Biomarkers and algorithms for diagnosis of ovarian cancer: CA125, HE4, RMI and ROMA, a review

Prediction of ovarian cancer using artificial intelligence tools

Purpose

Ovarian cancer is a common type of cancer and a leading cause of death in women. Therefore, accurate and fast prediction of ovarian tumors is crucial. One of the appropriate and precise methods for predicting and diagnosing this cancer is to build a model based on artificial intelligence methods. These methods provide a tool for predicting ovarian cancer according to the characteristics and conditions of each person.

Method

In this study, a data set included records related to 171 cases of benign ovarian tumors, and 178 records related to cases of ovarian cancer were analyzed. The data set contains the records of blood test results and tumor markers of the patients. After data preprocessing, including removing outliers and replacing missing values, the weight of the effective factors was determined using information gain indices and the Gini index. In the next step, predictive models were created using random forest (RF), support vector machine (SVM), decision trees (DT), and artificial neural network (ANN) models. The performance of these models was evaluated using the 10-fold cross-validation method using the indicators of specificity, sensitivity, accuracy, and the area under the receiver operating characteristic curve. Finally, by comparing the performance of the models, the best predictive model of ovarian cancer was selected.

Results

The most important predictive factors were HE4, CA125, and NEU. The RF model was identified as the best predictive model, with an accuracy of more than 86%. The predictive accuracy of DT, SVM, and ANN models was estimated as 82.91%, 85.25%, and 79.35%, respectively. Various artificial intelligence (AI) tools can be used with high accuracy and sensitivity in predicting ovarian cancer.

Conclusion

Therefore, the use of these tools can help specialists and patients with early, easier, and less expensive diagnosis of ovarian cancer. Future studies can leverage AI to integrate image data with serum biomarkers, thereby facilitating the creation of novel models and advancing the diagnosis and treatment of ovarian cancer.

Comprehensive Review of Uterine Leiomyosarcoma: Pathogenesis, Diagnosis, Prognosis, and Targeted Therapy

Uterine leiomyosarcoma (uLMS) is the most common subtype of uterine sarcomas. They have a poor prognosis with high rates of recurrence and metastasis. The five-year survival for uLMS patients is between 25 and 76%, with survival rates approaching 10-15% for patients with metastatic disease at the initial diagnosis. Accumulating evidence suggests that several biological pathways are involved in uLMS pathogenesis. Notably, drugs that block abnormal functions of these pathways remarkably improve survival in uLMS patients. However, due to chemotherapy resistance, there remains a need for novel drugs that can target these pathways effectively. In this review article, we provide an overview of the recent progress in ascertaining the biological functions and regulatory mechanisms in uLMS from the perspective of aberrant biological pathways, including DNA repair, immune checkpoint blockade, protein kinase and intracellular signaling pathways, and the hedgehog pathway. We review the emerging role of epigenetics and epitranscriptome in the pathogenesis of uLMS. In addition, we discuss serum markers, artificial intelligence (AI) combined with machine learning, shear wave elastography, current management and medical treatment options, and ongoing clinical trials for patients with uLMS. Comprehensive, integrated, and deeper insights into the pathobiology and underlying molecular mechanisms of uLMS will help develop novel strategies to treat patients with this aggressive tumor.

An electrochemical immunosensor based on MXene-GQD/AuNPs for the detection of trace amounts of CA-125 as specific tracer of ovarian cancer

An electrochemical immunoassay system was developed to detect CA-125 using a glassy carbon electrode (GCE) modified with MXene, graphene quantum dots (GQDs), and gold nanoparticles (AuNPs). The combined MXene-GQD/AuNPs modification displayed advantageous electrochemical properties due to the synergistic effects of MXene, GQDs, and AuNPs. The MXene-GQD composite in the modified layer provided strong mechanical properties and a large specific surface area. Furthermore, the presence of AuNPs significantly improved conductivity and facilitated the binding of anti-CA-125 on the modified GCE, thereby enhancing sensitivity. Various analytical techniques such as FE-SEM and EDS were utilized to investigate the structural and morphological characteristics as well as the elemental composition. The performance of the developed immunosensor was assessed using electrochemical impedance spectroscopy (EIS), cyclic voltammetry (CV), square wave voltammetry (SWV), and differential pulse voltammetry (DPV). Under optimized conditions in a working potential range of -0.2 to 0.6 V (vs. Ag/AgCl), the sensitivity, linear range (LR), limit of detection (LOD), and correlation coefficient (R2) were determined to be 315.250 µA pU.mL-1/cm2, 0.1 to 1 nU/mL, 0.075 nU/mL, and 0.9855, respectively. The detection of CA-125 in real samples was investigated using the developed immunoassay platform, demonstrating satisfactory results including excellent selectivity and reproducibility.

Diagnostic Utility of Selected Matrix Metalloproteinases (MMP-2, MMP-3, MMP-11, MMP-26), HE4, CA125 and ROMA Algorithm in Diagnosis of Ovarian Cancer

Ovarian cancer (OC) has an unfavorable prognosis. Due to the lack of effective screening tests, new diagnostic methods are being sought to detect OC earlier. The aim of this study was to evaluate the concentration and diagnostic utility of selected matrix metalloproteinases (MMPs) as OC markers in comparison with HE4, CA125 and the ROMA algorithm. The study group consisted of 120 patients with OC; the comparison group consisted of 70 patients with benign lesions and 50 healthy women. MMPs were determined via the ELISA method, HE4 and CA125 by CMIA. Patients with OC had elevated levels of MMP-3 and MMP-11, similar to HE4, CA125 and ROMA values. The highest SE, SP, NPV and PPV values were found for MMP-26, CA125 and ROMA in OC patients. Performing combined analyses of ROMA with selected MMPs increased the values of diagnostic parameters. The topmost diagnostic power of the test was obtained for MMP-26, CA125, HE4 and ROMA and performing combined analyses of MMPs and ROMA enhanced the diagnostic power of the test. The obtained results indicate that the tested MMPs do not show potential as stand-alone OC biomarkers, but can be considered as additional tests to raise the diagnostic utility of the ROMA algorithm.

Diagnosing and staging epithelial ovarian cancer by serum glycoproteomic profiling

BACKGROUND

There is a need for diagnostic tests for screening, triaging and staging of epithelial ovarian cancer (EOC). Glycoproteomics of blood samples has shown promise for biomarker discovery.

METHODS

We applied glycoproteomics to serum of people with EOC or benign pelvic masses and healthy controls. A total of 653 analytes were quantified and assessed in multivariable models, which were tested in an independent cohort. Additionally, we analyzed glycosylation patterns in serum markers and in tissues.

RESULTS

We identified a biomarker panel that distinguished benign lesions from EOC with sensitivity and specificity of 83.5% and 90.1% in the training set, and of 86.7 and 86.7% in the test set, respectively. ROC analysis demonstrated strong performance across a range of cutoffs. Fucosylated multi-antennary glycopeptide markers were higher in late-stage than in early-stage EOC. A comparable pattern was found in late-stage EOC tissues.

CONCLUSIONS

Blood glycopeptide biomarkers have the potential to distinguish benign from malignant pelvic masses, and early- from late-stage EOC. Glycosylation of circulating and tumor tissue proteins may be related. This study supports the hypothesis that blood glycoproteomic profiling can be used for EOC diagnosis and staging and it warrants further clinical evaluation.

Evaluation of serum CA125, HE4 and CA724 and the risk of ovarian malignancy algorithm score in the diagnosis of high-grade serous ovarian cancer

AIM

To develop a new algorithm for the detection of high-grade serous ovarian cancer (HGSOC).

METHODS

Patients diagnosed with HGSOC, borderline ovarian tumours (BOTs) or benign ovarian masses (BOMs) were enrolled between February 2019 and December 2020. Patients with BOTs or BOMs were grouped as non-HGSOC. The cases were divided randomly into a training cohort (two-thirds of cases) and a validation cohort (one-third of cases). Logistic regression was used to find risk factors for HGSOC and to create a new algorithm in the training cohort. Receiver operating characteristic curves were used to compare the diagnostic value of tumour biomarkers. Sensitivity and specificity of tumour markers and the new algorithm were calculated in the training cohort and validation cohort.

RESULTS

This study found significant differences in age; BRCA1/2 mutation status; CA125, CA724 and HE4 levels; and Risk of Ovarian Malignancy Algorithm score between the two groups.Logistic regression analysis showed that CA125 and BRCA1/2 were risk factors for HGSOC. A new algorithm combining CA125 and BRCA1/2 increased the specificity of CA125 for diagnosis of HGSOC. The new algorithm had sensitivity of 81.08% and specificity of 93.10% in the training cohort.

CONCLUSION

The new algorithm using CA125 and BRCA1/2 helped to distinguish between patients with HGSOC and patients with non-HGSOC.

Human epididymis protein 4: Analysis of national health and nutrition examination survey data

BACKGROUND

Human epididymis protein 4 (HE4) is a tumor marker overexpressed in ovarian cancer and is commonly utilized to aid with diagnosis of an adnexal mass. HE4 levels vary based on pregnancy, age, menopausal status, and tobacco use.

