Different Stages Of Ovarian Cancer Research Articles

The Value of Red Cell Distribution Width and Its Correlation with Other Parameters in Ovarian Cancer

Abstract Background: The red cell distribution width (RDW) is a measure of the range of variation in the red blood cell size. It reflects red blood cell volume heterogeneity and is a part of the whole blood cell count. The use of the RDW in the diagnosis of malignant tumors has recently attracted much attention. Related research has mainly focused on endometrial cancer, lung cancer, and liver cancer. Aim of the Study: This study investigated the correlation between the RDW and ovarian cancer by observing changes in the RDW in patients with different stages of ovarian cancer. Patients and Methods: A case–control study involving 50 females with ovarian cancer and 50 healthy females conducted at Baghdad Oncology Teaching Hospital, and Al-Imamian Al-Kadhimiyain Medical City from January 1, 2022, to December 30, 2022. Data collection included demographic data, complete blood count with the calculation of platelets to lymphocyte ratio (PLR) and neutrophil-to-lymphocyte ratio (NLR), transferrin saturation, and cancer antigen 125 (CA-125). Adult females with histologically proven ovarian cancer were included in this research. Patients with conditions that affect red blood cells were excluded from the study. Results: The mean age was 52.6 ± 10.4 years old for the cases and 50.0 ± 11.4 years for the control group. Platelets count (PLT), absolute neutrophil count (ANC), PLR, NLR, RDW, and CA-125 were significantly higher in the women with ovarian cancer in comparison to the control group (P < 0.001). RDW values were found to be positively correlated with white blood cell, PLT, ANC, NLR, PLR, and CA-125 values (P < 0.001). RDW values were also positively associated with cancer stage; thus, higher values of RDW are to be expected with a higher stage of cancer (P < 0.001). The optimal cutoff point for RDW was found to be 14.6% with a sensitivity of 92%, specificity of 90%, and an area under the curve of 96%. Conclusion: The RDW is increased with ovarian cancer, and a clear cutoff point for the prediction of ovarian cancer has been observed. Furthermore, RDW was positively correlated with cancer stage and inflammatory markers including PLT, ANC, PLR, NLR, and a tumor marker CA-125.

Altered expression of the L-arginine/nitric oxide pathway in ovarian cancer: metabolic biomarkers and biological implications

MotivationOvarian cancer (OC) is a highly lethal gynecological malignancy. Extensive research has shown that OC cells undergo significant metabolic alterations during tumorigenesis. In this study, we aim to leverage these metabolic changes as potential biomarkers for assessing ovarian cancer.MethodsA functional module-based approach was utilized to identify key gene expression pathways that distinguish different stages of ovarian cancer (OC) within a tissue biopsy cohort. This cohort consisted of control samples (n = 79), stage I/II samples (n = 280), and stage III/IV samples (n = 1016). To further explore these altered molecular pathways, minimal spanning tree (MST) analysis was applied, leading to the formulation of metabolic biomarker hypotheses for OC liquid biopsy. To validate, a multiple reaction monitoring (MRM) based quantitative LCMS/MS method was developed. This method allowed for the precise quantification of targeted metabolite biomarkers using an OC blood cohort comprising control samples (n = 464), benign samples (n = 3), and OC samples (n = 13).ResultsEleven functional modules were identified as significant differentiators (false discovery rate, FDR < 0.05) between normal and early-stage, or early-stage and late-stage ovarian cancer (OC) tumor tissues. MST analysis revealed that the metabolic L-arginine/nitric oxide (L-ARG/NO) pathway was reprogrammed, and the modules related to "DNA replication" and "DNA repair and recombination" served as anchor modules connecting the other nine modules. Based on this analysis, symmetric dimethylarginine (SDMA) and arginine were proposed as potential liquid biopsy biomarkers for OC assessment. Our quantitative LCMS/MS analysis on our OC blood cohort provided direct evidence supporting the use of the SDMA-to-arginine ratio as a liquid biopsy panel to distinguish between normal and OC samples, with an area under the ROC curve (AUC) of 98.3%.ConclusionOur comprehensive analysis of tissue genomics and blood quantitative LC/MSMS metabolic data shed light on the metabolic reprogramming underlying OC pathophysiology. These findings offer new insights into the potential diagnostic utility of the SDMA-to-arginine ratio for OC assessment. Further validation studies using adequately powered OC cohorts are warranted to fully establish the clinical effectiveness of this diagnostic test.

