Diagnostic Biomarker For Ovarian Cancer Research Articles

Identification of circulating microRNAs as diagnostic biomarkers for ovarian cancer:A pooled analysis of individual studies.

Circulating microRNAs (miRNAs) are proposed as promising non-invasive diagnostic biomarkers for many cancers. However, the diagnostic value of circulating miRNAs in ovarian cancer is inconsistent in different studies. Thus we performed this meta-analysis to systematically evaluate the diagnostic value of circulating miRNAs in ovarian cancer. Eligible studies that were published prior to 30 June 2017 were searched from the PubMed, EMBASE, Cochrane Library, and Chinese National Knowledge Infrastructure. All analyses were performed using STATA 12.0 software. A bivariate regression was used to calculate pooled diagnostic accuracy estimates. A total of 36 studies from 16 publications were included in this meta-analysis. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio of circulating miRNAs for ovarian cancer diagnosis were 0.76 (95% confidence intervals (CI): 0.69, 0.81), 0.81 (95% CI 0.74, 0.87), 4.00 (95% CI 2.70, 5.30), 0.30(95% CI 0.24, 0.37) and 13.00 (95% CI 9.00, 19.00), respectively. The area under the summary receiver operating characteristic curve was 0.85 (95% CI 0.82, 0.88). Subgroup analyses showed that multiple miRNA assays yielded better diagnostic characteristics than a single miRNA assay, and plasma miRNAs were better than serum miRNAs for ovarian cancer detection. Circulating miRNAs, especially the combination of multiple circulating miRNAs, are promising biomarkers for the diagnosis of ovarian cancer. However, further large-scale prospective studies are necessary to validate the applicability of the miRNAs in the early detection of ovarian cancer.

Method development of glycoprotein biomarkers for cancers.

Method development of glycoprotein biomarkers for cancers.

Phosphoserine imprinted nanosensor for detection of Cancer Antigen 125

Cancer antigen 125 (CA 125) is widely used as diagnostic biomarker for ovarian cancer. Change in the concentration level of CA 125 is associated with disease progression or regression. CA 125 posseses a phosphorylation site and protein backbone is phosphorylated on serine, before secretion. In this study, we have developed an imprinting method for CA 125 recognition and determination. In this method, methacryloyl antipyrine europium (III) [(MAAP)2-Eu(III)] and methacryloyl antipyrine terbium (III) [(MAAP)2-Tb(III)] have been used as new metal-chelating monomers via metal coordination–chelation interactions. Phosphoserine (PS) has been used as a template for the detection of CA 125. PS imprinted carbon nanotube (CNT) and Fe2O3 nanoparticle (SPN) have cavities that are selective for CA 125. The binding affinity of the PS imprinted CNT and SPN nanosensor has been investigated using Langmuir adsorption isotherms and affinity constants (Kaffinity) have found to be 1.85 105M−1 for PS and 13.5 10−3 mLU−1 and 7.73 10−3 mLU−1 for CA 125 (for CNT and SPN, respectively). Detection limit of PS imprinted CNT nanosensor for PS and CA 125 have been found to be 1.77 10−10M and 0.49 UmL−1, respectively. Human serum samples have been spiked with different concentrations of CA 125 (in pH 7.4 PBS) to investigate the feasibility of the nanosensors for clinical applications. Experimental results have been revealed that prepared nanosensors have been exhibited better sensivity, recovery and reproducibility.

Diagnostic accuracy of urine HE4 in patients with ovarian cancer: a meta-analysis.

Urine HE4 has been reported as the potential novel diagnostic biomarker for ovarian cancer in several studies, but their results were inconsistent. Therefore, we conducted a systematic analysis to evaluate the diagnostic value of urine HE4 in detecting ovarian cancer. A comprehensive electronic and manual search was conducted for relevant literatures through several databases up to May 5, 2016. The quality of the studies included in the systematic review was assessed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. All analyses were conducted using Meta-DiSc 1.4 and STATA 12.0 software. A total of seven publications were included in this study, and these studies included 413 ovarian cancer patients and 573 controls. The summary estimates were: sensitivity 0.76 (95% confidence interval [CI]: 0.72–0.80), specificity 0.92 (95% CI: 0.89–0.94), positive likelihood ratio 8.39 (95%CI: 4.81–14.63), negative likelihood ratio 0.23 (95% CI: 0.13–0.39), diagnostic odds ratio 37.90 (95% CI: 18.69–76.83), and area under the curve 0.93. According to our results, urine HE4 has greater diagnostic value in detecting ovarian cancer. In addition, considering the high heterogeneity, further research studies with more well-designed and large sample sizes are needed in the future.