OBJECTIVE(S)

The objective of this study was to evaluate population-based data to examine factors that affect HE4 among adult women in the United States and stratify levels of HE4 by demographic and gynecologic factors.

STUDY DESIGN

A retrospective analysis was conducted using data from 2,480 women aged 20 + who participated in the National Health and Nutrition Examination Survey (2001-2002). From these cross-sectional data, serum HE4 and cotinine, a marker of tobacco exposure, were combined with demographic and interview data. Estimated glomerular filtration rates (eGFR) were based on serum creatinine, age, sex, and race. Other variables of interest included menopausal status, pregnancy, and various gynecologic factors. Summary HE4 data are provided as geometric means with associated 95 % confidence intervals.

RESULTS

HE4 levels were independently associated with age, renal function, and nicotine use, all p < 0.001. Pre-menopausal women with a history of endometriosis were found to have elevated HE4 levels compared to those without, p < 0.01; however, we found no such difference among post-menopausal women. Adjusting for age, no differences in HE4 were found based on race/ethnicity, p = 0.29. HE4 levels showed statistically significant associations with income level; however, these were small and clinically irrelevant.

CONCLUSION

This study provides evaluation of HE4 levels among a data set representative of 98.5 million non-institutionalized women in the United States and gives insight into extraneous factors that may influence these levels.

Clinical value of serum tumor markers in assessing the efficacy of neoadjuvant chemotherapy in advanced ovarian cancer: single-center prospective clinical study

Objective

This study aimed to assess the clinical importance of various biomarkers, including NLR, CEA, CA199, CA125, CA153, and HE4, through dynamic testing to evaluate the effectiveness of neoadjuvant chemotherapy (NACT) for individuals facing advanced ovarian cancer. This provides valuable information for tailoring treatment plans to individual patients, thereby leading to a more personalized and effective management of individuals facing ovarian cancer.

Methods

The levels of NLR, CA125, CA199, CEA, CA153, and HE4 were detected before chemotherapy and after 3 courses of chemotherapy. Patients were categorized into ineffective and effective groups according to the effectiveness of NACT. To evaluate the factors influencing NACT's effectiveness in individuals facing advanced ovarian cancer, receiver operating characteristic (ROC) curves, predictive modeling, and multifactorial regression analysis were employed.

Results

In the effective group, the patients' age, maximum tumor diameter, and CEA and HE4 levels of the patients were significantly higher compared to those in the ineffective group (P <.05). Additionally, the difference in HE4 levels before and after treatment between the effective and ineffective groups was statistically significant (P<.05). Multifactorial analysis showed that age and maximum tumor diameter were independent risk factors impacting the effectiveness of NACT in individuals facing advanced ovarian cancer (P<.05). The ROC curve for predicting the effectiveness of NACT in individuals facing advanced ovarian cancer showed a sensitivity of 93.3% for NLR and a specificity of 92.3% for CA199. HE4 emerged as the most reliable predictor, demonstrating a specificity of 84.6% and a sensitivity of 75.3%. The area under the curve of the combined CA125 and HE4 assays for predicting the ineffectiveness of NACT in individuals facing advanced ovarian cancer was 0.825, showcasing a specificity of 74.2% and a sensitivity of 84.6%.

Conclusion

The predictive capacity for the effectiveness of NACT in individuals facing advanced ovarian cancer is notably high when considering the sensitivity of NLR and the specificity of CA199. Additionally, the combination of CA125 and HE4 assays can obtain a better predictive effect, which can accurately select patients suitable for NACT, determine the appropriate timing of the interval debulking surgery (IDS) surgery, and achieve a satisfactory tumor reduction effect.

Dual-Aptamer Recognition of DNA Logic Gate Sensor-Based Specific Exosomal Proteins for Ovarian Cancer Diagnosis

Clinical diagnosis of ovarian cancer lacks high accuracy due to the weak selection of specific biomarkers along with the circumstance biomarkers localization. Clustering analysis of proteins transported on exosomes enables a more precise screening of effective biomarkers. Herein, through bioinformatics analysis of ovarian cancer and exosome proteomes, two coexpressed proteins, EpCAM and CD24, specifically enriched, were identified, together with the development of an as-derived dual-aptamer targeted exosome-based strategy for ovarian cancer screening. In brief, a DNA ternary polymer with aptamers targeting EpCAM and CD24 was designed to present a logic gate reaction upon recognizing ovarian cancer exosomes, triggering a rolling circle amplification chemiluminescent signal. A dynamic detection range of 6 orders of magnitude was achieved by quantifying exosomes. Moreover, for clinical samples, this strategy could accurately differentiate exosomes from healthy persons, other cancer patients, and ovarian cancer patients, enabling promising in situ detection. By accurately selecting biomarkers and constructing a dual-targeted exosomal protein detection strategy, the limitation of insufficient specificity of traditional protein markers was circumvented. This work contributed to the development of exosome-based prognosis monitoring in ovarian cancer through the identification of disease-specific exosome protein markers.

Cancer risk and male Infertility: Unravelling predictive biomarkers and prognostic indicators

In recent years, there has been a global increase in cases of male infertility. There are about 30 million cases of male infertility worldwide and male reproductive health is showing rapid decline in last few decades. It is now recognized as a potential risk factor for developing certain types of cancer, particularly genitourinary malignancies like testicular and prostate cancer. Male infertility is considered a potential indicator of overall health and an early biomarker for cancer. Cases of unexplained male factor infertility have high levels of oxidative stress and oxidative DNA damage and this induces both denovo germ line mutations and epimutations due to build up of 8-hydroxy 2 deoxygunaosine abase which is highly mutagenic and also induces hypomethylation and genomic instability. Consequently, there is growing evidence to explore the various factors contributing to an increased cancer risk. Currently, the available prognostic and predictive biomarkers associated with semen characteristics and cancer risk are limited but gaining significant attention in clinical research for the diagnosis and treatment of elevated cancer risk in the individual and in offspring. The male germ cell being transcriptionally and translationally inert has a highly truncated repair mechanism and has minimal antioxidants and thus most vulnerable to oxidative injury due to environmental factors and unhealthy lifestyle and social habits. Therefore, advancing our understanding requires a thorough evaluation of the pathophysiologic mechanisms at the DNA, RNA, protein, and metabolite levels to identify key biomarkers that may underlie the pathogenesis of male infertility and associated cancer. Advanced methodologies such as genomics, epigenetics, proteomics, transcriptomics, and metabolomics stand at the forefront of cutting-edge approaches for discovering novel biomarkers, spanning from infertility to associated cancer types. Henceforth, in this review, we aim to assess the role and potential of recently identified predictive and prognostic biomarkers, offering insights into the success of assisted reproductive technologies, causes of azoospermia and idiopathic infertility, the impact of integrated holistic approach and lifestyle modifications, and the monitoring of cancer susceptibility, initiation and progression. Comprehending these biomarkers is crucial for providing comprehensive counselling to infertile men and cancer patients, along with their families.

Combination of clinical and spectral-CT iodine concentration for predicting liver metastasis in gastric cancer: a preliminary study

PURPOSE

This study aimed to determine the diagnostic efficacy of various indicators and models for the prediction of gastric cancer with liver metastasis.

METHODS

Clinical and spectral computed tomography (CT) data from 80 patients with gastric adenocarcinoma who underwent surgical resection were retrospectively analyzed. Patients were divided into metastatic and non-metastatic groups based on whether or not to occur liver metastasis, and the region of interest (ROI) was measured manually on each phase iodine map at the largest level of the tumor. Iodine concentration (IC), normalized iodine concentration (nIC), and clinical data of the primary gastric lesions were analyzed. Logistic regression analysis was used to construct the clinical indicator (CI) and clinical indicator-spectral CT iodine concentration (CI-Spectral CT-IC) Models, which contained all of the parameters with statistically significant differences between the groups. Receiver operating characteristic (ROC) curves were constructed to evaluate the accuracy of the models.

RESULTS

The metastatic group showed significantly higher levels of Cancer antigen125 (CA125), carcinoembryonic antigen (CEA), IC, and nIC in the arterial phase, venous phase, and delayed phase than the non-metastatic group (all p < 0.05). Normalized iodine concentration Venous Phase (nICVP) exhibited a favorable performance among all IC and nIC parameters for forecasting gastric cancer with liver metastasis (area under the curve (AUC), 0.846). The combination model of clinical data with significant differences and nICVP showed the best diagnostic accuracy for predicting liver metastasis from gastric cancer, with an AUC of 0.897.

CONCLUSION

nICVP showed the best diagnostic efficacy for predicting gastric cancer with liver metastasis. Clinical Indicators-normalized ICVP model can improve the prediction accuracy for this condition.

