Biomarker For Ovarian Cancer Research Articles

Circulating CD4+ Treg, CD8+ Treg, and CD3+ γδ T Cell Subpopulations in Ovarian Cancer.

Background and Objectives: Regulatory T cells (Tregs) are usually enriched in ovarian cancer (OC), and their immunosuppressive function plays a key role in tumorigenesis and progression. We mainly explored the phenotypical characterization of Treg-related markers on αβ and γδ T cell subsets in patients with OC. Materials and Methods: Thirty-six untreated patients with OC at the Women's Hospital of Nanjing Medical University from September 2019 to August 2021 were enrolled. Phenotypical characterization of Tregs-related markers were detected by flow cytometry (FCM). Enzyme-linked immunosorbent assay was used to detect the levels of carbohydrate antigen (CA125) and transforming growth factor β (TGF-β). The level of human epididymis protein 4 (HE4) was detected by electrochemiluminescence immunoassay. Results: Circulating CD4+ Tregs, CD8+ Tregs, and CD3+γδ T cell subpopulations from OC patients have elevated Foxp3, CD25, CD122, Vδ1, and reduced CD28 expression compared to benign ovarian tumor (BOT) patients and healthy controls (HC). The upregulation of Foxp3 and Vδ1 and the downregulation of CD28 were highly specific for maintaining the immunosuppression function of CD4+ Tregs, CD3+γδ T cells, and CD8+ Tregs in OC patients. These Treg subpopulations were able to discriminate OC from BOT and HC. The levels of CA125, HE4, and TGF-β were increased in OC patients. A significant positive correlation between Treg subpopulations and CA125, HE4, and TGF-β was revealed. Conclusions: Proportions of CD4+ Tregs, CD8+ Tregs, and CD3+γδ T cell subsets were significantly increased in OC patients and were positively correlated with FIGO stage/metastasis status, CA125, HE4, and TGF-β. These indicators have the potential to be used as immunosurveillance biomarkers for OC.

Pathogenicity of PKCγ Genetic Variants-Possible Function as a Non-Invasive Diagnostic Biomarker in Ovarian Cancer.

Ovarian cancer has the highest mortality rate among gynecologic malignancies, owing to its misdiagnosis or late diagnosis. Identification of its genetic determinants could improve disease outcomes. Conventional Protein Kinase C-γ (PKCγ) dysregulation is reported in several cancers. Similarly, its variant rs1331262028 is also reported to have an association with hepatocellular carcinoma. Therefore, the aim of the present study was to analyze the variant rs1331262028 association with ovarian cancer and to determine its impact on PKCγ's protein interactions. Association of variation was determined through genotyping PCR (cohort size:100). Protein-protein docking and molecular dynamic simulation were carried out to study the variant impact of PKCγ interactions. The study outcome indicated the positive association of variant rs1331262028 with ovarian cancer and its clinicopathological features. Molecular dynamics simulation depicted the potential influence of variation on PKCγ molecular signaling. Hence, this study provided the foundations for assessing variant rs1331262028 as a potential prognostic marker for ovarian cancer. Through further validation, it can be applied at the clinical level.

Peripheral and tumor-infiltrating immune cells are correlated with patient outcomes in ovarian cancer.

At present, there is still a lack of reliable biomarkers for ovarian cancer (OC) to guide prognosis prediction and accurately evaluate the dominant population of immunotherapy. In recent years, the relationship between peripheral blood markers and tumor-infiltrating immune cells (TICs) with cancer has attracted much attention. However, the relationship between the survival of OC patients and intratumoral- or extratumoral-associated immune cells remains controversial. In this study, four machine-learning algorithms were used to predict overall survival in OC patients based on peripheral blood indicators. To further screen out immune-related gene and molecular targets, we systematically explored the correlation between TICs and OC patient survival based on The Cancer Genome Atlas database. Using the TICs score method, patients were divided into a low immune infiltrating cell group and a high immune infiltrating cell group. The results showed that there was a significant statistical significance between the peripheral blood indicators and the survival prognosis of OC patients. Survival analysis showed that TICs play a crucial role in the survival of OC patients. Four core genes, CXCL9, CD79A, MS4A1, and MZB1, were identified by cross-PPI and COX regression analysis. Further analysis found that these genes were significantly associated with both TICs and survival in OC patients. These results suggest that both peripheral blood markers and TICs can be used as prognostic predictors in patients with OC, and CXCL9, CD79A, MS4A1, and MZB1 may be potential therapeutic targets for OC immunotherapy.

