Novel Biomarker For Ovarian Cancer Research Articles

Integrated bioinformatics and experimental validation reveal the role of JARID2 in ovarian cancer.

Aim: To develop novel prognostic markers for early detection and prognosis of ovarian cancer (OC). Materials & methods: We utilized bioinformatics analysis to identify and construct a prognostic model consisting of lncRNAs centered around JARID2 and explored the potential ceRNA network in OC. Cell functional experiments were conducted to validate the reliability of the ceRNA network and investigate the functional role of JARID2 in OC. Results: We constructed a nomogram composed of ten lncRNAs and identified the PKD1P6/miR-424-5p/JARID2 axis. Furthermore, our findings indicated that JARID2 promotes the proliferation of SKOV3 cells, suggesting its oncogenic role in OC. Conclusion: JARID2, potentially regulated by the PKD1P6/miR-424-5p/JARID2 axis, represents a potential novel biomarker for OC.

Gene co-expression network analysis revealed novel biomarkers for ovarian cancer.

Ovarian cancer is the second most common gynecologic cancer and remains the leading cause of death of all gynecologic oncologic disease. Therefore, understanding the molecular mechanisms underlying the disease, and the identification of effective and predictive biomarkers are invaluable for the development of diagnostic and treatment strategies. In the present study, a differential co-expression network analysis was performed via meta-analysis of three transcriptome datasets of serous ovarian adenocarcinoma to identify novel candidate biomarker signatures, i.e. genes and miRNAs. We identified 439 common differentially expressed genes (DEGs), and reconstructed differential co-expression networks using common DEGs and considering two conditions, i.e. healthy ovarian surface epithelia samples and serous ovarian adenocarcinoma epithelia samples. The modular analyses of the constructed networks indicated a co-expressed gene module consisting of 17 genes. A total of 11 biomarker candidates were determined through receiver operating characteristic (ROC) curves of gene expression of module genes, and miRNAs targeting these genes were identified. As a result, six genes (CDT1, CNIH4, CRLS1, LIMCH1, POC1A, and SNX13), and two miRNAs (mir-147a, and mir-103a-3p) were suggested as novel candidate prognostic biomarkers for ovarian cancer. Further experimental and clinical validation of the proposed biomarkers could help future development of potential diagnostic and therapeutic innovations in ovarian cancer.

Circulating Biomarkers of Inflammation and Ovarian Cancer Risk in the Nurses' Health Studies.

Chronic inflammation is a well-established mechanism of ovarian carcinogenesis; however, the specific immunogenic processes influencing ovarian tumor development remain unclear. In a case-control study nested within the Nurses' Health Study (NHS) and the NHSII, we examined the association between six inflammatory chemokines and cytokines [B-cell activating factor (BAFF), C-X-C motif chemokine ligand 13 (CXCL13), IL8, soluble(s)IL2-receptor-α(Rα), sIL6Rα] and epithelial ovarian cancer risk. Among 299 epithelial ovarian cancer cases and 334 matched controls, six inflammatory biomarkers were measured in plasma collected 1-24 years before diagnosis or index date using two custom multiplex Luminex panels. ORs and 95% confidence intervals (CI) were estimated for the association between each biomarker and risk using multivariable conditional logistic regression with adjustment for relevant confounders. We additionally assessed heterogeneity in the risk associations by histotype [high-grade serous carcinoma (HGSC) vs. non-HGSC], body mass index, smoking status, menopausal status, and aspirin use. Women with the highest versus lowest quartile (Q) levels of CXCL13 had a 72% increased ovarian cancer risk (OR = 1.72; 95% CI = 1.04-2.83; P trend = 0.007). The positive association with CXCL13 was stronger in magnitude for non-HGSC, overweight or obese women, and postmenopausal women, although only menopausal status demonstrated statistically significant heterogeneity (P interaction = 0.04). The remaining biomarkers were not associated with risk. This first evidence that prediagnostic CXCL13, a B-cell chemoattractant, is associated with an increased risk of epithelial ovarian cancer expands current understanding of the role of inflammation in ovarian carcinogenesis. CXCL13 may represent a novel biomarker for ovarian cancer.

