Diagnosis Of Ovarian Cancer Research Articles

Ovarian serous carcinoma with stomach metastasis: a rare case report and literature review.

Ovarian cancer is a common tumor among women. It is often asymptomatic in the early stages, with most cases already at stage III to IVE at the time of diagnosis. Direct spread and lymphatic metastasis are the primary modes of metastasis, whereas hematogenous spread is rare. An initial diagnosis of ovarian cancer that has metastasized to the stomach is also uncommon. Therefore, clear treatment methods and prognostic data for such metastasis are lacking. In our hospital, we encountered a patient with an initial imaging diagnosis of a gastric tumor and a history of an ovarian tumor with endoscopic abdominal metastasis. Based on the characteristics of the case, the two tumors were considered to be the same. After chemotherapy, a partial response was observed in the stomach and pelvic lesions, suggesting the effectiveness of the treatment. Through three treatments of recurrence, gastroscopy confirmed the stomach to be a metastatic site. Therefore, determining the primary source of advanced tumors is crucial in guiding treatment decisions. Clinicians must approach this comprehensively, relying on thorough evaluation and personal experience.

Nanopore Testing: A Paradigm Shift in Ovarian Cancer Screening

Nanopore Testing: A Paradigm Shift in Ovarian Cancer Screening

Impact of the Coronavirus Disease 2019 pandemic on neoadjuvant chemotherapy use in patients diagnosed with epithelial type ovarian cancer.

The Coronavirus Disease 2019 (COVID-19) pandemic posed critical challenges in providing care to ovarian cancer (OC) patients, including delays in OC diagnosis and treatment initiation. To accommodate for delays in OC surgery, the Society of Gynecologic Oncology (SGO) recommended preferential use of neoadjuvant chemotherapy during the pandemic. The purpose of this study was to assess the association of the COVID-19 pandemic with neoadjuvant chemotherapy use in patients diagnosed with OC. This retrospective cohort study included patients diagnosed with stage II-IV ovarian cancer of epithelial subtype between 01/01/2017-06/30/2021 at Kaiser Permanente Southern California (KPSC), a large integrated healthcare system in the United States. Ovarian cancer patients diagnosed between 2017-2020 were identified from KPSC's Surveillance, Epidemiology, and End Results (SEER)-affiliated cancer registry. Patients diagnosed in 2021 were identified from the electronic medical records (EMR) using ICD-10 diagnosis codes, followed by medical chart review to validate diagnosis and extract information on histology and stage at diagnosis. March 4, 2020 was used as the cut-off to define pre-pandemic and pandemic periods. Patients diagnosed with COVID-19 between OC diagnosis and treatment completion were excluded. Data on neoadjuvant chemotherapy use were extracted from the cancer registry and EMR, supplemented by chart review. Modified Poisson regression was used to evaluate the association of the pandemic with neoadjuvant chemotherapy use. Of 566 OC patients, 160 (28.3%) were diagnosed in the pandemic period. Patients diagnosed in the pandemic period were slightly younger (mean age 62.7 vs 64.9 years, p=0.07) and had a higher burden of Charlson comorbidities (p=0.05) than patients diagnosed in pre-pandemic period. No differences in time to treatment initiation were observed by pandemic periods. Neoadjuvant chemotherapy use was documented in 58.7% patients during the pandemic period compared to 47.3% in pre-pandemic period (p=0.01). After adjusting for covariates, patients diagnosed in the pandemic period were 29% more likely to receive neoadjuvant chemotherapy than patients diagnosed in pre-pandemic period [RR(95%CI): 1.29(1.12-1.49)]. Ovarian cancer patients diagnosed in the COVID-19 pandemic were more likely to receive neoadjuvant chemotherapy than patients diagnosed before the pandemic. Future research on patient outcomes and trends in the post-pandemic period are warranted.

Ovarian cancer think tank: An overview of the current status of ovarian cancer screening and recommendations for future directions

Early diagnosis and screening of ovarian cancer remain significant challenges to improving patient outcomes. There is an urgent need to implement both established and modern strategies to address the “early detection” conundrum, especially as new research continues to uncover the complexities of the disease. The discussion provided is the result of a unique research conference focused on reviewing early detection modalities and providing insight into future approaches.

