Exosomal CA125 as A Promising Biomarker for Ovarian Cancer Diagnosis

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.

Extracellular vesicles in nanomedicine and regenerative medicine: A review over the last decade

Small extracellular vesicles (sEVs) are known to be secreted by a vast majority of cells. These sEVs, specifically exosomes, induce specific cell-to-cell interactions and can activate signaling pathways in recipient cells through fusion or interaction. These nanovesicles possess several desirable properties, making them ideal for regenerative medicine and nanomedicine applications. These properties include exceptional stability, biocompatibility, wide biodistribution, and minimal immunogenicity. However, the practical utilization of sEVs, particularly in clinical settings and at a large scale, is hindered by the expensive procedures required for their isolation, limited circulation lifetime, and suboptimal targeting capacity. Despite these challenges, sEVs have demonstrated a remarkable ability to accommodate various cargoes and have found extensive applications in the biomedical sciences. To overcome the limitations of sEVs and broaden their potential applications, researchers should strive to deepen their understanding of current isolation, loading, and characterization techniques. Additionally, acquiring fundamental knowledge about sEVs origins and employing state-of-the-art methodologies in nanomedicine and regenerative medicine can expand the sEVs research scope. This review provides a comprehensive overview of state-of-the-art exosome-based strategies in diverse nanomedicine domains, encompassing cancer therapy, immunotherapy, and biomarker applications. Furthermore, we emphasize the immense potential of exosomes in regenerative medicine.

Deciphering the potential of proteomic-based biomarkers in women's reproductive diseases: empowering precision medicine in gynecology

CONTEXT

Gynecological disorders represent a complex set of malignancies that result from a diverse array of molecular changes affecting the lives of over a million women worldwide. Ovarian, Endometrial, and Cervical cancers, Endometriosis, PCOS are the most prevalent ones that pose a grave threat to women's health. Proteomics has emerged as an invaluable tool for developing novel biomarkers, screening methods, and targeted therapeutic agents for gynecological disorders. Some of these biomarkers have been approved by the FDA, but regrettably, they have a constrained diagnostic accuracy in early-stage diagnosis as all of these biomarkers lack sensitivity and specificity. Lately, high-throughput proteomics technologies have made significant strides, allowing for identification of potential biomarkers with improved sensitivity and specificity. However, limited successes have been shown with translation of these discoveries into clinical practice.

OBJECTIVE

This review aims to provide a comprehensive overview of the current and potential protein biomarkers for gynecological cancers, endometriosis and PCOS, discusses recent advances and challenges, and highlights future directions for the field.

CONCLUSION

We propose that proteomics holds great promise as a powerful tool to revolutionize the fight against female reproductive diseases and can ultimately improve personalized patient outcomes in women's biomedicine.

High-Grade Serous Ovarian Cancer-A Risk Factor Puzzle and Screening Fugitive

High-grade serous ovarian cancer (HGSOC) is the most lethal tumor of the female genital tract. Despite extensive studies and the identification of some precursor lesions like serous tubal intraepithelial cancer (STIC) or the deviated mutational status of the patients (BRCA germinal mutation), the pathophysiology of HGSOC and the existence of particular risk factors is still a puzzle. Moreover, a lack of screening programs results in delayed diagnosis, which is accompanied by a secondary chemo-resistance of the tumor and usually results in a high recurrence rate after the primary therapy. Therefore, there is an urgent need to identify the substantial risk factors for both predisposed and low-risk populations of women, as well as to create an economically and clinically justified screening program. This paper reviews the classic and novel risk factors for HGSOC and methods of diagnosis and prediction, including serum biomarkers, the liquid biopsy of circulating tumor cells or circulating tumor DNA, epigenetic markers, exosomes, and genomic and proteomic biomarkers. The novel future complex approach to ovarian cancer diagnosis should be devised based on these findings, and the general outcome of such an approach is proposed and discussed in the paper.

Exosomal biomarkers in the differential diagnosis of ovarian tumors: the emerging roles of CA125, HE4, and C5a

OBJECTIVE

Investigating the utility of serum exosomal markers CA125, HE4, and C5a, both individually and in combination, for distinguishing between benign and malignant ovarian tumors.

