Serum 27-nor-5β-Cholestane-3,7,12,24,25 Pentol ... - ACS Publications

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Serum 27-nor-5β-Cholestane-3,7,12,24,25 Pentol Glucuronide Discovered by Metabolomics as Potential Diagnostic Biomarker for Epithelium Ovarian Cancer Jing Chen,†,‡ Xiaoyan Zhang,†,§ Rui Cao,|| Xin Lu,‡ Sumin Zhao,‡ Agnes Fekete,^ Qiang Huang,‡ Philippe Schmitt-Kopplin,^ Yisheng Wang,§ Zhiliang Xu,‡ Xiaoping Wan,z Xiaohua Wu,# Naiqing Zhao,O Congjian Xu,*,§ and Guowang Xu*,‡ ‡

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CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 116023 Dalian, China § Obstetrics & Gynecology Hospital, Shanghai Medical School, Institute of Biomedical Science, Fudan University, Shanghai Key Laboratory of Female Reproductive Endocrine Related Diseases, Shanghai 200011, China Department of the Obstetrics and Gynecology Hospital, Dalian Medical University, 116033 Dalian, China ^ Department of BioGeoChemistry and Analytics, Institute of Ecological Chemistry, Helmholtz-Zentrum Muenchen-German Research Center for Environmental Health, Ingoldstaedter Landstrasse 1, D-85764 Neuherberg, Germany z The International Peace Maternity and Child Health Hospital, Shanghai Jiaotong University School of Medicine, 200030 Shanghai, China # Department of Gynecologic Oncology, Cancer Hospital, Fudan University, 200032 Shanghai, China O Department of Biostatistics and Social Medicine, School of Public Health, Fudan University, 200032 Shanghai, China

bS Supporting Information ABSTRACT: The aim of this study was to use a two steps strategy metabolomics to screen/identify and validate novel metabolic biomarker(s) for epithelial ovarian cancer (EOC). In the screening step, serum samples from 27 healthy women, 28 benign ovarian tumors, and 29 EOCs were analyzed by using LC
MS based nontargeted metabolomics. The three groups were separated with OSC filtered PLS-DA model, and six metabolites (27-nor-5β-cholestane-3,7,12,24,25 pentol glucuronide (CPG), phenylalanine, glycocholic acid, propionylcarnitine, Phe-Phe and Lyso PC (18:2)) were considered as potential biomarker candidates. In the validation step, the six metabolites were analyzed in targeted metabolomics by LC-selective ion monitoring mass spectrometry in another 685 serum samples with various clinical backgrounds. As a result, CPG was evaluated to be a potential biomarker and its content was elevated in EOC tissues compared with benign ovarian tumor tissues (p = 0.0005). Besides, CPG levels were found to be up-regulated in early stage EOC and in the three types of EOC histological types. Other variables such as nonovarian diseases, medicine consumption, gynecological inflammations, and menopausal state did not interfere in using CPG as diagnosis marker. CPG was found to be complementary to CA125. Our findings suggest that CPG can be considered a statistical relevant biomarker of EOC, ready for early stage detection. KEYWORDS: metabolomics, LC
MS, biomarker discovery, epithelium ovarian cancer, 27-nor-5β-cholestane-3,7,12,24,25 pentol glucuronide

’ INTRODUCTION Epithelium ovarian cancer (EOC)1 is the most common form of ovarian cancer leading to the most frequent cause of death within gynecological cancers.2 Because the etiology of EOC is not well-understood,1,3,4 only a few symptoms can be noticed at an early stage until the tumor becomes larger or metastases are disseminated,2 and thus EOC can usually only be diagnosed at an advanced stage when the 5-year survival rate is poor.1 Cancer antigen 125 (CA125) is the primarily used diagnostic biomarker for EOC.2 However, it is not overexpressed in every r 2011 American Chemical Society