Development and validation of an interpretable model integrating multimodal information for improving ovarian cancer diagnosis

Ovarian cancer, a group of heterogeneous diseases, presents with extensive characteristics with the highest mortality among gynecological malignancies. Accurate and early diagnosis of ovarian cancer is of great significance. Here, we present OvcaFinder, an interpretable model constructed from ultrasound images-based deep learning (DL) predictions, Ovarian–Adnexal Reporting and Data System scores from radiologists, and routine clinical variables. OvcaFinder outperforms the clinical model and the DL model with area under the curves (AUCs) of 0.978, and 0.947 in the internal and external test datasets, respectively. OvcaFinder assistance led to improved AUCs of radiologists and inter-reader agreement. The average AUCs were improved from 0.927 to 0.977 and from 0.904 to 0.941, and the false positive rates were decreased by 13.4% and 8.3% in the internal and external test datasets, respectively. This highlights the potential of OvcaFinder to improve the diagnostic accuracy, and consistency of radiologists in identifying ovarian cancer.

Boosting edgeR (Robust) by dealing with missing observations and gene-specific outliers in RNA-Seq profiles and its application to explore biomarker genes for diagnosis and therapies of ovarian cancer

The edgeR (Robust) is a popular approach for identifying differentially expressed genes (DEGs) from RNA-Seq profiles. However, it shows weak performance against gene-specific outliers and is unable to handle missing observations. To address these issues, we proposed a pre-processing approach of RNA-Seq count data by combining the iLOO-based outlier detection and random forest-based missing imputation approach for boosting the performance of edgeR (Robust). Both simulation and real RNA-Seq count data analysis results showed that the proposed edgeR (Robust) outperformed than the conventional edgeR (Robust). To investigate the effectiveness of identified DEGs for diagnosis, and therapies of ovarian cancer (OC), we selected top-ranked 12 DEGs (IL6, XCL1, CXCL8, C1QC, C1QB, SNAI2, TYROBP, COL1A2, SNAP25, NTS, CXCL2, and AGT) and suggested hub-DEGs guided top-ranked 10 candidate drug-molecules for the treatment against OC. Hence, our proposed procedure might be an effective computational tool for exploring potential DEGs from RNA-Seq profiles for diagnosis and therapies of any disease.

Prediagnostic use of menopausal hormone therapy and long-term survival of localized epithelial ovarian cancer: The Extreme study.

Use of menopausal hormone therapy (MHT) prior to an epithelial ovarian cancer (EOC) diagnosis has been suggested to be associated with improved survival. In a recent nationwide cohort study, we found that prediagnostic long-term MHT use, especially estrogen therapy (ET), was associated with improved long-term survival in women with nonlocalized EOC. Our aim was to investigate the influence of prediagnostic MHT use on long-term survival among women with localized EOC in the same nationwide study. Our study cohort comprised all women aged 50 years or older with an EOC diagnosis in Denmark 2000-2014 (n = 2097) identified from the Extreme study. We collected information on usage of systemic ET and estrogen plus progestin therapy (EPT) from the Danish National Prescription Registry. By using pseudo-values, 5- and 10-year absolute and relative survival probabilities were estimated with 95% confidence intervals (CIs) while adjusting for histology, comorbidity, and income. Relative survival probabilities >1 indicate better survival. The 5-year absolute survival probabilities were 61% and 56%, respectively, among women who were nonusers and users of prediagnostic MHT, whereas these numbers were 46% and 41%, respectively, regarding 10-year survival. Use of MHT was not significantly associated with an improved 5- or 10-year survival in women with localized EOC (5-year relative survival probability = 0.95, 95% CI: 0.89-1.02; 10-year relative survival probability = 0.92, 95% CI: 0.84-1.02). Similar findings were seen for systemic ET or EPT use. Our findings do not suggest a positive benefit from prediagnostic MHT use on long-term survival of localized EOC.

Diagnostic accuracy of contrast-enhanced CT versus PET/CT for advanced ovarian cancer staging: a comparative systematic review and meta-analysis.

