Potential Biomarker For Ovarian Cancer Research Articles

Computational insights into irinotecan's interaction with UBE2I in ovarian and endometrial cancers

Endometrial and Ovarian cancers are two highly prevalent and fatal reproductive diseases with poor prognoses among women. Elevated estrogen levels in Ovarian Cancer (OC) stimulate the endometrium, causing Endometrial Cancer (EC). Although numerous studies have reported the crucial genes and pathways in this cancer, the pathogenesis of this disease remains unclear. In this study, used bioinformatics tools to analyse GSE63678, GSE115810, GSE36389, GSE26712, GSE36668, GSE27651, GSE6008, GSE69429, GSE69428, GSE18521, GSE185209, GSE54388 gene expression microarray datasets for both the cancers. We analyzed the differential gene expression, functional association, and structural studies. The analysis identified crucial differentially expressed genes (DEGs) in both cancers associated with DNA damage, DNA integrity, and cell-cycle checkpoint signaling pathways. CLDN7, UBE2I, WT1, JAM2, FOXL2, F11R, JAM3, ZFPM2, MEF2C, and PIAS1 are the top 10 hub genes commonly identified in both cancer types. Only CLDN7 and F11R are upregulated, whereas the remaining hub genes are downregulated in both cancers, suggesting a common framework for contributing to tumorigenesis. Molecular docking and dynamics were performed on the UBE2I protein with Irinotecan Hydrochloride, which could serve as the new approach for treating and managing both cancers. The study reveals the common molecular pathways, pointing out the role of cell cycle and DNA damage and integrity checkpoint signaling in the pathogenesis of both cancer types. This study explored the UBE2I gene as a potential biomarker in OC and EC. Further, this study concludes that the irinotecan hydrochloride drug has higher therapeutic effects on UBE2I protein through docking and dynamics studies.

Defining three ferroptosis-based molecular subtypes and developing a prognostic risk model for high-grade serous ovarian cancer.

As a newly defined regulated cell death, ferroptosis is a potential biomarker in ovarian cancer (OV). However, its underlying mechanism in tumor microenvironment (TME) and clinical prediction significance in OV remained to be elucidated. The transcriptome data of high-grade serous OV from The Cancer Genome Atlas (TCGA) database were downloaded. Molecular subtypes were classified based on ferroptosis-correlated genes from the FerrDb database by performing consensus clustering analysis. The associations between the subtypes and clinicopathologic characteristics, mutation, regulatory pathways and immune landscape were assessed. A ferroptosis-related prognostic model was constructed and verified using International Cancer Genome Consortium (ICGC) cohort and GSE70769. Three molecular subtypes of OV were defined. Patients in subtype C3 tended to have the most favorable prognosis, while subtype C1 showing more mesenchymal cells, increased immune infiltration of Macrophages_M2, lower tumor purity, and epithelial-to-mesenchymal transition (EMT) features had the poorest prognosis. A ferroptosis-related risk model was constructed using 8 genes (PDP1, FCGBP, EPHA4, GAS1, SLC7A11, BLOC1S1, SPOCK2, and CXCL9) and manifested a strong prediction performance. High-risk patients had enriched EMT pathways, more Macrophages_M2, less plasma cells and CD8 cell infiltration, greater tendency of immune escape and worse prognosis. The risk score has negatively correlated relation with LAG3, TIGIT, CTLA4, IDO1, CD27, ICOS, and IL2RB but positively correlated with PVR, CD276, and CD28. Moreover, low-risk patients were more sensitive to Cisplatin and Gefitinib, Gemcitabine. Our results could improve the understanding of ferroptosis in OV, providing promising insights for the clinical targeted therapy for the cancer.

Effects of clinical covariates on serum miRNA expression among women without ovarian cancer.