Novel frontiers in urogenital cancers: from molecular bases to preclinical models to tailor personalized treatments in ovarian and prostate cancer patients

Over the last few decades, the incidence of urogenital cancers has exhibited diverse trends influenced by screening programs and geographical variations. Among women, there has been a consistent or even increased occurrence of endometrial and ovarian cancers; conversely, prostate cancer remains one of the most diagnosed malignancies, with a rise in reported cases, partly due to enhanced and improved screening efforts.Simultaneously, the landscape of cancer therapeutics has undergone a remarkable evolution, encompassing the introduction of targeted therapies and significant advancements in traditional chemotherapy. Modern targeted treatments aim to selectively address the molecular aberrations driving cancer, minimizing adverse effects on normal cells. However, traditional chemotherapy retains its crucial role, offering a broad-spectrum approach that, despite its wider range of side effects, remains indispensable in the treatment of various cancers, often working synergistically with targeted therapies to enhance overall efficacy.For urogenital cancers, especially ovarian and prostate cancers, DNA damage response inhibitors, such as PARP inhibitors, have emerged as promising therapeutic avenues. In BRCA-mutated ovarian cancer, PARP inhibitors like olaparib and niraparib have demonstrated efficacy, leading to their approval for specific indications. Similarly, patients with DNA damage response mutations have shown sensitivity to these agents in prostate cancer, heralding a new frontier in disease management. Furthermore, the progression of ovarian and prostate cancer is intricately linked to hormonal regulation. Ovarian cancer development has also been associated with prolonged exposure to estrogen, while testosterone and its metabolite dihydrotestosterone, can fuel the growth of prostate cancer cells. Thus, understanding the interplay between hormones, DNA damage and repair mechanisms can hold promise for exploring novel targeted therapies for ovarian and prostate tumors.In addition, it is of primary importance the use of preclinical models that mirror as close as possible the biological and genetic features of patients' tumors in order to effectively translate novel therapeutic findings "from the bench to the bedside".In summary, the complex landscape of urogenital cancers underscores the need for innovative approaches. Targeted therapy tailored to DNA repair mechanisms and hormone regulation might offer promising avenues for improving the management and outcomes for patients affected by ovarian and prostate cancers.

Ovarian Cancer surgical consideration is markedly improved by the neural network powered-MIA3G multivariate index assay

Background

Surgery remains the main treatment option for an adnexal mass suspicious of ovarian cancer. The malignancy rate is, however, only 10-15% in women undergoing surgery. This results in a high number of unnecessary surgeries. A surveillance-based approach is recommended to form the basis for surgical referrals. We have previously reported the clinical performance of MIA3G, a deep neural network-based algorithm, for assessing ovarian cancer risk. In this study, we show that MIA3G markedly improves the surgical selection for women presenting with adnexal masses.

Methods

MIA3G employs seven serum biomarkers, patient age, and menopausal status. Serum samples were collected from 785 women (IQR: 39-55 years) across 12 centers that presented with adnexal masses. MIA3G risk scores were calculated for all subjects in this cohort. Physicians had no access to the MIA3G risk score when deciding upon a surgical referral. The performance of MIA3G for surgery referral was compared to clinical and surgical outcomes. MIA3G was also tested in an independent cohort comprising 29 women across 14 study sites, in which the physicians had access to and utilized MIA3G prior to surgical consideration.

Results

When compared to the actual number of surgeries (n = 207), referrals based on the MIA3G score would have reduced surgeries by 62% (n = 79). The reduction was higher in premenopausal patients (77%) and in patients ≤55 years old (70%). In addition, a 431% improvement in malignancy prediction would have been observed if physicians had utilized MIA3G scores for surgery selection. The accuracy of MIA3G referral was 90.00% (CI 87.89-92.11), while only 9.18% accuracy was observed when the MIA3G score was not used. These results were corroborated in an independent multi-site study of 29 patients in which the physicians utilized MIA3G in surgical consideration. The surgery reduction was 87% in this cohort. Moreover, the accuracy and concordance of MIA3G in this independent cohort were each 96.55%.

Conclusion

These findings demonstrate that MIA3G markedly augments the physician's decisions for surgical intervention and improves malignancy prediction in women presenting with adnexal masses. MIA3G utilization as a clinical diagnostic tool might help reduce unnecessary surgeries.

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.

Mucin-Producing Lobular Breast Carcinoma Metastasis to an Ovarian Fibroma: Histopathological and Immunohistochemical Analysis of a Rare Case and Literature Review

Breast cancer stands as the primary cause of cancer-related mortality among women worldwide, often presenting with distant metastases upon diagnosis. Ovarian metastases originating from breast cancer represent a range of 3-30% of all ovarian neoplasms. Case Report: Herein, we present the histopathological, histochemical, and immunohistochemical findings of a rare case involving mucin-producing lobular breast carcinoma metastasizing to an ovarian fibroma in an 82-year-old female previously diagnosed with lobular breast carcinoma. Histopathological examination of the excised tissues revealed a biphasic neoplasm characterized by tumor cells expressing AE-1/AE-3 cytokeratin, mammaglobin, GCDFP-15, inhibin, and calretinin. Positive mucin staining was observed using histochemical techniques, and reticulin fibers were demonstrated using the Gordon-Sweets technique. A final diagnosis of mucin-producing lobular breast carcinoma metastatic to a benign ovarian fibroma was rendered. Conclusion: The occurrence of metastatic breast carcinoma overlaid on an ovarian tumor represents a rare and diagnostically challenging scenario.

Exploring the Immunological Profile in Breast Cancer: Recent Advances in Diagnosis and Prognosis through Circulating Tumor Cells

This review offers a comprehensive exploration of the intricate immunological landscape of breast cancer (BC), focusing on recent advances in diagnosis and prognosis through the analysis of circulating tumor cells (CTCs). Positioned within the broader context of BC research, it underscores the pivotal role of the immune system in shaping the disease's progression. The primary objective of this investigation is to synthesize current knowledge on the immunological aspects of BC, with a particular emphasis on the diagnostic and prognostic potential offered by CTCs. This review adopts a thorough examination of the relevant literature, incorporating recent breakthroughs in the field. The methodology section succinctly outlines the approach, with a specific focus on CTC analysis and its implications for BC diagnosis and prognosis. Through this review, insights into the dynamic interplay between the immune system and BC are highlighted, with a specific emphasis on the role of CTCs in advancing diagnostic methodologies and refining prognostic assessments. Furthermore, this review presents objective and substantiated results, contributing to a deeper understanding of the immunological complexity in BC. In conclusion, this investigation underscores the significance of exploring the immunological profile of BC patients, providing valuable insights into novel advances in diagnosis and prognosis through the utilization of CTCs. The objective presentation of findings emphasizes the crucial role of the immune system in BC dynamics, thereby opening avenues for enhanced clinical management strategies.

Research progress in endometriosis-associated ovarian cancer

Endometriosis-associated ovarian cancer (EAOC) is a unique subtype of ovarian malignant tumor originating from endometriosis (EMS) malignant transformation, which has gradually become one of the hot topics in clinical and basic research in recent years. According to clinicopathological and epidemiological findings, precancerous lesions of ovarian clear cell carcinoma (OCCC) and ovarian endometrioid carcinoma (OEC) are considered as EMS. Given the large number of patients with endometriosis and its long time window for malignant transformation, sufficient attention should be paid to EAOC. At present, the pathogenesis of EAOC has not been clarified, no reliable biomarkers have been found in the diagnosis, and there is still a lack of basis and targets for stratified management and precise treatment in the treatment. At the same time, due to the long medical history of patients, the fast growth rate of cancer cells, and the possibility of eliminating the earliest endometriosis-associated ovarian cancer, it is difficult to find the corresponding histological evidence. As a result, few patients are finally diagnosed with EAOC, which increases the difficulty of in-depth study of EAOC. This article reviews the epidemiology, pathogenesis, risk factors, clinical diagnosis, new treatment strategies and prognosis of endometriosis-associated ovarian cancer, and prospects the future direction of basic research and clinical transformation, in order to achieve stratified management and personalized treatment of ovarian cancer patients.

Approaches to prevention of gynecological malignancies

Gynecological malignancies represent one of the prevalent diseases in the female sex and prevention is essential to limit their incidence and mortality. Nowadays, not all malignancies benefit from adequate screening methods for this reason new biomarkers and methods are being developed to undertake timely and effective therapies.

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.

Current strategies for early epithelial ovarian cancer detection using miRNA as a potential tool

Ovarian cancer is one of the most aggressive and significant malignant tumor forms in the female reproductive system. It is the leading cause of death among gynecological cancers owing to its metastasis. Since its preliminary disease symptoms are lacking, it is imperative to develop early diagnostic biomarkers to aid in treatment optimization and personalization. In this vein, microRNAs, which are short sequence non-coding molecules, displayed great potential as highly specific and sensitive biomarker. miRNAs have been extensively advocated and proven to serve an instrumental part in the clinical management of cancer, especially ovarian cancer, by promoting the cancer cell progression, invasion, delayed apoptosis, epithelial-mesenchymal transition, metastasis of cancer cells, chemosensitivity and resistance and disease therapy. Here, we cover our present comprehension of the most up-to-date microRNA-based approaches to detect ovarian cancer, as well as current diagnostic and treatment strategies, the role of microRNAs as oncogenes or tumor suppressor genes, and their significance in ovarian cancer progression, prognosis, and therapy.