Accurate staging of ovarian cancer is critical to guide optimal management pathways. North American guidelines recommend contrast-enhanced CT as the primary work-up for staging ovarian cancer. This meta-analysis aims to compare the diagnostic accuracy of contrast-enhanced CT alone to PET/CT for detecting abdominal metastases in patients with a new or suspected diagnosis of ovarian cancer. A systematic review of MEDLINE, EMBASE, Scopus, the Cochrane Library, and the gray literature from inception to October 2022 was performed. Studies with a minimum of 5 patients evaluating the diagnostic accuracy of contrast-enhanced CT and/or PET/CT for detecting stage 3 ovarian cancer as defined by a surgical/histopathological reference standard ± clinical follow-up were included. Study, clinical, imaging, and accuracy data for eligible studies were independently acquired by two reviewers. Primary meta-analysis was performed in studies reporting accuracy on a per-patient basis using a bivariate mixed-effects regression model. Risk of bias was evaluated using QUADAS-2. From 3701 citations, 15 studies (918 patients with mean age ranging from 51 to 65years) were included in the systematic review. Twelve studies evaluated contrast-enhanced CT (6 using a per-patient assessment and 6 using a per-region assessment) and 11 studies evaluated PET/CT (7 using a per-patient assessment and 4 using a per-region assessment). All but one reporting study used consensus reading. Respective sensitivity and specificity values on a per-patient basis were 82% (67-91%, 95% CI) and 72% (59-82%) for contrast-enhanced CT and 87% (75-94%) and 90% (82-95%) for PET/CT. There was no significant difference in sensitivities between modalities (p = 0.29), but PET/CT was significantly more specific than CT (p < 0.01). Presumed variability could not be assessed in any single category due to limited studies using per-patient assessment. Studies were almost entirely low risk for bias and applicability concerns using QUADAS-2. Contrast-enhanced CT demonstrates non-inferior sensitivity compared to PET/CT, although PET/CT may still serve as an alternative and/or supplement to CT alone prior to and/or in lieu of diagnostic laparoscopy in patients with ovarian cancer. Future revisions to existing guidelines should consider these results to further refine the individualized pretherapeutic diagnostic pathway.

A novel ratiometric electrochemical immunosensor for the detection of cancer antigen 125 based on three-dimensional carbon nanomaterial and MOFs

Accurate and sensitive detection of cancer antigen 125 (CA125) is important in early ovarian cancer diagnosis and monitoring. In the present work, a novel electrochemical ratiometric immunosensor for the determination of CA125 based on three-dimensional reduced graphene oxide/multi-walled carbon nanotube carboxylic acid- thionine (3DrGO/MWCNTs-Thi) and UIO-66-NH2/ferrocenecarboxylic acid (UiO-66-Fc) was constructed. 3DrGO/MWCNTs-Thi of immobilized primary antibody (Ab1) and UiO-66-Fc of labeled second antibody (Ab2) were used as capture probes and signaling probes for CA125, respectively. As the concentration of CA125 increases, the oxidation peak currents (IThi and IFc) generated by the two materials were changed in opposite directions, and therefore the ratio of the currents (IThi/IFc) was negatively correlated with the concentration of CA125. Thanks to the amplification of high loading of signaling molecules by the framework nanocomposites and the ratiometric strategy, the constructed electrochemical immunosensor exhibits outstanding accuracy, stability, and sensitivity. Under optimal conditions, the detection range spanned from 0.01 U mL−1 to 80 U mL−1, with a calculated limit of detection (LOD) of 0.0089 U mL−1. The immunosensor was successfully applied to real serum sample tests, indicating its potential for clinical detection applications.