EOC patient and especially the early diagnosis rate (diagnosed stage I) is poor.5 Moreover, higher levels of CA125 are also elevated in other cancers,6 pelvis benign ovarian tumors and gynecological inflammations7 and thus unspecific to EOC. Development of other specific and sensitive biomarkers to replace or complement CA125 is a hot research area,1,8,9 aiming at elevating the 5-year survival rate. Metabolomics is a recent Received: February 24, 2011 Published: April 04, 2011 2625

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Figure 1. Flowchart of analytical strategy for potential biomarker discovery of EOC.

omics approach10 that aims at comprehensively studying endogenous small molecules; it has widely been applied in disease biomarker discovery,11,12 drug development,13,14 microorganisms and plant,15
17 environment18 and nutrition areas,19,20 etc. Metabolites are either substrates or products involved in various metabolic pathways;21 thus, any changes in pathology or physiology can be reflected from the metabotype (metabolic phenotype). Metabolomics has been successfully applied in biomarker discovery of prostate cancer,22 biliary tract cancer,23 Crohn disease24 and other diseases11,12,21,25
30 involving biofluids such as plasma, urine or tissues and feces extracts. Ovarian cancer was recently the focus of several studies.31
36 In this study, potential biomarkers of EOC were evaluated based on a two-step metabolomics approach on serum samples analyzed with an ultra performance liquid chromatographic-mass spectrometry (UPLC
MS). Samples from women without ovarian tumor, with benign ovarian tumor and with EOC were included. Our objective was to find a potential biomarker specific only for EOC diagnosis. The strategy included (i) nontargeted metabolomics analysis of 84 serum samples for the discovery of discriminant metabolites and (ii) a targeted analysis in a larger cohort of 685 samples with different clinical backgrounds to validate the metabolic biomarker (Figure 1).

(St. Louis, MO). K2HPO4 and KH2PO4 were from Kermel (China), and Lyso PC (12:0) was supplied by Avanti Polar Lipids (Alabaster, AL). Distilled water was further purified with a Milli-Q system (Millipore, MA). The chemical standard of 27-nor-5βcholestane-3,7,12,24,25 pentol (CP) was prepared from cholic acid by Prof. Ming Li's group in the Ocean University of China, Qingdao, China under the support of Dalian Institute of Chemical Physics. Clinical Samples

’ EXPERIMENTAL PROCEDURES

Informed consent was signed by each participant, and the study was approved by the Ethics Committee of Obstetrics and Gynecology Hospital ([2007]-No. 6), Fudan University. The study was divided into a screening step (sample set 1) and a validation step (sample set 2). The detailed information of samples is summarized in Supplementary Tables S1 and S2 (Supporting Information). In the screening step, samples were enrolled under distinct control in which samples associated with nonovarian diseases, gynecological inflammations and medication intake were avoided. In the validation step, samples with different clinical backgrounds were collected. These background factors included histological types, FIGO stages, menopausal status, at least one of 33 other nonovarian diseases, gynecological inflammations and medication intake.

Chemicals

Serum Preparation

Acetonitrile (HPLC grade) was from Merck (Rockville, MD). Formic acid, acetic acid and ammonium acetate (HPLC grade) were purchased from TEADIT (Houston, TX). Leucine
enkephalin and β-glucuronidase (Type IX-A) were from Sigma-Aldrich

Serum samples were collected at 6
8:00 a.m. under fasting condition and stored immediately in
80 C. Before analysis, serum was thawed at room temperature, and 180 μL of aliquot was mixed with 720 μL of acetonitrile for deproteinization. The 2626

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Journal of Proteome Research mixture was vortexed for 30 s, followed by centrifugation at 15 000 g at 4 C for 10 min. The supernatant was then lyophilized. Before analysis, the dried sample was reconstituted in 150 μL water/acetonitrile solution (v/v 1/4). LC
MS Analysis

