Distinct Urinary Metabolic Profile of Human Colorectal Cancer

Article pubs.acs.org/jpr

Distinct Urinary Metabolic Profile of Human Colorectal Cancer Yu Cheng,†,‡ Guoxiang Xie,§ Tianlu Chen,‡ Yunping Qiu,§ Xia Zou,‡ Minhua Zheng,∥,⊥ Binbin Tan,‡ Bo Feng,∥,⊥ Taotao Dong,∥,⊥ Pingang He,*,† Linjing Zhao,# Aihua Zhao,# Lisa X. Xu,‡ Yan Zhang,*,‡ and Wei Jia*,§,# †

Department of Chemistry, East China Normal University, Shanghai 200062, China Ministry of Education Key Laboratory of Systems Biomedicine, Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai 200240, China § Department of Nutrition, University of North Carolina at Greensboro, North Carolina Research Campus, Kannapolis, North Carolina 28081, United States ∥ Department of Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China ⊥ Shanghai Institute of Digestive Surgery, Shanghai 200025, China # School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China ‡

S Supporting Information *

ABSTRACT: A full spectrum of metabolic aberrations that are directly linked to colorectal cancer (CRC) at early curable stages is critical for developing and deploying molecular diagnostic and therapeutic approaches that will significantly improve patient survival. We have recently reported a urinary metabonomic profiling study on CRC subjects (n = 60) and health controls (n = 63), in which a panel of urinary metabolite markers was identified. Here, we report a second urinary metabonomic study on a larger cohort of CRC (n = 101) and healthy subjects (n = 103), using gas chromatography time-offlight mass spectrometry and ultra performance liquid chromatography quadrupole time-of-flight mass spectrometry. Consistent with our previous findings, we observed a number of dysregulated metabolic pathways, such as glycolysis, TCA cycle, urea cycle, pyrimidine metabolism, tryptophan metabolism, polyamine metabolism, as well as gut microbial−host co-metabolism in CRC subjects. Our findings confirm distinct urinary metabolic footprints of CRC patients characterized by altered levels of metabolites derived from gut microbial−host co-metabolism. A panel of metabolite markers composed of citrate, hippurate, pcresol, 2-aminobutyrate, myristate, putrescine, and kynurenate was selected, which was able to discriminate CRC subjects from their healthy counterparts. A receiver operating characteristic curve (ROC) analysis of these markers resulted in an area under the receiver operating characteristic curve (AUC) of 0.993 and 0.998 for the training set and the testing set, respectively. These potential metabolite markers provide a novel and promising molecular diagnostic approach for the early detection of CRC. KEYWORDS: colorectal cancer, metabonomics, metabolic profiling, gas chromatography time-of-flight mass spectrometry, ultra performance liquid chromatography quadrupole time-of-flight mass spectrometry, urine

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INTRODUCTION Colorectal cancer (CRC) is one of the most prevalent and fatal cancers worldwide.1 It is the third most common cancer in both men and women, with an estimated 101340 cases of colon and 39870 cases of rectal cancer and 49380 deaths in 2011, accounting for almost 9% of all cancer deaths in the United States in 2011.2 Recent research revealed a rapid increase in CRC cases in those metropolitan areas in fast developing Asia countries with altered lifestyle.3 Since the 5-year survival rate can reach 90% for stage I CRC patients but only 12% for stage IV patients,2 detection of CRC at an early stage will dramatically improve survival rates. To date, the standard © 2011 American Chemical Society

screening tool for accurate diagnosis of precancerous lesions and cancer morbidity in the colon and rectum (e.g., aberrant crypt foci, polyps, and tumors) has been colonoscopy.4 Due to the limited use of this invasive and unpleasant clinical procedure, certain tumor tests, such as carcinoembryonic antigen (CEA) and fecal occult blood testing (FOBT), have been developed for clinical use, but with relatively poor sensitivity and specificity.5−7 CEA has a sensitivity ranges from 30 to 80% at a cutoff of 2.5 μg/L and a specificity of 76.98% Received: October 6, 2011 Published: December 9, 2011 1354