Abstract 4974: Development of an ovarian cancer diagnosis score using electronic health records

Abstract Background: Diagnosis of invasive epithelial ovarian, fallopian tube, and primary peritoneal cancers (hereafter referred to as ovarian cancer) is often delayed, worsening both survival and quality of life for ovarian cancer patients. Diagnosis is delayed in ovarian cancer both because there is no effective screening method and because the symptoms commonly associated with ovarian cancer (i.e., abdominal/pelvic pain, bloating, loss of appetite, urinary symptoms) are non-specific to the disease. It would be beneficial to develop a flag in the electronic health record (EHR) when a patient’s healthcare utilization indicates further investigation for possible ovarian cancer is warranted. Thus, we used EHR data to develop a diagnosis score that identifies people who should be further assessed for potential ovarian cancer. Methods: EHR data from 211,123 female patients, including 135 ovarian cancer patients, at the University of Michigan Medical System during 2012-2023 were analyzed. Time-varying Cox proportional hazard models were fit to identify the association between ovarian cancer and the diagnostic codes recorded for healthcare visits in the EHR, including the temporal sequence of the diagnostic codes. The beta coefficients for the diagnosis codes from the Cox models were summed to create a weighted diagnosis score for each patient. The weighted diagnosis score was tested for association with ovarian cancer in the same population. Results: A total of 79 diagnostic codes were statistically significantly associated with ovarian cancer after applying a Bonferroni correction (p&amp;lt;9x10-5). The temporal sequence of the diagnoses was not associated with ovarian cancer. There were 11 pairs of diagnosis codes that were correlated (correlation coefficient &amp;gt;0.25); the diagnosis code with the higher p-value was excluded from further consideration. The remaining 68 codes were used to construct the diagnosis score. There was a statistically significant trend of increasing rate of ovarian cancer per quartile of the diagnosis score (hazard ratio=3.75, 95% confidence interval 3.50-4.03). Conclusion: A score for ovarian cancer diagnosis was developed based on 68 diagnostic codes in the EHR. The next step is to validate the diagnosis score in an external dataset. This validation is underway using administrative data from British Columbia, Canada. Citation Format: Minh Tung Phung, Karen McLean, Gillian E. Hanley, Celeste Leigh Pearce. Development of an ovarian cancer diagnosis score using electronic health records [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4974.

Abstract 1065: Two-step method for early detection of ovarian cancer with high specificity via platelet RNA

Abstract Owing to lack of disease-specific symptoms and screening methods, most of patients with ovarian cancer are diagnosed at an advanced stage. Recent technology based on platelet RNA aids in detection of tumors at the early stages. Also, for population screening of low-prevalence diseases like cancer, accurate detection with high specificity is essential to improve diagnostic methods. Thus, we proposed platelet RNA-based early ovarian cancer detection with great specificity. It is a two-step method for detecting ovarian tumors with more than 99% specificity using exon-exon junction features and sampling invariant normalization and finding their malignancy with more than 99% negative predictive value using exon-exon junction and hematology parameters. First, we downloaded public platelet transcriptome data of gynecological cancer including ovarian cancer (n=151), benign tumor (n=30), and healthy counterparts (n=218) from GEO (GSE158508, 89843, and 183635). Second, we prospectively enrolled patients with gynecological cancer (n=31), benign tumors (n=20), and healthy women (n=18) at Seoul National University Hospital (SNUH) and Boaz Medical Center and obtained platelet transcriptome data (FMH data). Feature selection and model development were conducted using public training set (n=243), public validation set (n=156), and FMH training/validation set (n=47). The batch-invariant normalization was performed, and 198 splice junctions were selected as features from training/validation set for the bootstrap aggregation of the SVM model diagnosing the existence of an ovarian tumor. The model performance was assessed from a separate test set only composed of FMH data (n=22). Our model that demonstrated 96.8% sensitivity, 100.0% specificity, with a predetermined cut-off value. Next, FMH training/validation set was used for model development of following malignancy test. 10 splice junctions and 19 hematology parameters were selected and SVM models were separately developed for splice junctions and hematology parameters using 5-fold cross validation. The predictions were combined using a logical OR operation to obtain the final prediction result. The result showed its diagnostic performance with 100% sensitivity or NPV, and 50.0% specificity in the test data set with a predetermined cut-off value. We proposed a two-step method to operationally improve the existing protocol for ovarian cancer screening. Our model initially excludes most healthy individuals who do not have ovarian tumors and excludes patients with benign ovarian tumors who do not necessitate medical testing. To conclude, our model allows physicians to identify a subset of ovarian tumor patients with a higher risk of malignancy even at early stages; it exhibits potential in enhancing early diagnosis of ovarian cancer, leading to increase of treatment effectiveness and ultimately augmenting the survival of patients with ovarian cancer. Citation Format: Eunyong Ahn, Se Ik Kim, Sungmin Park, Sarah Kim, Yeochan Kim, Eunchong Huang, Suyeon Lee, Dong Won Hwang, Heeyeon Kim, HyunA Jo, Untack Cho, Juwon Lee, TaeJin Ahn, Yong-Sang Song. Two-step method for early detection of ovarian cancer with high specificity via platelet RNA [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1065.

