitoring and early relapse detection.\u003c/p\u003e","manuscriptTitle":"Novel methodology for the digital analysis of circulating tumor cells in ovarian cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-01 08:38:42","doi":"10.21203/rs.3.rs-8106577/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"245026679621982107833606785993026785494","date":"2025-12-07T20:11:55+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-04T12:32:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"160255475480451014326097752660322482926","date":"2025-11-25T13:41:33+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-25T13:13:24+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-11-19T11:44:39+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-15T03:56:35+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-15T03:55:24+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-11-13T14:04:45+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4e7ce8d0-ac3b-40f0-97d2-a9eba0b8afb9","owner":[],"postedDate":"December 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":58587282,"name":"Biological sciences/Biological techniques"},{"id":58587284,"name":"Health sciences/Biomarkers"},{"id":58587286,"name":"Biological sciences/Cancer"},{"id":58587289,"name":"Biological sciences/Molecular biology"},{"id":58587290,"name":"Health sciences/Oncology"}],"tags":[],"updatedAt":"2025-12-01T08:38:42+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-01 08:38:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8106577","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8106577","identity":"rs-8106577","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}