The role of biomarkers in the management of epithelial ovarian cancer

Role of Proteins in Oncology: Advances in Cancer Diagnosis, Prognosis, and Targeted Therapy-A Narrative Review

Modern oncology increasingly relies on the role of proteins as key components in cancer diagnosis, prognosis, and targeted therapy. This review examines advancements in protein biomarkers across several cancer types, including breast cancer, lung cancer, ovarian cancer, and hepatocellular carcinoma. These biomarkers have proven critical for early detection, treatment response monitoring, and tailoring personalized therapeutic strategies. The article highlights the utility of targeted therapies, such as tyrosine kinase inhibitors and monoclonal antibodies, in improving treatment efficacy while minimizing systemic toxicity. Despite these advancements, challenges like tumor resistance, variability in protein expression, and diagnostic heterogeneity persist, complicating universal application. The review underscores future directions, including the integration of artificial intelligence, advanced protein analysis technologies, and the development of combination therapies to overcome these barriers and refine personalized cancer treatment.

Immunohistochemical expression of SATB2 and PAX8 in differentiating primary from metastatic ovarian mucinous neoplasms

Accurate stratification of an ovarian mucinous neoplasm as primary or secondary is always challenging as they show overlapping histomorphological and immunohistochemical features. Immunohistochemical staining for SATB2 and PAX8 was performed on 80 cases of mucinous ovarian neoplasms subdivided into 53 primary [25 primary ovarian mucinous carcinomas (POMCs) and 28 mucinous borderline tumors (MBTs)] and 27 secondary (12 of colonic origin, 7 of appendiceal origin, and 8 of gastric origin). Expression was correlated with different clinicopathologic parameters. PAX8-positive immunostaining was detected in 38 out of 53 cases (71.69%) of primary ovarian mucinous neoplasms (POMNs) with null positivity in the secondary ovarian mucinous tumors (0/27). SATB2-positive expression was detected in 16 out of 27 cases (59.26%) of the secondary ovarian mucinous tumors. None of the studied POMNs showed any positive immunostaining for SATB2 (0/53). A profile of SATB2-/PAX8+ and SATB2+/PAX8- can be used to differentiate POMNCs from secondary ovarian mucinous tumors of GI origin, respectively, with 100% specificity. PAX8 expression is associated with some clinicopathologic parameters providing the basis for the possible usage of PAX8 as prognostic marker.

Ultrasensitive Quantification of MUC16 Antigen/Amine-Terminated Aptamer Interaction by Surface Plasmon Resonance: Kinetic and Thermodynamic Studies

Purpose

MUC16 is a commonly employed biomarker to identify and predict ovarian cancer (OC). Precise measurement of MUC16 levels is essential for the accurate diagnosis, prediction, and management of OC. This research seeks to introduce a new surface plasmon resonance (SPR) biosensor design that utilizes aptamer-based technology to enable the sensitive and real-time detection of MUC16.

Methods

In this study, the sensor chip was immobilized with an anti-MUC16 aptamer (Ap) by utilizing 11-mercaptoundecanoic acid (MUA) as a linker to attach the amine-terminated Ap to the chip using EDC/NHS chemistry.

Results

The results indicated that the newly created aptasensor had a detection limit of 0.03 U/mL for MUC16 concentration, with a linear range of 0.09 to 0.27 U/mL. The findings demonstrate good precision and accuracy (<15%) for each MUC16 concentration, with recoveries ranging from 93% to 96%. Additionally, the aptasensor exhibited high selectivity, good repeatability, stability, and applicability in real human serum samples, indicating its potential as a valuable tool for the diagnosis and treatment of OC.

Conclusion

According to the outcomes, the designed aptasensor exhibited acceptable specificity to detect the CA125 antigen and could be utilized for the serum detection of target antigen by SPR method.

Circulating tumor cells help differentiate benign ovarian lesions from cancer before surgery: A literature review and proof of concept study using flow cytometry with fluorescence imaging

Current tools are insufficient for distinguishing patients with ovarian cancer from those with benign ovarian lesions before extensive surgery. The present study utilized a readily accessible platform employing a negative selection strategy, followed by flow cytometry, to enumerate circulating tumor cells (CTCs) in patients with ovarian cancer. These counts were compared with those from patients with benign ovarian lesions. CTC counts at baseline, before and after anticancer therapy, and across various clinical scenarios involving ovarian lesions were assessed. A negative-selection protocol we proposed was applied to patients with suspected ovarian cancer and prospectively utilized in those subsequently confirmed to have malignancy. The protocol was implemented before anticancer therapy and at months 3, 6, 9 and 12 post-treatment. A cut-off value for CTC number at 4.75 cells/ml was established to distinguish ovarian malignancy from benign lesions, with an area under the curve of 0.900 (P<0.001). In patients with ovarian cancer, multivariate Cox regression analysis revealed that baseline CTC counts and the decline in CTCs within the first three months post-therapy were significant predictors of prolonged progression-free survival. Additionally, baseline CTC counts independently prognosticated overall survival. CTC counts obtained with the proposed platform, used in the present study, suggest that pre-operative CTC testing may be able to differentiate between malignant and benign tumors. Moreover, CTC counts may indicate oncologic outcomes in patients with ovarian cancer who have undergone cancer therapies.

Sensitivity and Specificity of Selected Biomarkers and Their Combinations in the Diagnosis of Ovarian Cancer

One of the greatest challenges in modern gynecological oncology is ovarian cancer. Despite the numerous studies currently being conducted, it is still sometimes detected at late clinical stages, where the prognosis is unfavorable. One significant contributing factor is the absence of sensitive and specific parameters that could aid in early diagnosis. An ideal screening test, in view of the low incidence of ovarian cancer, should have a sensitivity of greater than 75% and a specificity of at least 99.6%. To enhance sensitivity and specificity, diagnostic panels are being created by combining individual markers. The drive to develop better screening tests for ovarian cancer focuses on modern diagnostic methods based on molecular testing, which in turn aims to find increasingly effective biomarkers. Currently, researchers' efforts are focused on the search for a complementary parameter to those most commonly used that would satisfactorily enhance the sensitivity and specificity of assays. Several biomarkers, including microRNA molecules, autoantibodies, cDNA, adipocytokines, and galectins, are currently being investigated by researchers. This article reviews recent studies comparing the sensitivity and specificity of selected parameters used alone and in combination to increase detection of ovarian cancer at an early stage.

From desert flora to cancer therapy: systematic exploration of multi-pathway mechanisms using network pharmacology and molecular modeling approaches

Ovarian cancer, often labeled a "silent killer," remains one of the most compelling and challenging areas of cancer research. In 2019 alone, a staggering 222,240 new cases of ovarian cancer were reported, with nearly 14,170 lives tragically lost to this relentless disease. The absence of effective diagnostic methods, increased resistance to chemotherapy, and the heterogeneous nature of ovarian cancer collectively contribute to the unfavorable prognosis observed in the majority of cases. Thus, there is a pressing need to explore therapeutic interventions that offer superior efficacy and safety, thereby enhancing the survival prospects for ovarian cancer patients. Recognizing this potential, our research synergizes bioinformatics with a network pharmacology approach to investigate the underlying molecular interactions of Saudi Arabian flora (Onopordum heteracanthum, Acacia ehrenbergiana, Osteospermum vaillantii, Cyperus rotundus, Carissa carandas, Carissa spinarum, and Camellia sinensis) in ovarian cancer treatment. At first, phytoconstituents of indigenous flora and their associated gene targets, particularly those pertinent to ovarian cancer, were obtained from open-access databases. Later, the shared targets of plants and diseases were compared to identify common targets. A protein-protein interaction (PPI) network of predicted targets was then constructed for the identification of key genes having the highest degree of connectivity among networks. Following that, a compound-target protein-pathway network was constructed, which uncovered that, namely, hispidulin, stigmasterol, ascorbic acid, octopamine, cyperene, kaempferol, pungenin, citric acid, d-tartaric acid, beta-sitosterol, (-)-epicatechin gallate, and (+)-catechin demonstrably influence cell proliferation and growth by impacting the AKT1 and VEGFA proteins. Molecular docking, complemented by a 20-ns molecular dynamic (MD) simulation, was used, and the binding affinity of the compound was further validated. Molecular docking, complemented by a 20-ns MD simulation, confirmed the binding affinity of these compounds. Specifically, for AKT1, ascorbic acid showed a docking score of -11.1227 kcal/mol, interacting with residues Ser A:240, Leu A:239, Arg A:243, Arg C:2, and Glu A:341. For VEGFA, hispidulin exhibited a docking score of -17.3714 kcal/mol, interacting with Asn A:158, Val A:190, Gln B:160, Ser A:179, and Ser B:176. To sum up, both a theoretical and empirical framework were established by this study, directing more comprehensive research and laying out a roadmap for the potential utilization of active compounds in the formulation of anti-cancer treatments.