Abstract 126: Effect of active cytomegalovirus infection on symptoms of fatigue, cancer-related cognitive impairment, and peripheral neuropathy in ovarian cancer survivors

Abstract Background: Fatigue, cancer-related cognitive impairment (CRCI) and neuropathy are among the most common and distressing side effects reported by individuals treated with chemotherapy for ovarian cancer. Cytomegalovirus (CMV) is a highly prevalent herpes virus infection worldwide, affects inflammation and immune function, and can reactivate with treatment for cancer. This study aimed to explore the associations between active CMV infection and patient-reported symptoms among ovarian cancer survivors who have completed front-line chemotherapy. Methods: We conducted a cross-sectional study among individuals with a history of a diagnosis of ovarian cancer, primary peritoneal cancer, or fallopian tube cancer from academic and community cancer clinics in Minnesota at any time point after completion of front-line chemotherapy. Participants completed a one-time survey and provided a blood sample. The exposure of interest was plasma CMV DNA level measured using digital PCR, with ≥100 copies/mL of plasma considered active infection (CMV+). We measured symptoms of fatigue (Fatigue Symptom Inventory), CRCI (PROMIS Cognitive Function 8a) and peripheral neuropathy (Functional Assessment of Cancer Therapy/Gynecologic Oncology Group - Neurotoxicity) using validated self-reported measures. Data were analyzed using descriptive statistics and compared by active CMV infection status (CMV+ vs. CMV-) in the full group and by treatment status (receipt of chemotherapy within 30 days prior to data collection) using t-tests. Results: A total of 160 participants were analyzed. Participants were median of 64.0 years old and 2.3 years from initial diagnosis. Most (75.2%) had advanced stage disease, 43.8% had experienced at least one recurrence, and 40.0% received chemotherapy within 30 days prior to participation. Overall, 40.0% were CMV+, with 40.6% CMV+ among those who received chemotherapy within 30 days and 39.6% CMV+ among those who had not. Participants who were CMV+, compared to those who were CMV-, had overall worse symptoms, with greater symptoms of neuropathy in the full group (11.9±9.2 vs. 9.1±7.8, p=0.04) and particularly greater symptoms of CRCI among those who had recently received chemotherapy (29.2±7.4 vs. 33.2±6.7, p=0.03). Conclusion: We identified that active CMV infection is common in this survivor population and found that worse symptoms of neuropathy and CRCI are associated with CMV+ status. These results are consistent with studies among other immune-compromised populations, and with further validation, provide a potential therapeutic path for reducing these symptoms. Citation Format: Xuan Li, Katherine Brown, Kate Honeyfield, Devon Hunter-Schlichting, Morgan Gruner, Deanna Teoh, Melissa Geller, Heather Nelson, Rachel I. Vogel. Effect of active cytomegalovirus infection on symptoms of fatigue, cancer-related cognitive impairment, and peripheral neuropathy in ovarian cancer survivors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 126.