In the screening step, the chromatographic separation was performed on a Waters Acquity UPLC system (Waters, Milford, MA) using a 50 mm  2.1 mm, 1.7 μm BEH C18 column (Waters, Milford, MA). The column was thermostatted at 35 C, and the sample manager was set to 4 C. Eluent A was water containing 0.1% formic acid and 2% acetonitrile, and eluent B was acetonitrile. The flow rate was 0.35 mL/min, and metabolites were eluted with solvent strength gradient. The gradient started at 100% A kept for 0.5 min; it was changed to 100% B in 24 min using a nonlinear gradient with curve 7 (Masslynx Guide: Waters 2690/2695 Pump gradient Page) and kept at 100% B for 3.5 min. Then it was quickly changed back to 100% A (0.1 min) and kept for another 2 min to equilibrate the column. A 5 μL aliquot was injected onto the column. Mass spectrometry was performed on a Waters Q-TOF micro MS (Waters MS Technologies, Manchester, U.K.). The capillary voltage and cone voltage were 3100 and 30 V. Nitrogen was used as nebulization (600 L/h, 300 C) and cone gas (50 L/h). The source temperature was set to 120 C. The mass spectrometer was operated in positive electrospray ionization mode, and m/z data between 100 and 920 were recorded. The data acquisition rate was set to 0.48 s with 0.1 s inter scan delay. In the validation step, the same LC system and column as those in the metabolic profiling study were used. Eluents A and B were also unaltered. To get rapid separation, the flow rate was increased to 0.4 mL/min and the gradient was started with 2% B, kept for 0.5 min, then changed to 100% B in 7 min and kept for 1 min, followed by 2 min column re-equilibrium. A 3 μL aliquot of each sample was analyzed. SQD mass spectrometer (Waters, U.K.) was employed as the detector. The parameters of the ion source were: capillary 3350 V, cone 33 V, source temperature 120 C, desolvation temperature 300 C, cone gas 50 L/h, and nebulization gas 500 L/h. Dwell time of each ion was set to 0.05 s except for the ion at m/z 166 (0.15s). Leucine
enkephalin (3 ng/μL) and Lyso PC (12:0) (3 ng/μL) were added as internal standard at the beginning of serum sample pretreatment. The details about analysis method of CPG from EOC tissue and benign ovarian tumor tissue samples are described in the Supporting Information. CA125 Assay

CA125 from 581 samples were measured commercially by First Affiliated Hospital Zhejiang University of Traditional Chinese Medicine. Chemiluminesent microparticle immunoassay (ARCHITECT i 2000) and ARCHITECT CA 125II reagent kit were used. The detailed information of samples is summarized in Supplementary Table S2 (Supporting Information). Data Analysis

Raw data were first transformed to NetCDF files by Databridge (Waters, U.K.) and input to XCMS software37 for peak alignment. Full width at half-maximum (fwhm) was set to 10 and the retention time window was set to 9 (bw = 9). Other parameters were default. Detected and matched peaks with retention time, m/z value and their corresponding peak area

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were listed. Normalization to total peak area for each sample was done before multivariate statistical analysis. The partial least squares-discriminant analysis (PLS-DA) and orthogonal signal correction (OSC) filtering were performed by SIMCA-P software version 11.0 (Umetrics AB, Umea, Sweden). Pareto scaling was used before multivariate analysis, and variable importance in the project (VIP) was calculated. The receiver operating characteristic curve (ROC), chi-square test and Wilcoxon rank sum test were performed by the SPSS version 13.0 (SPSS, Chicago, IL). Details on the analytical method for tissue samples are described in the Supporting Information. Structure Identification of the Metabolite with m/z 403