The analysis of preoperative or intraoperative factors in predicting the escalation of surgical pathological staging of patients with clinical stage I endometrioid carcinoma: A retrospective clinical study

To retrospectively analyze the preoperative and intraoperative influencing factors in predicting the escalation of surgical pathological staging in patients with clinical stage I endometrioid carcinoma. Patients with clinical stage I endometrioid carcinoma at Women's Hospital, School of Medicine, Zhejiang University, between January 2002 and December 2015 were enrolled in this study. Due to preoperative or intraoperative surgical exploration, the patients with one or more preoperative or intraoperative high-risk factors underwent total hysterectomy, bilateral salpingo-oophorectomy and lymphadenectomy, totaling 535 cases. The preoperative and intraoperative influencing factors that could lead to the escalation of postoperative surgical pathological staging were further analyzed. 1. There were 535 patients diagnosed with clinical stage I endometrioid carcinoma before surgery, 125 patients were upgraded with postoperative pathological staging, for a rate of 23.36%. 2. Kaplan-Meier survival curve analysis showed that the prognosis in postoperative surgical pathological staging upgraded cases was worse than that in nonupgraded cases. The tumor-free survival and overall survival rates in the 2 groups were significantly different (P < .001). 3. Univariate analysis showed that preoperative degree of myometrial infiltration, intraoperative visual myometrial infiltration depth, massive size of tumor (diameter ≥ 4 cm) and preoperative abnormal serum cancer antigen 125 (CA125) level were associated with the escalation of surgical pathological staging (P < .05). Multivariate analysis indicated that massive size of tumor and preoperative serum abnormal CA125 level were independent predictors of whether postoperative pathological staging would be upgraded (P < .05). 4. The receiver operating characteristic curve drawn with the massive size of tumor and/or the preoperative serum CA125 level abnormality could be used to predict the probability of postoperative pathological upstaging. The results showed that the area from the combination of the 2 factors under the receiver operating characteristic curve was 0.723 (95% confidence interval, 0.672-0.773), suggesting that the combination of massive size of tumor and abnormal preoperative serum CA125 level may serve as an influencing factor for predicting the postoperative pathological staging upgrades. The clinical stage I endometrioid carcinoma patients with massive size of tumor and abnormal preoperative serum CA125 level need to be fully evaluated to ensure appropriate management as soon as possible, since they are more likely to experience postoperative pathological staging upgrades.

Autoantibodies, antigen-autoantibody complexes and antigens complement CA125 for early detection of ovarian cancer

BACKGROUND

Multiple antigens, autoantibodies (AAb), and antigen-autoantibody (Ag-AAb) complexes were compared for their ability to complement CA125 for early detection of ovarian cancer.

METHODS

Twenty six biomarkers were measured in a single panel of sera from women with early stage (I-II) ovarian cancers (n = 64), late stage (III-IV) ovarian cancers (186), benign pelvic masses (200) and from healthy controls (502), and then split randomly (50:50) into a training set to identify the most promising classifier and a validation set to compare its performance to CA125 alone.

RESULTS

Eight biomarkers detected ≥ 8% of early stage cases at 98% specificity. A four-biomarker panel including CA125, HE4, HE4 Ag-AAb and osteopontin detected 75% of early stage cancers in the validation set from among healthy controls compared to 62% with CA125 alone (p = 0.003) at 98% specificity. The same panel increased sensitivity for distinguishing early-stage ovarian cancers from benign pelvic masses by 25% (p = 0.0004) at 95% specificity. From 21 autoantibody candidates, 3 AAb (anti-p53, anti-CTAG1 and annt-Il-8) detected 22% of early stage ovarian cancers, potentially lengthening lead time prior to diagnosis.

CONCLUSION

A four biomarker panel achieved greater sensitivity at the same specificity for early detection of ovarian cancer than CA125 alone.

Preoperative serum level of CA153 and a new model to predict the sub-optimal primary debulking surgery in patients with advanced epithelial ovarian cancer

OBJECTIVE

The aim of this study was to establish a preoperative model to predict the outcome of primary debulking surgery (PDS) for advanced ovarian cancer (AOC) patients by combing Suidan predictive model with HE4, CA125, CA153 and ROMA index.

METHODS

76 AOC Patients in revised 2014 International Federation of Gynecology and Obstetrics (FIGO) stage III-IV who underwent PDS between 2017 and 2019 from Yunnan Cancer Hospital were included. Clinical data including the levels of preoperative serum HE4, CA125, CA153 and mid-lower abdominal CT-enhanced scan results were collected. The logistics regression analysis was performed to find factors associated with sub-optimal debulking surgery (SDS). The receiver operating characteristic curve was used to evaluate the predictive performances of selected variables in the outcome of primary debulking surgery. The predictive index value (PIV) model was constructed to predict the outcome of SDS.

RESULTS

Optimal surgical cytoreduction was achieved in 61.84% (47/76) patients. The value for CA125, HE4, CA153, ROMA index and Suidan score was lower in optimal debulking surgery (ODS) group than SDS group. Based on the Youden index, which is widely used for evaluating the performance of predictive models, the best cutoff point for the preoperative serum HE4, CA125, CA153, ROMA index and Suidan score to distinguish SDS were 431.55 pmol/l, 2277 KU/L, 57.19 KU/L, 97.525% and 2.5, respectively. Patients with PIV≥5 may not be able to achieve optimal surgical cytoreduction. The diagnostic accuracy, NPV, PPV and specificity for diagnosing SDS were 73.7%, 82.9%, 62.9% and 72.3%, respectively. In the constructed model, the AUC of the SDS prediction was 0.770 (95% confidence interval: 0.654-0.887), P<0.001.

CONCLUSION

Preoperative serum CA153 level is an important non-invasive predictor of primary SDS in advanced AOC, which has not been reported before. The constructed PIV model based on Suidan's predictive model plus HE4, CA125, CA153 and ROMA index can noninvasively predict SDS in AOC patients, the accuracy of this prediction model still needs to be validated in future studies.

Immune checkpoints as potential theragnostic biomarkers for epithelial ovarian cancer

BACKGROUND

Epithelial ovarian cancer (EOC) is the leading cause of death associated with gynecologic tumors. EOC is asymptomatic in early stages, so most patients are not diagnosed until late stages, highlighting the need to develop new diagnostic biomarkers. Mediators of the tumoral microenvironment may influence EOC progression and resistance to treatment.

AIM

To analyze immune checkpoints to evaluate them as theranostic biomarkers for EOC.

PATIENTS AND METHODS

Serum levels of 16 immune checkpoints were determined in EOC patients and healthy controls using the MILLIPLEX MAP® Human Immuno-Oncology Checkpoint Protein Magnetic Bead Panel.

RESULTS

Seven receptors: BTLA, CD40, CD80/B7-1, GITRL, LAG-3, TIM-3, TLR-2 are differentially expressed between EOC and healthy controls. Serum levels of immune checkpoints in EOC patients are positively significantly correlated with levels of their ligands, with a higher significant correlation between CD80 and CTLA4 than between CD28 and CD80. Four receptors, CD40, HVEM, PD-1, and PD-L1, are positively associated with the development of resistance to Taxol-platinum-based chemotherapy. All of them have an acceptable area under the curve (>0.7).

CONCLUSION

This study has yielded a first panel of seven immune checkpoints (BTLA, CD40, CD80/B7-1, GITRL, LAG-3, TIM-3, TLR-2) associated with a higher risk of EOC and a second panel of four immune checkpoints (CD40, HVEM, PD-1, PD-L1) that may help physicians to identify EOC patients who are at high risk of developing resistance to EOC chemotherapy.

Long noncoding RNA FTX promotes epithelial-mesenchymal transition of epithelial ovarian cancer through modulating miR-7515/TPD52 and activating Met/Akt/mTOR

Overexpressed long noncoding RNA FTX is associated with low survival rate of epithelial ovarian cancer (EOC) patients, and enhances tumor infiltration. Thus, we aim to illuminate the undefined underlying mechanisms. Real-time quantitative polymerase chain reaction was applied to detect the expressions of FTX, miR-7515, miR-342-3p, miR-940, miR-150-5p, miR-205-5p and tumor protein D52 (TPD52). Cell counting kit-8 and transwell assays were utilized to explore the cell viability, migration or invasion of EOC cells. Western blot was conducted to measure the expressions of E-cadherin, N-cadherin, Met, phosphorylated (p)-Met, Akt, p-Akt, mTOR and p-mTOR. LncBase and TargetScan predicted the binding of miR-7515 with FTX, and the binding of TPD52 with miR-7515, respectively. The two bindings were further validated by dual luciferase reporter assay. As a result, FTX sponged miR-7515 and miR-7515 targeted to TPD52. FTX was overexpressed in four EOC cell lines. Overexpressed FTX enhanced the cell viability, migration or invasion of EOC cells, elevated N-cadherin and TPD52 expressions, phosphorylated Met/Akt/mTOR, and inhibited E-cadherin expression. All these influences were subsequently reversed by miR-7515 mimic. Collectively, FTX regulates miR-7515/TPD52 to facilitate the migration, invasion or epithelial-mesenchymal transition of EOC through activating Met/Akt/mTOR signaling pathway.