β-Galactosidase-activated near-infrared AIEgen for ovarian cancer imaging in vivo

Near-infrared (NIR) aggregation induced-emission luminogens (AIEgens) circumvent the noisome aggregation-caused quenching (ACQ) effect in physiological milieu, thus holding high promise for real-time and sensitive imaging of biomarkers in vivo. β-Galactosidase (β-Gal) is a biomarker for primary ovarian carcinoma, but current AIEgens for β-Gal sensing display emissions in the visible region and have not been applied in vivo. We herein propose an NIR AIEgen QM-TPA-Gal and applied it for imaging β-Gal activity in vitro and in ovarian tumor model. After being internalized by ovarian cancer cells (e.g., SKOV3), the hydrophilic nonfluorescent QM-TPA-Gal undergoes hydrolyzation by β-Gal to yield hydrophobic QM-TPA-OH, which subsequently aggregates into nanoparticles to turn NIR fluorescence “on” through the AIE mechanism. In vitro experimental results indicate that QM-TPA-Gal has a sensitive and selective response to β-Gal with a limit of detection (LOD) of 0.21 U/mL. Molecular docking simulation confirms that QM-TPA-Gal has a good binding ability with β-Gal to allow efficient hydrolysis. Furthermore, QM-TPA-Gal is successfully applied for β-Gal imaging in SKOV3 cell and SKOV3-bearing living mouse models. It is anticipated that QM-TPA-Gal could be applied for early diagnosis of ovarian cancers or other β-Gal-associated diseases in near future.

A comparison of the clinical and histological appearances after treatment of advanced stage ovarian cancer with PlasmaJet® device

ObjectiveTo compare the clinical appearance of “no residual disease” to the histological assessment of the same tissue when treated with PlasmaJet®. To determine if the treated tissue with a clinical appearance of “no residual disease” demonstrated histologically apparent damage to underlying structures. AimThe main aims of the study were to compare the clinical appearance of ‘no residual disease’ to the histological assessment of the same tissue and to assess whether treatment with PlasmaJet® to produce a clinical appearance of ‘no residual disease’ causes no histologically apparent damage to the underlying structures. MethodThis prospective cohort study was conducted in Liverpool Women’s NHS Foundation Trust between January 2019 and June 2020. Women with a diagnosis of advanced or presumed advanced stage ovarian cancer were approached and 20 women were recruited into the study. Tissue samples were collected from women with stage 3 or 4 ovarian cancer at either primary or interval debulking surgery. ResultsThe clinical appearance of no residual disease was confirmed histologically in 84 % (n = 16) of cases. Fat was the only underlying tissue seen damaged in 21 % (n = 4) of cases. Bowel resection with stoma formation was needed in one case (5.26 %). ConclusionPlasmaJet® ablated the malignant tissue in majority of the cases without causing any significant damage to the underlying tissue, it also reduced the need for stoma formation. This is a small study with encouraging results. PlasmaJet® could be a valuable tool in ovarian cancer surgery, it potentially could reduce the need for bowel surgery and allow treatment of significant mesenteric disease with reduced morbidity for the patient.

Unraveling the molecular mechanisms of lymph node metastasis in ovarian cancer: focus on MEOX1

BackgroundLymph node metastasis (LNM) is a major factor contributing to the high mortality rate of ovarian cancer, making the treatment of this disease challenging. However, the molecular mechanism underlying LNM in ovarian cancer is still not well understood, posing a significant obstacle to overcome.ResultsThrough data mining from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, we have identified MEOX1 as a specific gene associated with LNM in ovarian cancer. The expression of MEOX1 was found to be relatively high in serous ovarian adenocarcinoma, and its higher expression were associated with increased tumor grade and poorer clinical prognosis for ovarian cancer patients. Bioinformatics analysis revealed that MEOX1 exhibited the highest mRNA levels among all cancer types in ovarian cancer tissues and cell lines. Furthermore, gene set enrichment analysis (GSEA) and pathway analysis demonstrated that MEOX1 was involved in various LNM-related biological activities, such as lymphangiogenesis, lymphatic vessel formation during metastasis, epithelial-mesenchymal transition (EMT), G2/M checkpoint, degradation of extracellular matrix, and collagen formation. Additionally, the expression of MEOX1 was positively correlated with the expression of numerous prolymphangiogenic factors in ovarian cancer. To validate our findings, we conducted experiments using clinical tissue specimens and cell lines, which confirmed that MEOX1 was highly expressed in high-grade serous ovarian cancer (HGSOC) tissues and various ovarian cancer cell lines (A2780, SKOV3, HO8910, and OVCAR5) compared to normal ovarian tissues and normal ovarian epithelial cell line IOSE-80, respectively. Notably, we observed a higher protein level of MEOX1 in tumor tissues of LNM-positive HGSOC compared to LNM-negative HGSOC. Moreover, our fundamental experiments demonstrated that suppression of MEOX1 led to inhibitory effects on ovarian cancer cell proliferation and EMT, while overexpression of MEOX1 enhanced the proliferation and EMT capacities of ovarian cancer cells.ConclusionsThe results of our study indicate that MEOX1 plays a role in the lymph node metastasis of ovarian cancer by regulating multiple biological activities, including the proliferation and EMT of ovarian cancer, lymphangiogenesis, and ECM remodeling. Our findings suggest that MEOX1 could serve as a potential biomarker for the diagnosis and treatment of ovarian cancer with LNM.