Circ-0001068 is a novel biomarker for ovarian cancer and inducer of PD1 expression in T cells.

Ovarian cancer is a primary gynecological malignancy with a global 5-year survival rate of 44%. The majority of patients present with advanced disease at initial diagnosis because of the lack of an effective early detection screening test. Circular RNAs (circRNAs) within exosomes in the circulatory system are effective diagnostic and therapeutic biomarkers for many diseases, especially tumors. In this study, we used microarrays to identify 6 circRNAs that were upregulated and 37 circRNAs that were downregulated in exosomes from ovarian cancer patients as compared to healthy volunteers. We validated the accumulation trends for the 6 upregulated circRNAs in the training set using qRT-PCR and found that circ-0001068 was significantly higher in the serum exosomes from the ovarian cancer patients as than healthy volunteers. Circ-0001068 was next evaluated further in a larger cohort. As with the training set, results from the larger cohort revealed that levels of circ-0001068 in the exosomes were significantly higher in ovarian cancer patients than healthy volunteers. Circ-0001068 was also delivered into T cells and induced PD1 expression by acting as a competing endogenous RNA (ceRNA) for miR-28-5p through the exosomes.

Ovarian Cancer: Differentially Expressed microRNAs in Tumor Tissue and Cell-Free Ascitic Fluid as Potential Novel Biomarkers

Ovarian cancer is the deadliest gynecologic cancer. The large-scale microRNA (miRNA) expression profiling and individual miRNA validation was performed to find potential novel biomarkers for ovarian cancer. The most consistent overexpression of miRs-200b-3p, 135 b-5p and 182-5p was found in both ascitic fluid and tumors and suggests their potential as oncogenes. miR-451a was consistently underexpressed so may be a tumor suppressor. Results were inconsistent for miR-204-5p, which was overexpressed in ascitic fluid but underexpressed in tumor tissue. miR-203a-3p was generally overexpressed but this failed to be proved in independent sample set in tissue validation.

Abstract 598: Genetically predicted concentration of circulating proteins and epithelial ovarian cancer risk

Abstract Epithelial ovarian cancer (EOC) is the most common gynecological cancer, and ranks fifth in cancer deaths among women in the United States. Previous studies have identified several circulating proteins biomarkers for EOC risk, such as CA125 antigen and insulin-like growth factor-1 (IGF-1). However, previous studies on protein biomarkers for EOC were limited with small sample sizes, and only a few proteins investigated. In this study, we integrated genomics and proteomics data to search for novel circulating protein biomarkers for EOC risk. We constructed study instruments using genetic variants for protein quantitative trait loci (pQTL) for over 1,400 circulating proteins. Beta coefficients and standard errors of the selected pQTL variants in relation to EOC risk were obtained from 25,509 EOC cases and 40,941 controls from the Ovarian Cancer Association Consortium (OCAC). An inverse-variance weighted method was used to evaluate the association of these genetically predicted protein levels and risk of overall and serous EOC. We identified 35 protein biomarkers, for which their genetically predicted levels were significantly associated with the risk of either overall or serous EOC (false discovery rate <0.05). There are 25 proteins showing significant associations with both overall and serous EOC; most of the associations were stronger for serous EOC than overall EOC. The association for 31 proteins could be attributed, in part, to variants at ABO 9q34.2, a known ovarian cancer susceptibility locus. Five ABO locus-related proteins are in the PI3K-Akt signaling pathway, including insulin-like growth factor 1 receptor, hepatocyte growth factor receptor, interleukin-3 receptor subunit alpha, insulin receptor, and vascular endothelial growth factor receptor 2. Four of these proteins are also in the Rap1 and Ras signaling pathway. These five proteins are all inversely associated with the risk of overall EOC, with odds ratios ranging from 0.64 to 0.93 per unit increase in genetic risk scores (p values: 7.55×10-7 to 5.66×10-2). Four EOC associated proteins which were not influenced by variants in 9q34.2 are microfibrillar-associated protein 2, protein delta homolog 1, semenogelin-2, and netrin-G1, all of which showed significant associations with overall EOC, but not the serous subtype. Our study identifies potential novel biomarkers for ovarian cancer and suggests that genetic variants at locus 9q34.2 may play a role in the relation between levels of these proteins and risk of ovarian cancer. Citation Format: Guochong Jia, Xiang Shu, Xiao-Ou Shu, Jirong Long, Qiuyin Cai, Kirsten B. Moysich, Ellen L. Goode, Thomas A. Sellers, Wei Zheng. Genetically predicted concentration of circulating proteins and epithelial ovarian cancer risk [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 598.