Identification and Characterization of Aptamers Targeting Ovarian Cancer Biomarker Human Epididymis Protein 4 for the Application in Urine.

Ovarian cancer is the deadliest gynecological cancer. With non-specific symptoms of the disease and the lack of effective diagnostic methods, late diagnosis remains the crucial hurdle of the poor prognosis. Therefore, development of novel diagnostic approaches are needed. The purpose of this study is to develop DNA-based aptamers as potential diagnostic probes to detect ovarian cancer biomarker Human epididymis protein 4 (HE4) in urine. HE4 is a protein overexpressed in ovarian cancer, but not in healthy or benign conditions. With high stability and diagnostic value for detection of ovarian cancer, urine HE4 appears as an attractive non-invasive biomarker. The high-affinity anti-HE4 DNA aptamers were selected through 10 cycles of High Fidelity Systematic Evolution of Ligands by EXponential enrichment (Hi-Fi SELEX), a method for aptamer selection based on digital droplet PCR. The anti-HE4 aptamers were identified using DNA sequencing and bioinformatics analysis. The candidate aptamer probes were characterized in urine for binding to HE4 protein using thermofluorimetry. Two anti-HE4 aptamers, AHE1 and AHE3, displayed binding to HE4 protein in urine, with a constant of dissociation in the nanomolar range, with Kd (AHE1) = 87 ± 9 nM and Kd (AHE3) aptamer of 127 ± 28 nM. Therefore, these aptamers could be promising tools for application in diagnostics and future development of urine tests or biosensors for ovarian cancer.

Generation and Culturing of High-Grade Serous Ovarian Cancer Patient-Derived Organoids.

Organoids are 3D dynamic tumor models that can be grown successfully from patient-derived ovarian tumor tissue, ascites, or pleural fluid and aid in the discovery of novel therapeutics and predictive biomarkers for ovarian cancer. These models recapitulate clonal heterogeneity, the tumor microenvironment, and cell-cell and cell-matrix interactions. Additionally, they have been shown to match the primary tumor morphologically, cytologically, immunohistochemically, and genetically. Thus, organoids facilitate research on tumor cells and the tumor microenvironment and are superior to cell lines. The present protocol describes distinct methods to generate patient-derived ovarian cancer organoids from patient tumors, ascites, and pleural fluid samples with a higher than 97% success rate. The patient samples are separated into cellular suspensions by both mechanical and enzymatic digestion. The cells are then plated utilizing a basement membrane extract (BME) and are supported with optimized growth media containing supplements specific to the culturing of high-grade serous ovarian cancer (HGSOC). After forming initial organoids, the PDOs can sustain long-term culture, including passaging for expansion for subsequent experiments.

PD-L1 is highly expressed in ovarian cancer and associated with cancer stem cells populations expressing CD44 and other stem cell markers