Small-molecule exhibits anti-tumor activity by targeting the RNA m6A reader IGF2BP3 in ovarian cancer

Based on its absence in normal tissues and its role in tumorigenesis and tumor progression, insulin-like growth factor 2 mRNA-binding protein 3 (IGF2BP3), a reader of N6-methyladenosine (M6A) on RNA, represents a putative valuable and specific target for some cancer therapy. In this study, we performed bioinformatic analysis and immunohistochemistry (IHC) to find that IGF2BP3 was highly expressed in tumor epithelial cells and fibroblasts of ovarian cancer (OC), and was associated with poor prognosis, metastasis, and chemosensitivity in OC patients. In particular, we discovered that knockdown IGF2BP3 expression inhibited the malignant phenotype of OC cell lines by decreasing the protein levels of c-MYC, VEGF, CDK2, CDK6, and STAT1. To explore the feasibility of IGF2BP3 as a therapeutic target for OC, a small molecular AE-848 was designed and screened by molecular operating environment (MOE), which not only could duplicate the above results of knockdown assay but also reduced the expression of c-MYC in M2 macrophages and tumor-associated macrophages and promoted the cytokine IFN-γ and TNF-α secretion. The pharmacodynamic models of two kinds of OC bearing animals were suggested that systemic therapy with AE-848 significantly inhibited tumor growth by reducing the expression of tumor-associated antigen (c-MYC/VEGF/Ki67/CDK2) and improving the anti-tumor effect of macrophages. These results suggest that AE-848 can inhibit the growth and progression of OC cells by disrupting the stability of the targeted mRNAs of IGF2BP3 and may be a targeted drug for OC treatment.

Prediction model of ocular metastasis from primary liver cancer: Machine learning-based development and interpretation study

BACKGROUND

Ocular metastasis (OM) is a rare metastatic site of primary liver cancer (PLC). The purpose of this study was to establish a clinical predictive model of OM in PLC patients based on machine learning (ML).

METHODS

We retrospectively collected the clinical data of 1540 PLC patients and divided it into a training set and an internal test set in a 7:3 proportion. PLC patients were divided into OM and non-ocular metastasis (NOM) groups, and univariate logistic regression analysis was performed between the two groups. The variables with univariate logistic analysis p < 0.05 were selected for the ML model. We constructed six ML models, which were internally verified by 10-fold cross-validation. The prediction performance of each ML model was evaluated by receiver operating characteristic curves (ROCs). We also constructed a web calculator based on the optimal performance ML model to personalize the risk probability for OM.

RESULTS

Six variables were selected for the ML model. The extreme gradient boost (XGB) ML model achieved the optimal differential diagnosis ability, with an area under the curve (AUC) = 0.993, accuracy = 0.992, sensitivity = 0.998, and specificity = 0.984. Based on these results, an online web calculator was constructed by using the XGB ML model to help clinicians diagnose and treat the risk probability of OM in PLC patients. Finally, the Shapley additive explanations (SHAP) library was used to obtain the six most important risk factors for OM in PLC patients: CA125, ALP, AFP, TG, CA199, and CEA.

CONCLUSION

We used the XGB model to establish a risk prediction model of OM in PLC patients. The predictive model can help identify PLC patients with a high risk of OM, provide early and personalized diagnosis and treatment, reduce the poor prognosis of OM patients, and improve the quality of life of PLC patients.

LncRNA ASB16-AS1 accelerates cellular process and chemoresistance of ovarian cancer cells by regulating GOLM1 expression via targeting miR-3918

BACKGROUND

Reportedly, ovarian cancer (OC) is a major threat to women's health. Long non-coding RNA (lncRNA) ASB16-AS1 has been uncovered to participate in cancer progression. Nevertheless, the role of ASB16-AS1 in OC remains to be revealed.

PURPOSE

This study aimed to unveil the biological function of ASB16-AS1 and its underlying mechanisms in OC cells.

METHODS

QRT-PCR was done to test ASB16-AS1 expression in OC cells. Functional assays were performed to evaluate the malignant behaviors and cisplatin resistance of OC cells. Mechanistic analyses were done to investigate the regulatory molecular mechanism in OC cells.

RESULTS

ASB16-AS1 was found to be highly expressed in OC cells. ASB16-AS1 knockdown repressed proliferation, migration, and invasion of OC cells, while facilitating cell apoptosis. ASB16-AS1 was further validated to up-regulate GOLM1 through competitively binding with miR-3918. Moreover, miR-3918 overexpression was corroborated to suppress OC cell growth. Rescue assays further uncovered that ASB16-AS1 modulated the malignant processes of OC cells via targeting miR-3918/GOLM1 axis.

CONCLUSION

ASB16-AS1 facilitates the malignant processes and chemoresistance of OC cells via serving as miR-3918 sponge and positively modulating GOLM1 expression.

Profiling the metabolome of uterine fluid for early detection of ovarian cancer

Ovarian cancer (OC) causes high mortality in women because of ineffective biomarkers for early diagnosis. Here, we perform metabolomics analysis on an initial training set of uterine fluid from 96 gynecological patients. A seven-metabolite-marker panel consisting of vanillylmandelic acid, norepinephrine, phenylalanine, beta-alanine, tyrosine, 12-S-hydroxy-5,8,10-heptadecatrienoic acid, and crithmumdiol is established for detecting early-stage OC. The panel is further validated in an independent sample set from 123 patients, discriminating early OC from controls with an area under the curve (AUC) of 0.957 (95% confidence interval [CI], 0.894-1). Interestingly, we find elevated norepinephrine and decreased vanillylmandelic acid in most OC cells, resulting from excess 4-hydroxyestradiol that antagonizes the catabolism of norepinephrine by catechol-O-methyltransferase. Moreover, exposure to 4-hydroxyestradiol induces cellular DNA damage and genomic instability that could lead to tumorigenesis. Thus, this study not only reveals metabolic features in uterine fluid of gynecological patients but also establishes a noninvasive approach for the early diagnosis of OC.

Evaluation of the Potential Diagnostic Utility of the Determination of Selected Immunological and Molecular Parameters in Patients with Ovarian Cancer

Ovarian cancer is one of the most serious challenges in modern gynaecological oncology. Due to its non-specific symptoms and the lack of an effective screening procedure to detect the disease at an early stage, ovarian cancer is still marked by a high mortality rate among women. For this reason, a great deal of research is being carried out to find new markers that can be used in the detection of ovarian cancer to improve early diagnosis and survival rates of women with ovarian cancer. Our study focuses on presenting the currently used diagnostic markers and the latest selected immunological and molecular parameters being currently investigated for their potential use in the development of new diagnostic and therapeutic strategies.

Through the Looking Glass: Updated Insights on Ovarian Cancer Diagnostics

Epithelial ovarian cancer (EOC) is the deadliest gynaecological malignancy and the eighth most prevalent cancer in women, with an abysmal mortality rate of two million worldwide. The existence of multiple overlapping symptoms with other gastrointestinal, genitourinary, and gynaecological maladies often leads to late-stage diagnosis and extensive extra-ovarian metastasis. Due to the absence of any clear early-stage symptoms, current tools only aid in the diagnosis of advanced-stage patients, wherein the 5-year survival plummets further to less than 30%. Therefore, there is a dire need for the identification of novel approaches that not only allow early diagnosis of the disease but also have a greater prognostic value. Toward this, biomarkers provide a gamut of powerful and dynamic tools to allow the identification of a spectrum of different malignancies. Both serum cancer antigen 125 (CA-125) and human epididymis 4 (HE4) are currently being used in clinics not only for EOC but also peritoneal and GI tract cancers. Screening of multiple biomarkers is gradually emerging as a beneficial strategy for early-stage diagnosis, proving instrumental in administration of first-line chemotherapy. These novel biomarkers seem to exhibit an enhanced potential as a diagnostic tool. This review summarizes existing knowledge of the ever-growing field of biomarker identification along with potential future ones, especially for ovarian cancer.

The role of urine and serum biomarkers in the early detection of ovarian epithelial tumours

Ovarian cancer (OC) is one of the leading causes of gynaecological cancer mortality in women worldwide. If detected at an early stage (I, II), OC has a 90% 5-year survival rate; nevertheless, symptoms are often hidden, leading to late-stage (III, IV) diagnosis and a poor prognosis. The current diagnostic procedures, such as a pelvic exam, transvaginal ultrasound, CA-125 blood tests, serum HE4 tests and multivariate index assays (MIA), are insufficient. Sadly, surgery is frequently required to confirm a positive diagnosis. Therefore, there has been an increased interest in different biomarkers using a non-invasive test as a tool for the earlier diagnosis of OC to resolve the need for precise and non-invasive diagnostic methods. This review article aims to investigate how biomarkers influence early OC detection and to emphasise the role of using a combination of serum biomarkers panel rather than a single biomarker. In addition, this review provides insights into the current serum biomarkers, urine biomarkers and other emerging biomarkers in the early detection of OC for better specificity and sensitivity and to improve the overall survival (OS) rate.

GD2 and GD3 gangliosides as diagnostic biomarkers for all stages and subtypes of epithelial ovarian cancer

Background

Ovarian cancer (OC) is the deadliest gynecological cancer, often diagnosed at advanced stages. A fast and accurate diagnostic method for early-stage OC is needed. The tumor marker gangliosides, GD2 and GD3, exhibit properties that make them ideal potential diagnostic biomarkers, but they have never before been quantified in OC. We investigated the diagnostic utility of GD2 and GD3 for diagnosis of all subtypes and stages of OC.