If not specifically defined, MS/MS experiments were mainly performed on a Waters Q-TOF micro MS. MSn information was also obtained from nano ESI Q-TOF 6510 (Agilent, Santa Clara, CA) equipped with nanosprayer. The nano ESI Q-TOF (Agilent, Santa Clara, CA) was calibrated for mass accuracy and the errors were below 5 ppm. High-resolution mass spectra were acquired on a Bruker APEX Qe 12T Fourier transform ion cyclotron resonance MS (Bremen, Germany), and the errors of external and internal calibration in the relevant m/z range were always below 0.1 ppm. The samples were infused with microspray infusion at 120 μL/h and ionized in positive and negative electrospray. Fractionation of the relevant peaks was performed on a UPLC system equipped with Waters Fraction Collector III (Waters, Milford, MA). A 1.7 μm BEH C18 column and fast gradient were used to guarantee high throughput and separation. The injection volume was 20 μL. A fraction was collected at least 50 times and concentrated for the accurate molecular mass measurements and qualitative analysis. A glucuronidase hydrolysis experiment was performed according to the product information offered by Sigma-Aldrich. TSKGel AFC SPE column 40  6 mm I.D. (Tosoh, Japan) was used to enrich the potential biomarker and its enzymatic hydrolysate. Dipotassium phosphate-monopotassium phosphate (K2HPO4
KH2PO4) buffer (150 mM, pH= 8.8) and ammonium acetate
acetic acid (NH4Ac-HAc) buffer (10 mM, PH=4.75) were used as loading and elution solutions.

’ RESULTS AND DISCUSSION The strategy of metabolomics on biomarker discovery is resumed in Figure 1. Discovery of Differential Metabolites in Healthy, Benign Ovarian Tumor and EOC Women

First, a UPLC
MS analytical method was developed according to the FDA guidance38 to obtain reproducible, sensitive and abundant metabolic information. A typical total ion chromatogram (TIC) of EOC serum samples is shown in Figure 2A. The analytical characteristics of metabolite profiling based on LC
MS were described through 6 representative signals in 13 QC samples (mixtures of the sera samples). Relative standard derivation (RSD) of retention times and peak areas was less than 1% and 2.0
12.3%, respectively, and thus of acceptable reproducibility and precision according to the FDA guidance. After peak alignment of 84 samples, 327 features (variables) were obtained that were further studied with the multivariate analysis. In order to screen metabolites representing the actual metabolic differences among healthy women and patients with benign ovarian tumor or EOC, 84 women were enrolled 2627

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Figure 2. Differential metabolites discovery. (A) TIC of typical EOC serum sample. (B) Score plot of PLS-DA model with OSC filtering, healthy (0), benign ovarian tumor (þ) and EOC (2). (C) P-plot. Top 25 variables in the VIP list are marked with a red square. (D) SUS-plot. The five regions (a
e) are explained in the text.

under controlled conditions to avoid the disturbance from environmental factors. Other nonovarian diseases, gynecological inflammations or medicine consumption were not detected or found within a week before sampling. Healthy, benign ovarian tumor and EOC women were separated according to the OSC filtered PLS-DA model, and the first component presents differences between EOC and other two groups (Figure 2B). Five samples from each group were selected randomly as a predictive set, and the others were set as a training set. As observed, all of the EOC samples were not mistakenly classified into the healthy women area, and the same result can be found in the healthy women (Supplementary Figure S1, Supporting Information). The serum metabolic differences are significant between healthy women, benign ovarian tumor and ovarian cancer, so we could detect ovarian cancer by metabolic profiling. However, it is unrealistic to apply the metabolic profiling in the clinic. At this moment, our interest was in single or several specific metabolites from the metabolic profiling, as the detection and quality control of target metabolites in LC
MS are much easier. Top 25 variables were picked out according to the VIP values at the first component of the OSC filtered PLS-DA model, were marked with red panes in the P-plot39 (Figure 2C) and also summarized in the Supplemental Table S3, Supporting Information. By using the SUS-plot40 (Figure 2D) constructed from the correlation of EOC vs healthy women and benign ovarian tumor vs healthy women models (Supplementary Figure S2, Supporting Information), 25 variables were then classified into five categories (Figure 2D), the variables in region a and b were elevated and decreased, respectively, only in EOC, the variables in region c and d represent the common ground of benign ovarian tumor and EOC, and the variables in region e have a

relationship with benign ovarian tumors. Thirteen variables in region a and b were considered as discriminant metabolites with significant differences (p < 0.05) in three groups. By studying the relationship among the variables, 6 compounds out of the 13 variables in region a and b were observed and are summarized in the Supplemental Table S3, Supporting Information. Though these metabolites were statistically significant, they cannot be applied in clinical diagnosis without further validation in a larger population to assess the biological variation. Target Analysis of Discriminant Metabolites for a Validation of Potential Biomarker(s)