The interplay between ovarian cancer and social relationships: an analysis of patients’ metaphors

Objective Research showed that ovarian cancer poses unique challenges to patients’ care experiences and that patients’ social networks could affect their care path significantly. The present study aimed to analyse the metaphors that patients used to signify the impact of the illness on their social relationships and the role of relationships in dealing with cancer. Design Following a qualitative description approach we conducted 38 semi-structured interviews with Australian (14) and Italian (24) women diagnosed at different stages of ovarian cancer. Results The analysis identified four themes bringing together the meanings expressed by participants’ metaphors: Lack of comprehension and communication; Isolation, marginalisation, and self-isolation; Discrepancy between the private and public self; and Social relationships as empowerment resources. Conclusion The polysemic nature of patients’ metaphors captures both the empowering and especially disempowering role of social relationships in dealing with ovarian cancer. Results also show that metaphors are used to make sense of the impact of ovarian cancer on social relationships and to express different strategies for managing patients’ networks.

Altered Levels of Blood Glucose and Serum Lipids in Sudanese Patients with Ovarian Cancer

Background: The etiology of ovarian cancer is not well-understood; numerous metabolomics profiling, epidemiological, and hospital-based case control studies have associated abnormal levels of blood glucose and serum lipids with the risk and the prognosis of various types of cancers including ovarian cancer. The association between the risk of the incidence of ovarian cancer and the alterations in the levels of blood glucose and serum lipids is not well defined. Objective: In this study we aimed to compare the levels of blood glucose, triacylglycerols, low-density lipoprotein cholesterol, and high-density lipoprotein cholesterol in patients with different stages of ovarian cancer and healthy controls to determine how they relate to the risk and prognosis of ovarian cancer. Methodology: In a case-control cross sectional study, we enrolled ninety-nine Sudanese women, diagnosed with ovarian cancer but had not received any kind of treatment as the study group, and a control group of forty-one age-matched, apparently healthy women. The patients were classified according to the International Federation of Obstetricians and Gynecologists staging system into two groups: early stages (stage I & II) and late stages (stages III & IV). Blood glucose and serum lipids; triacylglycerols, total cholesterol, low-density lipoprotein cholesterol and high-density lipoprotein cholesterol were determined by enzymatic colorimetric methods using commercially available analytical kits. The IBM SPSS version 20 software was used for statistical analysis. A Mann-Whitney U test was used for comparison of the median concentrations of blood glucose, triacylglycerols, total cholesterol, low-density lipoprotein cholesterol and high-density lipoprotein cholesterol in the study groups. Logistic regression model was used to estimate the relative risk of ovarian cancer in relation to levels of blood glucose and serum lipids. P value of 0.05 was considered significant. Results: Our data indicated significantly higher levels of blood glucose (p , triacylglycerols (p = 0.002), and low-density lipoprotein cholesterol (p , and lower levels of high-density lipoprotein cholesterol (p = 0.023), in ovarian cancer patients compared to the control subjects. No significant difference was found in the levels of blood glucose or any of the serum lipids between patients in the early stages (stage I & II) and those in late stages (stage III & IV) of ovarian cancer. The logistic regression analysis indicated significant association between the elevated levels of the blood glucose, triacylglycerols and low-density lipoprotein cholesterol and the risk of the ovarian cancer. Conclusion: We conclude that the levels of blood glucose, triacylglycerols, low-density lipoprotein cholesterol and high-density lipoprotein cholesterol differ significantly between ovarian cancer patients and the healthy control subjects. The risk of ovarian cancer was positively associated with the levels of blood glucose, triacylglycerols and low-density lipoprotein cholesterol, and negatively associated with levels of high-density lipoprotein cholesterol. Therefore, determination of blood glucose and serum lipids, particularly, triacylglycerols, low-density lipoprotein cholesterol may be helpful as diagnostic indicators of ovarian cancer (OC).

Dynamic TF-lncRNA Regulatory Networks Revealed Prognostic Signatures in the Development of Ovarian Cancer.