Serum microRNAs (miRNAs) are potential biomarkers for ovarian cancer; however, many factors may influence miRNA expression. To understand potential confounders in miRNA analysis, we examined how sociodemographic factors and comorbidities, including known ovarian cancer risk factors, influence serum miRNA levels in women without ovarian cancer. Data from 1,576 women from the Mass General Brigham Biobank collected between 2012 and 2019, excluding subjects previously or subsequently diagnosed with ovarian cancer, were examined. Using a focused panel of 179 miRNA probes optimized for serum profiling, miRNA expression was measured by flow cytometry using the Abcam Fireplex® assay and correlated with subjects' electronic medical records. The study population broadly reflected the New England population. The median age of subjects was 49 years, 34% were current or prior smokers, 33% were obese (BMI >30kg/m2), 49% were postmenopausal, and 11% had undergone prior bilateral oophorectomy. Significant differences in miRNA expression were observed among ovarian risk factors such as age, obesity, menopause, BRCA1 or BRCA2 germline mutations or breast cancer in family history. Additionally, miRNA expression was significantly altered by prior bilateral oophorectomy, hypertension, and hypercholesterolemia. Other variables, such as smoking, parity, age at menarche, hormonal replacement therapy, oral contraception, breast, endometrial, or colon cancer, and diabetes were not associated with significant changes in the panel when corrected for multiple testing. Serum miRNA expression patterns are significantly affected by patient demographics, exposure history, and medical comorbidities. Understanding confounders in serum miRNA expression is important for refining clinical assays for cancer screening.

Abstract B048: Evaluating the impact of pulmonary diseases on the use of microRNAs as biomarkers for early detection of ovarian cancer

Abstract B048: Evaluating the impact of pulmonary diseases on the use of microRNAs as biomarkers for early detection of ovarian cancer

Mendelian randomization analysis to elucidate the causal relationship between small molecule metabolites and ovarian cancer risk

BackgroundSmall molecule metabolites are potential biomarkers for ovarian cancer. However, the causal relationship between small molecule metabolites and ovarian cancer remains unclear.MethodsSingle nucleotide polymorphisms (SNPs) correlated with 53 distinct small molecule metabolites were identified as instrumental variables (IVs) from comprehensive genome-wide association studies. Aggregate data encompassing 25,509 cases of ovarian cancer and 40,941 controls of European descent were procured from the Ovarian Cancer Association Consortium. To evaluate causative associations, four Mendelian randomization techniques—including inverse-variance weighted, weighted median, maximum likelihood, and MR-Egger regression—were employed.ResultsIn total, 242 SNPs were delineated as IVs for the small molecule metabolites under consideration. A significant association with the overarching risk of ovarian cancer was observed for six distinct metabolites. Hexadecenoylcarnitine and methioninesulfoxide were associated with a 32% and 31% reduced risk, respectively. Fifteen metabolites were linked to subtype ovarian cancers. For instance, both methionine sulfoxide and tetradecanoyl carnitine exhibited an inverse association with the risk of clear cell and high-grade serous ovarian cancers. Conversely, tryptophan demonstrated a 1.72-fold elevated risk for endometrioid ovarian cancer.ConclusionThis study identified several metabolites with putative causal effects on ovarian cancer risk using Mendelian randomization analysis. The findings provide insight into the etiological role of small molecule metabolites and highlight potential early detection biomarkers for ovarian cancer. Subsequent investigations are imperative to corroborate these findings and elucidate the underlying pathophysiological mechanisms.

MUM1L1 as a Tumor Suppressor and Potential Biomarker in Ovarian Cancer: Evidence from Bioinformatics Analysis and Basic Experiments.