The more samples are validated, the more statistically relevant are the markers. Therefore, the six compounds were validated with a larger sample set consisting of various different clinical backgrounds (sample set 2, Supplemental Table S2, Supporting Information) by using selective ion monitoring (SIM) mass spectrometry (MS). The analytical characteristics of the targetanalysis method were studied. The 24 h stability of the instrument was evaluated by 13 injections of leucine
enkephalin and lysophosphatidylcholine (Lyso PC) (12:0), and RSDs of their peak area were 8.27 and 6.02%, respectively. The 685 samples were divided into 12 batches (one batch could be analyzed within one day) for the target analysis of the 6 discriminant metabolites. The results of different subsets are listed in Supplementary Figure S3 and Table S4, Supporting Information. As a result, 5 serum metabolites were no longer only relevant with EOC (Supplemental Table S4, Supporting Information), but one metabolite with m/z 403 could be further considered as a potential biomarker (Figure 3A). The p values were 4.68  10
17 (nonovarian tumor vs EOC) and 1.52  10
12 (benign ovarian tumor vs EOC), which represent greatly significant differences. 2628

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Figure 3. Content of CPG in (A) sera and (B) tissues. White, gray and black columns represent nontumor women, benign ovarian tumor women and ovarian cancer women, respectively. The details of the detection method are described in the Supporting Information. The error bar means the standard deviation.

Identification of the Metabolite with m/z 403

The structure identification of the biomarker with m/z 403 was mainly based on the strategy we published previously.41 Quasi-molecular ion was determined by studying the mass spectra (Figure S4, Supporting Information). ESIþ mass spectrum was complicated (Figure S4B), and ions marked with an arrow were considered to be derived from the same compound as m/z 403 (according to Pearson correlate index). Ions with m/z 615 in ESIþ and ion with m/z 613 in ESI- (Figure S4A) gave a strong hint that they were [MþH]þ and [M
H]
, respectively. Moreover, ions with m/z 637 and m/z 653 can be attributed to [MþNa]þ and [MþK]þ adducts. Next, the fraction of the metabolite with m/z 403 was collected from LC runs for subsequent flow injection FT-ICRMS measurement, the accuracy mass of [M
H]
was determined as 613.3588. The formula was deduced as being C32H54O11 with a perfect mass error below 1 ppm, and the corresponding isotopology was also verified. For further structure clarification, the fragmentation behavior of the molecule ion was investigated. Because it was easily fragmented in ESIþ, nanospray was employed for “softer” ionization. Five hydroxyl groups, a glucuronic acid group, and a sterol ABCD ring could be deduced from MS/MS spectra of m/z 615 and m/z 385 (Figure S4C and S4D, Supporting Information). The presence of glucuronic acid was proven by the analysis of glucuronidase hydrolysis experiments (data not shown). The metabolite was further confirmed as a C26 bile alcohol containing two hydroxyl groups in the side chain. Because this metabolite and its enzymatic hydrolysate could be selectively enriched by the TSK-Gel AFC SPE column, the two hydroxyl groups in the side chain are indicated to be cis-diol structured. According to its MS/MS spectra, retention behavior, database query and literatures,42
44 this potential metabolic biomarker was identified as 27-nor-5β-cholestane-3,7,12,24,25 pentol glucuronide (CPG). Its glucuronidase hydrolysis product (27-nor5β-cholestane-3,7,12,24,25 pentol, CP) was confirmed with a synthetic standard sample from the Ocean University of China. Correlation of CPG with Clinicopathological Factors