Establishment of a cholangiocarcinoma risk evaluation model based on mucin expression levels

BACKGROUND

Cholangiocarcinoma (CCA) is a highly malignant cancer, characterized by frequent mucin overexpression. MUC1 has been identified as a critical oncogene in the progression of CCA. However, the comprehensive understanding of how the mucin family influences CCA progression and prognosis is still incomplete.

AIM

To investigate the functions of mucins on the progression of CCA and to establish a risk evaluation formula for stratifying CCA patients.

METHODS

Single-cell RNA sequencing data from 14 CCA samples were employed for elucidating the roles of mucins, complemented by bioinformatic analyses. Subsequent validations were conducted through spatial transcriptomics and immunohistochemistry. The construction of a risk evaluation model utilized the least absolute shrinkage and selection operator regression algorithm, which was further confirmed by independent cohorts and diverse data types.

RESULTS

CCA tumor cells with elevated levels of MUC1 and MUC4 showed activated nucleotide metabolic pathways and increased invasiveness. MUC5AC-high cells were found to promote CCA progression through WNT signaling. MUC5B-high cells exhibited robust cellular oxidation activities, leading to resistance against antitumoral treatments. MUC13-high cells were observed to secret chemokines, recruiting and transforming macrophages into the M2-polarized state, thereby suppressing antitumor immunity. MUC16-high cells were found to promote tumor progression through interleukin-1/nuclear factor kappa-light-chain-enhancer of activated B cells signaling upon interaction with neutrophils. Utilizing the expression levels of these mucins, a risk factor evaluation formula for CCA was developed and validated across multiple cohorts. CCA samples with higher risk factors exhibited stronger metastatic potential, chemotherapy resistance, and poorer prognosis.

CONCLUSION

Our study elucidates the functional mechanisms through which mucins contribute to CCA development, and provides tools for risk stratification in CCA.

Bayesian and deep-learning models applied to the early detection of ovarian cancer using multiple longitudinal biomarkers

BACKGROUND

Ovarian cancer is the most lethal of all gynecological cancers. Cancer Antigen 125 (CA125) is the best-performing ovarian cancer biomarker which however is still not effective as a screening test in the general population. Recent literature reports additional biomarkers with the potential to improve on CA125 for early detection when using longitudinal multimarker models.

METHODS

Our data comprised 180 controls and 44 cases with serum samples sourced from the multimodal arm of UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS). Our models were based on Bayesian change-point detection and recurrent neural networks.

RESULTS

We obtained a significantly higher performance for CA125-HE4 model using both methodologies (AUC 0.971, sensitivity 96.7% and AUC 0.987, sensitivity 96.7%) with respect to CA125 (AUC 0.949, sensitivity 90.8% and AUC 0.953, sensitivity 92.1%) for Bayesian change-point model (BCP) and recurrent neural networks (RNN) approaches, respectively. One year before diagnosis, the CA125-HE4 model also ranked as the best, whereas at 2 years before diagnosis no multimarker model outperformed CA125.

CONCLUSIONS

Our study identified and tested different combination of biomarkers using longitudinal multivariable models that outperformed CA125 alone. We showed the potential of multivariable models and candidate biomarkers to increase the detection rate of ovarian cancer.

Effects of silencing hsa_circ_0015326 on proliferation, migration, invasion, and apoptosis of epithelial ovarian cancer cells

Although the treatment of ovarian cancer has made great progress, there are still many patients who are not timely detected and given targeted therapy due to unknown pathogenesis. Recent studies have found that hsa_circ_0015326 is upregulated in ovarian cancer and is involved in the proliferation, invasion, and migration of ovarian cancer cells. However, whether hsa_circ_0015326 can be used as a new target of ovarian cancer needs further investigation. Therefore, the effect of hsa_circ_0015326 on epithelial ovarian cancer was investigated in this study. At first, si-hsa_circ_0015326 lentivirus was transfected into epithelial ovarian cancer cells. Then real-time fluorescence quantitative PCR (qRT-PCR) was used to detect hsa_circ_0015326 level. The proliferation of ovarian cancer cells was detected by CCK-8 assay. The horizontal and vertical migration abilities of the cells were detected by wound-healing assay and Transwell assay, respectively. Transwell assay was also used to determine the invasion rate. As for the apoptosis rate, it was assessed by flow cytometry. As a result, the expression level of hsa_circ_0015326 in A2780 and SKOV3 was found to be higher than that in IOSE-80. However, after transfecting si-hsa_circ_0015326 and si-NC into the cells, the proliferation, migration, and invasion abilities of A2780 and SKOV3 cells in the si-hsa_circ_0015326 group were significantly reduced in comparison to those in the si-NC and mock groups, while their apoptosis rates were elevated. Collectively, silencing hsa_circ_0015326 bears the capability of inhibiting the proliferation, migration, and invasion of ovarian cancer cells while increasing apoptosis rate. It can be concluded that hsa_circ_0015326 promotes the malignant biological activities of epithelial ovarian cancer cells.

Decoding the glycoproteome: a new frontier for biomarker discovery in cancer

Cancer early detection and treatment response prediction continue to pose significant challenges. Cancer liquid biopsies focusing on detecting circulating tumor cells (CTCs) and DNA (ctDNA) have shown enormous potential due to their non-invasive nature and the implications in precision cancer management. Recently, liquid biopsy has been further expanded to profile glycoproteins, which are the products of post-translational modifications of proteins and play key roles in both normal and pathological processes, including cancers. The advancements in chemical and mass spectrometry-based technologies and artificial intelligence-based platforms have enabled extensive studies of cancer and organ-specific changes in glycans and glycoproteins through glycomics and glycoproteomics. Glycoproteomic analysis has emerged as a promising tool for biomarker discovery and development in early detection of cancers and prediction of treatment efficacy including response to immunotherapies. These biomarkers could play a crucial role in aiding in early intervention and personalized therapy decisions. In this review, we summarize the significant advance in cancer glycoproteomic biomarker studies and the promise and challenges in integration into clinical practice to improve cancer patient care.

Use of Sensor Array Analysis to Detect Ovarian Cancer through Breath, Urine, and Blood: A Case-Control Study

Ovarian cancer (OC) is the eighth most common cancer in women. Since screening programs do not exist, it is often diagnosed in advanced stages. Today, the detection of OC is based on clinical examination, transvaginal ultrasound (US), and serum biomarker (Carbohydrate Antigen 125 (CA 125) and Human Epididymis Protein 4 (HE4)) dosage, with a sensitivity of 88% and 95%, respectively, and a specificity of 84% for US and 76% for biomarkers. These methods are clearly not enough, and OC in its early stages is often missed. Many scientists have recently focused their attention on volatile organic compounds (VOCs). These are gaseous molecules, found in the breath, that could provide interesting information on several diseases, including solid tumors. To detect VOCs, an electronic nose was invented by a group of researchers. A similar device, the e-tongue, was later created to detect specific molecules in liquids. For the first time in the literature, we investigated the potential use of the electronic nose and the electronic tongue to detect ovarian cancer not just from breath but also from urine, blood, and plasma samples.

Artificial intelligence-based models enabling accurate diagnosis of ovarian cancer using laboratory tests in China: a multicentre, retrospective cohort study

BACKGROUND

Ovarian cancer is the most lethal gynecological malignancy. Timely diagnosis of ovarian cancer is difficult due to the lack of effective biomarkers. Laboratory tests are widely applied in clinical practice, and some have shown diagnostic and prognostic relevance to ovarian cancer. We aimed to systematically evaluate the value of routine laboratory tests on the prediction of ovarian cancer, and develop a robust and generalisable ensemble artificial intelligence (AI) model to assist in identifying patients with ovarian cancer.

METHODS

In this multicentre, retrospective cohort study, we collected 98 laboratory tests and clinical features of women with or without ovarian cancer admitted to three hospitals in China during Jan 1, 2012 and April 4, 2021. A multi-criteria decision making-based classification fusion (MCF) risk prediction framework was used to make a model that combined estimations from 20 AI classification models to reach an integrated prediction tool developed for ovarian cancer diagnosis. It was evaluated on an internal validation set (3007 individuals) and two external validation sets (5641 and 2344 individuals). The primary outcome was the prediction accuracy of the model in identifying ovarian cancer.

FINDINGS

Based on 52 features (51 laboratory tests and age), the MCF achieved an area under the receiver-operating characteristic curve (AUC) of 0·949 (95% CI 0·948-0·950) in the internal validation set, and AUCs of 0·882 (0·880-0·885) and 0·884 (0·882-0·887) in the two external validation sets. The model showed higher AUC and sensitivity compared with CA125 and HE4 in identifying ovarian cancer, especially in patients with early-stage ovarian cancer. The MCF also yielded acceptable prediction accuracy with the exclusion of highly ranked laboratory tests that indicate ovarian cancer, such as CA125 and other tumour markers, and outperformed state-of-the-art models in ovarian cancer prediction. The MCF was wrapped as an ovarian cancer prediction tool, and made publicly available to provide estimated probability of ovarian cancer with input laboratory test values.