Tissue miR-193b as a Novel Biomarker for Patients with Ovarian Cancer.

BackgroundMiRNAs play important roles in regulating many fundamental biological processes. Deregulation of miRNAs is involved in the initiation and progression of cancer. MiR-193b is regarded as tumor suppressor in many types of cancers. However, the role of miR-193b in ovarian cancer is poorly understood.Material/MethodsThe expression level of miR-193b in ovarian cancer cell lines and ovarian cancer samples was evaluated using quantitative real-time reverse transcription-PCR (qRT-PCR). The ovarian cancer patients were categorized into a high miR-193b expression group and a low miR-193b expression group according to the median miR-193b expression level. The correlation between tissue miR-193b expression and the patients’ clinicopathological factors, as well as survival, was also analyzed.ResultsThe results showed that the miR-193b expression was significantly down-regulated in ovarian cancer cell lines and tumor tissues compared with normal controls. In addition, tissue miR-193b expression was positively correlated with FIGO stage (P=0.001), histological grade (P=0.032), ascites (P=0.019), lymph node metastasis (P=0.003), and tumor size (P=0.041). Among 116 patients with ovarian cancer examined, the 5-year overall survival (OS) rates were 62.5% and 22.01% in patients with high and low miR-193b expression, respectively (P=0.003). Multivariate analysis showed that tissue miR-193b is an independent prognostic factor in patients with ovarian cancer (HR=4.219; P=0.015).ConclusionsReduction of miR-193b was found in ovarian cancer and its lower expression was associated with poorer prognosis. Tissue miR-193b showed potential as novel biomarker for ovarian cancer.

Extracellular Vesicles from Ovarian Carcinoma Cells Display Specific Glycosignatures.

Cells release vesicles to the extracellular environment with characteristic nucleic acid, protein, lipid, and glycan composition. Here we have isolated and characterized extracellular vesicles (EVs) and total cell membranes (MBs) from ovarian carcinoma OVMz cells. EVs were enriched in specific markers, including Tsg101, CD63, CD9, annexin-I, and MBs contained markers of cellular membrane compartments, including calnexin, GRASP65, GS28, LAMP-1, and L1CAM. The glycoprotein galectin-3 binding protein (LGALS3BP) was strongly enriched in EVs and it contained sialylated complex N-glycans. Lectin blotting with a panel of lectins showed that EVs had specific glycosignatures relative to MBs. Furthermore, the presence of glycoproteins bearing complex N-glycans with α2,3-linked sialic acid, fucose, bisecting-GlcNAc and LacdiNAc structures, and O-glycans with the T-antigen were detected. The inhibition of N-glycosylation processing from high mannose to complex glycans using kifunensine caused changes in the composition of EVs and induced a decrease of several glycoproteins. In conclusion, the results showed that glycosignatures of EVs were specific and altered glycosylation within the cell affected the composition and/or dynamics of EVs release. Furthermore, the identified glycosignatures of EVs could provide novel biomarkers for ovarian cancer.

Detection of novel biomarkers for ovarian cancer with an optical nanotechnology detection system enabling label-free diagnostics

Ovarian carcinoma has the highest lethality rate of gynecologic tumors, largely attributed to the late-stage diagnosis of the disease. Reliable tools for both accurate diagnosis and early detection of disease onset are lacking, and presently less than 20% of ovarian cancers are detected at an early stage. Protein biomarkers that allow the discrimination of early and late stages of ovarian serous carcinomas are urgently needed as they would enable monitoring pre-symptomatic aspects of the disease, disease progression, and the efficacy of intervention therapies. We compare the absolute and relative protein levels of six protein biomarkers for ovarian cancer in five different established ovarian cancer cell lines, utilizing both quantitative immunoblot analysis and a guided-mode resonance (GMR) bioassay detection system that utilizes a label-free optical biosensor readout. The GMR sensor approach provided highly accurate, consistent, and reproducible quantification of protein biomarkers as validated by quantitative immunoblotting, as well as enhanced sensitivity, and is therefore suitable for quantification and detection of novel biomarkers for ovarian cancer. We identified fibronectin, apolipoprotein A1, and TIMP3 as potential protein biomarkers for the differential diagnosis of primary versus metastatic ovarian carcinoma. Future studies are needed to confirm the suitability of protein biomarkers tested herein in patient samples.