Circulating Cell Free DNA as the Diagnostic Marker for Ovarian Cancer: A Systematic Review and Meta-Analysis.

BackgroundQuantitative analyses of circulating cell-free DNA (cfDNA) are potential methods for the detection of ovarian cancer. Many studies have evaluated these approaches, but the results were too inconsistent to be conclusive. This study is the first to systematically evaluate the accuracy of circulating cfDNA for the diagnosis of ovarian cancer by conducting meta-analysis.MethodsWe searched PubMed, Embase, Cochrane Library and the Chinese National Knowledge Infrastructure (CNKI) databases systematically for relevant literatures up to December 10, 2015. All analyses were conducted using Meta-DiSc1.4 and Stata 12.0 software. Sensitivity, specificity and other measures of accuracy of circulating cfDNA for the diagnosis of ovarian cancer were pooled. Meta-regression was performed to identify the sources of heterogeneity.ResultsThis meta-analysis included a total of 9 studies, including 462 ovarian cancer patients and 407 controls. The summary estimates for quantitative analysis of circulating cfDNA in ovarian cancer screen were as follows: sensitivity, 0.70 (95% confidence interval (CI), 0.65–0.74); specificity, 0.90 (95% CI, 0.87–0.93); positive likelihood ratio, 6.60 (95% CI, 3.90–11.17); negative likelihood ratio, 0.34 (95% CI, 0.25–0.47); diagnostic odds ratio, 26.05 (95% CI, 14.67–46.26); and area under the curve, 0.89 (95% CI, 0.83–0.95), respectively. There was no statistical significance for the evaluation of publication bias.ConclusionsCurrent evidence suggests that quantitative analysis of cfDNA has unsatisfactory sensitivity but acceptable specificity for the diagnosis of ovarian cancer. Further large-scale prospective studies are required to validate the potential applicability of using circulating cfDNA alone or in combination with conventional markers as diagnostic biomarker for ovarian cancer and explore potential factors that may influence the accuracy of ovarian cancer diagnosis.

Diagnostic values of osteopontin combined with CA125 for ovarian cancer: a meta-analysis.

Osteopontin (OPN) is currently one of the most studied serum biomarkers of ovarian cancer (OC). This meta-analysis aims to ascertain whether OPN is a useful diagnostic biomarker for OC and determine the overall diagnostic accuracy of OPN measurement when combined with cancer antigen 125 (CA125). A systematic literature search was conducted in Cochrane Library, PubMed, EMBASE, CBM, and China National Knowledge Infrastructure databases. Information was independently extracted by two investigators. The Quality Assessment for Studies of Diagnostic Accuracy tool was applied to examine the quality of eligible studies, and a bivariate model was used to calculate the pooled estimates. We identified 17 and 10 studies that evaluated the role of OPN alone and OPN combined with CA125 in diagnosing ovarian tumor, respectively. The overall diagnostic sensitivity and specificity of OPN in OC were 0.766 (95% CI 0.685-0.831) and 0.897 (95% CI 0.849-0.931), correspondingly. When OPN was combined with CA125, the sensitivity and specificity were 0.871 (95% CI 0.788-0.924) and 0.881 (95% CI 0.837-0.914), respectively. OPN is a useful tumor biomarker in future screening tests of OC and can be a promising adjunct to CA125. Additional studies with multicenter trials and carefully selected controls are needed to further verify the results.

Towards an Animal Model of Ovarian Cancer: Cataloging Chicken Blood Proteins Using Combinatorial Peptide Ligand Libraries Coupled with Shotgun Proteomic Analysis for Translational Research