Ovarian cancer (OC) is the most prevalent gynecologic malignancy, with high mortality rates. However, its pathogenesis remains unclear. The current study aimed to explore potential biomarkers and suppressor genes for diagnosing and treating OC. Biochemical and bioinformatics approaches were used to detect differentially expressed genes (DEGs) in ovarian tissues via integration analysis. Kaplan-Meier plot analysis was performed to assess progression-free survival and overall survival according to DEGs. Then, we constructed a protein-protein interaction (PPI) network based on data from the STRING database to identify the related target genes of DEGs. Finally, DEGs regulating the proliferation, migration, and invasion of SKOV3 cell lines were validated via in vitro experiments. Four DEGs (MUM1L1, KLHDC8A, CRYGD, and GREB1) with enriched expression in ovarian tissues were explicitly expressed in the ovary based on an analysis of all human proteins. MUM1L1 had high specificity, and its expression was higher in normal ovarian tissues than in OC tissues. Kaplan-Meier plot analysis showed that a high MUM1L1 expression was associated with longer progression-free survival and overall survival in OC. Based on the PPI analysis results, CBLN4, CBLN1, PTH2R, TMEM255B, and COL23A1 were associated with MUM1L1. In vitro studies revealed that MUM1L1 overexpression decreased the proliferation, migration, and invasion ability of SKOV3 cell lines. Meanwhile, MUM1L1 knockdown had contrasting results. MUM1L1 is a tumor suppressor gene and is a potential biomarker for diagnosing and treating OC.

Graphene-Oxide-Assisted Biosensor With Optimum Response Selection Algorithm for Detecting and Quantifying Vimentin, a Potential Biomarker for Ovarian Cancer

Graphene-Oxide-Assisted Biosensor With Optimum Response Selection Algorithm for Detecting and Quantifying Vimentin, a Potential Biomarker for Ovarian Cancer

P23: Serum nuclear matrix proteins (NMPs) as potential biomarkers of ovarian cancer (OVCA)

P23: Serum nuclear matrix proteins (NMPs) as potential biomarkers of ovarian cancer (OVCA)

Endocan-microvascular Density in Primary Ovarian Carcinoma.

Background:Epithelial ovarian cancer is among the leading causes of death in women and is driven by angiogenesis. Microvascular density (MVD) can be used to evaluate angiogenesis in carcinomas and thus it can be used as a potential biomarker for ovarian cancer. This study is aimed to establish the association between endocan-MVD with clinicopathological factors in primary epithelial ovarian cancer. Methods:The clinicopathological characteristics were acquired from the medical records filed between January 2008 and December 2018 of 89 epithelial ovarian cancer cases in Hospital Universiti Sains Malaysia, Kelantan, Malaysia. Sectioned samples were analyzed for endocan through immunohistochemistry followed by the quantification of MVD. The association between clinicopathological characteristics and endocan-MVD was analyzed using the Pearson chi-square test and Fischer’s exact test. Results:All cases of epithelial ovarian carcinomas were positive for endocan. The mean ± standard deviation value of endocan-MVD level was 21.6±14.60 microvessels per 200x field. A total of 53 (59.6%) cases had low and 36 (40.04%) had high endocan-MVD values. High endocan-MVD level had a significant association with the older age group (p-value = 0.009), smaller tumor size (p-value<0.001), type II tumor (p-value<0.001), high-grade tumor (p-value<0.001), advanced FIGO stage (p-value=0.002), and presence of tumor recurrence (p-value=0.017). No significant association was found between endocan-MVD and the other clinicopathological characteristics such as race, pre-operative serum CA-125 level, presence of diabetes mellitus, endometriosis, lymph node involvement, distant metastasis, and family history of malignancy. Conclusion:Endocan-MVD showed a significant association with age, tumor size, tumor type, tumor grade, FIGO stage, and recurrence in primary epithelial ovarian cancer. Thus, endocan-MVD could be implemented as a reliable marker to predict prognosis in epithelial ovarian cancer in the future.

The Role of Lectin as Potential Biomarker in Ovarian Cancer.

Lectin acts as an effective tool for screening potential biomarkers and gives an indication of highly valued research. Lectin offers the advantage of having the ability to recognize carbohydrate moiety of glycoprotein, peptidoglycan, glycosides, glycopeptides, lipopolysaccharide etc. makes powerful aid in detecting new cancer biomarkers in most complex tissues and fluids. The unique specificity of lectin in detecting single out anomalously expressed glycosylation pave lectin-based method that can often go down the line for future cancer biomarkers. This article explores the different types of lectin, their sources and possible application in masking the activity of ovarian cancer cells.

LPA3 is a precise therapeutic target and potential biomarker for ovarian cancer.