The pathological development of ovarian cancer (OC) is a complex progression that depends on multiple alterations of coding and non-coding genes. Therefore, it is important to capture the transcriptional-regulating events during the progression of OC development and to identify reliable markers for predicting clinical outcomes in patients. A dataset of 399 ovarian serous cystadenocarcinoma patients at different stages from The Cancer Genome Atlas (TCGA) was analyzed. Stage-specific transcription factor (TF)-long non-coding RNA (lncRNA) regulatory networks were constructed by integrating high-throughput RNA molecular profiles and TF binding information. Systematic analysis was performed to characterize the TF-lncRNA-regulating behaviors across different stages of OC. Cox regression analysis and Kaplan-Meier survival curves were used to evaluate the prognostic efficiency of TF-lncRNA regulations and cliques. The stage-specific TF-lncRNA regulatory networks at three OC stages (II, III, and IV) exhibited common structures and specific topologies of risk TFs and lncRNAs. A TF-lncRNA activity profile across different stages revealed that TFs were highly stage-selective in regulating lncRNAs. Functional analysis indicated that groups of TF-lncRNA interactions were involved in specific pathological processes in the development of OC. In a STAT3-FOS co-regulating clique, the TFs STAT3 and FOS were selectively regulating target lncRNAs across different OC stages. Further survival analysis indicated that this TF-lncRNA biclique may have the potential for predicting OC prognosis. This study revealed the topological and dynamic principles of TF-lncRNA regulatory networks and provided a resource for further analysis of stage-specific regulating mechanisms of OC.

Inhibition of cisplatin-resistant tumor cells on activated CD4+ T cells in ovarian cancer and its mechanism

Objective: To investigate the inhibition of cisplatin-resistant tumor cells on activated CD4+ T cells in ovarian cancer and its mechanism. Methods: 50 patients with ovarian cancer admitted to our hospital from March 2016 to July 2017 were selected as the research group, and 50 normal subjects were selected as the control group. The same separation method of CD4+ T cells was applied to both two groups. The serum levels of ovarian cancer markers and inflammation factors were compared between two groups. Results: The serum levels of ovarian cancer markers in the research group were higher than those in the control group (P<0.05). The average proportion of CD4+ T cells in peasant patients was higher than that in workers and cadres (P<0.05). The average proportion of CD4+ T cells in patients with tumor infiltration was higher than that in patients without infiltration (P<0.05). The level of IL-6 in the research group was higher than that in the control group. While the levels of IL-10, IL-4, IL-2, TNF-α and IFN-γ were lower than those in the control group (P<0.05), and the immune function of the research group was still lower than that of the control group (P<0.05). Conclusion: Cisplatin-resistant ovarian cancer cells can inhibit the activation of CD4+ T cells, which can effectively judge the different stages of ovarian cancer. Moreover, the number of CD4+ T cells can be used to determine the prognosis of patients, providing a certain clue for clinical treatment.

Identification of molecular biomarkers for ovarian cancer using computational approaches.

Ovarian cancer is one of the major causes of mortality among women. This is partly because of highly asymptomatic nature, lack of reliable screening techniques and non-availability of effective biomarkers of ovarian cancer. The recent availability of high-throughput data and consequently the development of network medicine approach may play a key role in deciphering the underlying global mechanism involved in a complex disease. This novel approach in medicine will pave the way in translating the new molecular insights into an effective drug therapy applying better diagnostic, prognostic and predictive tests for a complex disease. In this study, we performed reconstruction of gene co-expression networks with a query-based method in healthy and different stages of ovarian cancer to identify new potential biomarkers from the reported biomarker genes. We proposed 17 genes as new potential biomarkers for ovarian cancer that can effectively classify a disease sample from a healthy sample. Most of the predicted genes are found to be differentially expressed between healthy and diseased states. Moreover, the survival analysis showed that these genes have a significantly higher effect on the overall survival rate of the patient than the established biomarkers. The comparative analyses of the co-expression networks across healthy and different stages of ovarian cancer have provided valuable insights into the dynamic nature of ovarian cancer.