INTERPRETATION

The MCF model consistently achieved satisfactory performance in ovarian cancer prediction when using laboratory tests from the three validation sets. This model offers a low-cost, easily accessible, and accurate diagnostic tool for ovarian cancer. The included laboratory tests, not only CA125 which was the highest ranked laboratory test in importance of diagnostic assistance, contributed to the characterisation of patients with ovarian cancer.

FUNDING

Ministry of Science and Technology of China; National Natural Science Foundation of China; Natural Science Foundation of Guangdong Province, China; and Science and Technology Project of Guangzhou, China.

TRANSLATION

For the Chinese translation of the abstract see Supplementary Materials section.

Unlocking the potential of oncology biomarkers: advancements in clinical theranostics

INTRODUCTION

Cancer biomarkers have revolutionized the field of oncology by providing valuable insights into tumor changes and aiding in screening, diagnosis, prognosis, treatment prediction, and risk assessment. The emergence of "omic" technologies has enabled biomarkers to become reliable and accurate predictors of outcomes during cancer treatment.

CONTENT

In this review, we highlight the clinical utility of biomarkers in cancer identification and motivate researchers to establish a personalized/precision approach in oncology. By extending a multidisciplinary technology-based approach, biomarkers offer an alternative to traditional techniques, fulfilling the goal of cancer therapeutics to find a needle in a haystack.

SUMMARY AND OUTLOOK

We target different forms of cancer to establish a dynamic role of biomarkers in understanding the spectrum of malignancies and their biochemical and molecular characterization, emphasizing their prospective contribution to cancer screening. Biomarkers offer a promising avenue for the early detection of human cancers and the exploration of novel technologies to predict disease severity, facilitating maximum survival and minimum mortality rates. This review provides a comprehensive overview of the potential of biomarkers in oncology and highlights their prospects in advancing cancer diagnosis and treatment.

Early Ovarian Cancer Detection in the Age of Fallopian Tube Precursors: A Systematic Review

OBJECTIVE

To determine biomarkers other than CA 125 that could be used in identifying early-stage ovarian cancer.

DATA SOURCES

Ovid MEDLINE ALL, EMBASE, Web of Science Core Collection, ScienceDirect, Clinicaltrials.gov , and CAB Direct were searched for English-language studies between January 2008 and April 2023 for the concepts of high-grade serous ovarian cancer, testing, and prevention or early diagnosis.

METHODS OF STUDY SELECTION

The 5,523 related articles were uploaded to Covidence. Screening by two independent reviewers of the article abstracts led to the identification of 245 peer-reviewed primary research articles for full-text review. Full-text review by those reviewers led to the identification of 131 peer-reviewed primary research articles used for this review.

TABULATION, INTEGRATION, AND RESULTS

Of 131 studies, only 55 reported sensitivity, specificity, or area under the curve (AUC), with 36 of the studies reporting at least one biomarker with a specificity of 80% or greater specificity or 0.9 or greater AUC.

CONCLUSION

These findings suggest that although many types of biomarkers are being tested in ovarian cancer, most have similar or worse detection rates compared with CA 125 and have the same limitations of poor detection rates in early-stage disease. However, 27.5% of articles (36/131) reported biomarkers with better sensitivity and an AUC greater than 0.9 compared with CA 125 alone and deserve further exploration.

Synergistic suppression of ovarian cancer by combining NRF2 and GPX4 inhibitors: in vitro and in vivo evidence

Ovarian cancer is a significant challenge in women's health due to the lack of effective screening and diagnostic methods, often leading to late detection and the highest mortality rate among all gynecologic tumors worldwide. Recent research has shown that ovarian cancer has an "iron addiction" phenotype which makes it vulnerable to ferroptosis inducers. We tested the combination of NRF2-targeted inhibitors with GPX4-targeted inhibitors in ovarian cancer through in vitro and in vivo experiment. The data showed that combination treatment effectively suppressed adherent cell growth, inhibited suspended cell spheroid formation, and restrained the ability of spheroid formation in 3D-culture. Mechanistically, the combination induced accumulation of ROS, 4-HNE, as well as activation of caspase-3 which indicates that this combination simultaneously increases cell ferroptosis and apoptosis. Notably, inhibition of GPX4 or NRF2 can suppress ovarian cancer spreading and growth in the peritoneal cavity of mice, while the combination of NRF2 inhibitor ML385 with GPX4 inhibitors showed a significant synergistic effect compared to individual drug treatment in a syngeneic mouse ovarian cancer model. Overall, these findings suggest that combining NRF2 inhibitors with GPX4 inhibitors results in a synergy suppression of ovarian cancer in vitro and in vivo, and maybe a promising therapeutic strategy for the treatment of ovarian cancer.

Paradigm Shift: A Comprehensive Review of Ovarian Cancer Management in an Era of Advancements

Ovarian cancer (OC) is the female genital malignancy with the highest lethality. Patients present a poor prognosis mainly due to the late clinical presentation allied with the common acquisition of chemoresistance and a high rate of tumour recurrence. Effective screening, accurate diagnosis, and personalised multidisciplinary treatments are crucial for improving patients' survival and quality of life. This comprehensive narrative review aims to describe the current knowledge on the aetiology, prevention, diagnosis, and treatment of OC, highlighting the latest significant advancements and future directions. Traditionally, OC treatment involves the combination of cytoreductive surgery and platinum-based chemotherapy. Although more therapeutical approaches have been developed, the lack of established predictive biomarkers to guide disease management has led to only marginal improvements in progression-free survival (PFS) while patients face an increasing level of toxicity. Fortunately, because of a better overall understanding of ovarian tumourigenesis and advancements in the disease's (epi)genetic and molecular profiling, a paradigm shift has emerged with the identification of new disease biomarkers and the proposal of targeted therapeutic approaches to postpone disease recurrence and decrease side effects, while increasing patients' survival. Despite this progress, several challenges in disease management, including disease heterogeneity and drug resistance, still need to be overcome.

Noninvasive serum N-glycans associated with ovarian cancer diagnosis and precancerous lesion prediction

BACKGROUND

Ovarian cancer (OC) is one of the most common gynecological tumors with high morbidity and mortality. Altered serum N-glycome has been observed in many diseases, while the association between serum protein N-glycosylation and OC progression remains unclear, particularly for the onset of carcinogenesis from benign neoplasms to cancer.

METHODS

Herein, a mass spectrometry based high-throughput technique was applied to characterize serum N-glycome profile in individuals with healthy controls, benign neoplasms and different stages of OC. To elucidate the alterations of glycan features in OC progression, an orthogonal strategy with lectin-based ELISA was performed.

RESULTS

It was observed that the initiation and development of OC was associated with increased high-mannosylationand agalactosylation, concurrently with decreased total sialylation of serum, each of which gained at least moderately accurate merits. The most important individual N-glycans in each glycan group was H7N2, H3N5 and H5N4S2F1, respectively. Notably, serum N-glycome could be used to accurately discriminate OC patients from benign cohorts, with a comparable or even higher diagnostic score compared to CA125 and HE4. Furthermore, bioinformatics analysis based discriminative model verified the diagnostic performance of serum N-glycome for OC in two independent sets.

CONCLUSIONS

These findings demonstrated the great potential of serum N-glycome for OC diagnosis and precancerous lesion prediction, paving a new way for OC screening and monitoring.

Tandem SERS and MS/MS Profiling of Plasma Extracellular Vesicles for Early Ovarian Cancer Biomarker Discovery

Extracellular vesicles (EVs) are vectors of biomolecular cargo that play essential roles in intercellular communication across a range of cells. Protein, lipid, and nucleic acid cargo harbored within EVs may serve as biomarkers at all stages of disease; however, the choice of methodology may challenge the specificity and reproducibility of discovery. To address these challenges, the integration of rigorous EV purification methods, cutting-edge spectroscopic technologies, and data analysis are critical to uncover diagnostic signatures of disease. Herein, we demonstrate an EV isolation and analysis pipeline using surface-enhanced Raman spectroscopy (SERS) and mass spectrometry (MS) techniques on plasma samples obtained from umbilical cord blood, healthy donor (HD) plasma, and plasma from women with early stage high-grade serous carcinoma (HGSC). Plasma EVs were purified by size exclusion chromatography and analyzed by surface-enhanced Raman spectroscopy (SERS), mass spectrometry (MS), and atomic force microscopy. After determining the fraction of highest EV purity, SERS and MS were used to characterize EVs from HDs, pooled donors with noncancerous gynecological ailments (n = 6), and donors with early stage [FIGO (I/II)] with HGSC. SERS spectra were subjected to different machine learning algorithms such as PCA, logistic regression, support vector machine, naïve Bayes, random forest, neural network, and k nearest neighbors to differentiate healthy, benign, and HGSC EVs. Collectively, we demonstrate a reproducible workflow with the potential to serve as a diagnostic platform for HGSC.