BackgroundImmune checkpoint inhibitors, including PD-L1 (programmed death ligand-1) inhibitors have well documented anticancer therapeutic effect in most types of cancers but its use in the treatment of ovarian cancer is not yet proven. The aim of our study is to explore the predictive biomarkers in ovarian cancer and its association with the outcomes. We have investigated the role of PD-L1 expressions in the tumor microenvironment cells including immune cells and cancer stem cells in different types of ovarian cancer.MethodsA total of 119 surgical archived ovarian cancer samples were collected from the pathology department at King Fahad Specialist Hospital, Dammam, Saudi Arabia that included serous carcinomas, clear cell carcinomas, mucinous carcinomas, endometrioid carcinomas, and granulosa cell tumors. Immunohistochemistry (IHC) staining was performed using (i) PD-L1 antibodies to detect PD-L1 expressions; (ii) CD8 and CD4 to detect Tumor Infiltrating Lymphocytes (TILs); and (iii) CD44, LGR5, and ALDH2 to detect stem cell markers. The clinicopathological data were collected from patients’ medical record to investigate the association with PD-L1, TILs, and stem cells expressions.ResultsWe report high PD-L1 expressions in 47.8% of ovarian cancer samples. PD-L1 expressions were detected in different types of epithelial ovarian cancer and were not associated with poor prognosis of ovarian cancer. However, determining the expression levels of TILs in the ovarian cancer tissues found that 81% (n = 97) of ovarian cancer samples have TILs that express both of CD8 and CD4 and significantly associated with high PD-L1 expressions. Interestingly, we have found that ovarian cancer tissues with high expressions of PD-L1 were associated with high expressions of stem cells expressing CD44 and LGR5.ConclusionsPD-L1 is highly expressed in the serous type of ovarian carcinomas and the overall expression of PD-L1 is not associated with poor survival rate. Furthermore, PD-L1 expressions are strongly associated with TILs and stem cell markers in ovarian cancer. Inhibiting the PD-L1 using immune checkpoint inhibitors might downregulate stem cell population that known to be associated with cancer recurrence.

Immune-Related Genes' Prognostic, Therapeutic and Diagnostic Value in Ovarian Cancer Immune-Related Gene Biomarker in Ovarian Cancer.

The abnormal expression of immune-related genes (IRGs) plays an important role in the occurrence and progression of ovarian cancer (OC), which is the main cause of mortality among gynecological cancer patients. This study aims to establish a prognostic risk model and comprehensively analyze the relationship between OC risk score and prognosis, immune cell infiltration (ICI) and therapeutic sensitivity in OC. We retrospectively evaluated the clinicopathological characteristics of consecutive OC patients in the Cancer Genome Atlas (TCGA) database. First, the prognostic risk model was constructed by bioinformatics methods. And then, we systematically assessed model robustness, and correlations between risk score and prognosis, and immune cell infiltration. The ICGC cohort was used to verify the prognostic risk model. Finally, we evaluated their value in the treatment of OC immunotherapy and chemotherapy. A total of 10 IRGs were identified to construct the prognostic risk model. Survival analysis revealed that patients in the low-risk group had a better prognosis (P < .01), and the risk score might be considered an independent predictor for predicting the prognosis. In addition, risk scores and patient clinical information were used to construct clinical nomograms, improving the prediction's precision. We also explored the relationship between the risk score and ICI, immunotherapy and drug sensitivity. Collectively, we identified a novel ten IRGs signature that may be applied as a prognostic predictor of OC, thereby benefiting clinical decision-making and personalized treatment of patients.

Abstract P030: Prospective analysis of plasma metabolites for early detection of high-grade serous ovarian cancer