Anterior Gradient-3: A novel biomarker for ovarian cancer that mediates cisplatin resistance in xenograft models

The Anterior Gradient (AGR) genes AGR2 and AGR3 are part of the Protein Disulfide Isomerase (PDI) family and harbour core thioredoxin folds (CxxS motifs) that have the potential to regulate protein folding and maturation. A number of proteomics and transcriptomics screens in the fields of limb regeneration, cancer cell metastasis, pro-oncogenic oestrogen-signalling, and p53 regulation have identified AGR2 as a novel component of these signalling pathways. Curiously, despite the fact that the AGR2 and AGR3 genes are contiguous on chromosome 7p21.1-3, the AGR3 protein has rarely been identified in such OMICs screens along with AGR2 protein. Therefore there is little information on how AGR3 protein is expressed in normal and diseased states. A panel of three monoclonal antibodies was generated towards AGR3 protein for identifying novel clinical models that can be used to define whether AGR3 protein could play a positive or negative role in human cancer development. One monoclonal antibody was AGR3-specific and bound a linear epitope that could be defined using both pep-scan and phage-peptide library screening. Using this monoclonal antibody, endogenous AGR3 protein expression was shown to be cytosolic in four human ovarian cancer subtypes; serous, endometrioid, clear cell, and mucinous. Mucinous ovarian cancers produced the highest number of AGR3 positive cells. AGR3 expression is coupled to AGR2 expression only in mucinous ovarian cancers, whereas AGR3 and AGR2 expressions are uncoupled in the other three types of ovarian cancer. AGR3 expression in ovarian cancer is independent of oestrogen-receptor expression, which is distinct from the oestrogen-receptor dependent expression of AGR3 in breast cancers. Isogenic cancer cell models were created that over-express AGR3 and these demonstrated that AGR3 mediates cisplatin-resistance in mouse xenografts. These data indicate that AGR3 is over-expressed by a hormone (oestrogen-receptor α)-independent mechanism and identify a novel protein-folding associated pathway that could mediate resistance to DNA-damaging agents in human cancers.

MicroRNA and the pathogenesis of ovarian cancer – a new horizon for molecular diagnostics and treatment?

Ovarian cancer is the leading cause of death among gynecologic malignancies and despite advances in treatment, more than 50% of all patients will experience recurrence, resulting in worse overall prognosis. Therefore, identification of novel biomarkers for ovarian cancer is of significant interest. microRNA (miRNA) constitute a class of small gene regulatory RNA molecules (18-24 nt) and by sequence complementarity, they negatively regulate messenger RNA (mRNA) translation of target genes. Rising data are available that miRNA are functionally involved in the pathogenesis of ovarian cancer. In this regard, recent advances in profiling studies revealed a variety of miRNA candidates, differently expressed in ovarian carcinomas and in disease-specific conditions like hypoxia or chemo-resistance. This review abstracts recent efforts on establishing miRNA as novel molecular biomarkers for ovarian cancer and depicts the existing preliminary framework for defining peripheral-blood derived miRNA as novel circulating biomarkers. Beside these clinical implications, we highlight the current functional understanding of miRNA alteration and discuss major challenges in miRNA profiling approaches. Finally, we briefly outline methodologies to therapeutically modulate miRNA expression in cancer and try to assess how miRNA can improve our conceptual understanding and the clinical management of ovarian cancer.