METHODS

In this study, we selected a total of 234 patients diagnosed with ovarian tumors, including 34 with malignant tumors, 10 with borderline ovarian tumors, and 190 with benign tumors. This study conducted comparisons of exosomal levels of CA125, HE4, and C5a among distinct groups, as well as making comparisons between serum and exosomal levels of CA125 and HE4. Furthermore, the diagnostic performance was assessed through Receiver Operating Characteristic (ROC) curve analysis. The Area Under the Curve (AUC) was computed, and a comparative evaluation of sensitivity and specificity was conducted to ascertain their effectiveness in determining the nature of ovarian tumors across different markers.

RESULTS

Serum CA125 and HE4 levels, the ROMA index, exosomal CA125, HE4, C5a levels, and their combined applied value (OCS value) were notably elevated in the ovarian non-benign tumor group compared to the benign tumor group, with statistical significance (P < 0.05). Exosomal and serum levels of CA125 and HE4 exhibited a positive correlation, with concentrations of these markers in serum surpassing those in exosomes. The combined OCS (AUC = 0.871) for CA125, HE4, and C5a in exosomes demonstrated superior sensitivity (0.773) and specificity (0.932) compared to serum tumor markers (CA125, HE4) and the ROMA index. The tumor stage represents an autonomous risk factor influencing the prognosis of individuals with ovarian malignancies.

CONCLUSION

The stage of ovarian malignancy is an independent risk factor for its prognosis. The combination of exosomal CA125, HE4 and C5a has a higher clinical value for the identification of the nature of ovarian tumours.

Tumor-derived systems as novel biomedical tools-turning the enemy into an ally

Cancer is a complex illness that presents significant challenges in its understanding and treatment. The classic definition, "a group of diseases characterized by the uncontrolled growth and spread of abnormal cells in the body," fails to convey the intricate interaction between the many entities involved in cancer. Recent advancements in the field of cancer research have shed light on the role played by individual cancer cells and the tumor microenvironment as a whole in tumor development and progression. This breakthrough enables the utilization of the tumor and its components as biological tools, opening new possibilities. This article delves deeply into the concept of "tumor-derived systems", an umbrella term for tools sourced from the tumor that aid in combatting it. It includes cancer cell membrane-coated nanoparticles (for tumor theranostics), extracellular vesicles (for tumor diagnosis/therapy), tumor cell lysates (for cancer vaccine development), and engineered cancer cells/organoids (for cancer research). This review seeks to offer a complete overview of the tumor-derived materials that are utilized in cancer research, as well as their current stages of development and implementation. It is aimed primarily at researchers working at the interface of cancer biology and biomedical engineering, and it provides vital insights into this fast-growing topic.

Lectins as potential tools for cancer biomarker discovery from extracellular vesicles

Extracellular vesicles (EVs) have considerable potential as diagnostic, prognostic, and therapeutic agents, in large part because molecular patterns on the EV surface betray the cell of origin and may also be used to "target" EVs to specific cells. Cancer is associated with alterations to cellular and EV glycosylation patterns, and the surface of EVs is enriched with glycan moieties. Glycoconjugates of EVs play versatile roles in cancer including modulating immune response, affecting tumor cell behavior and site of metastasis and as such, paving the way for the development of innovative diagnostic tools and novel therapies. Entities that recognize specific glycans, such as lectins, may thus be powerful tools to discover and detect novel cancer biomarkers. Indeed, the past decade has seen a constant increase in the number of published articles on lectin-based strategies for the detection of EV glycans. This review explores the roles of EV glycosylation in cancer and cancer-related applications. Furthermore, this review summarizes the potential of lectins and lectin-based methods for screening, targeting, separation, and possible identification of improved biomarkers from the surface of EVs.

The diagnostic and therapeutic prospects of exosomes in ovarian cancer

Exosomes are nano-sized vesicles derived from the endosomal system and are involved in many biological and pathological processes. Emerging evidence has demonstrated that exosomes with cell-specific constituents are associated with the tumorigenesis and progression of ovarian cancer. Therefore, exosomes derived from ovarian cancers can be potential diagnostic biomarkers and therapeutic targets. In this review, we briefly present the biological characteristics of exosomes and the recent advances in isolating and detecting exosomes. Furthermore, we summarise the many functions of exosomes in ovarian cancer, hoping to provide a theoretical basis for clinical applications of exosomes in the diagnosis and treatment of ovarian cancer.