Current studies have demonstrated that significant increased LPA levels to be observed in ascites in patients with ovarian cancer. Although several studies have shown that Lysophosphatidic acid (LPA) related to the progression of ovarian cancer, which LPA receptors (LPARs) and G-coupled protein subtypes mediated in LPA actions have not been clearly elucidated. This study aimed to clarify the roles of LPA and it is subtype-specific LPARs mediating mechanisms in ovarian cancer integrated using bioinformatic analysis and biological experimental approaches. The big data analysis shown that LPA3 was the only differentially expressed LPA receptor among the six LPARs in ovarian cancer and further verified in immunohistochemistry of tissue microarrays. Also found that LPA3 was also highly expressed in ovarian cancer tissue and ovarian cancer cells. Importantly, LPA significantly promoted the proliferation and migration of LPA3-overexpressing ovarian cancer cells, while the LPA-induced actions blocked by Ki16425, a LPAR1/3 antagonist treated, and LPA3-shRNA transfected. In vivo study indicated that the LPA3-overexpressing cell-derived tumors metastasis, tumors volume, and tumors mass were apparently increased in xenografted nude mice. In addition, we also observed that LPA3 was differential high expression in ovarian cancer tissue of the patients. Our studies further confirmed the LPA3/Gi/MAPKs/NF-κB signals were involved in LPA-induced oncogenic actions in ovarian cancer cells. Our findings indicated that the LPA3 might be a novel precise therapeutic target and potential biomarker for ovarian cancer.

Nanomaterial-based biosensor developing as a route toward in vitro diagnosis of early ovarian cancer.

The grand challenges of ovarian cancer early diagnosis have led to an alarmingly high mortality rate from ovarian cancer (OC) in the past half century. In vitro diagnosis (IVD) has great potential in the early diagnosis of OC through non-invasive and dynamic analysis of biomarkers. However, common IVDs often fail to provide reliable test results due to lack of sensitivity, specificity, and convenience. In recent years, the discovery of new biomarkers and the progress of nanomaterials can solve the shortcomings of traditional IVD for early OC. These emerging biosensors based on nanomaterials offer great improvements in convenience, speed, selectivity, and sensitivity of IVD. In this review, we firstly systematically summarized the limits of commercial IVD biosensors of OC and the latest discovery of new biomarkers for OC. The representative optimization strategies for six potential ovarian cancer biomarkers are systematically discussed with emphasis on nanomaterial selection and the design of detection principles. Then, various strategies adopted by emerging biosensors based on nanomaterials are also introduced in detail, including optical, electrochemical, microfluidic, and surface plasmon sensors. Finally, current challenges of early OC IVD are proposed, and future research directions on this promising field are also discussed.

Low Expression of SLC7A11 Confers Drug Resistance and Worse Survival in Ovarian Cancer via Inhibition of Cell Autophagy as a Competing Endogenous RNA.

Drug resistance is the main cause of chemotherapy failure in ovarian cancer (OC), and identifying potential druggable targets of autophagy is a novel and promising approach to overcoming drug resistance. In this study, 131 genes associated with autophagy were identified from three autophagy-related databases, and of these, 14 were differentially expressed in 90 drug-resistant OC tissues versus 197 sensitive tissues according to the Cancer Genome Atlas ovarian cancer cohort. Among these 14 genes, SLC7A11 was significantly decreased in two paclitaxel-resistant OC cells (HeyA8-R and SKOV3-R) and in 90 drug-resistant tissues compared with their controls. In vitro overexpression of SLC7A11 significantly increased the sensitivity of HeyA8-R cells to paclitaxel, inhibited colony formation, induced apoptosis, and arrested cell cycle. Further, low SLC7A11 expression was correlated with poor overall survival (OS), progression-free survival (PFS), and post-progression survival (PPS) in 1815 OC patients. Mechanistically, SLC7A11 strongly regulated cell autophagy as a competing endogenous RNA (ceRNA) based on pan-cancer analyses of 32 tumor types. Specifically, as a ceRNA for autophagy genes STX17, RAB33B, and UVRAG, SLC7A11 was strongly and positively co-expressed with these three genes in 20, 12, and 12 different tumors, respectively, in 379 OC tissues and in 90 drug-resistant OC tissues, and the former two were significantly upregulated in SLC7A11-overexpressed HeyA8-R cells. Further, SLC7A11 induced the protein expression of other autophagy genes, such as LC3, Atg16L1, and Atg7, and the expression of the respective proteins was further increased when the cells were treated with paclitaxel. The results strongly suggest that SLC7A11 regulates autophagy via ceRNA interactions with the three abovementioned genes in pan-cancer and in drug-resistant OC. Moreover, low expression of STX17 and UVRAG also significantly predicted low OS, PFS, and PPS. The combination of SLC7A11 with STX17 was more predictive of OS and PFS than either individually, and the combination of SLC7A11 with UVRAG was highly predictive of OS and PPS. The above results indicated that decreased SLC7A11 resulted in drug resistance and effected low rates of survival in OC patients, probably via ceRNA interactions with autophagy genes, and thus the gene could serve as a therapeutic target and potential biomarker in OC.