dx.doi.org/10.1021/pr201001a | J. Proteome Res. 2012, 11, 1354−1363

Journal of Proteome Research

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with a reference value of 5 μg/L.8 The sensitivity of FOBT for CRC varied from 51 to 100% and specificity varied from 90.4 to 97%.9 Metabolic profiling technology has been used to measure significant metabolic variations in tissue specimens of CRC patients, which revealed altered expression of amino acids, fatty acids, lactate, carboxylic acids and metabolites in urea cycle.10−16 Our recent serum17 and urinary18 metabonomic studies identified dysregulated glycolysis, tricarboxylic acid (TCA) cycle, urea cycle, pyrimidine metabolism, polyamine metabolism and gut flora metabolism associated with CRC morbidity. A striking observation is that metabolites derived from gut microbial-host co-metabolism contributed significantly to the classification between CRC and their healthy counterparts in urine metabonomic profiling.18 Recently the composition of gut microbiota in the CRC patients from the same cohort in our previous urinary metabonomic study was analyzed, which revealed a major structural imbalance of gut microbiota in CRC patients.19 One operational taxonomic unit (OTU) closely related to Bacteroides f ragilis and 11 OTUs belonging to the genera Enterococcus, Escherichia/Shigella, Klebsiella, Streptococcus and Peptostreptococcus were observed with increased abundances in the gut microbiota of CRC patients. Meanwhile, 3 OTUs related to Bacteroides vulgatus and Bacteroides uniformis and 5 OTUs belonging to the genus Roseburia and other butyrate-producing bacteria of the family Lachnospiraceae became significantly less abundant in CRC patients compared to healthy controls. The altered structure of gut microbiota observed in this metagenomic study suggests that CRC patients may demonstrate a distinct urinary metabolic profile involving gut microbial metabolites. Here, we report a urinary metabonomic study on a larger cohort of CRC (n = 101) and healthy subjects (n = 103) using a combined gas chromatography−time-of-flight mass spectrometry (GC−TOFMS) and ultra performance liquid chromatography−quadrupole time-of-flight mass spectrometry (UPLC− QTOFMS). The study was intended to confirm previously detected metabolic variations associated with CRC morbidity. In this study, we also intend to identify and characterize these differentially expressed metabolites as CRC diagnostic markers.

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with inflammatory conditions or gastrointestinal tract disorders were excluded. CEA levels for each CRC patient were also assessed. Clinical information on participants was provided in Table 1. To guarantee the effect of the statistical model, Table 1. Clinical Information for Colorectal Cancer Patients and Healthy Controls CRC patients Training set Number Age (median, range) Male/female ratio CEA (>5.0 ng/mL, median, range) TNM-I TNM-II TNM-III TNM-IV Testing set Number Age (median, range) Male/female ratio CEA (>5.0 ng/mL, median, range) TNM-I TNM-II TNM-III TNM-IV In total Number Age (median, range) Male/female ratio CEA (>5.0 ng/mL, median, range) TNM-I TNM-II TNM-III TNM-IV

61 59, 24−83 34/27 21, 3.705, 0.7−891.16

healthy controls 62 60, 31−75 31/31

15 25 17 4 40 63.5, 36−80 24/16 13, 4.02, 0.94−376.43

41 57, 35−76 0/41

9 20 10 1 101 60, 24−83 58/43 34, 3.9, 0.7−891.16

103 58, 31−76 31/72

24 45 27 5

training set and testing set were established as in Table 1. The training set was well-matched between the CRC and healthy control groups in age and sex. There was no significant difference for the age and sex in the training set between CRC and healthy controls. The p-value of Student’s t test for age between the two groups was 0.91. The value of Chi-square with Yate’s correction was 0.85 for sex between the two groups. Urine samples were collected in the morning before breakfast from a total of 101 CRC patients and 103 healthy volunteers at Ruijin Hospital (Shanghai, China). The collected urine samples were centrifuged at 3000 rpm for 10 min at 4 °C to remove suspended debris, and the resulting supernatants were immediately stored at −80 °C without any preservatives. The protocol was approved by Ruijin Hospital Institutional Review Board and written consents were signed by all participants prior to the study.