Fabrication of a Novel Au Star@AgAu Yolk-Shell Nanostructure for Ovarian Cancer Early Diagnosis and Targeted Therapy

Purpose

A novel CYPA-targeted, SiO₂ encapsulated Au star@AgAu yolk-shell nanostructure (YSNS) was synthesized and used for ovarian cancer early diagnosis and therapy.

Methods

Diverse spectroscopic and microscopic methods were utilized to investigate the pattern of the yolk-shell nanostructure. In addition, in vitro and in vivo experiments were carried out.

Results

It can be found that the ratio of HAuCl₄ and AgNO₃ played a critical role in the constitution of the yolk-shell nanostructure. The as-prepared yolk-shell nanostructure showed excellent SERS performance, which could be utilized as SERS substrate for specific sensitivity analysis of ovarian cancer markers cyclophilin A (CYPA) with detectable limit of 7.76*10^-10 μg/mL. In addition, the as-prepared yolk-shell nanostructure possessed outstanding photothermal performance, which could be used as photothermal agent for ovarian cancer therapy. Experiments in vitro and in vivo proved that the as-prepared yolk-shell nanostructures are ideal candidate for early diagnosis and therapy for ovarian cancer in one platform.

Conclusion

This work holds promise to offer a new method for the detection and therapy of ovarian cancer in the early stage.

The hidden potential of glycomarkers: Glycosylation studies in the service of cancer diagnosis and treatment

Changes in the glycosylation process appear early in carcinogenesis and evolve with the growth and spread of cancer. The correlation of the characteristic glycosylation signature with the tumor stage and the appropriate therapy choice is an important issue in translational medicine. Oncologists also pay attention to extracellular vesicles as reservoirs of new cancer glycomarkers that can be potent for cancer diagnosis/prognosis. In this review, we recall glycomarkers used in oncology and show their new glycoforms of improved clinical relevance. We summarize current knowledge on the biological functions of glycoepitopes in cancer-derived extracellular vesicles and their potential use in clinical practice. Is glycomics a future of cancer diagnosis? It may be, but in combination with other omics analyses than alone.

Lectin-Based Study Reveals the Presence of Disease-Relevant Glycoepitopes in Bladder Cancer Cells and Ectosomes

Bladder cancer is a malignancy that remains a therapeutic challenge and requires the identification of new biomarkers and mechanisms of progression. Several studies showed that extracellular vesicles promote angiogenesis, migration and metastasis, and inhibit apoptosis in bladder cancer. This effect may depend on their glycosylation status. Thus, the aim of this study was to compare glycosylation profiles of T-24 urothelial bladder cancer cells, HCV-29 normal ureter epithelial cells, and ectosomes released by both cell lines using lectin blotting and flow cytometry. Ectosomes displayed distinct total and surface glycosylation profiles with abundance of β-1,6-branched glycans and sialilated structures. Then, it was investigated whether the glycosylation status of the T-24 and HCV-29 cells is responsible for the effect exerted by ectosomes on the proliferation and migration of recipient cells. Stronger proproliferative and promigratory activity of T-24-derived ectosomes was observed in comparison to ectosomes from HCV-29 cells. When ectosomes were isolated from DMJ-treated cells, the aforementioned effects were diminished, suggesting that glycans carried by ectosomes were involved in modulation of recipient cell function. HCV-29- and T-24-derived ectosomes also increased the viability and motility of endothelial HUVEC cells and Hs27 fibroblasts. This supports the hypothesis that ectosomes can modulate the function of various cells present in the tumor microenvironment.

Molecular Biomarkers for the Early Detection of Ovarian Cancer

Ovarian cancer is the deadliest gynecological cancer, leading to over 152,000 deaths each year. A late diagnosis is the primary factor causing a poor prognosis of ovarian cancer and often occurs due to a lack of specific symptoms and effective biomarkers for an early detection. Currently, cancer antigen 125 (CA125) is the most widely used biomarker for ovarian cancer detection, but this approach is limited by a low specificity. In recent years, multimarker panels have been developed by combining molecular biomarkers such as human epididymis secretory protein 4 (HE4), ultrasound results, or menopausal status to improve the diagnostic efficacy. The risk of ovarian malignancy algorithm (ROMA), the risk of malignancy index (RMI), and OVA1 assays have also been clinically used with improved sensitivity and specificity. Ongoing investigations into novel biomarkers such as autoantibodies, ctDNAs, miRNAs, and DNA methylation signatures continue to aim to provide earlier detection methods for ovarian cancer. This paper reviews recent advancements in molecular biomarkers for the early detection of ovarian cancer.