Heart transplantation and biomarkers: a review about their usefulness in clinical practice

Advanced heart failure (AdvHF) can only be treated definitively by heart transplantation (HTx), yet problems such right ventricle dysfunction (RVD), rejection, cardiac allograft vasculopathy (CAV), and primary graft dysfunction (PGD) are linked to a poor prognosis. As a result, numerous biomarkers have been investigated in an effort to identify and prevent certain diseases sooner. We looked at both established biomarkers, such as NT-proBNP, hs-troponins, and pro-inflammatory cytokines, and newer ones, such as extracellular vesicles (EVs), donor specific antibodies (DSA), gene expression profile (GEP), donor-derived cell free DNA (dd-cfDNA), microRNA (miRNA), and soluble suppression of tumorigenicity 2 (sST2). These biomarkers are typically linked to complications from HTX. We also highlight the relationships between each biomarker and one or more problems, as well as their applicability in routine clinical practice.

Quantification of putative ovarian cancer serum protein biomarkers using a multiplexed targeted mass spectrometry assay

BACKGROUND

Ovarian cancer is the most lethal gynecologic malignancy in women, and high-grade serous ovarian cancer (HGSOC) is the most common subtype. Currently, no clinical test has been approved by the FDA to screen the general population for ovarian cancer. This underscores the critical need for the development of a robust methodology combined with novel technology to detect diagnostic biomarkers for HGSOC in the sera of women. Targeted mass spectrometry (MS) can be used to identify and quantify specific peptides/proteins in complex biological samples with high accuracy, sensitivity, and reproducibility. In this study, we sought to develop and conduct analytical validation of a multiplexed Tier 2 targeted MS parallel reaction monitoring (PRM) assay for the relative quantification of 23 putative ovarian cancer protein biomarkers in sera.

METHODS

To develop a PRM method for our target peptides in sera, we followed nationally recognized consensus guidelines for validating fit-for-purpose Tier 2 targeted MS assays. The endogenous target peptide concentrations were calculated using the calibration curves in serum for each target peptide. Receiver operating characteristic (ROC) curves were analyzed to evaluate the diagnostic performance of the biomarker candidates.

RESULTS

We describe an effort to develop and analytically validate a multiplexed Tier 2 targeted PRM MS assay to quantify candidate ovarian cancer protein biomarkers in sera. Among the 64 peptides corresponding to 23 proteins in our PRM assay, 24 peptides corresponding to 16 proteins passed the assay validation acceptability criteria. A total of 6 of these peptides from insulin-like growth factor-binding protein 2 (IBP2), sex hormone-binding globulin (SHBG), and TIMP metalloproteinase inhibitor 1 (TIMP1) were quantified in sera from a cohort of 69 patients with early-stage HGSOC, late-stage HGSOC, benign ovarian conditions, and healthy (non-cancer) controls. Confirming the results from previously published studies using orthogonal analytical approaches, IBP2 was identified as a diagnostic biomarker candidate based on its significantly increased abundance in the late-stage HGSOC patient sera compared to the healthy controls and patients with benign ovarian conditions.

CONCLUSIONS

A multiplexed targeted PRM MS assay was applied to detect candidate diagnostic biomarkers in HGSOC sera. To evaluate the clinical utility of the IBP2 PRM assay for HGSOC detection, further studies need to be performed using a larger patient cohort.

OvaPrint-A Cell-free DNA Methylation Liquid Biopsy for the Risk Assessment of High-grade Serous Ovarian Cancer

PURPOSE

High-grade serous ovarian carcinoma (HGSOC) is the most lethal epithelial ovarian cancer (EOC) and is often diagnosed at late stage. In women with a known pelvic mass, surgery followed by pathologic assessment is the most reliable way to diagnose EOC and there are still no effective screening tools in asymptomatic women. In the current study, we developed a cell-free DNA (cfDNA) methylation liquid biopsy for the risk assessment of early-stage HGSOC.

EXPERIMENTAL DESIGN

We performed reduced representation bisulfite sequencing to identify differentially methylated regions (DMR) between HGSOC and normal ovarian and fallopian tube tissue. Next, we performed hybridization probe capture for 1,677 DMRs and constructed a classifier (OvaPrint) on an independent set of cfDNA samples to discriminate HGSOC from benign masses. We also analyzed a series of non-HGSOC EOC, including low-grade and borderline samples to assess the generalizability of OvaPrint. A total of 372 samples (tissue n = 59, plasma n = 313) were analyzed in this study.

RESULTS

OvaPrint achieved a positive predictive value of 95% and a negative predictive value of 88% for discriminating HGSOC from benign masses, surpassing other commercial tests. OvaPrint was less sensitive for non-HGSOC EOC, albeit it may have potential utility for identifying low-grade and borderline tumors with higher malignant potential.

CONCLUSIONS

OvaPrint is a highly sensitive and specific test that can be used for the risk assessment of HGSOC in symptomatic women. Prospective studies are warranted to validate OvaPrint for HGSOC and further develop it for non-HGSOC EOC histotypes in both symptomatic and asymptomatic women with adnexal masses.

Prediction model of adnexal masses with complex ultrasound morphology

Background

Based on the ovarian-adnexal reporting and data system (O-RADS), we constructed a nomogram model to predict the malignancy potential of adnexal masses with sophisticated ultrasound morphology.

Methods

In a multicenter retrospective study, a total of 430 subjects with masses were collected in the adnexal region through an electronic medical record system at the Fourth Hospital of Harbin Medical University during the period of January 2019-April 2023. A total of 157 subjects were included in the exception validation cohort from Harbin Medical University Tumor Hospital. The pathological tumor findings were invoked as the gold standard to classify the subjects into benign and malignant groups. All patients were randomly allocated to the validation set and training set in a ratio of 7:3. A stepwise regression analysis was utilized for filtering variables. Logistic regression was conducted to construct a nomogram prediction model, which was further validated in the training set. The forest plot, C-index, calibration curve, and clinical decision curve were utilized to verify the model and assess its accuracy and validity, which were further compared with existing adnexal lesion models (O-RADS US) and assessments of different types of neoplasia in the adnexa (ADNEX).

Results

Four predictors as independent risk factors for malignancy were followed in the preparation of the diagnostic model: O-RADS classification, HE4 level, acoustic shadow, and protrusion blood flow score (all p < 0.05). The model showed moderate predictive power in the training set with a C-index of 0.959 (95%CI: 0.940-0.977), 0.929 (95%CI: 0.884-0.974) in the validation set, and 0.892 (95%CI: 0.843-0.940) in the external validation set. It showed that the predicted consequences of the nomogram agreed well with the actual results of the calibration curve, and the novel nomogram was clinically beneficial in decision curve analysis.

Conclusion

The risk of the nomogram of adnexal masses with complex ultrasound morphology contained four characteristics that showed a suitable predictive ability and provided better risk stratification. Its diagnostic performance significantly exceeded that of the ADNEX model and O-RADS US, and its screening performance was essentially equivalent to that of the ADNEX model and O-RADS US classification.

Exosomal Cargo in Ovarian Cancer Dissemination

Ovarian cancer (OC) has the highest mortality rate among all gynecologic cancers and is characterized by early peritoneal spread. The growth and development of OC are associated with the formation of ascitic fluid, creating a unique tumor microenvironment. Understanding the mechanisms of tumor progression is crucial in identifying new diagnostic biomarkers and developing novel therapeutic strategies. Exosomes, lipid bilayer vesicles measuring 30-150 nm in size, are known to establish a crucial link between malignant cells and their microenvironment. Additionally, the confirmed involvement of exosomes in carcinogenesis enables them to mediate the invasion, migration, metastasis, and angiogenesis of tumor cells. Functionally active non-coding RNAs (such as microRNAs, long non-coding RNAs, circRNAs), proteins, and lipid rafts transported within exosomes can activate numerous signaling pathways and modify gene expression. This review aims to expand our understanding of the role of exosomes and their contents in OC carcinogenesis processes such as epithelial-mesenchymal transition (EMT), angiogenesis, vasculogenic mimicry, tumor cell proliferation, and peritoneal spread. It also discusses the potential for utilizing exosomal cargo to develop novel "liquid biopsy" biomarkers for early OC diagnosis.

Advances in screening and diagnostic lab-on-chip tools for gynaecological cancers - a review

Gynaecological cancers are a major global health concern due to the lack of effective screening programmes for ovarian and endometrial cancer, for example, and variable access to vaccination and screening tests for cervical cancer in many countries. Recent research on portable and cost-effective lab-on-a-chip (LoC) technologies show promise for mass screening and diagnostic procedures for gynaecological cancers. However, most LoCs for gynaecological cancer are still in development, with a need to establish and clinically validate factors such as the type of biomarker, sample and method of detection, before patient use. Multiplex approaches, detecting a panel of gynaecological biomarkers in a single LoC, offer potential for more reliable diagnosis. This review highlights the current research on LoCs for gynaecological cancer screening and diagnosis, emphasizing the need for further research and validation prior to their widespread adoption in clinical practice.