Active DNA end processing in micronuclei of ovarian cancer cells

BackgroundOvarian cancer is one of the most deadly gynecological malignancies and inclined to recurrence and drug resistance. Previous studies showed that the tumorigenesis of ovarian cancers and their major histotypes are associated with genomic instability caused by defined sets of pathogenic mutations. In contrast, the mechanism that influences the development of drug resistance and disease recurrence is not well elucidated. Solid tumors are prone to chromosomal instability (CIN) and micronuclei formation (MN). Although MN is traditionally regarded as the outcome of genomic instability, recent investigation on its origin and final consequences reveal that the abnormal DNA metabolism in MN is a driver force for some types of catastrophic genomic rearrangements, accelerating dramatic genetic variation of cancer cells.MethodsWe used Indirect Immunofluorescent staining to visualize micronuclei and activation of DNA repair factors in ovarian cancer cell lines and biopsies.ResultsWe show that ovarian cancer cells are disposed to form micronuclei upon genotoxic insults. Double strand DNA breaks (DSBs)-triggered insurgence of micronuclei is associated with unrepaired chromosomes passing through mitosis. According to their morphology and DNA staining, micronuclei compartments are divided into early and late stages that can be further characterized by differential staining of γH2AX and 53BP1. We also show that MN compartments do not halt controlled DNA metabolism as sequestered nuclear repair factors are enriched at DNA breaks in MN compartments and efficiently process DNA ends to generate single-stranded DNA (ssDNA) structures. Interestingly, unknown factors are required for DNA end processing in MN in addition to the nuclear resection machinery. Finally, these hallmarks of micronuclei evolution depicted in cell culture were recapitulated in different stages of ovarian cancer biopsies.ConclusionsIn aggregate, our findings demonstrate that ovarian cancer cells are inclined to form micronuclei that undergo robust DNA metabolism and generate ssDNA structures, potentially destabilizing genomic structures and triggering genetic variation.

Role of Platelet-Derived Tgfβ1 in the Progression of Ovarian Cancer.

Purpose: Transforming growth factor β1 (Tgfβ1) plays an important role in cancer. Most of Tgfβ1 in plasma is from platelets; thus, we studied whether platelet Tgfβ1 has any role in the progression of ovarian cancer, and whether this role is limited to metastasis or also involves the growth of primary tumors.Experimental Design: We compared the growth of murine ovarian cancer cell-induced tumors in platelet-specific Tgfβ1-deficient mice and wild-type mice. Using resected tumor nodules, we studied the effect of platelet Tgfβ1 on neoangiogenesis and on platelet extravasation into tumors. To investigate the effect of Tgfβ1 at different stages of ovarian cancer, we reduced expression of Tgfβ1 receptor (its TgfβR1 component) in tumors at different time points after injection of cancer cells, and compared the final tumor size.Results: Lack of platelet Tgfβ1 in mice reduced tumor growth, neoangiogenesis, and platelet extravasation. Ovarian cancer tumors in platelet-specific Tgfβ1-deficient mice reached less than half of their size in wild-type littermates. Knockdown of TgfβR1 on cancer cells in the first 2 weeks after their injection reduced tumor growth, but was less effective if initiated after 3 weeks.Conclusions: We showed that platelet Tgfβ1 increased the growth of primary tumors in murine models of ovarian cancer. We also showed that inhibition of TgfβR1 is more effective in reducing the growth of ovarian cancer if initiated earlier. Our results supported a therapeutic benefit in preventing platelet activation, degranulation, and release of Tgfβ1 in ovarian cancer. Clin Cancer Res; 23(18); 5611-21. ©2017 AACR.

Next-Generation Sequencing Approach in Methylation Analysis of HNF1B and GATA4 Genes: Searching for Biomarkers in Ovarian Cancer.

DNA methylation is well-known to be associated with ovarian cancer (OC) and has great potential to serve as a biomarker in monitoring response to therapy and for disease screening. The purpose of this study was to investigate methylation of HNF1B and GATA4 and correlate detected methylation with clinicopathological characteristic of OC patients. The study group consisted of 64 patients with OC and 35 control patients. To determine the most important sites of HNF1B and GATA4, we used next-generation sequencing. For further confirmation of detected methylation of selected regions, we used high-resolution melting analysis and methylation-specific real-time polymerase chain reaction (PCR). Selected regions of HNF1B and GATA4 were completely methylation free in all control samples, whereas methylation-positive pattern was observed in 32.8% (HNF1B) and 45.3% (GATA4) of OC samples. Evaluating both genes together, we were able to detect methylation in 65.6% of OC patients. We observed a statistically significant difference in HNF1B methylation between samples with different stages of OC. We also detected subtype specific methylation in GATA4 and a decrease of methylation in late stages of OC. The combination of unmethylated HNF1B and methylated GATA4 was associated with longer overall survival. In our study, we employed innovative approach of methylation analysis of HNF1B and GATA4 to search for possible epigenetic biomarkers. We confirmed the significance of the HNF1B and GATA4 hypermethylation with emphasis on the need of selecting the most relevant sites for analysis. We suggest selected CpGs to be further examined as a potential positive prognostic factor.