Abstract Background: Ovarian cancer has poor prognosis due to &amp;gt;60% of the patients being diagnosed at late-stage. However, there is currently no ovarian cancer biomarker appropriate for screening. This may be, in part, due to many prior studies using clinical samples obtained at the time of diagnosis for biomarker discovery. Here, we sought to discover novel plasma metabolomic biomarkers for ovarian cancer early detection using prospectively collected blood samples. Methods: We examined 250 plasma metabolites in 93 high-grade serous ovarian cancer (HGSOC) cases with blood drawn up to five years prior to diagnosis and 93 matched controls in the prospective Nurses’ Health Studies and Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial. We also measured metabolites in 40 early-stage HGSOC cases with blood collected at diagnosis and 40 matched controls in a hospital-based study. We used conditional logistic regression to calculate the odds ratios (ORs) and 95% confidence intervals (CIs) of the association between one standard deviation increase in metabolite levels and ovarian cancer risk, and used metabolite set enrichment analysis (MSEA) to identify metabolite group associations. False discovery rate (FDR) was used to correct for multiple testing. For the prospectively collected blood samples, we conducted analyses by time between blood draw and diagnosis. Results: Plasma metabolites associated with ovarian cancer in blood samples collected prospectively were different than in blood samples collected at diagnosis of early-stage disease compared to controls. There were 45 individual metabolites associated with early-stage disease (FDR&amp;lt;0.05), and none of these metabolites were associated with ovarian cancer in the prospectively collected blood samples (FDR&amp;lt;0.05). There were 10 metabolites associated with ovarian cancer diagnosed within 3 years of blood collection (p&amp;lt;0.05), including 7 lipid metabolites (2 lysophosphatidylcholines, 2 diglycerides, 1 phosphatidylcholine, 1 cholesteryl esters, 1 phosphatidylethanolamines) that were inversely associated with ORs ranging from 0.61-0.66. Interestingly, the direction of association remained similar when restricting to cases with blood collected up to 2 years and 1 year prior to diagnosis, and the magnitude of association generally became stronger for cases closer to diagnosis. Similarly, distinct profiles were observed in MSEA, with diglycerides being inversely associated with ovarian cancer diagnosis in prospectively collected blood samples (FDR ranging from &amp;lt;0.05 to &amp;lt;0.20) and positively associated in blood collected at early-stage disease diagnosis (FDR&amp;lt;0.05).Conclusion: Our results revealed plasma metabolomic profiles differ between prospectively collected blood samples up to 3 years prior to diagnosis and blood samples collected at time of diagnosis, even for early-stage disease, suggesting the use of prospectively collected blood samples may discover promising novel early detection biomarkers for ovarian cancer. Citation Format: Naoko Sasamoto, Oana A. Zeleznik, Allison F. Vitonis, Daniel W. Cramer, Julian Avila-Pacheco, Clary B. Clish, Britton Trabert, Shelley S. Tworoger, Kathryn L. Terry. Prospective analysis of plasma metabolites for early detection of high-grade serous ovarian cancer. [abstract]. In: Proceedings of the AACR Special Conference: Precision Prevention, Early Detection, and Interception of Cancer; 2022 Nov 17-19; Austin, TX. Philadelphia (PA): AACR; Can Prev Res 2023;16(1 Suppl): Abstract nr P030.

Systemic inflammation factors as survival prognosis markers in ovarian neoplasm and the relationship with cancer-associated inflammatory mediators-a review.

At the level of the genital system, ovarian neoplasm is the most frequent cause of morbidity and mortality. In the specialized literature, the coexistence of an inflammatory process is admitted from the early stages of the evolution of this pathology. Starting from the importance of this process, both in determinism and in the evolution of carcinogenesis and summarizing the field of knowledge, for this study we considered two objectives: the first was the presentation of the pathogenic mechanism, through which chronic +ovarian inflammation is involved in the process of carcinogenesis, and the second is the justification of the clinical utility of the three parameters, accepted as biomarkers of systemic inflammation: neutrophil-lymphocyte ratio, platelet lymphocyte ratio, and lymphocyte-monocyte ratio in the assessment of prognosis. The study highlights the acceptance of these hematological parameters, with practical utility, as prognostic biomarkers in ovarian cancer, based on the intrinsic link with cancer-associated inflammatory mediators. Based on the data from the specialized literature, the conclusion is that in ovarian cancer, the inflammatory process induced by the presence of the tumor, induces changes in the types of circulating leukocytes, with immediate effects on the markers of systemic inflammation.

Integrative multi-omics and machine learning approach reveals tumor microenvironment-associated prognostic biomarkers in ovarian cancer

Integrative multi-omics and machine learning approach reveals tumor microenvironment-associated prognostic biomarkers in ovarian cancer

High expression of SPINT2 promotes immune infiltration and tumor progression in ovarian cancer