Methods

This retrospective study evaluated GD2 and GD3 expression in biobanked tissue and serum samples from patients with invasive epithelial OC, healthy donors, non-malignant gynecological conditions, and other cancers. GD2 and GD3 levels were evaluated in tissue samples by immunohistochemistry (n=299) and in two cohorts of serum samples by quantitative ELISA. A discovery cohort (n=379) showed feasibility of GD2 and GD3 quantitative ELISA for diagnosing OC, and a subsequent model cohort (n=200) was used to train and cross-validate a diagnostic model.

Results

GD2 and GD3 were expressed in tissues of all OC subtypes and FIGO stages but not in surrounding healthy tissue or other controls. In serum, GD2 and GD3 were elevated in patients with OC. A diagnostic model that included serum levels of GD2+GD3+age was superior to the standard of care (CA125, p<0.001) in diagnosing OC and early-stage (I/II) OC.

Conclusion

GD2 and GD3 expression was associated with high rates of selectivity and specificity for OC. A diagnostic model combining GD2 and GD3 quantification in serum had diagnostic power for all subtypes and all stages of OC, including early stage. Further research exploring the utility of GD2 and GD3 for diagnosis of OC is warranted.

Growth Differentiation Factor-15 as a candidate biomarker in gynecologic malignancies: A meta-analysis

Growth Differentiation Factor-15 (GDF-15), though emerged as a novel marker in gynecological cancers, is yet to be recognized in clinical diagnostics. Eligible studies were sorted from multiple online databases, namely PubMed, Cochrane, ClinicalTrials.gov, Google Scholar, Web of Science, Embase, Scopus, LILACS, Opengrey. From six studies, histopathologically diagnosed cases without prior treatment, and with diagnostic accuracy data for GDF-15 in gynecological cancers, were included. Our meta-analysis shows that GDF-15 has pooled diagnostic odds ratio of 12.74 at 80.5% sensitivity and 74.1% specificity, and an AUC of 0.84. Hence, GDF-15 is a potential marker to differentiate gynecological malignancy from non-malignant tumors.

CT imaging phenotypes linked to CA125 and HE4 biomarkers are highly predictive in discriminating between hereditary and sporadic ovarian cancer patients

BACKGROUND: Hereditary ovarian cancers (HOC) represent about 23% of ovarian cancer (OC) cases: they are most frequently related to germline mutations in the BRCA genes. OBJECTIVE: We aimed to compare CA125/HE4 serum levels and Computed Tomography (CT) features at time of ovarian cancer (OC) diagnosis in two populations: BRCA mutant and BRCA wild-type (WT) OC, and to investigate the relationship between this laboratory and radiological biomarker and BRCA mutation status. METHODS: This retrospective study included 60 newly diagnosed OC patients with FIGO stage IIIC-IV disease, tested for BRCA1/2 germline mutation status of which preoperative CT scan and serum tumor marker assay were available. RESULTS: The median level of CA125 (708 U/mL) was significantly higher (p < 0.002) in BRCA1/2 mutated patients than in WT patients (176 U/mL), whereas the median level of HE4 (492 pmol/L) was significantly higher (p < 0.002) in WT than in BRCA-mutated patients (252 pmol/L). BRCA mutation carriers showed a higher incidence of bilateral ovarian masses (p = 0.0303) characterized by solid structures (p < 0.00001), higher peritoneal tumor load, macronodular implants >2 cm (p = 0.000099), increased frequency of lymphadenopathies (p = 0.019), and metastasis (p = 0.052) compared to patients with BRCA WT. CONCLUSIONS: Tumor markers and CT patterns may help in identifying BRCA mutation status in OC directing patients towards a personalized treatment.

HE4 as a serum biomarker for the diagnosis of pelvic masses: a prospective, multicenter study in 965 patients

Background

To evaluate the diagnostic value of adding human epididymis protein 4 (HE4), cancer antigen 125 (CA125) and risk of malignancy algorithm (ROMA) to ultrasound for detecting ovarian cancer in patients with a pelvic mass.

Methods

This was a prospective, observational, multicenter study. Patients aged > 18 years who were scheduled to undergo surgery for a suspicious pelvic mass had CA125 and HE4 levels measured prior to surgery, in addition to a routine transvaginal ultrasound scan. The diagnostic performance of CA125, HE4 and ROMA for distinguishing between benign and malignant adnexal masses was assessed using receiver operating characteristic (ROC) analysis and the corresponding area under the curve (AUC).

Results

Of 965 evaluable patients, 804 were diagnosed with benign tumors and 161 were diagnosed with ovarian cancer. In late-stage ovarian cancer, CA125, HE4 and ROMA all had an excellent diagnostic performance (AUC > 0.92), whereas in stage I and II, diagnostic performance of all three biomarkers was less adequate (AUC < 0.77). In the differential diagnosis of ovarian cancer and endometriosis, ROMA and HE4 performed better than CA125 with 99 and 98.1% versus 75.0% sensitivity, respectively, at 75.4% specificity.

Conclusions

ROMA and HE4 could be valuable biomarkers to help with the diagnosis of ovarian cancer in premenopausal patients in order to differentiate from endometriosis, whereas CA125 may be more adequate for postmenopausal patients.

• Serum biomarkers can help to distinguish benign from malignant pelvic masses

• We evaluated the diagnostic value of adding HE4, CA125 and ROMA to ultrasound for detecting ovarian cancer

• In stage III and IV ovarian cancer all three biomarkers showed excellent performance

• ROMA and HE4 performed better than CA125 in the differential diagnosis of ovarian cancer and endometriosis

Identification of Malignant Cell Populations Associated with Poor Prognosis in High-Grade Serous Ovarian Cancer Using Single-Cell RNA Sequencing

Simple Summary

Ovarian cancer has a high recurrence rate (~75%), and tumor heterogeneity is associated with such tumor recurrence. However, it is still poorly understood in ovarian cancer. To reveal tumor heterogeneity, we performed single-cell RNA sequencing (RNA-seq) of serous ovarian cancer cells from four different patients: two with primary carcinoma, one with recurrent carcinoma, and one with carcinoma treated with interval debulking surgery. As a result, we found two malignant tumor cell subtypes associated with poor prognosis. One malignant population included the earliest cancer cells and cancer stem-like cells. SLC3A1 and PEG10 were identified as the marker genes of cancer-initiating cells. The other malignant population expressing CA125 (MUC16), the most common biomarker for ovarian cancer, is associated with a decrease in the number of tumor-infiltrating cytotoxic T lymphocytes (CTLs). Our findings will offer new markers for diagnosis and choosing treatments targeting the malignant populations in ovarian cancer.

Abstract

To reveal tumor heterogeneity in ovarian cancer, we performed single-cell RNA sequencing (RNA-seq). We obtained The Cancer Genome Atlas (TCGA) survival data and TCGA gene expression data for a Kaplan–Meier plot showing the association of each tumor population with poor prognosis. As a result, we found two malignant tumor cell subtypes associated with poor prognosis. Next, we performed trajectory analysis using scVelo and Monocle3 and cell–cell interaction analysis using CellphoneDB. We found that one malignant population included the earliest cancer cells and cancer stem-like cells. Furthermore, we identified SLC3A1 and PEG10 as the marker genes of cancer-initiating cells. The other malignant population expressing CA125 (MUC16) is associated with a decrease in the number of tumor-infiltrating cytotoxic T lymphocytes (CTLs). Moreover, cell–cell interaction analysis implied that interactions mediated by LGALS9 and GAS6, expressed by this malignant population, caused the CTL suppression. The results of this study suggest that two tumor cell populations, including a cancer-initiating cell population and a population expressing CA125, survive the initial treatment and suppress antitumor immunity, respectively, and are associated with poor prognosis. Our findings offer a new understanding of ovarian cancer heterogeneity and will aid in the development of diagnostic tools and treatments.

Downregulation of lncRNA ASMTL-AS1 in Epithelial Ovarian Cancer Correlates with Worse Prognosis and Cancer Progression

Given the characters of "Silent killer", epithelial ovarian cancer (EOC) usually suffered late diagnosis and poor prognosis. Therefore, this study aimed to explore the prognostic significance of ASMTL-AS1 in EOC and investigated the effect of lncRNA ASMTL-AS1 dysregulation on tumor cellular function. ASMTL-AS1 expression was analyzed in 133 EOC tissues and five kinds of cell lines by RT-qPCR. The expression of ASMTL-AS1 was tested for correlation with clinical data using the chi-square test and clinical follow-up using Kaplan-Meier method with log-rank test. Further, the prognostic parameters in predicting EOC overall survival were assessed by using multivariate Cox proportional hazards analysis. In vitro assays, including MTT assay and transwell assay, were conducted using EOC cell lines with overexpression of ASMTL-AS1. In tumorous tissues and cell lines, ASMTL-AS1 was lowly expressed compared with normal ones. This downregulation was associated with the advanced FIGO stage, positive ascites cytology, and lymph node. In particular, low levels of ASMTL-AS1 were revealed to have a high prognostic impact on EOC. ASMTL-AS1 overexpression strongly decreased cell proliferation, migration, and invasion in vitro partly by moderating miR-1228-3p. This study demonstrates a significant role for lowly expressed ASMTL-AS1 in EOC allowing for the prediction of prognosis for EOC. Considering that ASMTL-AS1 is strongly involved in cell growth and invasion, ASMTL-AS1 may be a promising marker for EOC prognosis and therapy.