Assessment of the Value of a Modified Risk of Malignancy Index (RMI) in Preoperative Discrimination Between Benign and Malignant Ovarian Masses

Objectives: To assess the diagnostic accuracy of different risk of malignancy index (RMI) scores and to evaluate the role of a modified RMI (RMI 5) in pre-operative discrimination between benign and malignant ovarian masses. Study Design: Prospective observational study. Patients and methods: Women with a suspicious ovarian mass scheduled for laparotomy or laparoscopy were potentially eligible for inclusion in the current study. Trans-abdominal and trans-vaginal ultrasound with Doppler assessment of the adnexal masses was done. Calculation of the RMI 1, RMI2, RMI 3, RMI 4, and RMI5 was done. We compared RMI to histopathological outcome. In the current study, a new RMI score was created by adding Doppler blood flow of the ovarian mass to the calculation of the previous RMI1. Results: One hundred and fifty women with ovarian masses were included in the current study. Ninety six women (64%) had benign ovarian masses while, malignant ovarian masses were found in 54 women (36%). Comparison between benign and malignant ovarian masses regarding to the risk of malignancy indices revealed that that there was a statistically significant difference between the two groups regarding to the risk of malignancy indices with Pvalue< 0.001. Receiver operator characteristic curve analysis of the 5 RMI indices shows that the best method for prediction of malignant ovarian tumor was RMI 1. Also there was no statistically significant difference between the five methods in prediction of malignant ovarian tumors. RMI5 with cut off value of 250 is reliable tool at a tertiarycenter to discriminate between ovarian cancer and benign ovarian masses with a sensitivity of 90.38% and specificity of 93.88%. There was statistically significant difference between the different stages of ovarian cancer and RMI 5 with (P<0.05). Conclusion: The RMI 1 is the gold standard for preoperative discrimination between benign and malignant ovarian masses. Adding Doppler flow to the parameters of RMI 1 (RMI 5) increased specificity of RMI 1 in detecting malignant ovarian masses.

Analysing Big Data to Build Knowledge Based System for Early Detection of Ovarian Cancer

Big data analysis plays a crucial role in the health care for early diagnosis of fatal disease. The data mining techniques are widely used for data analysis problem to discover valuable knowledge from a large amount of data. This paper uses the data mining methods such as feature selection and classification to provide a predictive model for ovarian cancer detection. A huge amount of dataset is gathered to build knowledge based system. Rough set theory is utilized to find the data reliance and reduce the feature set contained in the data set. The Hybrid Particle Genetic Swarm Optimization (PGSO) is used to optimize the selected features to efficiently classify the ovarian cancer, either normal or early or different stages of ovarian cancer. Multi class SVM is adopted as the classifier to classify normal or different stages of ovarian cancer using the optimized feature set. The experiment is done on different ovarian cancer dataset and the proposed system has obtained better results for all datasets.

Segregation of ovarian cancer stage exploiting spectral biomarkers derived from blood plasma or serum analysis: ATR-FTIR spectroscopy coupled with variable selection methods.

Ovarian cancer is a solid tumor and a leading cause of mortality. Diagnostic tools for the detection of early stage (stage I) ovarian cancer are urgently needed. For this purpose, attenuated total reflection Fourier-transform infrared spectroscopy (ATR-FTIR) coupled with variable selection methods, successive projection algorithm or genetic algorithm (GA) combined with linear discriminant analysis (LDA), were employed to identify spectral biomarkers in blood plasma or serum samples for accurate diagnosis of different stages of ovarian cancer, histological type and segregation based on age. Three spectral datasets (stage I vs. stage II-IV; serous vs. non-serous carcinoma; and, ≤60 years vs. >60 years) were processed: sensitivity and specificity required for real-world diagnosis of ovarian cancer was achieved. Toward segregating stage I vs. stage II-IV, sensitivity and specificity (plasma blood) of 100% was achieved using a GA-LDA model with 33 wavenumbers. For serous vs. non-serous category (plasma blood), the sensitivity and specificity levels, using 29 wavenumbers by GA-LDA, were remarkable (up to 94%). For ≤60 years and >60 years categories (plasma blood), the sensitivity and specificity, using 42 wavenumbers by GA-LDA, gave complete accuracy (100%). For serum samples, sensitivity and specificity results gave relatively high accuracy (up to 91.6% stage I vs. stage II-IV; up to 93.0% serous vs. non-serous; and, up to 96.0% ≤60 years vs. >60 years) using several wavenumbers. These findings justify a prospective population-based assessment of biomarkers signatures using ATR-FTIR spectroscopy as a screening tool for stage of ovarian cancer.