EXPERIMENTAL SECTION

Chemicals

HPLC grade methanol, acetonitrile, and formic acid were purchased from Merck Chemicals (Darmstadt, Germany). Pyridine was analytical grade and purchased from China National Pharmaceutical Group Corporation (Shanghai, China). L-2-Chlorophenylalanine was purchased from Intechem Tech. Co. Ltd. (Shanghai, China). BSTFA (1% TMCS), heptadecanoic acid, methoxyamine, leucine-enkephalin were purchased from Sigma Aldrich (St. Louis, MO). Clinical Samples

The 101 patients, ages 24−83 years and diagnosed with CRC (38 colon cancers and 63 rectal cancers), were categorized according to histopathological features and stages according to TNM classification of malignant tumors: stage I, 24 patients; stage II, 45 patients; stage III, 27 patients; stage IV, 5 patients. Patients enrolled in this research were not on any medication before sample collection. The clinical diagnosis and pathological reports of all the patients were obtained from the hospital. The healthy volunteers, ages 31−76 years, were selected by a routine physical examination and any subjects

Urine Sample Preparation and Analysis by GC−TOFMS

Urine samples were derivatized and subsequently analyzed by GC-TOFMS based on a revised protocol originally from our previous publications.17 A 200 μL aliquot of urine sample was centrifuged at 12000 rpm for 10 min. A 50 μL aliquot of the supernatant was transferred to a 1.5 mL PE tube, adding 10 μL urease (type C, 30 U/10 μL) and incubating for 15 min at 37 1355



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°C. The supernatant was spiked with two internal standards (10 μL 2-chlorophenylalanine in water, 0.3 mg/mL; 10 μL heptadecanoic acid in methanol, 1 mg/mL). The mixed solution was extracted with 170 μL methanol and vortexed for 30 s. The samples were centrifuged at 12000 rpm for 5 min. An aliquot of the 200 μL supernatant was transferred to a glass sampling vial to vacuum-dry at room temperature. The residue was derivatized using a two-step procedure. First, 80 μL methoxyamine (15 mg/mL in pyridine) was added to the vial, vortexed for 30 s and kept at 30 °C for 90 min followed by 80 μL BSTFA (1%TMCS) at 70 °C for 60 min. Each 1 μL aliquot of the derivatized solution was injected in spitless mode into an Agilent 6890N gas chromatography coupled with a Pegasus HT time-of-flight mass spectrometer (Leco Corporation, St Joseph, MI). Separation was achieved on a DB-5MS capillary column (30 m × 250 μm I.D., 0.25-μm film thickness; (5%-phenyl) methyl-polysiloxane bonded and cross-linked; Agilent J&W Scientific, Folsom, CA) with helium as the carrier gas at a constant flow rate of 1.0 mL/min. The temperature of injection, transfer interface, and ion source was set to 270 °C, 260 °C, and 200 °C, respectively. The GC temperature programming was set to 2 min isothermal heating at 80 °C, followed by 10 °C/min oven temperature ramps to 140 °C, 4 °C/min to 210 °C, and 10 °C/min to 240 °C, and 25 °C/min to 290 °C, a final 4.5 min maintenance at 290 °C. Electron impact ionization (70 eV) at full scan mode (m/z 30−600) was used, with an acquisition rate of 20 spectrum/second in the TOFMS setting.

concentration of 100 ng/mL and flow rate of 0.1 mL/min for all analyses. Data Analysis