To validate the metabolic origination of CPG, the contents of CPG in 13 ovarian cancer and 12 benign ovarian tumor tissues were also analyzed by using SIM MS. CPG could be detected in every tissue sample, and a higher level of CPG was observed in ovarian cancer tissue than in benign ovarian tumors tissues (p = 0.0005, Figure 3B). It is very possible that abnormal

metabolism of ovarian cancer tissue resulted in an elevated CPG level and, thus, an elevated CPG level in serum (Figure 3A). To evaluate the clinical significance of CPG, the serum level was measured in the EOC patients with different histological types and FIGO stages. Four histological types of EOC (set 2.2 of Table S2, Supporting Information) were studied (Figure 4A) including serous tumor, endometrioid tumor, mucinous tumor and clear cell carcinoma. The serum concentrations in the first three types of EOC were significantly up-regulated compared with those of non-EOC women, but those of clear cell carcinoma were not. Since only nine samples of clear cell carcinoma were analyzed, further study is needed. The serum contents of CPG at early (FIGO stage I) and advanced (FIGO stage III) EOC were investigated as shown in Figure 4B (set 2.3 of Table S2, Supporting Information). Both early and advanced EOC were found significantly up-regulated compared with healthy women and benign ovarian tumors. It has been known that CA125 did not show a good sensitivity in early diagnosis.5 From our result, early detection of EOC based on the level of CPG can be achieved. Correlation of CPG with Different Clinical Backgrounds

In clinical applications, the samples under investigation were not always “ideal” with a single factor (health or one disease); the influence of other nonovarian diseases, gynecological inflammations and medication intake must also be considered (sets 2.4 and 2.5 of Table S2, Supplemental Figure S3, Supporting Information). It can be seen from Figure 4C that CPG was not influenced by the above-mentioned three factors in the women without ovarian tumor since its level in the serum was similar to that in the healthy women. Similar results were also obtained in the benign ovarian tumor patients. It can be concluded that the above-mentioned three factors in the women without ovarian tumor and women with benign ovarian tumor did not cause false positive result in the diagnosis of EOC when the serum CPG was determined. It also can be seen from Figure 4C that the content of CPG in EOC patients with nonovarian diseases and medication intake were not significantly decreased compared with those of EOC patients but were decreased to the level of nontumor patients in EOC patients with gynecological inflammations giving negative contribution to diagnosis sensitivity. Because only nine samples were collected, further study is still needed before a definite conclusion could be achieved. 2629

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Figure 4. Effect of different backgrounds on CPG. (A) Four EOC histological types, (B) early and advanced EOC, and (C) samples with different clinical backgrounds. “Ideal” samples were from those persons who were not associated with other nonovarian diseases, medicine consumption, and gynecological inflammations. “Without detailed information” means samples without information on nonovarian diseases, gynecological inflammations or medicine consumption. (D) Pre- and postmenopausal samples; g and f represent significant differences (p < 0.05) compared with healthy or benign ovarian tumor. * means significant difference (p < 0.05) compared with EOC. The error bar means the standard deviation.

Two-hundred seventy-four samples (set 2.7 of Table S2, Supporting Information) were also collected from the women diagnosed without ovarian tumor, with benign ovarian tumor or EOC, but no other further information was recorded, they were classified into “without detailed information” in Figure 4C. The analytical results of these samples were very similar to those with the detailed information (Figure 4C). The effect of menopause status on CPG was also investigated. Healthy women, benign ovarian tumor women and EOC women were divided into pre- and postmenopausal subsets (set 2.1 of Table S2, Supporting Information). As a result, there was no significant difference between pre- and postmenopausal women (Figure 4D). There were various menstrual states among these premenopausal women, so the effect of menstrual cycles on CPG levels could be minimized. Besides, the CPG content of healthy women samples from southern China (vegetable-based diet) and northern China (meatbased diet) were compared, and no difference was observed (Wilcoxon p > 0.05). Diagnostic Sensitivity and Specificity of Serum CPG