IGF2BP3‑stabilized SIX4 promotes the proliferation, migration, invasion and tube formation of ovarian cancer cells

The mortality rate of ovarian cancer (OC) is the highest among the different types of female reproductive system cancers. SIX homeobox 4 (SIX4), a member of the homeobox family, subfamily SIX, fulfills an important role in metastasis and angiogenesis in a variety of types of cancer. The aim of the present study was to investigate both the effects and the underlying mechanism of SIX4 on angiogenesis in OC. The Gene Expression Profiling Interactive Analysis and Encyclopedia of RNA Interactomes databases were employed to predict the expression levels of SIX4 in OC tissues, and its association with the overall survival (OS) rate of patients with OC. The expression levels of SIX4 in OC cell lines were detected by reverse transcription‑quantitative PCR (RT‑qPCR) and western blot analysis. Following silencing of SIX4, the proliferation, invasion, migration and angiogenesis of OC cells were investigated via Cell Counting Kit‑8, colony formation, wound healing, Transwell and tube formation assays. Subsequently, the levels of insulin‑like growth factor 2 mRNA binding protein 3 (IGF2BP3) in OC cell lines were detected by RT‑qPCR and western blot analysis. The ability of IGF2BP3 to bind to SIX4 mRNA was detected via an RNA immunoprecipitation assay, and the stability of SIX4 mRNA was assessed by RT‑qPCR following Actinomycin D treatment. Finally, the effects of transfection of sh‑SIX4 and overexpression of IGF2BP3 simultaneously were examined to further delineate the mechanism involved. It was revealed that SIX4 was highly expressed in OC tissues and cells, and its expression was associated with low OS rates in patients with OC. SIX4 knockdown with short hairpin RNA inhibited the proliferation, migration and invasion of cells, as well as angiogenesis. In addition, IGF2BP3 overexpression led to an improvement in the stability of SIX4 mRNA. Overexpression of IGF2BP3 also reversed the inhibitory effect of SIX4 interference on the malignant phenotypes of OC cells. Taken together, the results of the present study demonstrated that IGF2BP3‑stabilized SIX4 promoted the proliferation, metastasis and angiogenesis of SKOV3 cells.

Diagnostic and prognostic role of TFF3, Romo-1, NF-кB and SFRP4 as biomarkers for endometrial and ovarian cancers: a prospective observational translational study

Purpose

This study aimed to evaluate trefoil factor 3 (TFF3), secreted frizzled-related protein 4 (sFRP4), reactive oxygen species modulator 1 (Romo1) and nuclear factor kappa B (NF-κB) as diagnostic and prognostic markers of endometrial cancer (EC) and ovarian cancer (OC).

Methods

Thirty-one patients with EC and 30 patients with OC undergone surgical treatment were enrolled together with 30 healthy controls in a prospective study. Commercial ELISA kits determined serum TFF-3, Romo-1, NF-кB and sFRP-4 concentrations.

Results

Serum TFF-3, Romo-1 and NF-кB levels were significantly higher in patients with EC and OC than those without cancer. Regarding EC, none of the serum biomarkers differs significantly between endometrial and non-endometrioid endometrial carcinomas. Mean serum TFF-3 and NF-кB levels were significantly higher in advanced stages. Increased serum levels of TFF-3 and NF-кB were found in those with a higher grade of the disease. Regarding OC, none of the serum biomarkers differed significantly among histological subtypes. Significantly increased serum levels of NF-кB were observed in patients with advanced-stage OC than those with stage I and II diseases. No difference in serum biomarker levels was found between those who had a recurrence and those who had not. The sensibility and specificity of these four biomarkers in discriminating EC and OC from the control group showed encouraging values, although no one reached 70%.

Conclusions

TFF-3, Romo-1, NF-кB and SFRP4 could represent new diagnostic and prognostic markers for OC and EC. Further studies are needed to validate our results.

Prognostic Impact of Circulating Methylated Homeobox A9 DNA in Patients Undergoing Treatment for Recurrent Ovarian Cancer

Methylated Homeobox A9 circulating tumor DNA (meth-HOXA9) has been suggested as a blood-based biomarker in epithelial ovarian cancer (EOC), although its prognostic significance remains unproven. The aim of the present study was to investigate the prognostic impact of meth-HOXA9 in patients with recurrent EOC. DNA was purified from 4 mL plasma and, following bilsulfite conversion, meth-HOXA9 was analyzed using a methylation-specific droplet digital PCR. Detection of meth-HOXA9 was reported as a percentage of total DNA and as a binary variable (detectable and undetectable). Meth-HOXA9 status and its dynamics during palliative treatment were correlated with overall survival (OS) as the primary endpoint. At baseline, meth-HOXA9 was detected in 65.9% (83/126) of the patients. The median OS was 8.9 and 17.9 months in patients with detectable and undetectable meth-HOXA9 at baseline (hazard ratio: 2.04, p = 0.002), which remained significant in the multivariate analysis. Median OS in patients with an increase in meth-HOXA9 after one treatment cycle was 5.3 months compared to 33 months in patients with undetectable meth-HOXA9 (p < 0.001). Meth-HOXA9 was significantly related to poor survival and may serve as a prognostic marker in patients with recurrent EOC. The longitudinal monitoring of meth-HOXA9 is clinically feasible with the perspective of aiding clinical decision making.

PTPN18 Stimulates the Development of Ovarian Cancer by Activating the PI3K/AKT Signaling

Objective

To illustrate the functions of protein tyrosine phosphatase nonreceptor type 18 (PTPN18) in the progression of ovarian cancer and the potential molecular mechanism.

Methods

Differential PTPN18 expression in ovarian cancer samples was determined. Following PTPN18 knockdown, changes in proliferation and migration in ovarian cancer cells were detected. Nude mice with ovarian cancer were used to uncover the effects of PTPN18 on ovarian cancer growth in vivo.

Results

PTPN18 was significantly upregulated in ovarian cancer samples and linked to pathological staging and metastasis rate. PTPN18 displayed prognostic and diagnostic potentials in ovarian cancer. Knockdown of PTPN18 and treatment of the PI3K inhibitor could inhibit proliferative and migratory abilities in ovarian cancer cells. Moreover, PTPN18 was capable of inactivating PI3K/AKT signaling. In vivo knockdown of PTPN18 suppressed ovarian cancer growth in nude mice.

Conclusions

PTPN18 is upregulated in ovarian cancer, which stimulates the malignant development by activating PI3K/AKT signaling. The PTPN18 level is also associated with pathological staging and metastasis in ovarian cancer patients, which may be utilized as a hallmark predicting the malignant level.

Serum Autoantibodies against LRDD, STC1, and FOXA1 as Biomarkers in the Detection of Ovarian Cancer

Purpose

This study is aimed at evaluating serum autoantibodies against four tumor-associated antigens, including LRDD, STC1, FOXA1, and EDNRB, as biomarkers in the immunodiagnosis of ovarian cancer (OC).

Methods

The autoantibodies against LRDD, STC1, FOXA1, and EDNRB were measured using an enzyme-linked immunosorbent assay (ELISA) in 94 OC patients and 94 normal healthy controls (NHC) in the research group. In addition, the diagnostic values of different autoantibodies were validated in another independent validation group, which comprised 136 OC patients, 136 NHC, and 181 patients with benign ovarian diseases (BOD).

Results

In the research group, autoantibodies against LRDD, STC1, and FOXA1 had higher serum titer in OC patients than NHC (P < 0.001). The area under receiver operating characteristic curves (AUCs) of these three autoantibodies were 0.910, 0.879, and 0.817, respectively. In the validation group, they showed AUCs of 0.759, 0.762, and 0.817 and sensitivities of 49.3%, 42.7%, and 48.5%, respectively, at specificity over 90% for discriminating OC patients from NHC. For discriminating OC patients from BOD, they showed AUCs of 0.718, 0.729, and 0.814 and sensitivities of 47.1%, 39.0%, and 51.5%, respectively, at specificity over 90%. The parallel analyses demonstrated that the combination of anti-LRDD and anti-FOXA1 autoantibodies achieved the optimal diagnostic performance with the sensitivity of 58.1% at 87.5% specificity and accuracy of 72.8%. The positive rate of the optimal autoantibody panel improved from 62.4% to 87.1% when combined with CA125 in detecting OC patients.

Conclusion

Serum autoantibodies against LRDD, STC1, and FOXA1 have potential diagnostic values in detecting OC.

Applications of artificial intelligence (AI) in ovarian cancer, pancreatic cancer, and image biomarker discovery

BACKGROUND

Artificial intelligence (AI), including machine learning (ML) and deep learning, has the potential to revolutionize biomedical research. Defined as the ability to "mimic" human intelligence by machines executing trained algorithms, AI methods are deployed for biomarker discovery.

OBJECTIVE

We detail the advancements and challenges in the use of AI for biomarker discovery in ovarian and pancreatic cancer. We also provide an overview of associated regulatory and ethical considerations.

METHODS

We conducted a literature review using PubMed and Google Scholar to survey the published findings on the use of AI in ovarian cancer, pancreatic cancer, and cancer biomarkers.

RESULTS

Most AI models associated with ovarian and pancreatic cancer have yet to be applied in clinical settings, and imaging data in many studies are not publicly available. Low disease prevalence and asymptomatic disease limits data availability required for AI models. The FDA has yet to qualify imaging biomarkers as effective diagnostic tools for these cancers.