Epithelial ovarian cancer is the most deadly gynecological cancer around the world, with high morbidity in industrialized countries. Early diagnosis is key in reducing its morbidity rate. Yet, robust biomarkers, diagnostics, and animal models are still limited for ovarian cancer. This calls for broader omics and systems science oriented diagnostics strategies. In this vein, the domestic chicken has been used as an ovarian cancer animal model, owing to its high rate of developing spontaneous epithelial ovarian tumors. Chicken blood has thus been considered a surrogate reservoir from which cancer biomarkers can be identified. However, the presence of highly abundant proteins in chicken blood has compromised the applicability of proteomics tools to study chicken blood owing to a lack of immunodepletion methods. Here, we demonstrate that a combinatorial peptide ligand library (CPLL) can efficiently remove highly abundant proteins from chicken blood samples, consequently doubling the number of identified proteins. Using an integrated CPLL-1DGE-LC-MSMS workflow, we identified a catalog of 264 unique proteins. Functional analyses further suggested that most proteins were coagulation and complement factors, blood transport and binding proteins, immune- and defense-related proteins, proteases, protease inhibitors, cellular enzymes, or cell structure and adhesion proteins. Semiquantitative spectral counting analysis identified 10 potential biomarkers from the present chicken ovarian cancer model. Additionally, many human homologs of chicken blood proteins we have identified have been independently suggested as diagnostic biomarkers for ovarian cancer, further triangulating our novel observations reported here. In conclusion, the CPLL-assisted proteomic workflow using the chicken ovarian cancer model provides a feasible platform for translational research to identify ovarian cancer biomarkers and understand ovarian cancer biology. To the best of our knowledge, we report here the most comprehensive survey of the chicken blood proteome to date.

Abstract 511: Characterization of humoral response profiles of ovarian cancer patients as diagnostic biomarkers

Abstract Ovarian cancer is the 5th leading cause of cancer death among women and is the leading cause of death among gynecologic malignancies as a result of limitations in early diagnosis. It is associated with the induction of humoral immunity, characterized by the generation of IgG against a wide range of tumor-associated antigens. Although the role of circulating tumor-reactive IgG is unclear, it has been demonstrated to correlate with poor prognosis and survival. The use of tumor-reactive IgG as a potential diagnostic and/or prognostic tool has been limited since most studies have identified components with shared expression in non-neoplastic tissue and failed to define targets exhibiting ubiquitous expression in ovarian cancers. A number of these limitations result from the use of recombinant proteins (translated in non-mammalian cells) as targets to define immunoreactivity. The power in utilizing the autologous humoral response is its ability to define alterations which might lead to the altered appearance or aberrant association of antigen with other cellular components, thereby resulting in the induction of humoral responses. We therefore hypothesize that the ovarian patient humoral response to specific tumor antigens can serve as diagnostic biomarkers of ovarian cancer. To investigate patients’ humoral response to “natural” tumor antigens, ovarian tumor cell lysates and tumor-derived exosomes were separated into antigenic proteins by first dimension isoelectric focusing via an immobilized pH gradient and/or rotofor fractionation. Reactive proteins were further separated by second dimension SDS-PAGE, followed by western immunoblotting using autologous or heterologous patient sera. Circulating tumor-reactive IgG was found to recognize shared (MW 37-75 kD, pI 3.0-5.5) and distinct (MWexo ∼50 kD, pI 9.0-10.5; MWlys∼15 kD, pI 4.3-6.5) ovarian tumor cell proteins. Recognition of these antigenic proteins was not observed using antibodies derived from normal control patients. Collectively, our data supports that the circulating IgG produced in the ovarian cancer humoral response recognizes specific tumor antigens (many of which are shared among cancer patients) and will thus have utility in diagnosing these patients. Further identification of these antigenic proteins by mass spectrometry will produce specific diagnostic targets and may provide insights on common driver mechanisms in ovarian cancer. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 511. doi:1538-7445.AM2012-511

In Silico discovery of transcription factors as potential diagnostic biomarkers of ovarian cancer

BackgroundOur study focuses on identifying potential biomarkers for diagnosis and early detection of ovarian cancer (OC) through the study of transcription regulation of genes affected by estrogen hormone.ResultsThe results are based on a set of 323 experimentally validated OC-associated genes compiled from several databases, and their subset controlled by estrogen. For these two gene sets we computationally determined transcription factors (TFs) that putatively regulate transcription initiation. We ranked these TFs based on the number of genes they are likely to control. In this way, we selected 17 top-ranked TFs as potential key regulators and thus possible biomarkers for a set of 323 OC-associated genes. For 77 estrogen controlled genes from this set we identified three unique TFs as potential biomarkers.ConclusionsWe introduced a new methodology to identify potential diagnostic biomarkers for OC. This report is the first bioinformatics study that explores multiple transcriptional regulators of OC-associated genes as potential diagnostic biomarkers in connection with estrogen responsiveness. We show that 64% of TF biomarkers identified in our study are validated based on real-time data from microarray expression studies. As an illustration, our method could identify CP2 that in combination with CA125 has been reported to be sensitive in diagnosing ovarian tumors.