Exosomes and cancer - Diagnostic and prognostic biomarkers and therapeutic vehicle

Exosomes belong to a subpopulation of extracellular vesicles secreted by the dynamic multistep endocytosis process and carry diverse functional molecular cargoes, including proteins, lipids, nucleic acids (DNA, messenger and noncoding RNA), and metabolites to promote intercellular communication. Proteins and noncoding RNA are among the most abundant contents in exosomes; they have biological functions and are selectively packaged into exosomes. Exosomes derived from tumor, stromal and immune cells contribute to the multiple stages of cancer progression as well as resistance to therapy. In this review, we will discuss the biogenesis of exosomes and their roles in cancer development. Since specific contents within exosomes originate from their cells of origin, this property allows exosomes to function as valuable biomarkers. We will also discuss the potential use of exosomes as diagnostic and prognostic biomarkers or predictors for different therapeutic strategies for multiple cancers. Furthermore, the applications of exosomes as direct therapeutic targets or engineered vehicles for drugs are an important field of exosome study. Better understanding of exosome biology may pave the way to promising exosome-based clinical applications.

Extracellular vesicles and particles impact the systemic landscape of cancer

Intercellular cross talk between cancer cells and stromal and immune cells is essential for tumor progression and metastasis. Extracellular vesicles and particles (EVPs) are a heterogeneous class of secreted messengers that carry bioactive molecules and that have been shown to be crucial for this cell-cell communication. Here, we highlight the multifaceted roles of EVPs in cancer. Functionally, transfer of EVP cargo between cells influences tumor cell growth and invasion, alters immune cell composition and function, and contributes to stromal cell activation. These EVP-mediated changes impact local tumor progression, foster cultivation of pre-metastatic niches at distant organ-specific sites, and mediate systemic effects of cancer. Furthermore, we discuss how exploiting the highly selective enrichment of molecules within EVPs has profound implications for advancing diagnostic and prognostic biomarker development and for improving therapy delivery in cancer patients. Altogether, these investigations into the role of EVPs in cancer have led to discoveries that hold great promise for improving cancer patient care and outcome.

Ultrasensitive Single Extracellular Vesicle Detection Using High Throughput Droplet Digital Enzyme-Linked Immunosorbent Assay

Extracellular vesicles (EVs) have attracted enormous attention for their diagnostic and therapeutic potential. However, it has proven challenging to achieve the sensitivity to detect individual nanoscale EVs, the specificity to distinguish EV subpopulations, and a sufficient throughput to study EVs among an enormous background. To address this fundamental challenge, we developed a droplet-based optofluidic platform to quantify specific individual EV subpopulations at high throughput. The key innovation of our platform is parallelization of droplet generation, processing, and analysis to achieve a throughput (∼20 million droplets/min) more than 100× greater than typical microfluidics. We demonstrate that the improvement in throughput enables EV quantification at a limit of detection = 9EVs/μL, a >100× improvement over gold standard methods. Additionally, we demonstrate the clinical potential of this system by detecting human EVs in complex media. Building on this work, we expect this technology will allow accurate quantification of rare EV subpopulations for broad biomedical applications.

Understanding the Correlation between Metabolic Regulator SIRT1 and Exosomes with CA-125 in Ovarian Cancer: A Clinicopathological Study

Background

Ovarian cancer (OvCa), the deadliest gynaecological malignancy, is associated with poor prognosis and high mortality rate. Ovarian cancer has been related with CA-125 and metabolic reprogramming by SIRT1 leading to metastasis with the involvement of exosomes.

Methods

Clinicopathological data of OvCa patients were collected to perform the analysis. Patients' samples were collected during surgery for immunohistochemistry and flow cytometric analysis of SIRT1, HIF-1α, exosomal markers (CD81 and CD63), ki-67, and PAS staining for glycogen deposition. Adjacent normal and tumor tissues were collected as per the CA-125 levels.