Epiregulin as a marker for the initial steps of ovarian cancer development

Epiregulin (Ep) was found to be produced in non-cancer ovarian cells in response to gonadotropin stimulation as well in ovarian cancer cells in an autonomous manner. However, there were no systematic follow-up studies of Ep expression in the development of different stages of ovarian cancer. Using specific antibodies to Ep and the indirect immunocytochemistry methods, we found that in normal ovary the staining for Ep was mainly confined to the epithelial cells, while the stromal cells were only occasionally and moderately stained. In contrast in benign serous and mucinous tumors most of the tumor cells showed a clear staining in the cytoplasm. In borderline serous and mucinous tumors the staining was much more intensive, and appear occasionally in aggregated form. In serous, mucinous and endometrioid carcinomas labeling remain high, with more frequent aggregated form. It is suggested that follow-up of the expression of Ep can serve as a reliable early indication of the development of ovarian cancer. Moreover, the cytoplasmic aggregation of Ep may suggest a specific mechanism of the release of this growth factor to the extracellular space in order to exert its autocrine and paracrine effect on the family of the EGF receptors.

Acute phase serum amyloid A in ovarian cancer as an important component of proteome diagnostic profiling

In the context of serum amyloid A (SAA) identification as ovarian cancer marker derived by SELDI-MS, its serum levels were measured by immunoassay in different stages of ovarian cancer, in benign gynecological tumors, and in healthy controls. In addition, SELDI-TOF-MS spectra were obtained by protocol optimized for the SAA peak intensity. SELDI data on small proteins (5.5-17.5 kDa) and SAA immunoassay data were combined with cancer antigen (CA)125 data in order to study the classification accuracy between cancer and noncancer by support vector machine (SVM), logistic regression, and top scoring pair classifiers. Although an addition of SAA immunoassay data to CA125 data did not significantly improve cancer/noncancer discrimination, SVM applied to combined biomarker data (CA125 and SAA immunoassay variables plus 48 SELDI peak variables) yielded the best classification rate (accuracy 95.2% vs. 86.2% for CA125 alone). Notably, most of discriminatory peaks selected by the classifiers have significant correlation with the major known peaks of SAA (11.7 kDa) and transthyretin (13.9 kDa). Acute phase serum amyloid A (A-SAA) was proved to be an important member of cancer discriminatory protein profile. Among the eight known ovarian cancer SELDI profile components, A-SAA is the most relevant to molecular pathogenesis of cancer and it has the highest degree of up-regulation in disease.

Peritoneal Fluid Lactate Dehydrogenase in Ovarian Cancer

In attempt to identify patients with ovarian carcinoma and differentiate them from patients with benign ovarian tumor or other gynecological malignancies, peritoneal fluid and serum lactate dehydrogenase (LDH) levels were measured in 51 patients: 15 with ovarian carcinoma, 15 with endometrial carcinoma, 4 with cervical carcinoma, and 17 with benign ovarian tumor. Peritoneal fluid and serum LDH levels in ovarian cancer patients were significantly higher than those in patients with benign ovarian tumor (P< 0.001) or other gynecological malignancies (P< 0.001 andP< 0.03, respectively). Yet, peritoneal fluid LDH demonstrated higher diagnostic sensitivity (87%) and greater diagnostic accuracy (90%) than serum LDH (60 and 77%, respectively) or serum CA-125 (73 and 83%, respectively). Comparing the histological types of ovarian cancer, serous cystadenocarcinoma presented higher peritoneal fluid LDH levels than endometrioid or mucinous cystadenocarcinoma. No difference in peritoneal fluid LDH was observed comparing different stages of ovarian cancer. The results suggest that peritoneal fluid LDH may be an efficient biochemical marker in diagnosis of ovarian cancer.