This study aimed to identify the function and mechanism of Serine Peptidase Inhibitor, Kunitz Type 2 (SPINT2) in ovarian cancer (OC). The expression of SPINT2 was analyzed using the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) database. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to identify enriched functional categories of SPINT2 and its correlated genes. Correlation of SPINT2 with OC immune infiltration level was analyzed using the Tumor IMmune Estimation Resource (TIMER) web server. The effect of SPINT2 on cell proliferation was detected using cell counting kit-8 (CCK-8) and colony formation assay. Its effects on cell migration and invasion were examined using the transwell assay. Function of SPINT2 in M2 macrophage recruitment was detected using the migration assay. The role of SPINT2 on M2 macrophage differentiation was evaluated using M2 macrophage markers and by detection of interleukin 10 (IL-10) release. An OC cohort study (GSE12470) showed that SPINT2 was highly expressed in OC. The high SPINT2 expression was related to shorter overall survival (OS) and poor recurrence-free survival (RFS). GO and KEGG analysis indicated that SPINT2 associated genes played roles in glycoprotein catabolism, cell adhesion and T cell differentiation. SPINT2 also played a key role in infiltration of macrophages in OC. shSPINT2 reduced viability and colony formation ability of ovarian cancer cell line SK-OV-3 cells. Moreover, shSPINT2 also inhibited the cell migration and invasion. Co-culture of shSPINT2 transfected SK-OV-3 cells with macrophages inhibited the migration of M2 macrophages, and inhibited macrophages polarization from M0 to M2. These results suggested that SPINT2 is involved in infiltration of tumor-associated macrophages (TAMs). SPINT2 also plays an important role in the polarization and migration of macrophages. These findings suggested that SPINT2 has the potential to be explored as a biomarker for OC and a potential target.

Two-phase dual-signal-readout immunosensing platform based on multifunctional carbon nano-onions for ovarian cancer biomarker detection.

In order to detect early tumor markers and gain valuable time for treatment, there is an urgent need to develop a fast, cheap, and ultrasensitive multi-reading sensing platform. Herein, a solid/liquid two-phase dual-output biosensor was explored based on a sensitized sonochemiluminescence (SCL) strategy and a multifunctional carbon nano-onion (CNO) probe. It is clear that ultrasonic radiation caused the formation of hydroxyl radicals (˙OH), triggering the SCL signal of the emitter lucigenin (Luc2+). Meanwhile, titanium carbide nanodots and ethanol were used to enhance the SCL signal, and an astonishingly linear enhancement of the SCL intensity was produced with increasing ethanol concentration. More importantly, the CNOs, with their excellent photothermal properties and adsorption capacity, can output both the temperature signal and an enhanced SCL strength from the solid-liquid phase. Through inter-calibration of the signals from the two-phases, this biosensor shows excellent analytical performance for the detection of the ovarian cancer biomarker, human epididymis-specific protein 4, from 10-5 to 10 ng mL-1 with a low detection limit of 3.3 fg mL-1. This work not only provides a novel two-phase signal-output mode that broadens the scope of multiperformance joint applications of CNOs, but also enriches the quantitative detection of point-of-care testing.

Analysis of Erythrocyte Sedimentation Rate Order in Epithelial Ovarian Cancer.

Erythrocyte sedimentation rate order (ESRO), platelet-lymphocyte ratio (PLR), red cell distribution width (RDW), and neutrophil-lymphocyte ratio (NLR) are hematological parameters that reflect the presence of biomarkers in epithelial ovarian cancer (EOC). This study evaluated the best haematological parameter in order to distinguish between EOC patients and healthy individuals. There were a total of 33 patients with EOC as subjects treated in Dr. Sardjito hospital, Yogyakarta, Indonesia and 32 volunteer subjects as controls. Curves of receiver operation, area under the curve, specificity, and sensitivity were estimated. To demonstrate EOC's presence, the ESRO was better than the hematological parameters of RDW, PLR, and NLR (AUC = 0.9245, 0.9010, 0.8351, and 0.7457, respectively; sensitivity and specificity = 83.3 and 90.6; 88.9 and 87.5; 100.0 and 68.8; and 94.4 and 53.1, respectively) at the cut-off points 1,125, 44.2, 163.2, and 2,551. Hence, ESRO is a better parameter to indicate the presence of EOC compared to RDW, PLR and NLR.