ROC curves were done to study the diagnose potential of serum CPG (Figure 5A, B). With all 685 samples, AUC was 0.747, and the sensitivity and specificity were determined as 67% and 77% (cutoff = 0.002, peak area ratio to internal standard), respectively, by the Youden index. Interestingly, for the EOC at

Figure 5. ROC curves. (A) EOC vs non-EOC (including benign ovarian tumor and nonovarian tumor women). (B) EOC at FIGO stage I vs non-EOC.

stage I, AUC was 0.750, and the sensitivity and specificity were 70% and 77% (cutoff = 0.002), respectively. The average accuracy to discriminate benign ovarian tumor and ovarian cancer was 71.3%. CA125 and CPG were also found to be independent biomarkers for EOC diagnosis by the chi-square test (p < 0.05). CA125 level of 25 EOC samples were below the cutoff line (35 units/ mL), but 16 of them (64%) could be detected by CPG (cutoff = 0.002). Furthermore, based on CA125 value, 49 non-EOC 2630

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Journal of Proteome Research samples have false positive results, but 34 of these samples (69%) showed negative results based on CPG. All of these data suggested that CPG has the potential to diagnose EOC, already at an early stage, and CPG could be used as a complementary biomarker of CA125 for EOC detection.

’ CONCLUSIONS In this study, a potential new EOC biomarker was identified based on metabolomics analysis using a UPLC
MS platform. The validity of the biomarker was evaluated in a two-step strategy. First, nontargeted metabolomics analysis and multivariate statistical analysis defined 6 relevant differentiating metabolites. Second, these 6 candidates were analyzed with a targeted UPLC
MS approach in 685 independent serum samples having different clinical backgrounds. Only 1 of the candidate molecules, CPG, was verified as a potential biomarker for EOC. Furthermore, CPG was proven to have an elevated concentration level in ovarian cancer tissues compared to benign ovarian tumor tissues. The experimental data showed that this metabolite could be potentially applied in clinical area for the diagnosis of EOC. The improvement in the early diagnosis should facilitate the management and improve the 5-year survival rate of EOC patients. However, further studies are still necessary to clarify its pathophysiological mechanism and significance. ’ ASSOCIATED CONTENT

bS

Supporting Information Supplementary tables and figures. This material is available free of charge via the Internet at http://pubs.acs.org.

’ AUTHOR INFORMATION Corresponding Author

*G.X.: e-mail, [email protected]; tel./fax, 0086-411-84379530. C.X.: e-mail, [email protected]; tel., 0086- 21-63455050; fax, 0086- 021-63455090. Author Contributions †

These two authors contribute equally.

’ ACKNOWLEDGMENT This study was supported by grants from the National Hightech R&D Program (863 Program) (No. 2006AA02Z342), the National Basic Research Program of China (No. 2007CB914701) from the State Ministry of Science & Technology of China, the key foundation (No. 20835006) and the creative research group project (No. 21021004) from the National Natural Science Foundation of China, the Sino-German Center for Research Promotion (DFG and NSFC, GZ 364) enabling the travel support of A.F. and P.S.K. to China and G.X. to Germany within the frame of this study. We also thank Prof. Ming Li, Ocean University of China, for the synthesis of CP standard sample. ’ REFERENCES (1) Williams, T.; Toups, K.; Saggese, D.; Kalli, K.; Cliby, W.; Muddiman, D. Epithelial ovarian cancer: disease etiology, treatment, detection, and investigational gene, metabolite, and protein biomarkers. J. Proteome Res. 2007, 6 (8), 2936–2962. (2) Jacobs, I.; Menon, U. Progress and challenges in screening for early detection of ovarian cancer. Mol. Cell. Proteomics 2004, 3 (4), 355–366.

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