Interrogating the Theranostic Capacity of a MUC16-Targeted Antibody for Ovarian Cancer.

Aberrantly expressed glycans on mucins such as mucin-16 (MUC16) are implicated in the biology that promotes ovarian cancer (OC) malignancy. Here, we investigated the theranostic potential of a humanized antibody, huAR9.6, targeting fully glycosylated and hypoglycosylated MUC16 isoforms. Methods: In vitro and invivo targeting of the diagnostic radiotracer [89Zr]Zr-DFO-huAR9.6 was investigated via binding experiments, immuno-PET imaging, and biodistribution studies on OC mouse models. Ovarian xenografts were used to determine the safety and efficacy of the therapeutic version, [177Lu]Lu-CHX-A″-DTPA-huAR9.6. Results: In vivo uptake of [89Zr]Zr-DFO-huAR9.6 supported invitro-determined expression levels: high uptake in OVCAR3 and OVCAR4 tumors, low uptake in OVCAR5 tumors, and no uptake in OVCAR8 tumors. Accordingly, [177Lu]Lu-CHX-A″-DTPA-huAR9.6 displayed strong antitumor effects in the OVCAR3 model and improved overall survival in the OVCAR3 and OVCAR5 models in comparison to the saline control. Hematologic toxicity was transient in both models. Conclusion: PET imaging of OC xenografts showed that [89Zr]Zr-DFO-huAR9.6 delineated MUC16 expression levels, which correlated with invitro results. Additionally, we showed that [177Lu]Lu-CHX-A″-DTPA-huAR9.6 displayed strong antitumor effects in highly MUC16-expressing tumors. These findings demonstrate great potential for 89Zr- and 177Lu-labeled huAR9.6 as theranostic tools for the diagnosis and treatment of OC.

Ovarian cancer awareness of women in Turkey: A cross-sectional study.

Early diagnosis of ovarian cancer (OC) increases survival rates; however, due to low awareness levels, women may be diagnosed with OC at the advanced stage. The aim of this cross-sectional study is to reveal the OC awareness of Turkish women and affecting factors. Participants were invited to study via social media tools between February-June 2022. Data was collected with Personal Information Form and the "OC Awareness Scale" from 446 women. 81% of the participants did not recall OC symptoms, 80.8% recognized OC risk factors. The most frequently recalled and recognized OC symptom is pelvic pain (19.8%; 55.8%, respectively). The most frequently recalled and recognized OC risk factors were smoking (43.1%, 67.9%, respectively) and family history (39%, 58.7%, respectively). 2% of the participants felt very confident in recognizing the signs, 72.9% would seek help within 1-2 days when they recognized the signs of OC. The awareness of OC was higher among women who had advanced age, higher education, family history and were in menopause. Turkish women have low level of awareness and knowledge about OC symptoms and risk factors. There is an urgent need for an OC awareness campaign that takes into account the socio-demographic characteristics of women. The results of the study may also guide strategies to prevent OC.

Intraoperative Ketorolac and Outcomes after Ovarian Cancer Surgery.