Involvement of Rab25 Biomarker in Pathogenesis of Ovarian Cancer

Ovarian tumor is the third leading common gynecologic tumor and the common leading cause of death in gynecological cancers to the entire global and studies suggested that Rab25 is insinuated in the pathological process of ovarian cancer. Despite the availability of biomarkers for ovarian cancer detection, there are no specific markers that enable the early detection of ovarian cancers which open an avenue to Rab25 to be review. A number of genes and proteins have been reported to be involved in the pathogenesis of ovarian cancers. Of them, Ras-related protein 25 (Rab25) is suggested to be linked to increased risk of ovarian cancer development. Rab25, an intracellular transport protein, belongs to the Rab small GTPase family and regulates various aspects of internalized membrane protein recycling and trafficking occurring inside the cells to the cell membrane. It is known to be involved in cell proliferation, and prevents apoptosis and invasion in ovarian cancer. Rab25 is highly found in epithelial cells and the expression of Rab25 proteins has been implicated to be ubiquitous. Upregulation of Rab25 has also been strongly shown to intensify the cancer cell proliferation and to prevent apoptosis in vitro and in vivo .Here in we will review the past and current studies implicating Rab 25 as potential biomarker in ovarian cancer in addition to pathogenesis

Screening and prognostic value of potential biomarkers for ovarian cancer.

BackgroundOvarian cancer is a common gynecological malignant tumor that greatly threatens women’s health, so we screened potential biomarkers of ovarian cancer and analyzed their prognostic value.MethodsThe Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets were used to analyze the ovarian cancer-related genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to analyze the function of ovarian cancer-related genes. The survival-related genes were screened out through the least absolute shrinkage and selection operator (LASSO) method. Multivariate Cox regression model and stepwise regression analysis were performed to construct the risk model. The receiver operating characteristic (ROC) and the area under the ROC curve (AUC) were used to evaluate the prediction accuracy of risk score model. Finally, gene set enrichment analysis (GSEA) and immune cell infiltration analysis were performed to investigate the biological function and immune cell infiltration.ResultsA total of 111 genes were found to have common effects on survival. These genes were mainly involved in metabolism, protein phosphorylation and immune-related signaling pathways. Seven risk genes (AP3D1, DCAF10, FBXO16, LRFN4, PTPN2, SAYSD1, ZNF426) were screened out. Among these genes, AP3D1 and LRFN4 are risk genes and DCAF10, FBXO16, PTPN2, SAYSD1, and ZNF426 are protective genes. These findings suggest that risk status may be an independent prognostic factor. The risk score had a high predictive value for the prognosis of ovarian cancer. In addition, GSEA revealed that the biological function of genes expressed in patients at a high risk was mostly related to immune-related function. The contents of CD4+ T cells, macrophages, myeloid dendritic cells (mDC) and neutrophils were high in samples at a high risk for ovarian cancer.ConclusionsThe abnormal expression of AP3D1, DCAF10, FBXO16, LRFN4, PTPN2, SAYSD1 and ZNF426 is highly related to the progression of ovarian cancer. These seven genes can be used as independent prognostic markers of ovarian cancer. This study not only adds evidence to the pathogenesis of ovarian cancer but also provides scientific basis for judging the prognosis of ovarian cancer.