The acquired MS data from GC-TOFMS and UPLCQTOFMS were analyzed according to our previously published work.17,22 The GC-TOFMS data was analyzed by ChromaTOF software (v 4.34, LECO, St Joseph, MI). After alignment with Statistic Compare component, the CSV file was obtained with three dimension data sets including sample information, peak retention time and peak intensities. The internal standard was used for data quality control (reproducibility). Internal standards and any known pseudo positive peaks, such as peaks caused by noise, column bleed and BSTFA derivatization procedure, were removed from the data set. The detectable spectral features in GC−TOFMS were 361 in total. The data set was normalized using the sum intensity of the 361 features in each sample. Metabolites annotation with NIST 05 Standard mass spectral databases linked to ChromaTOF software were manually checked with a similarity of more than 70% in addition to the reference standard compounds. The UPLC-QTOFMS ESI+ raw data were analyzed by the MarkerLynx Applications Manager version 4.1 (Waters, Manchester, U.K.) using the following parameters. The parameters used were RT range 0.5−9.5 min, mass range 50−1000 Da, mass tolerance 0.02 Da, internal standard detection parameters were deselected for peak retention time alignment, isotopic peaks were excluded for analysis, noise elimination level was set at 10.00, minimum intensity was set to 15% of base peak intensity, maximum masses per RT was set at 6 and, finally, RT tolerance was set at 0.01 min. A list of the ion intensities of each peak detected was generated, using retention time (RT) and the m/z data pairs as the identifier for each ion. The resulting three-dimensional matrix contains arbitrarily assigned peak index (RT-m/z pairs), sample names (observations), and ion intensity information (variables). To obtain consistent differential variables, the resulting matrix was further reduced by removing any peaks with missing value (ion intensity = 0) in more than 60% samples. The internal standard was used for data quality control (reproducibility). The ion peaks generated by the internal standard were also removed. Internal standards and any known pseudo positive peaks, such as peaks caused by noise, column bleed and solvents, were removed from the data set. There were 2581 detectable spectral features in UPLC−QTOFMS data set, which was then normalized using the sum intensity of the 2581 features in each sample. Metabolites obtained from ESI+ mode of UPLC− QTOFMS analysis were annotated with the aid of available reference standards in our lab and the web-based resources such as the Human Metabolome Database (HMDB) (http:// www.hmdb.ca/). The annotated metabolites in the two data sets resulting from GC−TOFMS and UPLC−QTOFMS ES+ (expressed as G and L, respectively) were combined into a new data set (261 metabolites in total, in which 163 from GC−TOFMS and 98 from UPLC−QTOFMS) for further statistical analysis by uniand multivariate statistical methods (the total list of 261 metabolites is provided in the Supplementary Table S2, Supporting Information). The combined data set was imported into SIMCA-P+ 12.0 software package (Umetrics, Umeå, Sweden). Principle component analysis (PCA) and orthogonal partial least-squares-discriminant analysis (OPLS-DA) were carried out to visualize the metabolic alterations between CRC

Urine Sample Preparation and Analysis by UPLC−QTOFMS

Urine sample preparation was processed according to our previous work.20−22 The collected urine samples were centrifuged at 3000 rpm for 10 min at 4 °C, and the resulting supernatants were immediately stored at −80 °C pending UPLC−QTOFMS analysis. Urine (150 μL) was spiked with an internal standard (20 μL 2-chlorophenylalanine in water, 0.3 mg/mL). Ultrapure water (300 μL) was added to it. The mixture was vortexed for 1 min, and then centrifuged at 12000 rpm for 15 min at 4 °C, and the resulting supernatants were for UPLC−QTOFMS analysis. A 5 μL aliquot of the supernatant was injected onto a 100 mm ×2.1 mm, 1.7 μm BEH C18 column (Waters, Milford, MA) held at 40 °C using an ultra performance liquid chromatography system (Waters, USA). The binary gradient elution system consisted of water with 0.1% v/v formic acid (A) and acetonitrile with 0.1% v/v formic acid (B) and separation was achieved using the following gradient: 1−20% B over 0−1 min, 20−70% B over 1−3 min, 70−85% B over 3−8 min, 85− 100% B over 8−9 min, the composition was held at 100% B for 0.5 min, then 9.5−10.5 min, 100% to 1% B, and 10.5−12 min holding at 1% B. The flow rate was 0.4 mL/min. All the samples were kept at 4 °C during the analysis. The mass spectrometric data was collected using a Waters QTOF premier (Manchester, UK) equipped with an electrospray ion source operating in positive ion mode. The source temperature was set at 100 °C with a cone gas flow of 50 L/ h, a desolvation gas temperature of 350 °C with a desolvation gas flow of 650 L/h. The capillary and cone voltage was set to 3.2 kV and 35 V, respectively. Centroid data was collected from 50 to 1000 m/z with a scan time of 0.3 s and interscan delay of 0.02 s over a 9.5 min analysis time. Leucine enkephalin was used as the lock mass (m/z 556.2771 in ES+ mode) at a 1356