Serum HE4 levels are less frequently elevated than CA125 in women with benign gynecologic disorders

The human epididymis protein 4 (HE4) is a novel biomarker for ovarian cancer. This study measured the HE4 and CA125 levels in women with benign gynecological disorders. Sera were obtained from women prior to surgery for a pelvic mass and HE4 and CA125 levels were determined. The proportions of patients with elevated biomarker levels were compared. There were 1042 women with benign disease. HE4 levels were less often elevated than CA125 (8% vs 29%, P < .001). A marked difference was observed in patients with endometriosis in which HE4 was elevated in 3% of patients and CA125 in 67% (P < .0001). Serous ovarian tumors were associated with elevated levels of HE4 in 8% of patients and CA125 in 20% (P = .0002); uterine fibroids in 8% vs 26% (P = .0083); dermoids in 1% vs 21% (P = .0004); and inflammatory disease in 10% vs 37% (P = .014). HE4 is elevated less frequently than CA125 in benign disease, particularly in premenopausal patients.

Abstract 4325: Localization and expression of osteopontin in a chitosan-treated ovarian cancer cell line, SKOV-3

Abstract Ovarian cancer is the deadliest gynecological malignancy and despite current treatments, such as chemotherapy and radiotherapy, reoccurrence of this deadly malignancy is high. Although elevated levels of osteopontin (OPN), a phosphorylated glycoprotein, are always present in the ovaries, recent studies have implicated osteopontin (OPN) protein as a novel biomarker for ovarian cancer. Early detection of ovarian cancer is imperative, and furthermore, novel treatments with fewer adverse effects than current treatments of chemotherapy and radiation, are currently being investigated. Chitosan, a natural polysaccharide that is biodegradable and biocompatible, may serve as a potential alternative treatment for ovarian cancer, and may function by regulating OPN expression. Therefore, the objective of this study was to determine the effects of chitosan on the expression and localization of OPN in SKOV-3 cells. It was hypothesized that the expression of OPN protein will be down-regulated in a dose-dependent manner. To test this hypothesis, SKOV-3 cells were treated with 0, 50, 100, 250, and 500, ng/mL of chitosan for 48 hours. An MTT assay was performed to determine cell viability and proliferation rate. Cells were harvested; total protein was isolated and then subjected to SDS-PAGE and Western blot analysis. The localization of OPN was determined using immunocytochemistry, after chitosan treatment of SKOV-3 cells on two-chamber slides for 48 hours. The expression of OPN decreased in SKOV-3 cells in a dose-dependent manner. In addition, cell proliferation tended to decrease with increasing dose of chitosan. These data indicate that chitosan may inhibit SKOV-3 cell proliferation by decreasing OPN production and secretion. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 4325. doi:10.1158/1538-7445.AM2011-4325

ER-60 (PDIA3) is highly expressed in a newly established serous ovarian cancer cell line, YDOV-139

Characterization of a newly established serous ovarian cancer cell line, YDOV-139 was performed and ER-60 (PDIA3), which was highly expressed in YDOV-139, was evaluated as novel biomarker for ovarian cancer. The YDOV-139 cell line was established using ascites samples from a 67-year-old Korean woman with recurrent ovarian cancer, and was characterized with respect to various biological and genetic features. Gene expression profiles were analyzed using cDNA microarrays, and proteomic evaluation was performed by two-dimensional gel electrophoresis (2-DE) and matrix-assisted laser desorption ionization-time of flight peptide mass fingerprinting (MALDI-TOF/PMF). Four candidate markers that were strongly up-regulated in YDOV-139 were validated by real-time polymerase chain reaction (PCR) and immunohistochemistry (IHC). The epithelial-like characteristics of YDOV-139 were evident from morphologic studies, and the average population doubling time was 120 h. When transplanted into nude mice, YDOV-139 cells successfully induced tumor masses in all three animals. Chemosensitivity tests showed that gemcitabine had the highest chemosensitivity index against YDOV-139 cells. HLA typing revealed A*24/A*31, B*07/B*35, Cw03*(09)/w*07, and DRB1*01/DRB1*15 alleles. Compared with human ovarian surface epithelial (HOSE) cells, 2,520 genes and 23 protein spots were differentially expressed in YDOV-139. Validation by real-time PCR showed that mRNA expression of LCN2, MDK, SLCO4A1, and ER-60 (PDIA3) were strongly elevated in ovarian cancers. In IHC analysis, ER-60 (PDIA3) was significantly overexpressed in both borderline tumors and invasive ovarian cancers (P<0.001). The molecular characteristics of YDOV-139 may have implications for future ovarian cancer research and ER-60 (PDIA3) should be investigated further as a potential biomarker of ovarian cancer.