Results

CA-125, a vital diagnostic marker, has shown significant correlation with body mass index (BMI) (P = 0.0153), tumor type (P = 0.0029), ascites level, ascites malignancy, degree of dissemination, tumor differentiation, FIGO stage, TNM stage, laterality, and tumor size at P < 0.0001. Since significant correlation was associated with BMI and degree of dissemination, as disclosed by IHC analysis, metabolic marker SIRT1 (P = 0.0003), HIF-1α (P < 0.0001), exosomal marker CD81 (P < 0.0001), ki-67 status (P = 0.0034), and glycogen deposition (P <0.0001) were expressed more in tumor tissues as compared to the normal ones. ROC analysis of CA-125 had shown 327.7 U/ml has the best cutoff point with 82.4% sensitivity and specificity of 52.3%. In addition, Kaplan-Meier plots of CA-125 (P < 0.0001), BMI (P = 0.001), degree of dissemination (P < 0.0001), and ascites level (P <0.0001) reflected significant correlation with overall survival (OS). Upon multivariate Cox-regression analysis for overall survival (OS), BMI (P = 0.008, HR 1.759, 95% CI 1.156-2.677), ascites malignancy (P = 0.032, HR 0.336, 95% CI 0.124-0.911), and degree of dissemination (P = 0.004, HR 1.994, 95% CI 1.251-3.178) were significant proving to be independent indicators of the disease.

Conclusion

Clinicopathological parameters like BMI, degree of dissemination, and ascites level along with CA-125 can be prognostic factors for the disease. Levels of CA-125 can depict the metabolic and metastatic factors. Thus, by targeting SIRT1 and assessing exosomal concentrations to overcome metastasis and glycogen deposition, individualized treatment strategy could be designed. In-depth studies are still required.

A nomogram model based on clinical markers for predicting malignancy of ovarian tumors

OBJECTIVE

The aim of this study was to build a nomogram based on clinical markers for predicting the malignancy of ovarian tumors (OTs).

METHOD

A total of 1,268 patients diagnosed with OTs that were surgically removed between October 2017 and May 2019 were enrolled. Clinical markers such as post-menopausal status, body mass index (BMI), serum human epididymis protein 4 (HE4) value, cancer antigen 125 (CA125) value, Risk of Ovarian Malignancy Algorithm (ROMA) index, course of disease, patient-generated subjective global assessment (PG-SGA) score, ascites, and locations and features of masses were recorded and analyzed (p 0.05). Significant variables were further selected using multivariate logistic regression analysis and were included in the decision curve analysis (DCA) used to assess the value of the nomogram model for predicting OT malignancy.

RESULT

The significant variables included post-menopausal status, BMI, HE4 value, CA125 value, ROMA index, course of disease, PG-SGA score, ascites, and features and locations of masses (p 0.05). The ROMA index, BMI (≥ 26), unclear/blurred mass boundary (on magnetic resonance imaging [MRI]/computed tomography [CT]), mass detection (on MRI/CT), and mass size and features (on type B ultrasound [BUS]) were screened out for multivariate logistic regression analysis to assess the value of the nomogram model for predicting OT malignant risk (p 0.05). The DCA revealed that the net benefit of the nomogram's calculation model was superior to that of the CA125 value, HE4 value, and ROMA index for predicting OT malignancy.

CONCLUSION

We successfully tailored a nomogram model based on selected clinical markers which showed superior prognostic predictive accuracy compared with the use of the CA125, HE4, or ROMA index (that combines both HE and CA125 values) for predicting the malignancy of OT patients.

Aptamer-based biosensing through the mapping of encoding upconversion nanoparticles for sensitive CEA detection

An aptamer-based assay through the mapping and enumeration of encoding UCNPs for digital detection of CEA is reported.