Kisspeptin 54 as biomarker for breast and ovarian cancer

Kisspeptin 54 as biomarker for breast and ovarian cancer

An Analytical Approach of Micro-RNA Interaction Study in Ovarian Cancer

A gynecological malignancy with a high mortality rate and a variety of forms, ovarian cancer is challenging to identify early and prevent. Numerous studies have examined the expression profile of microRNAs (miRNAs) in tissue and serum samples of patients to discover pertinent biomarkers for ovarian cancer. Functional tests also demonstrated that a number of miRNAs had oncogenic or suppressive effects. Despite the invention of various biological markers, mostly mRNA and protein, ovarian cancer mortality remains a problem due to late detection, which is related to low specificities and sensitivities. Recent advancements in expression biology have turned towards this compulsive direction for cancer diagnosis and prognosis; focusing on identification and development of specific and sensitive biomarkers, such as microRNAs (miRNAs). A number of miRNAs, including miR-23b, miR- 146b, miR-200-a/b/c, miR-630, miR-31 influence the epithelial-mesenchymal transition pathway, modulating ovarian cancer cell invasiveness. MiRNAs have such a broad range of functions in ovarian cancer that they have been recommended as potential treatment strategies in the future. In this review, we discuss the role of various micro RNAs and the genes they target in the pathogenesis of ovarian cancer, as well as the potential applications of these molecules as biomarkers.

NcRNA-mediated low expression of P2RY14 correlates with poor prognosis and tumor immune infiltration in ovarian carcinoma.

Ovarian cancer (OV) has been puzzling clinicians because of its poor prognosis. More and more evidence show that the G protein coupled receptor P2RY14 plays a key role in the initiation and progression of various types of human cancer. The purpose of our study is to explore the correlation between P2RY14 and the prognosis of ovarian cancer patients and the relevant mechanism. First, the differentially expressed gene P2RY14 was screened from The Cancer Genome Atlas (TCGA) database. Explored possible P2RY14 related miRNAs and lncRNAs through multiple public databases, predicted and analyzed the expression level of candidate miRNAs and candidate lncRNAs that can bind to candidate miRNAs in OV through StarBase database. The TIMER database was used to comprehensively analyze the expression of tumor infiltrating immune cells, and to analyze the correlation between the expression level of P2RY14 and the level of immune cell infiltration in OV or the expression level of immune checkpoints. Patients with P2RY14 overexpression had better overall survival (OS) and progression-free interval (PFI). In the Targetscan database, 22 upstream miRNAs that may bind to P2RY14 were predicted. According to the regulatory network constructed by the Cytoscape software, correlation analysis and the role of miRNAs in the prognosis of OV, we first determined that the candidate miRNAs were miR-34c-5p. Then, we predicted the upstream lncRNAs of miR-34c-5p in the StarBase database, the expression level of these lncRNAs in OV in the Gene Expression Profiling Interactive Analysis (GEPIA) database, and the role in prognosis. We determined that LINC00665 is the most potential lncRNA upstream of ovarian cancer miRNA (hsa-miR-34c-5p)-P2RY14. Then, we analyzed the results in the Timer database, suggesting that P2RY14 expression was positively correlated with CD8+T Cell, CD4+T Cell, Macrophage, Neutral and Dendritic cells, and negatively correlated with B cells. Meanwhile, P2RY14 was positively correlated with CD274 and PDCD1. P2RY14 can be used as a new predictive biomarker of ovarian cancer. Intervention of P2RY14 can affect the prognosis of ovarian cancer by affecting LINC00665-miR-34c-5p-P2RY14 axis. These findings provide a potential target for the development of anti-cancer strategies for ovarian cancer.

Current and emerging biomarkers in ovarian cancer diagnosis; CA125 and beyond.