Knowledge-guided multi-scale independent component analysis for biomarker identification.

BackgroundMany statistical methods have been proposed to identify disease biomarkers from gene expression profiles. However, from gene expression profile data alone, statistical methods often fail to identify biologically meaningful biomarkers related to a specific disease under study. In this paper, we develop a novel strategy, namely knowledge-guided multi-scale independent component analysis (ICA), to first infer regulatory signals and then identify biologically relevant biomarkers from microarray data.ResultsSince gene expression levels reflect the joint effect of several underlying biological functions, disease-specific biomarkers may be involved in several distinct biological functions. To identify disease-specific biomarkers that provide unique mechanistic insights, a meta-data "knowledge gene pool" (KGP) is first constructed from multiple data sources to provide important information on the likely functions (such as gene ontology information) and regulatory events (such as promoter responsive elements) associated with potential genes of interest. The gene expression and biological meta data associated with the members of the KGP can then be used to guide subsequent analysis. ICA is then applied to multi-scale gene clusters to reveal regulatory modes reflecting the underlying biological mechanisms. Finally disease-specific biomarkers are extracted by their weighted connectivity scores associated with the extracted regulatory modes. A statistical significance test is used to evaluate the significance of transcription factor enrichment for the extracted gene set based on motif information. We applied the proposed method to yeast cell cycle microarray data and Rsf-1-induced ovarian cancer microarray data. The results show that our knowledge-guided ICA approach can extract biologically meaningful regulatory modes and outperform several baseline methods for biomarker identification.ConclusionWe have proposed a novel method, namely knowledge-guided multi-scale ICA, to identify disease-specific biomarkers. The goal is to infer knowledge-relevant regulatory signals and then identify corresponding biomarkers through a multi-scale strategy. The approach has been successfully applied to two expression profiling experiments to demonstrate its improved performance in extracting biologically meaningful and disease-related biomarkers. More importantly, the proposed approach shows promising results to infer novel biomarkers for ovarian cancer and extend current knowledge.

Identification of heat shock protein 90 and other proteins as tumour antigens by serological screening of an ovarian carcinoma expression library.

Serological screening of recombinant cDNA expression libraries has been widely used for the identification of tumour antigens in various cancer types. Identification of tumour antigens in ovarian cancer may facilitate the development of vaccine-based therapies and of disease biomarkers. The purpose of our investigation is to identify tumour antigens in ovarian cancer by using the serological analysis of recombinant cDNA expression libraries method. A recombinant ovarian carcinoma cDNA expression library was screened with ascites fluid, pooled from five ovarian cancer patients. Twelve tumour antigens encoded by known genes were isolated, including ribosomal protein S18, heat shock protein 90, JK-recombination signal binding protein, ribonucleoprotein H1, RAN binding protein 7, TG-interacting factor, eukaryotic translation initiation factor p40 subunit, human amyloid precursor protein-binding protein 1, ribosomal protein L8, CDC23, IQ motif containing GTPase activating protein 1, and ribosomal protein L3. Heat shock protein 90 was chosen for further investigation. The prevalence of hsp90 autoantibodies in ovarian cancer was determined with immunoassay. Sera from 22 normal females, 32 from ovarian cancer (22 stage III/IV, 10 stage I/II), 37 colorectal cancer, 13 breast cancer, 10 lung cancer, 20 benign gynaecologic diseases, and 10 benign breast lesions were screened. Seven (32%) stage III/IV ovarian cancer, 1 (10%) stage I/II ovarian cancer, 1 (3%) colorectal cancer, 1 (8%) breast cancer, and 1 (5%) benign gynaecologic disease sera were found to contain hsp90 autoantibodies. These data support the view that hsp90 autoantibodies are frequently found in late stage ovarian cancer. Hsp90 may, therefore, represent a novel biomarker for ovarian cancer and a candidate ovarian cancer vaccine target.British Journal of Cancer (2002) 87, 339–343. doi:10.1038/sj.bjc.6600439 www.bjcancer.com© 2002 Cancer Research UK