Molecular Profile Study of Extracellular Vesicles for the Identification of Useful Small “Hit” in Cancer Diagnosis

Tumor-secreted extracellular vesicles (EVs) are the main mediators of cell-cell communication, permitting cells to exchange proteins, lipids, and metabolites in varying physiological and pathological conditions. They contain signature tumor-derived molecules that reflect the intracellular status of their cell of origin. Recent studies have shown that tumor cell-derived EVs can aid in cancer metastasis through the modulation of the tumor microenvironment, suppression of the immune system, pre-metastatic niche formation, and subsequent metastasis. EVs can easily be isolated from a variety of biological fluids, and their content makes them useful biomarkers for the diagnosis, prognosis, monitorization of cancer progression, and response to treatment. This review aims to explore the biomarkers of cancer cell-derived EVs obtained from liquid biopsies, in order to understand cancer progression and metastatic evolution for early diagnosis and precision therapy.

Modified HE4, CA125, and ROMA cut-off values and predicted probability of ovarian tumor in Chinese patients

BACKGROUND

Most prior studies investigating the risk of ovarian malignancy algorithm (ROMA) with cancer antigen 125 (CA125) and human epididymis protein 4 (HE4) have involved Caucasian population or other populations. To date, there have been no unique calculations of predicted probability (PP) risk specifically for Chinese populations to help physicians in primary care settings.

METHODS

A group of 534 women with ovarian tumor diagnoses were enrolled and serum HE4 and CA125 were measured in each individual. Modified cut-off values were obtained by maximizing area under the curve (AUC) values and adjusted by using logistic regression with corresponding sensitivity (SN), specificity (SP), Youden index (YI), positive predictive value (PPV), and negative predictive value (NPV).

RESULTS

By utilizing the ideal PPV, NPV, and AUC values, in premenopausal women modified HE4, CA125, ROMA, and PP cut-off values were 73.87 pmol/L, 61.60 U/mL, 18.47%, and 0.168, respectively. The same test values for postmenopausal women were 120.90 pmol/L, 76.21 U/mL, 26.48%, and 0.485, respectively. The SN for HE4 with the modified cut-off value was significantly lower than that for CA125 (P=0.040) in premenopausal women and lower than that for ROMA (P=0.001) and PP (P=0.044) in postmenopausal women. The AUC values for CA125, ROMA, and PP were all significantly higher than that for HE4 (P=0.006, 0.007, and 0.002, respectively) in postmenopausal women.

CONCLUSIONS

The modified cut-off values for HE4, CA125, ROMA, and PP with ideal SN, SP, YI, NPV, PPV were useful of ruling out ovarian malignancy among both pre- and post-menopausal women. In premenopausal women modified HE4, CA125, ROMA, and PP cut-off values were 73.87 pmol/L, 61.60 U/mL, 18.47%, and 0.168, respectively and in postmenopausal women were 120.90 pmol/L, 76.21 U/mL, 26.48%, and 0.485, respectively.

Current Search through Liquid Biopsy of Effective Biomarkers for Early Cancer Diagnosis into the Rich Cargoes of Extracellular Vesicles

There exist many different human cancers, but regardless of the cancer type, an early diagnosis is a necessary condition for further optimal outcomes from the disease. Therefore, efficient specific and sensitive cancer biomarkers are urgently needed. This is especially true for the cancers depicting a silent progression, and those only diagnosed in an already metastatic state with a poor survival prognostic. After a rapid overview of the previous methods for cancer diagnosis, the outstanding characteristics of extracellular vesicles (EVs) will be presented, as new interesting candidates for early cancer diagnosis in human biofluid non-invasive liquid biopsy. The present review aims to give the state-of-the-art of the numerous searches of efficient EV-mediated cancer diagnosis. The corresponding literature quest was performed by means of an original approach, using a powerful Expernova Questel big data platform, which was specifically adapted for a literature search on EVs. The chosen collected scientific papers are presented in two parts, the first one drawing up a picture of the current general status of EV-mediated cancer diagnosis and the second one showing recent applications of such EV-mediated diagnosis for six important human-specific cancers, i.e., lung, breast, prostate, colorectal, ovary and pancreatic cancers. However, the promising perspective of finally succeeding in the worldwide quest for the much-needed early cancer diagnosis has to be moderated by the many remaining challenges left to solve before achieving the efficient clinical translation of the constantly increasing scientific knowledge.