Ovarian cancer (OC) is one of the most common causes of cancer-related death in women worldwide. Its five-year survival rates are worse than the two most common gynecological cancers, cervical and endometrial. This is because it is asymptomatic in the early stages and usually detected in the advanced metastasized stage. Thus, survival is increasingly dependent on timely diagnosis. The delay in detection is contributed partly by the occurrence of non-specific clinical symptoms in the early stages and the lack of effective biomarkers and detection approaches. This underlines the need for biomarker identification and clinical validation, enabling earlier diagnosis, effective prognosis, and response to therapy. Apart from the traditional diagnostic biomarkers for OC, several new biomarkers have been delineated using advanced high-throughput molecular approaches in recent years. They are currently being clinically evaluated for their true diagnostic potential. In this chapter, we document the commonly utilized traditional screening markers and recently identified emerging biomarkers in OC diagnosis, focusing on secretory and protein biomarkers. We also briefly reviewed the recent advances and prospects in OC diagnosis.

Duplex-immunoassay of ovarian cancer biomarker CA125 and HE4 based carbon dot decorated dendritic mesoporous silica nanoparticles.

Fluorescent lateral flow immunoassay (FLFIA) is widely used mainly because of its low cost and instant detection. Its limit of detection (LOD) is closely related to fluorescence signals, and the development of fluorescence signals with fine performance remains a challenge. In this work, dendritic mesoporous silica nanoparticles (DMSNs) were used as fine carriers due to their large pore size and stable performance. We successfully synthesized carbon dots (CDs) with a 560 nm maximum emission wavelength (CD560) by the hydrothermal method. A new type of fluorescence signal for FLFIA was observed by loading CD560 on DMSNs through the Si-O bond which is denoted as DMSNs@CD560. Applying DMSNs@CD560 to the FLFIA can eliminate the influence of interfacial background blue fluorescence thus improving its detection sensitivity. The formed DMSNs@CD560-FLFIA achieved high sensitivity detection of ovarian cancer biomarkers carbohydrate antigen 125 (CA125) and human epididymal protein 4 (HE4). The LOD of CA125 is 0.5 U mL-1 and the correlation coefficient R2 = 0.985, and the LOD of HE4 reaches 0.05 ng mL-1 and the correlation coefficient R2 = 0.981. The DMSNs@CD560-FLFIA is sensitive and efficient providing a new method for the early diagnosis of ovarian cancer.

Nanoscale detection of ovarian cancer biomarker

Nanoscale detection of ovarian cancer biomarker

The extracellular secretion of miR-1825 wrapped by exosomes increases CLEC5A expression: A potential oncogenic mechanism in ovarian cancer

<b>Background:</b> Ovarian cancer (OC) is a leading cause of gynecological cancer-linked deaths worldwide. Exosomal <i>miR-1825</i> and its target gene C-type lectin domain family 5 member A (<i>CLEC5A</i>) are associated with tumorigenesis in cancers that was further probed. <b>Methods:</b> Exosomal <i>miR-1825</i> expression in exosomes and its impact on overall survival (OS) prediction were determined using Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) data. Target genes of <i>miR-1825</i> were searched in five prediction databases and prognostically significant differentially expressed genes were identified. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were carried out. The ability of <i>CLEC5A</i> to predict OS was evaluated using univariate and multivariate Cox regression analyses and Kaplan-Meier curves. The <i>CLEC5A</i> expression pattern in OC was validated using immunohistochemistry. The CIBERSORT algorithm was used to compare the immune cell landscape, and the results were validated in a GEO cohort. Finally, the predicted half maximal inhibitory concentration (IC50) values for five commonly used chemotherapy agents were also compared. <b>Results:</b> <i>MiR-1825</i> level was higher in exosomes derived from OC cells and served as a tumor suppressor. The <i>CLEC5A</i> gene was found to be a target of <i>miR-1825</i>, the upregulation of which was correlated with a poor prognosis. M2 macrophage infiltration was significantly enhanced in the <i>CLEC5A</i> high expression group, while T follicular helper cell infiltration was reduced in it. While the predicted IC50 for cisplatin and doxorubicin was higher in the <i>CLEC5A</i> high expression group, that of docetaxel, gemcitabine, and paclitaxel was lower. <b>Conclusion:</b> <i>MiR-1825</i>, a promising OC biomarker, may promote OC progression by increasing <i>CLEC5A</i> expression via exosome-mediated efflux from tumor cells.