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patients and healthy controls after mean centering and unit variance scaling. In this study, the default 7-round crossvalidation was applied with 1/seventh of the samples being excluded from the mathematical model in each round, in order to guard against overfitting. The variable importance in the projection (VIP) values of all the peaks from the 7-fold crossvalidated OPLS-DA model was taken as a coefficient for peak selection. VIP ranks the overall contribution of each variable to the OPLS-DA model, and those variables with VIP > 1.0 are considered relevant for group discrimination.23 In addition to the multivariate statistical method, the Student’s t-test and Wilcoxon−Mann−Whitney test were also applied to measure the significance of each metabolite. Metabolites with both multivariate and univariate statistical significance (VIP >1 and p < 0.001) are considered markers responsible for the differentiation of CRC from healthy controls. To further interpret the biological significance associated with CRC morbidity, we applied the Kyoto Encyclopedia of Genes and Genomes (KEGG) database to link these metabolites to metabolic pathways.24 Logistic regression and ROC analysis were performed by SPSS software (IBM, SPSS 19.0).

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RESULTS A total of 361 and 2581 spectral features were consistently detected in the urine samples with GC−TOFMS and UPLC− QTOFMS after excluding internal standards, respectively. With the 361 features generated from GC−TOFMS, a PCA scores plot using 5 components (R2Xcum = 0.386, Q2 cum = 0.283) and a cross-validated OPLS-DA model using 1 predictive component and 3 orthogonal components (R2Xcum = 0.32, R2Ycum = 0.907, Q2Ycum = 0.802) were constructed, showing a separation between CRC and healthy subjects (Supplementary Figure S1A, Supporting Information). Similarly, a PCA scores plot using 5 components (R2Xcum = 0.401, Q2 cum = 0.345) and a cross-validated OPLS-DA model using one predictive component and three orthogonal components (R2Xcum = 0.329, R2Ycum = 0.936, Q2Ycum = 0.825) were constructed with the resulting 2581 features generated from UPLC−QTOFMS, which indicates a clear separation between CRC patients and healthy controls (Supplementary Figure S1B, Supporting Information). A total of 261 metabolites were annotated from the detected spectral features from GC−TOFMS and UPLC−QTOFMS using reference standards as well as available database (NIST library 2005 and HMDB). A PCA (5 components, R2Xcum = 0.35, Q2 cum = 0.248) and a cross-validated OPLS-DA model (1 predictive component and 2 orthogonal components, R2Xcum = 0.273, R2Ycum = 0.897, Q2Ycum = 0.816) were constructed with satisfactory discriminating ability using the 261 annotated metabolites (Supplementary Figure S2A and S2B, Supporting Information). To further test the performance of this model, 61 CRC subjects and age and gender-matched 62 healthy controls were selected as training samples. Figure 1 shows the prediction results of the 81 testing samples (black triangles and green boxes) using the OPLS-DA model established with the training samples (R2Xcum = 0.281, R2Ycum = 0.90, Q2Ycum = 0.756). All the test samples are correctly classified as CRC patients and healthy controls. A permutation test (500 times) of the PLSDA model (Supplementary Figure S3A, Supporting Information) including correlation coefficient between the original Y and the permuted Y versus the cumulative R2 and Q2, with the regression line is shown in Supplementary Figure S3B. All of the cancer patients were correctly discriminated from the

Figure 1. Scores plot of the OPLS-DA prediction model of colorectal cancer (CRC). An OPLS-DA model was constructed using data from 62 healthy controls (blue dots) and 61 CRC patients (red diamonds) (the “training set”), this model was then used to predict CRC of a further 81 samples including 41 healthy controls (black triangles) and 40 CRC patients (green boxes) that were not used in the construction of the model (the “testing set”).

healthy controls in the predictive component, including 24 patients diagnosed at TNM stage I. This result indicates great potential for early diagnosis of CRC using these urinary metabolite markers. However, similar to our previous urine metabonomics study, we were not able to further classify CRC patients based on their different pathological stages using PLS or OPLS-DA models of current urinary metabolite profiles. We were not able to stratify patients based on their CEA levels (CRC patients with CEA >5 ng/mL vs lower levels) using metabonomic data. A panel of 35 metabolites with VIP threshold (VIP > 1) from the training set and p-value (p < 0.001) were selected as metabolite markers in Table 2. These metabolites represent key metabolic pathways involving glycolysis and TCA cycle, urea cycle, pyrimidine metabolism, tryptophan metabolism, polyamine metabolism and gut flora metabolism. Additionally, vitamin and organic acids appeared to be associated in CRC morbidity. Particularly notable among these 35 markers are metabolites deriving from gut microbial-host co-metabolism. Seven representative metabolites in urine, citrate, hippurate, p-cresol, 2-aminobutyrate, myristate, putrescine, and kynurenate, were selected as a panel of candidate markers based on their high fold changes, AUC, and VIP values. Logistic regression was used to combine the 7 variables into a multivariable. The ROC curves based on the multivariable yielded satisfactory results using the training sample set and the testing set respectively, as shown in Figure 2A. The area under the curve (AUC) reached 0.993 (95% confidence interval: 0.979−1.000) with a sensitivity of 97.5% and specificity of 97.6% for the training set, while AUC reached 0.998 (95% confidence interval: 0.992−1.000) for the testing set, with a sensitivity of 97.5% and specificity of 100%. Box plots of citrate, kynurenate, hippurate, and 21357