CONCLUSIONS

Challenges associated with data availability, quality, bias, as well as AI transparency and explainability, will likely persist. Explainable and trustworthy AI efforts will need to continue so that the research community can better understand and construct effective models for biomarker discovery in rare cancers.

Machine Learning analysis of high-grade serous ovarian cancer proteomic dataset reveals novel candidate biomarkers

Ovarian cancer is one of the most common gynecological malignancies, ranking third after cervical and uterine cancer. High-grade serous ovarian cancer (HGSOC) is one of the most aggressive subtype, and the late onset of its symptoms leads in most cases to an unfavourable prognosis. Current predictive algorithms used to estimate the risk of having Ovarian Cancer fail to provide sufficient sensitivity and specificity to be used widely in clinical practice. The use of additional biomarkers or parameters such as age or menopausal status to overcome these issues showed only weak improvements. It is necessary to identify novel molecular signatures and the development of new predictive algorithms able to support the diagnosis of HGSOC, and at the same time, deepen the understanding of this elusive disease, with the final goal of improving patient survival. Here, we apply a Machine Learning-based pipeline to an open-source HGSOC Proteomic dataset to develop a decision support system (DSS) that displayed high discerning ability on a dataset of HGSOC biopsies. The proposed DSS consists of a double-step feature selection and a decision tree, with the resulting output consisting of a combination of three highly discriminating proteins: TOP1, PDIA4, and OGN, that could be of interest for further clinical and experimental validation. Furthermore, we took advantage of the ranked list of proteins generated during the feature selection steps to perform a pathway analysis to provide a snapshot of the main deregulated pathways of HGSOC. The datasets used for this study are available in the Clinical Proteomic Tumor Analysis Consortium (CPTAC) data portal (https://cptac-data-portal.georgetown.edu/).

Histological Grade and Tumor Stage Are Correlated with Expression of Receptor Activator of Nuclear Factor Kappa b (Rank) in Epithelial Ovarian Cancers

The receptor activator of nuclear factor kappa B (RANK) is becoming recognized as a master regulator of tumorigenesis, yet its role in gynecological cancers remains mostly unexplored. We investigated whether there is a gradation of RANK protein and mRNA expression in epithelial ovarian cancer (EOC) according to malignancy and tumor staging. Immunohistochemical expression of RANK was examined in a cohort of 135 (benign n = 29, borderline n= 23 and malignant n = 83) EOCs. Wild type and truncated RANK mRNA isoform quantification was performed in a cohort of 168 (benign n = 26, borderline n = 13 and malignant n = 129) EOCs. RANK protein and mRNA values were increased in malignant vs. benign or borderline conditions across serous, mucinous and endometrioid cancer subtypes. Additionally, a trend of increased RANK values with staging was observed for the mucinous and serous histotype. Thus, increased expression of RANK appears associated with the evolution of disease to the onset of malignancy in EOC. Moreover, in some EOC histotypes, RANK expression is additionally associated with clinicopathological markers of tumor aggressiveness, suggesting a role in further progression of tumor activity.

A combined biomarker panel shows improved sensitivity and specificity for detection of ovarian cancer

Background

Combined biomarkers can improve the sensitivity and specificity of ovarian cancer (OC) diagnosis and effectively predict patient prognosis. This study explored the diagnostic and prognostic values of serum CCL18 and CXCL1 antigens combined with C1D, FXR1, ZNF573, and TM4SF1 autoantibodies in OC.

Methods

CCL18 and CXCL1 monoclonal antibodies and C1D, FXR1, ZNF573, and TM4SF1 antigens were coated with microspheres. Logistic regression was used to construct a serum antigen‐antibody combined detection model; receiver‐operating characteristic curve (ROC) was used to evaluate the diagnostic efficacy of the model; and the Kaplan‐Meier method and Cox regression models were used for survival analysis to evaluate the prognosis of OC. Data from The Cancer Genome Atlas (TCGA) and Genotype‐Tissue Expression (GTEx) projects and online survival analysis tools were used to evaluate prognostic genes for OC. The CIBERSORT immune score was used to explore the factors influencing prognosis and their relationship with tumor‐infiltrating immune cells.

Results

The levels of each index in the blood samples of patients with OC were higher than those of the other groups. The combined detection model has higher specificity and sensitivity in the diagnosis of OC, and its diagnostic efficiency is better than that of CA125 alone and diagnosing other malignant tumors. CCL18 and TM4SF1 may be factors affecting the prognosis of OC, and CCL18 may be related to immune‐infiltrating cells.

Conclusions

The serum antigen‐antibody combined detection model established in this study has high sensitivity and specificity for the diagnosis of OC.

Ultrasound-Targeted Microbubble Destruction-Mediated Inhibition of Livin Expression Accelerates Ovarian Cancer Cell Apoptosis

Objective

Ultrasound-targeted microbubble destruction (UTMD) technique has recently been developed as a nonviral delivery of gene therapy. This study aimed at investigating the survival and apoptosis of ovarian cancer cell line OVCA-433 by inhibiting Livin expression through ultrasound-targeted microbubble destruction.

Methods

We synthesized a targeted microbubble agent for UTMD-mediated shRNA against Livin gene in human ovarian cancer OVCA-433 cells. Lipid microbubbles were conjugated with a luteinizing hormone-releasing hormone analog (LHRHa) by an avidin-biotin linkage to target the ovarian cancer OVCA-433 cells expressing LHRH receptors. The microbubbles were mixed with the recombinant plasmid harboring shRNA-Livin. shRNA-Livin was transfected into OVCA-433 cells upon exposure to 1 MHz pulsed ultrasound beam (0.5 W/cm²) for 8 s. Cell survival was measured by the MTT assay, cell apoptosis by flow cytometry using annexin V/PI double staining, and cell ultrastructure by using the transmission electron microscope. The mRNA and protein expression levels of caspase-3 and caspase-8 were detected by RT-qPCR and western blotting.

Results

UTMD-mediated delivery of shRNA-Livin remarkably reduced the survival of OVCA-433 cells but promoted the apoptosis compared with shRNA-Livin alone, shRNA-Livin plus nontargeted microbubbles, and shRNA-Livin plus LHRHa-conjugated microbubbles containing shRNA-Livin with or without exposure to ultrasound pulses. It was also found that UTMD-mediated delivery of shRNA-Livin notably declined the mRNA and protein expression levels of caspase-3 and caspase-8 in OVCA-433 cells compared with shRNA-Livin alone, shRNA-Livin plus nontargeted microbubbles, and shRNA-Livin plus LHRHa-conjugated microbubbles containing shRNA-Livin with or without exposure to ultrasound pulses.

Conclusion

Our experiment verifies the hypothesis that ultrasound mediation of targeted microbubbles can enhance the transfection efficiency of shRNA-Livin in ovarian cancer cells.

Model constructions of chemosensitivity and prognosis of high grade serous ovarian cancer based on evaluation of immune microenvironment and immune response

BACKGROUND

The prognosis of high grade serous ovarian cancer (HGSOC) patients is closely related to the immune microenvironment and immune response. Based on this, the purpose of this study was to construct a model to predict chemosensitivity and prognosis, and provide novel biomarkers for immunotherapy and prognosis evaluation of HGSOC.

METHODS

GSE40595 (38 samples), GSE18520 (63 samples), GSE26712 (195 samples), TCGA (321 samples) and GTEx (88 samples) were integrated to screen differential expressed genes (DEGs) of HGSOC. The prognosis related DEGs (DEPGs) were screened through overall survival analysis. The DEGs-encoded protein-protein interaction network was constructed and hub genes of DEPGs (DEPHGs) were generated by STRING. Immune characteristics of the samples were judged by ssGSEA, ESTIMATE and CYBERSORT. TIMER was used to analyze the relationship between DEPHGs and tumor-infiltrating immunocytes, as well as the immune checkpoint genes, finally immune-related DEPHGs (IDEPHGs) were determined, and whose expression in 12 pairs of HGSOC tissues and tumor-adjacent tissues were analyzed by histological verification. Furthermore, the chemosensitivity genes in IDEPHGs were screened according to GSE15622 (n = 65). Finally, two prediction models of paclitaxel sensitivity score (PTX score) and carboplatin sensitivity score (CBP score) were constructed by lasso algorithm. The area under curve was calculated to estimate the accuracy of candidate gene models in evaluating chemotherapy sensitivity.

RESULTS

491 DEGs were screened and 37 DEGs were identified as DEPGs, and 11 DEPHGs were further identified. Among them, CXCL13, IDO1, PI3, SPP1 and TRIM22 were screened as IDEPHGs and verified in the human tissues. Further analysis showed that IDO1, PI3 and TRIM22 could independently affect the chemotherapy sensitivity of HGSOC patients. The PTX score was significantly better than TRIM22, PI3, SPP1, IDO1 and CXCL13 in predicting paclitaxel sensitivity, so was CBP score in predicting carboplatin sensitivity. What's more, both of the HGSOC patients with high PTX score or high CBP score had longer survival time.

CONCLUSIONS

Five IDEPHGs identified through comprehensive bioinformatics analysis were closely related with the prognosis, immune microenvironment and chemotherapy sensitivity of HGSOC. Two prediction models based on IDEPHGs might have potential application of chemotherapy sensitivity and prognosis for patients with HGSOC.