Aptamers as Potential Therapeutic Tools for Ovarian Cancer: Advancements and Challenges

Ovarian cancer (OC) is the most common lethal gynecologic cause of death in women worldwide, with a high mortality rate and increasing incidence. Despite advancements in the treatment, most OC patients still die from their disease due to late-stage diagnosis, the lack of effective diagnostic methods, and relapses. Aptamers, synthetic, short single-stranded oligonucleotides, have emerged as promising anticancer therapeutics. Their ability to selectively bind to target molecules, including cancer-related proteins and receptors, has revolutionized drug discovery and biomarker identification. Aptamers offer unique insights into the molecular pathways involved in cancer development and progression. Moreover, they show immense potential as drug delivery systems, enabling targeted delivery of therapeutic agents to cancer cells while minimizing off-target effects and reducing systemic toxicity. In the context of OC, the integration of aptamers with non-coding RNAs (ncRNAs) presents an opportunity for precise and efficient gene targeting. Additionally, the conjugation of aptamers with nanoparticles allows for accurate and targeted delivery of ncRNAs to specific cells, tissues, or organs. In this review, we will summarize the potential use and challenges associated with the use of aptamers alone or aptamer-ncRNA conjugates, nanoparticles, and multivalent aptamer-based therapeutics for the treatment of OC.

Value of ultrasound and magnetic resonance imaging combined with tumor markers in the diagnosis of ovarian tumors

BACKGROUND

Compare the diagnostic performance of ultrasound (US), magnetic resonance imaging (MRI), and serum tumor markers alone or in combination for detecting ovarian tumors.

AIM

To investigate the diagnostic value of US, MRI combined with tumor markers in ovarian tumors.

METHODS

The data of 110 patients with ovarian tumors, confirmed by surgery and pathology, were collected in our hospital from February 2018 to May 2023. The dataset included 60 cases of benign tumors and 50 cases of malignant tumors. Prior to surgery, all patients underwent preoperative US and MRI examinations, as well as serum tumor marker tests [carbohydrate antigen 125 (CA125), human epididymis protein 4 (HE4)]. The aim of the study was to compare the diagnostic performance of these three methods individually and in combination for ovarian tumors.

RESULTS

This study found statistically significant differences in the ultrasonic imaging characteristics between benign and malignant tumors. These differences include echo characteristics, presence or absence of a capsule, blood flow resistance index, clear tumor shape, and blood flow signal display rate (P < 0.05). The apparent diffusion coefficient values of the solid and cystic parts in benign tumors were found to be higher compared to malignant tumors (P < 0.05). Additionally, the time-intensity curve image features of benign and malignant tumors showed significant statistical differences (P < 0.05). The levels of serum CA125 and HE4 in benign tumors were lower than those in malignant tumors (P < 0.05). The combined use of US, MRI, and tumor markers in the diagnosis of ovarian tumors demonstrates higher accuracy, sensitivity, and specificity compared to using each method individually (P < 0.05).

CONCLUSION

US, MRI, and tumor markers each have their own advantages and disadvantages when it comes to diagnosing ovarian tumors. However, by combining these three methods, we can significantly enhance the accuracy of ovarian tumor diagnosis, enabling early detection and identification of the tumor’s nature, and providing valuable guidance for clinical treatment.

Development and validation of radiomics nomograms for preoperative prediction of characteristics in non-small cell lung cancer and circulating tumor cells

To develop and validate 3 radiomics nomograms for preoperative prediction of pathological and progression diagnosis in non-small cell lung cancer (NSCLC) as well as circulating tumor cells (CTCs). A total of 224 and 134 patients diagnosed with NSCLC were respectively gathered in 2018 and 2019 in this study. There were totally 1197 radiomics features that were extracted and quantified from the images produced by computed tomography. Then we selected the radiomics features with predictive value by least absolute shrinkage and selection operator and combined them into radiomics signature. Logistic regression models were built using radiomics signature as the only predictor, which were then converted to nomograms for individualized predictions. Finally, the performance of the nomograms was assessed on both cohorts. Additionally, immunohistochemical correlation analysis was also performed. As for discrimination, the area under the curve of pathological diagnosis nomogram and progression diagnosis nomogram in NSCLC were both higher than 90% in the training cohort and higher than 80% in the validation cohort. The performance of the CTC-diagnosis nomogram was somehow unexpected where the area under the curve were range from 60% to 70% in both cohorts. As for calibration, nonsignificant statistics (P > .05) yielded by Hosmer-Lemeshow tests suggested no departure between model prediction and perfect fit. Additionally, decision curve analyses demonstrated the clinically usefulness of the nomograms. We developed radiomics-based nomograms for pathological, progression and CTC diagnosis prediction in NSCLC respectively. Nomograms for pathological and progression diagnosis were demonstrated well-performed to facilitate the individualized preoperative prediction, while the nomogram for CTC-diagnosis prediction needed improvement.

A Novel Predictive Multi-Marker Test for the Pre-Surgical Identification of Ovarian Cancer

Ovarian cancer remains the most lethal of gynecological malignancies, with the 5-year survival below 50%. Currently there is no simple and effective pre-surgical diagnosis or triage for patients with malignancy, particularly those with early-stage or low-volume tumors. Recently we discovered that CXCL10 can be processed to an inactive form in ovarian cancers and that its measurement has diagnostic significance. In this study we evaluated the addition of processed CXCL10 to a biomarker panel for the discrimination of benign from malignant disease. Multiple biomarkers were measured in retrospectively collected plasma samples (n = 334) from patients diagnosed with benign or malignant disease, and a classifier model was developed using CA125, HE4, Il6 and CXCL10 (active and total). The model provided 95% sensitivity/95% specificity for discrimination of benign from malignant disease. Positive predictive performance exceeded that of "gold standard" scoring systems including CA125, RMI and ROMA% and was independent of menopausal status. In addition, 80% of stage I-II cancers in the cohort were correctly identified using the multi-marker scoring system. Our data suggest the multi-marker panel and associated scoring algorithm provides a useful measurement to assist in pre-surgical diagnosis and triage of patients with suspected ovarian cancer.

Diagnostic value of serum CA125 combined with PET/CT in ovarian cancer and tuberculous peritonitis in female patients

PURPOSE

To evaluate the diagnostic value of serum CA125 combined with 18F-FDG PET/CT in ovarian cancer (OC) and tuberculous peritonitis (TBP) in female patients and to establish a diagnostic scoring system.

METHOD

A total of 86 female patients (64 OC and 22 TBP) were included in this study. Serum CA125, PET/CT maximal intensity projection (MIP), maximal standardized uptake value, ovarian mass, ascites volume, and other indicators were analyzed and a diagnostic scoring system was established according to the weights of statistically significant indicators.

RESULTS

Univariate analysis showed that serum CA125 in OC and TBP patients were 2079.9 ± 1651.3 U/mL and 448.3 ± 349.5 U/mL (P < 0.001). In MIP images, abdominal lesions were focal distribution in 92.2% (59/64) of OC patients and diffuse distribution in 95.5% (21/22) of TBP patients (P < 0.001). Ovarian masses could be observed in 82.8% (53/64) OC patients and 31.8% (7/22) TBP patients (P <0.001). The other indicators were not statistically significant. Logistic regression analysis showed that serum CA125 and MIP were independent risk factors for diagnosis. A diagnostic scoring system could be established based on serum CA125, MIP and ovarian mass, and the diagnostic sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were 98.4% (63/64), 95.5% (21/22), 97.7% (84/86), 98.4% (63/64), and 95.5% (21/22), respectively.

CONCLUSION

Serum CA125 combined with PET/CT is of great value in the diagnosis of OC and TBP. A simple and efficient diagnostic scoring system can be established using serum CA125, MIP image feature, and ovarian mass.

Carbohydrate antigen 125 in congestive heart failure: ready for clinical application?

Congestion is the permanent mechanism driving disease progression in patients with acute heart failure (AHF) and also is an important treatment target. However, distinguishing between the two different phenotypes (intravascular congestion and tissue congestion) for personalized treatment remains challenging. Historically, carbohydrate antigen 125 (CA125) has been a frequently used biomarker for the screening, diagnosis, and prognosis of ovarian cancer. Interestingly, CA125 is highly sensitive to tissue congestion and shows potential for clinical monitoring and optimal treatment of congestive heart failure (HF). Furthermore, in terms of right heart function parameters, CA125 levels are more advantageous than other biomarkers of HF. CA125 is expected to become a new biological alternative marker for congestive HF and thereby is expected be widely used in clinical practice.