Proteomic Contributions to Personalized Cancer Care

Cancer impacts each patient and family differently. Our current understanding of the disease is primarily limited to clinical hallmarks of cancer, but many specific molecular mechanisms remain elusive. Genetic markers can be used to determine predisposition to tumor development, but molecularly targeted treatment strategies that improve patient prognosis are not widely available for most cancers. Individualized care plans, also described as personalized medicine, still must be developed by understanding and implementing basic science research into clinical treatment. Proteomics holds great promise in contributing to the prevention and cure of cancer because it provides unique tools for discovery of biomarkers and therapeutic targets. As such, proteomics can help translate basic science discoveries into the clinical practice of personalized medicine. Here we describe how biological mass spectrometry and proteome analysis interact with other major patient care and research initiatives and present vignettes illustrating efforts in discovery of diagnostic biomarkers for ovarian cancer, development of treatment strategies in lung cancer, and monitoring prognosis and relapse in multiple myeloma patients.

RETRACTED: MicroRNA signatures of tumor-derived exosomes as diagnostic biomarkers of ovarian cancer

Most ovarian cancer patients are diagnosed at an advanced stage (67%) and prospects for significant improvement in survival reside in early diagnosis. While expression patterns of a recently identified biomarker family, microRNA, appear to be characteristic of tumor type and developmental origin, microRNA profiling has been limited to tissue specimens. Tumors actively release exosomes into the peripheral circulation and we now demonstrate the association of microRNAs with circulating tumor-derived exosomes. Circulating tumor exosomes were isolated using a modified MACS procedure with anti-EpCAM. Initially, microRNA profiles of ovarian tumors were compared to those of tumor exosomes isolated from the same patients. Levels of 8 microRNAs (miR-21, miR-141, miR-200a, miR-200c, miR-200b, miR-203, miR-205 and miR-214) previously demonstrated as diagnostic, were compared in exosomes isolated from sera specimens of women with benign disease and various stages of ovarian cancer. MicroRNA from ovarian tumor cells and exosomes from the same patients were positive for 218 of 467 mature microRNAs analyzed. The levels of the 8 specific microRNAs were similar between cellular and exosomal microRNAs (exhibiting correlations from 0.71 to 0.90). While EpCAM-positive exosomes were detectable in both patients with benign ovarian disease and ovarian cancer, exosomal microRNA from ovarian cancer patients exhibited similar profiles, which were significantly distinct from profiles observed in benign disease. Exosomal microRNA could not be detected in normal controls. These results suggest that microRNA profiling of circulating tumor exosomes could potentially be used as surrogate diagnostic markers for biopsy profiling, extending its utility to screening asymptomatic populations.

Recent technical strategies to identify diagnostic biomarkers for ovarian cancer

Ovarian cancer is the fifth leading cause of cancer deaths among North American women. Regrettably, there is currently no reliable circulating biomarker that can detect ovarian cancer in its early stages. The CA125 biomarker is very useful for treatment response monitoring, but its sensitivity is very low for early detection. Thus, there is an urgent need for the identification of new circulating biomarkers/panel of biomarkers that could be used to diagnose ovarian cancer before it becomes clinically detectable and advanced. Unfortunately, the strategies used in the past years to identify such biomarkers have not led to any outstanding candidate. This review summarizes the different approaches used in the last decade and suggests which strategies should be adopted in the near future in order to lead to the successful identification of new ovarian cancer diagnostic biomarkers.

Preoperative plasma osteopontin level as a biomarker complementary to carbohydrate antigen 125 in predicting ovarian cancer

New biomarkers other than carbohydrate antigen (CA) 125 are needed for the detection of ovarian cancer. Osteopontin (OPN) is one of the candidates identified by high-throughput complementary DNA microarray techniques. We evaluated the preoperative plasma OPN level as a diagnostic biomarker for ovarian cancer in comparison with CA125. Preoperative plasma OPN and CA125 levels were measured and compared in 32 patients with ovarian cancer, 34 patients with benign ovarian tumor, 30 patients with other gynecologic cancers and 31 healthy women. Preoperative plasma OPN levels were also assessed according to tumor stage, the volume of ascites and histological types. The sensitivity and specificity for predicting ovarian cancer was compared between OPN and CA125. Preoperative plasma OPN levels were significantly higher in patients with ovarian cancer than in those with benign ovarian tumor, in other gynecologic patients or in healthy women. Stage IV ovarian cancer patients and ovarian cancer patients with ascites had higher plasma OPN levels than those without ascites and in a lower stage. There was no relation between OPN and the histological type. The sensitivity of preoperative plasma OPN in detecting ovarian cancer was 81.3% and almost reached that of CA125. The specificity was moderate. Sensitivity increased to 93.8% with the combination of CA125, compared to 84.4% with CA125 alone. Preoperative OPN is a useful biomarker for predicting ovarian cancer. It is especially useful when used complementary to CA125. Larger studies of patients with ovarian cancer showing a low CA125 level or in early stages of ovarian cancer are needed.