LRP1B Expression as a Putative Predictor of Response to Pegylated Liposomal Doxorubicin Treatment in Ovarian Cancer

BACKGROUND

Pegylated liposomal doxorubicin (PLD) is among the most active therapies for recurrent/progressive ovarian cancer (OC). Low-density lipoprotein receptor-related protein 1B (LRP1B) is one of the 10 most significantly deleted genes in human cancers. It mediates endocytosis of several factors from the cellular environment including liposomes. Although the LRP1B role in cancer has not been fully disclosed, its contribution to resistance to liposomal therapies has been hypothesized. This study aimed to evaluate the impact of LRP1B protein as a possible marker of response to PLD in patients with OC.

METHODS

LRP1B expression and response to PLD were analyzed in OC cell lines by qRT-PCR and PrestoBlue viability assay, respectively. LRP1B protein expression was evaluated for the first time, in tumor samples from PLD-treated patients and controls (other chemotherapies) by immunohistochemistry. Association of LRP1B staining score (determined based on intensity and percentage of positively stained cells) with clinicopathological features, response to therapy and survival outcomes was evaluated.

RESULTS

OC cells with increased expression of LRP1B were more sensitive to PLD. LRP1B staining score was associated with clinicopathological features, response to therapy, and survival outcomes. Higher LRP1B levels were associated with prolonged progression-free survival. This association was more evident in patients treated with PLD and in responders to PLD.

CONCLUSION

Our results support a possible role of LRP1B as a predictor of response to PLD in patients with OC.

Identification of a glycolysis-related gene signature for survival prediction of ovarian cancer patients

Background

Ovarian cancer (OV) is deemed the most lethal gynecological cancer in women. The aim of this study was to construct an effective gene prognostic model for predicting overall survival (OS) in patients with OV.

Methods

The expression profiles of glycolysis‐related genes (GRGs) and clinical data of patients with OV were extracted from The Cancer Genome Atlas (TCGA) database. Univariate, multivariate, and least absolute shrinkage and selection operator Cox regression analyses were conducted, and a prognostic signature based on GRGs was constructed. The predictive ability of the signature was analyzed using training and test sets.

Results

A gene risk signature based on nine GRGs (ISG20, CITED2, PYGB, IRS2, ANGPTL4, TGFBI, LHX9, PC, and DDIT4) was identified to predict the survival outcome of patients with OV. The signature showed a good prognostic ability for OV, particularly high‐grade OV, in the TCGA dataset, with areas under the curve (AUC) of 0.709 and 0.762 for 3‐ and 5‐year survival, respectively. Similar results were found in the test sets, and the AUCs of 3‐, 5‐year OS were 0.714 and 0.772 in the combined test set. And our signature was an independent prognostic factor. Moreover, a nomogram combining the prediction model and clinical factors was developed.

Conclusion

Our study established a nine‐GRG risk model and nomogram to better predict OS in patients with OV. The risk model represents a promising and independent prognostic predictor for patients with OV. Moreover, our study on GRGs could offer guidance for the elucidation of underlying mechanisms in future studies.

HOXA9-methylated DNA as a diagnostic biomarker of ovarian malignancy

Aim: In ovarian cancer, methylated HOXA9 (meth-HOXA9) has been proposed as a relevant biomarker, however, its role in the carcinogenic development remains unknown. This study aimed at evaluating meth-HOXA9 as a diagnostic biomarker in ovarian cancer. Materials & methods: The meth-HOXA9 status was examined in 138 tissue specimens encompassing normal ovaries, benign- and borderline tumors, and ovarian cancer using droplet digital PCR. Results: Meth-HOXA9 was detected in 93% (82/88) and 88% (14/16) of ovarian cancer and borderline tumors, respectively. In patients with benign ovarian tumors meth-HOXA9 was detected in 17% (3/18). Using receiver operating characteristic (ROC) analysis meth-HOXA9 had a diagnostic accuracy of 98%. Conclusion: Meth-HOXA9 is highly cancer specific and could serve as a general diagnostic marker of ovarian malignancy.

Transcriptomic Analyses Reveal B-Cell Translocation Gene 2 as a Potential Therapeutic Target in Ovarian Cancer

Ovarian cancer is the most common and aggressive type of tumor of the female reproductive system. Two factors account for this detrimental clinical presentation: (i) the lack of early detection methods and (ii) the inherently aggressive nature of this malignancy. Currently, transcriptomic analyses have become important tools to identify new targets in different cancer types. In this study, by measuring expression levels in ovarian cancer samples and stem cell samples, we identified 24 tumor suppressor genes consistently associated with poor prognosis. Combined results further revealed a potential therapeutic candidate, BTG2, which belongs to the antiproliferative gene family. Our results showed that BTG2 expression regulated ovarian cancer cell proliferation via G1/S phase cell cycle arrest by regulating Cyclin D1, CDK4, p-AKT, and p-ERK expression. BTG2 also inhibited cell migration by modulating MMP-2 and MMP-9 expression. Furthermore, xenograft models confirmed a growth inhibitory effect of BTG2 in ovarian cancer in vivo. BTG2 was significantly associated with ovarian cancer FIGO stage and grade in the clinic. Our findings indicated that BTG2 exerts a suppressive impact on ovarian cancer and could be a potential biomarker.

Malignant Ascites in Ovarian Cancer: Cellular, Acellular, and Biophysical Determinants of Molecular Characteristics and Therapy Response

Ascites refers to the abnormal accumulation of fluid in the peritoneum resulting from an underlying pathology, such as metastatic cancer. Among all cancers, advanced-stage epithelial ovarian cancer is most frequently associated with the production of malignant ascites and is the leading cause of death from gynecologic malignancies. Despite decades of evidence showing that the accumulation of peritoneal fluid portends the poorest outcomes for cancer patients, the role of malignant ascites in promoting metastasis and therapy resistance remains poorly understood. This review summarizes the current understanding of malignant ascites, with a focus on ovarian cancer. The first section provides an overview of heterogeneity in ovarian cancer and the pathophysiology of malignant ascites. Next, analytical methods used to characterize the cellular and acellular components of malignant ascites, as well the role of these components in modulating cell biology, are discussed. The review then provides a perspective on the pressures and forces that tumors are subjected to in the presence of malignant ascites and the impact of physical stress on therapy resistance. Treatment options for malignant ascites, including surgical, pharmacological and photochemical interventions are then discussed to highlight challenges and opportunities at the interface of drug discovery, device development and physical sciences in oncology.

Analysis of HOXA9 methylated ctDNA in ovarian cancer using sense-antisense measurement

DNA promoter methylation is an early event in tumorigenesis and holds promise as a valuable marker in ovarian cancer (OC). It can be measured using circulating tumor specific DNA (ctDNA) isolated from the bloodstream. Sensitivity, however, is a limiting factor of its diagnostic feasibility in OC. DNA methylation analyses are based on bisulfite conversion, resulting in two DNA strands that are no longer complementary. The current standard strategy would then target only one of the double stranded DNA strands, but the potential to increase the sensitivity by targeting both DNA strands is available. In this study, we aimed at evaluating the diagnostic potential of methylated HOXA9 ctDNA in OC by targeting both the DNA sense and antisense strand. Methylated HOXA9 was detected in the plasma of 47/79 (59.5%) patients with newly diagnosed OC using sense-antisense droplet digital PCR. Simultaneous sense-antisense measurement increased the sensitivity by 14.6% (51.9% to 59.5%) as compared to antisense only. In patients with FIGO stage I-II disease the sensitivity was increased by 25%. In conclusion, simultaneous measurement targeting both DNA strands can increase the sensitivity and the analytical approach appears valuable in the diagnostic setting of OC.

Preoperative serum CA-125 level as a predictor for the extent of cytoreduction in patients with advanced stage epithelial ovarian cancer

BACKGROUND

Ovarian cancer is the seventh most common cancer in women worldwide and the eighth most common cause of cancer death. Due to the lack of effective early detection strategies and the unspecific onset of symptoms, it is diagnosed at an advanced stage in 75% of cases. The cancer antigen (CA) 125 is used as a prognostic marker and its level is elevated in more than 85% of women with advanced stages of epithelial ovarian cancer (EOC). The standard treatment is primary debulking surgery (PDS) followed by adjuvant chemotherapy (ACT), but the later approach is neoadjuvant chemotherapy (NACT) followed by interval debulking surgery (IDS). Several studies have been conducted to find out whether preoperative CA-125 serum levels influence treatment choice, surgical resection and survival outcome. The aim of our study was to analyse experience of single institution as Cancer comprehensive center with preoperative usefulness of CA-125.

PATIENTS AND METHODS

At the Institute of Oncology Ljubljana a retrospective analysis of 253 women with stage FIGO IIIC and IV ovarian cancer was conducted. Women were divided into two groups based on their primary treatment. The first group was the NACT group (215 women) and the second the PDS group (38 women). The differences in patient characteristics were compared using the Chi-square test and ANOVA and the Kaplan-Meier method was used for calculating progression-free survival (PFS) and overall survival (OS).

RESULTS

The median serum CA-125 level was higher in the NACT group than in the PDS group, 972 IU/ml and 499 IU/ ml, respectively. The PFS in the NACT group was 8 months (95% CI 6.4-9.5) and 18 months (95% CI 12.5-23.4) in the PDS group. The median OS was lower in the NACT group than in the PDS group, 25 months (95% CI 20.6-29.5) and 46 months (95% CI 32.9-62.1), respectively.