Top-Down Proteomics of Human Saliva, Analyzed with Logistic Regression and Machine Learning Methods, Reveal Molecular Signatures of Ovarian Cancer

Ovarian cancer (OC) is the most lethal of all gynecological cancers. Due to vague symptoms, OC is mostly detected at advanced stages, with a 5-year survival rate (SR) of only 30%; diagnosis at stage I increases the 5-year SR to 90%, suggesting that early diagnosis is essential to cure OC. Currently, the clinical need for an early, reliable diagnostic test for OC screening remains unmet; indeed, screening is not even recommended for healthy women with no familial history of OC for fear of post-screening adverse events. Salivary diagnostics is considered a major resource for diagnostics of the future. In this work, we searched for OC biomarkers (BMs) by comparing saliva samples of patients with various stages of OC, breast cancer (BC) patients, and healthy subjects using an unbiased, high-throughput proteomics approach. We analyzed the results using both logistic regression (LR) and machine learning (ML) for pattern analysis and variable selection to highlight molecular signatures for OC and BC diagnosis and possibly re-classification. Here, we show that saliva is an informative test fluid for an unbiased proteomic search of candidate BMs for identifying OC patients. Although we were not able to fully exploit the potential of ML methods due to the small sample size of our study, LR and ML provided patterns of candidate BMs that are now available for further validation analysis in the relevant population and for biochemical identification.

The Role of Interleukin 6 (IL6), Cancer Antigen-125 (CA-125), and Human Epididymis Protein 4 (HE4) to predict tumor resectability in the advanced epithelial ovarian cancer patients

INTRODUCTION

A study of tumor resectability in pre-operative patients with advanced epithelial ovarian cancer is required to predict primary surgical benefits accurately. This study aims to investigate IL6, CA-125 and HE4 to predict tumor resectability in the pre-operative patients with advanced epithelial ovarian cancer.

METHODS

This cross-sectional study was conducted in the polyclinic, oncology and gynecology inpatient room of Dr. M. Jamil Padang Hospital from June until December 2022. Advanced epithelial ovarian cancer stage based on histology result from FIGO stages IIIB-IVA. IL6, CA-125, and HE4 were measured using ECLIA (electrochemiluminescence immunoassay). Categorical data were assessed using Chi-square and Mann-Whitney tests. Numerical variable correlations were analyzed using Pearson Correlation tests. While the correlation between numerical and nominal variables was analyzed using the Eta correlation test. A p-value of <0,05 was considered a significant correlation. The cut-off value of serum IL6, CA-125, and HE4 was determined with a ROC curve. The sensitivity and specificity of each clinical parameter were calculated.

RESULTS

There was a significant difference in IL-6 (1328 vs 752 pg/ml; p<0,001), CA-125 (1260,5 vs 819,5 U/ml; p<0,001), and HE4 levels (1320 vs 760 pmol/L; p<0,001) between patients with tumor resectability of > 1 cm (suboptimal) vs < 1 cm (optimal). There was a correlation between IL6 (r = 0,832), CA-125 (r = 0,716), and HE4 (r = 0,716) with tumor resectability.

CONCLUSION

Measuring IL6, CA-125, and HE4 levels is useful for clinicians to predict tumor resectability in pre-operative patients with advanced epithelial ovarian cancer.

Revised Cut-Off Value of Human Epididymis Protein 4 Enhances Its Use as an Ovarian Tumor Marker

OBJECTIVES

Human epididymis protein 4 (HE4), a protein secreted by ovarian tumors, has been used as an ovarian tumor marker. This study aimed to improve the usefulness of HE4 to detect malignant ovarian tumors by reviewing the cut-off values.

DESIGN

A retrospective study without intervention was conducted.

PARTICIPANTS

One hundred forty-nine healthy women (premenopausal, 126; postmenopausal, 23) and 24 patients with ovarian tumors (malignant, 12; benign, 12) participated in the study.

SETTING

The study used the Department of Obstetrics and Gynecology of a university hospital in Japan and the university hospital as a workplace from 2016 to 2018.

METHODS

The basic performance of the HE4 assay was evaluated, and the serum HE4 levels of participants were measured. Receiver operating characteristic analysis was performed using the HE4 data of the patients.

RESULTS

There were no significant differences in HE4 levels between the pre- and postmenopausal groups of healthy women. When the global cut-off values (premenopausal, 70 pmol/L; postmenopausal, 140 pmol/L) were adopted, the clinical sensitivity, specificity, positive predictive value, and negative predictive value were 41.7%, 91.7%, 83.3%, and 61.1%, respectively. Based on the results of the receiver operating characteristic analysis, we set the HE4 cut-off level at 60 pmol/L, regardless of the menopausal status. With the newly set cut-off value, the clinical sensitivity, specificity, positive predictive value, and negative predictive value were 66.7%, 91.7%, 88.9%, and 73.3%, respectively. That is, the clinical sensitivity of HE4 was improved without lowering specificity.

LIMITATIONS

The small number of subjects and the fact that the health status of the healthy women was evaluated based on questionnaires were limitations to the study.

CONCLUSION

A clinically useful cut-off value for HE4 as an ovarian tumor marker was established regardless of the menopausal status of the women, with improved clinical sensitivity, positive predictive value, and negative predictive value without lowering specificity. Currently, different cut-off values for HE4 in pre- and postmenopausal women are used globally. The cut-off value for CA125 was the same between pre- and postmenopausal women. Therefore, with the newly established cut-off value, HE4 can be used more conveniently in a non-specialized setting, especially when it is used in combination with CA125.

Mutations in circulating tumor DNA detected in the postoperative period predict poor survival in patients with ovarian cancer

BACKGROUND

We investigated whether mutations in plasma circulating tumor DNA (ctDNA) can provide prognostic insight in patients with different histological types of ovarian carcinoma. We also examined the concordance of mutations detected in ctDNA samples with those identified in the corresponding formalin-fixed paraffin-embedded (FFPE) tumor specimens.

METHODS

Between July 2016 and December 2017, 29 patients with ovarian carcinoma were prospectively enrolled. FFPE tumor specimens were obtained from all participants. A total of 187 blood samples for ctDNA analysis were collected before surgery (C0), immediate after surgery before adjuvant chemotherapy (C1), and at six-month intervals. Progression-free survival (PFS) and overall survival (OS) served as the main outcome measures.

RESULTS

The study cohort consisted of 13 (44.8%) patients with high-grade serous carcinomas (HGSC), 9 (31.0%) with clear cell carcinoma, 2 (6.9%) with mucinous carcinomas, 4 (13.8%) with low-grade serous carcinomas, and 1 (3.4%) with endometrioid carcinoma. Twenty-four (82.8%) patients had at least one detectable ctDNA variant. The concordance rate between mutations identified in pretreatment ctDNA and corresponding FFPE tumor specimens was 92.3% for patients with HGSC and 58.6% for the entire cohort. The median follow-up time was 33.15 months (range: 0.79-46.13 months). Patients with an advanced stage disease more likely had detectable ctDNA mutations before surgery (C0) and after surgery at C1, while those with HGSC more likely had ctDNA mutations detected before surgery. The presence of ctDNA mutations at C1 was an independent predictor of worse OS with a hazard ratio of 6.56 (95% confidence interval, (1.07-40.17) for detectable versus undetectable C1 ctDNA variants, p = 0.042).

CONCLUSIONS

ctDNA mutations are common in patients with ovarian carcinoma. The presence of ctDNA mutations after surgery was an independent predictor of less favorable PFS and OS.

CT-based radiomics nomogram analysis for assessing BRCA mutation status in patients with high-grade serous ovarian cancer

BACKGROUND

Radiomics nomogram analysis is widely preoperatively used to assess gene mutations in various tumors.

PURPOSE

To explore the value of computed tomography (CT)-based radiomics nomogram analysis for assessing BRCA gene mutation status of patients with high-grade serous ovarian cancer (HGSOC).

MATERIAL AND METHODS

In total, 96 patients with HGSOC were retrospectively screened and randomly divided into primary (n = 68) and validation cohorts (n = 28). The clinical model was constructed based on clinical features and CT morphological features using univariate and multivariate logistic analyses. Maximum-relevance and minimum-redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) were performed for feature dimensionality reduction and radiomics score was calculated. The nomogram model combining the clinical model and the radiomics score was constructed using multivariate logistic regression. Receiver operating characteristic (ROC) curves were generated to assess models' performance. The calibration analysis and decision curve analysis (DCA) were also performed.

RESULTS

The clinical model consisted of CA125 level and supradiaphragmatic lymphadenopathy and yielded an area under the curve (AUC) of 0.69 (primary cohort) and 0.81 (validation cohort). The radiomics model was built with seven selected features and showed an AUC of 0.87 (primary cohort) and 0.81 (validation cohort). The nomogram finally showed the highest AUC of 0.89 (primary cohort) and 0.87 (validation cohort). The nomogram presented favorable calibrations in both the primary and validation cohorts. DCA further confirmed the clinical benefits of the constructed nomogram.

CONCLUSION

CT-based radiomics nomogram provides a non-invasive method to discriminate BRCA gene mutation status of HGSOC and potentially helps develop precise medical strategies.