Fibronectin 1: A Potential Biomarker for Ovarian Cancer.

Methods OVCAR3 and A2780 are the two common cell lines that are used for ovarian cancer studies. The different invasion and migration abilities were observed by scratch tests and transwell experiments in our preliminary study. Gene chip was used to screen the expression gene in these two different cell lines, and then, the differentially expressed genes (at least 2-fold difference, P value < 0.05) were analyzed using KEGG. Result Fibronectin 1 (FN1) was found to be the most strongly correlated with the invasion and migration abilities of the OVCAR3 cells. Real-time PCR and FN1 knockout cell line was conducted and confirmed this finding. Based on the Oncomine database analysis, comparing with normal people, ovarian cancer patients exhibited high levels of FN1 expression. Additionally, higher FN1 expression was found in patients with higher FIGO stages of cancer. Conclusion FN1 could be a new biomarker for ovarian cancer detection and progress indicator.

Bioinformatics Analysis of Potential Therapeutic Targets and Prognostic Biomarkers amid CXC Chemokines in Ovarian Carcinoma Microenvironment.

Background Ovarian cancer (OC) is one of the leading lethal gynecologic cancers of women around the world. More than 70% of patients are diagnosed with stage III or IV with poor outcome. This is partly because of lacking early effective screening techniques and potential biomarkers of OC. CXC chemokines in tumor microenvironment (TME) and their interaction with relative receptors can excite the downstream signaling pathways to influence tumor progression. However, the role of CXC chemokines in OC has not been identified. Methods ONCOMINE, GEPIA, Kaplan–Meier plotter, cBioPortal, TIMER, Metascape, and LinkedOmics were applied in our study. Results The transcriptional levels of CXCL1/8/9/10/11/12/13/14/16/17 were significantly elevated while CXCL3 was obviously reduced in OC vs normal ovarian tissue. CXCL8/9/11/13 were correlated with clinic pathological stage. Patients with low expression of CXCL8/9/11/13 were associated with better prognosis. We also found that CXCL3 and CXC12 could be used as potential prognostic markers of OC through Kaplan–Meier plotter. Patients with high expression of CXCL3/12 had a significantly better prognosis. Their functions focus on locomotion, signaling, response to stimulus, undergoing the process of multiorganism, immune system, biological regulation, etc. The differentiated CXC chemokines mainly participate in cytokine-cytokine receptor interaction, chemokine signaling pathway, IL-17 signaling pathway, and toll-like receptor signaling pathway. Our results showed that CXC chemokines were highly correlated with infiltration of immune cells. The kinase targets of differentially expressed CXC chemokines are mainly in ATM, LYN, LCK, PLK1, FYN, CDK2, and ATR. Conclusions Our results may provide a new insight for selecting precision biomarkers of targeted therapy of OC.

Identification of PKP 2/3 as potential biomarkers of ovarian cancer based on bioinformatics and experiments

BackgroundPlakophilins (PKPs) are widely involved in gene transcription, translation, and signal transduction, playing a crucial role in tumorigenesis and progression. However, the function and potential mechanism of PKP1/2/3 in ovarian cancer (OC) remains unclear. It’s of great value to explore the expression and prognostic values of PKP1/2/3 and their potential mechanisms, immune infiltration in OC.MethodsThe expression levels, prognostic values and genetic variations of PKP1/2/3 in OC were explored by various bioinformatics tools and databases, and PKP2/3 were selected for further analyzing their regulation network and immune infiltration. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathways (KEGG) enrichment were also conducted. Finally, the expression and prognosis of PKP2 were validated by immunohistochemistry.ResultsThe expression level and prognosis of PKP1 showed little significance in ovarian cancer, and the expression of PKP2/3 mRNA and protein were upregulated in OC, showing significant correlations with poor prognosis of OC. Functional enrichment analysis showed that PKP2/3 and their correlated genes were significantly enriched in adaptive immune response, cytokine receptor activity, organization of cell–cell junction and extracellular matrix; KEGG analysis showed that PKP2/3 and their significantly correlated genes were involved in signaling pathways including cytokine-mediated signaling pathway, receptor signaling pathway and pathways in cancer. Moreover, PKP2/3 were correlated with lymphocytes and immunomodulators. We confirmed that high expression of PKP2 was significantly associated with advanced stage, poor differentiation and poor prognosis of OC patients.ConclusionMembers of plakophilins family showed various degrees of abnormal expressions and prognostic values in ovarian cancer. PKP2/3 played crucial roles in tumorigenesis, aggressiveness, malignant biological behavior and immune infiltration of OC, and can be regarded as potential biomarker for early diagnosis and prognosis evaluation in OC.