Surgery is the cornerstone of ovarian cancer treatment. However, surgery and perioperative inflammation have been described as potentially pro-metastagenic. In various animal models and other human cancers, intraoperative administration of non-steroidal anti-inflammatory drugs (NSAIDs) appears to have a positive impact on patient outcomes. In this unicentric retrospective study, we provide an exploratory analysis of the safety and potential benefit of intraoperative administration of ketorolac on the outcome of patients undergoing surgery for ovarian cancer. The study population included all patients who were given a diagnosis of ovarian, fallopian tube or peritoneal cancer by the multidisciplinary oncology committee (MOC) of the Cliniques universitaires Saint-Luc between 2015 and 2020. We included 166 patients in our analyses, with a median follow-up of 21.8 months. Both progression-free survival and overall survival were superior in patients who received an intraoperative injection of ketorolac (34.4 months of progression-free survival in the ketorolac group versus 21.5 months in the non-ketorolac group (p = 0.002), and median overall survival was not reached in either group but there was significantly higher survival in the ketorolac group (p = 0.004)). We also performed subgroup analyses to minimise bias due to imbalance between groups on factors that could influence patient survival, and the group of patients receiving ketorolac systematically showed a better outcome. Uni- and multivariate analyses confirmed that administration of ketorolac intraoperatively was associated with better progression-free survival (HR = 0.47 on univariate analysis and 0.43 on multivariate analysis, p = 0.003 and 0.023, respectively). In terms of complications, there were no differences between the two groups, either intraoperatively or postoperatively. Our study has shown a favourable association between the use of ketorolac during surgery and the postoperative progression of ovarian cancer in a group of 166 patients, without any rise in intra- or postoperative complications. These encouraging results point to the need for a prospective study to confirm the benefit of intraoperative administration of ketorolac in ovarian cancer surgery.

Ultrasensitive and label-free electrochemical immunosensor for the detection of the ovarian cancer biomarker CA125 based on CuCo-ONSs@AuNPs nanocomposites

Cancer antigen 125 (CA125) is pivotal as a tumor marker in early ovarian cancer prevention and diagnosis. In this work, we introduced an ultrasensitive label-free electrochemical immunosensor tailored for CA125 detection, leveraging nanogold-functionalized copper-cobalt oxide nanosheets (CuCo-ONSs@AuNPs) as nanocomposites. For the inaugural application, copper-cobalt oxide nanosheets delivered the requisite DPV electrochemical response for the immunosensors. Their large specific surface area and commendable electrical conductivity amplify electron transfer and enable significant gold nanoparticle loading. Concurrently, AuNPs offer a plethora of active sites, facilitating easy immobilization of biomolecules via the bond between amino groups and AuNPs. We employed scanning electron microscopy, transmission electron microscopy, and x-ray photoelectron spectroscopy to characterize the nanomaterials' surface morphology and elemental composition. The electrochemical sensor response signals were ascertained using differential pulse voltammetry. Under optimal conditions, the immunosensor exhibited a linear detection range from 1×10−7 U/mL to 1×10−3 U/mL and a detection limit of 3.9×10−8 U/mL (S/N=3). The proposed label-free electrochemical immunosensor furnishes a straightforward, dependable, and sensitive approach for CA125 quantification and stands as a promising method for clinical detection of other tumor markers.

Multi-modality deep learning model reaches high prediction accuracy in the diagnosis of ovarian cancer

We evaluated the diagnostic performance of a multimodal deep-learning (DL) model for ovarian mass differential diagnosis. This single-center retrospective study included 1,054 ultrasound (US)-detected ovarian tumors (699 benign and 355 malignant). Patients were randomly divided into training (n= 675), validation (n= 169), and testing (n= 210) sets. The model was developed using ResNet-50. Three DL-based models were proposed for benign-malignant classification of these lesions: single-modality model that only utilized US images; dual-modality model that used US images and menopausal status as inputs; and multi-modality model that integrated US images, menopausal status, and serum indicators. After 5-fold cross-validation, 210 lesions were tested. We evaluated the three models using the area under the curve (AUC), accuracy, sensitivity, and specificity. The multimodal model outperformed the single- and dual-modality models with 93.80% accuracy and 0.983 AUC. The Multimodal ResNet-50 DL model outperformed the single- and dual-modality models in identifying benign and malignant ovarian tumors.