Biomarker discovery for ovarian cancer using SELDI-TOF-MS

Objectives. The purpose of this study is to discover potential biomarkers for the detection and monitoring of adjuvant chemotherapy for ovarian cancer. Methods. Serum samples from ovarian cancers and non-cancer controls were analyzed using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS). To discover the possible diagnostic biomarker for ovarian cancer, a preliminary training set of spectra derived from 31 primary ovarian cancer patients, 16 patients with benign ovarian diseases, and 25 healthy women was used to develop a proteomic model that discriminated cancer from non-cancer effectively. A blind test set, including 43 new cases, was used to validate the sensitivity and specificity of this multivariate model. To explore treatment-induced serum protein change, the protein profiles generated from 16 postoperative patients before chemotherapy are compared with those obtained after chemotherapy. Results. A Four-peak model was established in the training set that discriminated cancer from non-cancer with sensitivity of 90.8% and specificity of 93.5%. A sensitivity of 87.0% and a specificity of 95.0% for the blind test were obtained, compared with 60.7%, 55% for CA125 for the same samples. These 4 markers performed significantly better than the current standard marker, CA125 ( P < 0.05). One protein peak (mass/charge ratio [ m/ z], 4475) was identified in 12 of 16 (75%) postoperative patients after chemotherapy, but was absent before chemotherapy. Conclusion. The proteins represented by these peaks are candidate biomarkers for ovarian cancer diagnosis and/or monitoring treatment response.

Osteopontin as a potential diagnostic biomarker for ovarian cancer.

Development of new biomarkers for ovarian cancer is needed for early detection and disease monitoring. Analyses involving complementary DNA (cDNA) microarray data can be used to identify up-regulated genes in cancer cells, whose products may then be further validated as potential biomarkers. To describe validation studies of an up-regulated gene known as osteopontin, previously identified using a cDNA microarray system. Experimental and cross-sectional studies were conducted involving ovarian cancer and healthy human ovarian surface epithelial cell lines and cultures, archival paraffin-embedded ovarian tissue collected between June 1992 and June 2001, and fresh tissue and preoperative plasma from 144 patients evaluated for a pelvic mass between June 1992 and June 2001 in gynecologic oncology services at 2 US academic institutions. Plasma samples from 107 women selected from an epidemiologic study of ovarian cancer initiated between May 1992 and March 1997 were used as healthy controls. Relative messenger RNA expression in cancer cells and fresh ovarian tissue, measured by real-time polymerase chain reaction as 2(-DeltaDeltaCT)(a quantitative value representing the amount of osteopontin expression); osteopontin production, localized and scored in ovarian healthy and tumor tissue with immunohistochemical studies; and amount of osteopontin in patient vs control plasma, measured using an enzyme-linked immunoassay. The geometric mean for 2(-DeltaDeltaCT)for osteopontin expression in 5 healthy ovarian epithelial cell cultures was 4.1 compared with 270.4 in 14 ovarian cancer cell lines (P =.03). The geometric mean 2(-DeltaDeltaCT)for osteopontin expression in tissue from 2 healthy ovarian epithelial samples was 9.0 compared with 164.0 in 27 microdissected ovarian tumor tissue samples (P =.06). Immunolocalization of osteopontin showed that tissue samples from 61 patients with invasive ovarian cancer and 29 patients with borderline ovarian tumors expressed higher levels of osteopontin than tissue samples from 6 patients with benign tumors and samples of healthy ovarian epithelium from 3 patients (P =.03). Osteopontin levels in plasma were significantly higher (P<.001) in 51 patients with epithelial ovarian cancer (486.5 ng/mL) compared with those of 107 healthy controls (147.1 ng/mL), 46 patients with benign ovarian disease (254.4 ng/mL), and 47 patients with other gynecologic cancers (260.9 ng/mL). Our findings provide evidence for an association between levels of a biomarker, osteopontin, and ovarian cancer and suggest that future research assessing its clinical usefulness would be worthwhile.