Ovarian cancer risk according to circulating zinc and copper concentrations: A meta-analysis and Mendelian randomization study

Ovarian cancer is a lethal disease with few modifiable risk factors. Circulating zinc and copper are potential biomarkers for ovarian cancer; however, evidence of their causal effects are scarce. This study aimed to examine the impact of circulating zinc and copper concentrations on ovarian cancer risk, using meta-analysis and Mendelian randomization (MR) approaches. Twenty case-control studies, including 699 patients with ovarian cancer, 567 patients with benign ovarian lesions, and 1194 healthy controls, were selected for meta-analysis. With a Two-sample MR approach, genetic instruments of 21 single nucleotide polymorphisms (SNPs) associated with circulating zinc and 25 SNPs associated with circulating copper were created. Their genetic associations with ovarian cancer were extracted from a genome-wide association study of 25,509 ovarian cancer cases and 40,941 controls. Ovarian cancer patients had significantly lower concentrations of circulating zinc than healthy controls (Standardized mean differences [SMD]=-1.01, 95% CI:-1.38 to-0.64). In contrast, circulating copper concentrations were significantly higher in ovarian cancer patients (SMD=1.46, 95% CI: 0.82 to 2.09). In MR analysis, we only found increased circulating zinc concentration causally associated with a lower risk of ovarian cancer (odds ratio=0.968, 95% CI: 0.941 to 0.995, per SD of ranked-inverse normalized concentration), especially in the high-grade serous subtype. Although increased circulating copper and decreased zinc concentrations were found in ovarian cancer patients, a suggestive causal association was only detected with zinc concentration, suggesting further studies on zinc interventions for ovarian cancer might have clinical impact.

EBST: An Evolutionary Multi-Objective Optimization Based Tool for Discovering Potential Biomarkers in Ovarian Cancer.

Ovarian cancer is the deadliest gynecologic malignancy, mainly due to limitations in early diagnosis. With advances in high-throughput technologies, research interest in identifying novel and customized tumor biomarkers for early detection and diagnosis is rapidly growing. Here we introduce a new tool called EBST to select microRNAs with biomarker potency in ovarian cancer. This tool has pre-processing options and Its core is the use of Modified Multi Objective Imperialist Competitive Algorithm and six objective functions based on the classifier performance/structure evaluation, clustering error and mRMR filter. In this paper, we used the FDR filter in the pre-processing stage and considered five objective functions, four of which relate to the l1-SVM classifier performance and one to the average mRMR ranking. The proposed method has identified 11 microRNAs including hsa-miR-6784-5p, hsa-miR-1228-5p, hsa-miR-8073, hsa-miR-6756-5p, hsa-miR-1307-3p, hsa-miR-4697-5p, hsa-miR-3663-3p, hsa-miR-328-5p, hsa-miR-1228-3p, hsa-miR-6821-5p, hsa-miR-1268a. Data classification by the proposed model showed 100 percent sensitivity, 99.38 percent specificity, 99.69 percent accuracy and 99.39 percent positive predictive value. In comparison with routine state-of-the-art methods, superiority of our method was confirmed. The biological evaluation of selected microRNAs using bioinformatics tools and published articles confirms their role in cancer signaling pathways. The tool and its MATLAB code are freely available at https://github.com/hanif-y.