Role of exosomes in the immune microenvironment of ovarian cancer

Exosomes are excretory vesicles that can deliver a variety of bioactive cargo molecules to the extracellular environment. Accumulating evidence demonstrates exosome participation in intercellular communication, immune response, inflammatory response and they even play an essential role in affecting the tumor immune microenvironment. The role of exosomes in the immune microenvironment of ovarian cancer is mainly divided into suppression and stimulation. On one hand exosomes can stimulate the innate and adaptive immune systems by activating dendritic cells (DCs), natural killer cells and T cells, allowing these immune cells exert an antitumorigenic effect. On the other hand, ovarian cancer-derived exosomes initiate cross-talk with immunosuppressive effector cells, which subsequently cause immune evasion; one of the hallmarks of cancer. Exosomes induce the polarization of macrophages in M2 phenotype and induce apoptosis of lymphocytes and DCs. Exosomes further activate additional immunosuppressive effector cells (myeloid-derived suppressor cells and regulatory T cells) that induce fibroblasts to differentiate into cancer-associated fibroblasts. Exosomes also induce the tumorigenicity of mesenchymal stem cells to exert additional immune suppression. Furthermore, besides mediating the intercellular communication, exosomes carry microRNAs (miRNAs), proteins and lipids to the tumor microenvironment, which collectively promotes ovarian cancer cells to proliferate, invade and tumors to metastasize. Studying proteins, lipids and miRNAs carried by exosomes could potentially be used as an early diagnostic marker of ovarian cancer for designing treatment strategies.

A radiomics approach for automated diagnosis of ovarian neoplasm malignancy in computed tomography

This paper develops a two-dimensional (2D) radiomics approach with computed tomography (CT) to differentiate between benign and malignant ovarian neoplasms. A retrospective study was conducted from July 2017 to June 2019 for 134 patients with surgically-verified benign or malignant ovarian tumors. The patients were randomly divided in a ratio of 7:3 into two sets, namely a training set (of n = 95) and a test set (of n = 39). The ITK-SNAP software was used to delineate the regions of interest (ROI) associated with lesions of the largest diameters in plain CT image slices. Texture features were extracted by the Analysis Kit (AK) software. The training set was used to select the best features according to the maximum-relevance minimum-redundancy (mRMR) criterion, in addition to the algorithm of the least absolute shrinkage and selection operator (LASSO). Then, we employed a radiomics model for classification via multivariate logistic regression. Finally, we evaluated the overall performance of our method using the receiver operating characteristics (ROC), the DeLong test. and tested in an external validation test sample of patients of ovarian neoplasm. We created a radiomics prediction model from 14 selected features. The radiomic signature was found to be highly discriminative according to the area under the ROC curve (AUC) for both the training set (AUC = 0.88), and the test set (AUC = 0.87). The radiomics nomogram also demonstrated good calibration and differentiation for both the training (AUC = 0.95) and test (AUC = 0.96) samples. External validation tests gave a good performance in radiomic signature (AUC = 0.83) and radiomics nomogram (AUC = 0.95). The decision curve explicitly indicated the clinical usefulness of our nomogram method in the sense that it can influence major clinical events such as the ordering or abortion of other tests, treatments or invasive procedures. Our radiomics model based on plain CT images has a high diagnostic efficiency, which is helpful for the identification and prediction of benign and malignant ovarian neoplasms.

Glycosylation of Cancer Extracellular Vesicles: Capture Strategies, Functional Roles and Potential Clinical Applications

Glycans are major constituents of extracellular vesicles (EVs). Alterations in the glycosylation pathway are a common feature of cancer cells, which gives rise to de novo or increased synthesis of particular glycans. Therefore, glycans and glycoproteins have been widely used in the clinic as both stratification and prognosis cancer biomarkers. Interestingly, several of the known tumor-associated glycans have already been identified in cancer EVs, highlighting EV glycosylation as a potential source of circulating cancer biomarkers. These particles are crucial vehicles of cell–cell communication, being able to transfer molecular information and to modulate the recipient cell behavior. The presence of particular glycoconjugates has been described to be important for EV protein sorting, uptake and organ-tropism. Furthermore, specific EV glycans or glycoproteins have been described to be able to distinguish tumor EVs from benign EVs. In this review, the application of EV glycosylation in the development of novel EV detection and capture methodologies is discussed. In addition, we highlight the potential of EV glycosylation in the clinical setting for both cancer biomarker discovery and EV therapeutic delivery strategies.