CONCLUSIONS

Preoperative CA-125 cut off value of 500 IU/ml is a promising threshold to predict a successful PDS.

Ovarian Cancer: Biomarkers and Targeted Therapy

Ovarian cancer is one of the most common causes of death in women as survival is highly dependent on the stage of the disease. Ovarian cancer is typically diagnosed in the late stage due to the fact that in the early phases is mostly asymptomatic. Genomic instability is one of the hallmarks of ovarian cancer. While ovarian cancer is stratified into different clinical subtypes, there still exists extensive genetic and progressive diversity within each subtype. Early detection of the disorder is one of the most important steps that facilitate a favorable prognosis and a good response to medical therapy for the patients. In targeted therapies, individual patients are treated by agents targeting the changes in tumor cells that help them grow, divide and spread. Currently, in gynecological malignancies, potential therapeutic targets include tumor-intrinsic signaling pathways, angiogenesis, homologous-recombination deficiency, hormone receptors, and immunologic factors. Ovarian cancer is usually diagnosed in the final stages, partially due to the absence of an effective screening strategy, although, over the times, numerous biomarkers have been studied and used to assess the status, progression, and efficacy of the drug therapy in this type of disorder.

Small Bowel PCI Score as a Prognostic Factor of Ovarian Cancer Patients Undergoing Cytoreductive Surgery (CRS) with Hyperthermic Intraperitoneal Chemotherapy (HIPEC), a Retrospective Analysis of 130 Patients

Ovarian cancer is the leading cause of death among gynecologic malignancies. Combining cytoreductive surgery and hyperthermic intraperitoneal chemotherapy (HIPEC) can benefit patients with advanced ovarian cancer. We evaluate the role of small bowel peritoneal cancer index (sb-PCI) score as a prognostic factor. We retrospectively analyzed characteristics and clinical outcomes of patients that underwent intermediate cytoreductive surgery combined with HIPEC after neoadjuvant chemotherapy and patient's characteristics underwent debulking surgery plus HIPEC for recurrence disease. One hundred thirty patients were included. Eighty-five of them (65.4%) were treated for recurrent ovarian cancer, while 45 (34.6%) underwent intermediate cytoreductive surgery after neoadjuvant chemotherapy with a mean age of 52 years. Mean intraoperative peritoneal cancer index (PCI) was 11.84 with a mean sb-PCI score of 5.57. Univariate analysis revealed that PCI, sb-PCI, and completeness of cytoreduction (CC) were parameters that correlated significantly with overall survival, while after multivariate analysis sb-PCI and CC were identified as independent prognostic factors of survival. A statistically significant correlation between sb-PCI score and overall survival of patients with advanced ovarian cancer was revealed. Further larger future studies are required to confirm our conclusion in order to change the treatment of advanced ovarian cancer patients.

Current update on malignant epithelial ovarian tumors

Epithelial ovarian cancer (EOC) represents the most frequently occurring gynecological malignancy, accounting for more than 70% of ovarian cancer deaths. Preoperative imaging plays an important role in assessing the extent of disease and guides the next step in surgical decision-making and operative planning. In this article, we will review the multimodality imaging features of various subtypes of EOC. We will also discuss the role of imaging in the staging, management, and surveillance of EOC.

Comprehending the Proteomic Landscape of Ovarian Cancer: A Road to the Discovery of Disease Biomarkers

Despite recent technological advancements allowing the characterization of cancers at a molecular level along with biomarkers for cancer diagnosis, the management of ovarian cancers (OC) remains challenging. Proteins assume functions encoded by the genome and the complete set of proteins, termed the proteome, reflects the health state. Comprehending the circulatory proteomic profiles for OC subtypes, therefore, has the potential to reveal biomarkers with clinical utility concerning early diagnosis or to predict response to specific therapies. Furthermore, characterization of the proteomic landscape of tumor-derived tissue, cell lines, and PDX models has led to the molecular stratification of patient groups, with implications for personalized therapy and management of drug resistance. Here, we review single and multiple marker panels that have been identified through proteomic investigations of patient sera, effusions, and other biospecimens. We discuss their clinical utility and implementation into clinical practice.

RNF144A suppresses ovarian cancer stem cell properties and tumor progression through regulation of LIN28B degradation via the ubiquitin-proteasome pathway

OBJECTIVE

Cancer stem cells (CSCs) are the main driving force of tumorigenesis, metastasis, recurrence, and drug resistance in epithelial ovarian cancer (EOC). The current study aimed to explore the regulatory effects of ring finger protein 144A (RNF144A), an E3 ubiquitin ligase, in the maintenance of CSC properties and tumor development in EOC.

METHODS

The expressions of RNF144A in EOC tissue samples and cells were examined. The knockdown or overexpression of a target gene was achieved by transfecting EOC cells with short hairpin RNA or adenoviral vectors. A mouse xenograft model was constructed by inoculating nude mice with EOC cells. Co-immunoprecipitation was used to determine the interaction between RNF144A and LIN28B.

RESULTS

Downregulated RNF144A expression was observed in ovarian tumor tissues and EOC cells. Low RNF144A expression was positively associated with poor survival of EOC patients. RNF144A knockdown significantly enhanced sphere formation and upregulated stem cell markers in EOC cells, while RNF144A overexpression prevented EOC cells from acquiring stem cell properties. Also, the upregulation of RNF144A inhibited ovarian tumor growth and aggressiveness in cell culture and mouse xenografts. Further analysis revealed that RNF144A induced LIN28B degradation through ubiquitination in EOC cells. LIN28B upregulation restored the expressions of stem cell pluripotency-associated transcription factors in EOC cells overexpressing RNF144A.

CONCLUSION

Taken together, our findings highlight the therapeutic potential of restoring RNF144A expression and thereby suppressing LIN28B-associated oncogenic signaling for EOC treatment. • Ring finger protein 144A (RNF144A) is downregulated in epithelial ovarian cancer (EOC) tissues and cell lines. • The overexpression of RNF144A prevents EOC cells from acquiring stem cell properties and inhibits ovarian tumor growth. • RNF144A induces LIN28B degradation through ubiquitination in EOC cells. • LIN28B upregulation restores the expressions of stem cell pluripotency-associated transcription factors in EOC cells overexpressing RNF144A.

Quantitative Mass Spectrometry-Based Proteomics for Biomarker Development in Ovarian Cancer

Ovarian cancer is the most lethal gynecologic malignancy among women. Approximately 70–80% of patients with advanced ovarian cancer experience relapse within five years and develop platinum-resistance. The short life expectancy of patients with platinum-resistant or platinum-refractory disease underscores the need to develop new and more effective treatment strategies. Early detection is a critical step in mitigating the risk of disease progression from early to an advanced stage disease, and protein biomarkers have an integral role in this process. The best biological diagnostic tool for ovarian cancer will likely be a combination of biomarkers. Targeted proteomics methods, including mass spectrometry-based approaches, have emerged as robust methods that can address the chasm between initial biomarker discovery and the successful verification and validation of these biomarkers enabling their clinical translation due to the robust sensitivity, specificity, and reproducibility of these versatile methods. In this review, we provide background information on the fundamental principles of biomarkers and the need for improved treatment strategies in ovarian cancer. We also provide insight into the ways in which mass spectrometry-based targeted proteomics approaches can provide greatly needed solutions to many of the challenges related to ovarian cancer biomarker development.

Screening and Identification of an Immune-Associated lncRNA Prognostic Signature in Ovarian Carcinoma: Evidence from Bioinformatic Analysis

Backgrounds

The dysregulated long noncoding RNAs (lncRNAs) have been described to be crucial regulators in the progression of ovarian carcinoma. The infiltration status of immune cells is also related to the clinical outcomes in ovarian carcinoma. The present research is aimed at constructing an immune-associated lncRNA signature with potential prognostic value for ovarian carcinoma patients.

Methods

We obtained 379 ovarian carcinoma cases with available clinical data and transcriptome data from The Cancer Genome Atlas database to evaluate the infiltration status of immune cells, thereby generating high and low immune cell infiltration groups. According to the expression of the immune-associated lncRNA signature, the risk score of each case was calculated. The high- and low-risk groups were classified using the median risk score as threshold.

Results

A total of 169 immune-associated lncRNAs that differentially expressed in ovarian carcinoma were included. According to the Lasso regression analysis and Cox univariate and multivariate analyses, 5 immune-associated lncRNAs, including AC134312.1, AL133467.1, CHRM3-AS2, LINC01722, and LINC02207, were identified as a predictive signature with significant prognostic value in ovarian carcinoma. The following Kaplan-Meier analysis, ROC analysis, and Cox univariate and multivariate analyses further suggested that the predicted signature may be an independent prognosticator for patients with ovarian carcinoma. The following gene set enrichment analysis showed that this 5 immune-associated lncRNAs signature was significantly related to the hedgehog pathway, basal cell carcinoma, Wnt signaling pathway, cytokine receptor interaction, antigen processing and presentation, and T cell receptor pathway.

Conclusion

: This study suggested a predictive model with 5 immune-associated lncRNAs that has an independent prognostic value for ovarian carcinoma patients.