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Table 2. Representative Differential Metabolites Contributed for the Separation between the CRC Patients and the Healthy Controls Derived from UPLC−QTOFMS and GC−TOFMS Analysis no. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35

VIPa 1.86 1.20 1.30 1.29 1.14 1.70 1.10 2.17 1.28 1.31 1.35 1.09 1.75 1.17 1.05 1.86 1.23 1.73 1.78 1.15 1.87 1.96 1.28 1.49 1.08 1.14 1.65 1.20 1.09 1.49 2.00 1.69 1.36 1.28 1.06

pb 5.70 7.90 1.44 4.34 2.85 6.38 2.73 5.03 2.23 1.19 1.48 2.65 1.05 2.60 6.33 5.70 4.66 2.84 8.14 7.78 3.57 1.56 1.44 7.87 1.89 3.16 8.08 3.04 9.87 5.20 2.76 1.25 9.62 7.25 3.69

× × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × ×

Pc −17

10 10−10 10−8 10−8 10−7 10−12 10−7 10−27 10−8 10−9 10−8 10−7 10−17 10−10 10−10 10−17 10−9 10−20 10−16 10−10 10−15 10−20 10−8 10−9 10−8 10−7 10−17 10−8 10−6 10−12 10−19 10−11 10−8 10−10 10−5

3.08 5.41 5.79 4.71 3.42 4.01 2.24 4.06 1.49 3.66 2.81 2.57 2.66 3.65 2.76 5.93 2.22 1.82 3.82 1.92 1.13 4.97 3.28 6.50 4.97 3.66 3.57 7.80 1.50 8.46 2.40 1.74 7.74 9.81 1.40

× × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × ×

−17

10 10−10 10−13 10−8 10−8 10−17 10−8 10−23 10−8 10−16 10−8 10−9 10−21 10−21 10−18 10−28 10−11 10−24 10−15 10−11 10−14 10−20 10−11 10−9 10−8 10−9 10−16 10−18 10−6 10−12 10−23 10−12 10−11 10−11 10−5

fold changed (CRC/control)

metabolite

metabolic pathway

−11.11 −1.89 1.81 −1.41 1.59 −3.03 −1.59 −2.50 −1.89 −2.94 −1.52 −1.56 −1.96 −3.13 −3.57 −11.11 −1.67 −3.70 −1.67 −1.59 −4.00 −1.82 1.95 1.75 −1.41 3.04 −3.23 −1.82 −1.33 −1.64 12.76 −2.04 −1.69 −1.35 −1.37

PyruvateG CitrateG FumarateG UreaL PutrescineG Myristate TryptophanL KynurenateL 5-Hydroxy-tryptophanL IndoleacetateL IndoleL TyrosineL HomovanillateG PhenolG p-CresolG HippurateG 4-AminohippurateL Trimethylamine N-oxideL UridineL UracilG Pyridoxal (Vitamin B6)L HydroxyacetateG 4-HydroxybutyrateG 2-AminobutyrateG XyloseG Acetyl-carnitineL 2-HydroxyestradiolL ArabitolG CreatinineL GlucuronateG HistidinolL N-Acetyl-L-lysineL SorboseG ThreonateG AlanineG

Glycolysis TCA cycle TCA cycle Urea cycle Polyamine metabolism Fatty Acid Biosynthesis Tryptophan metabolism Tryptophan metabolism Tryptophan metabolism Tryptophan metabolism Tryptophan metabolism Tyrosine metabolism Tyrosine metabolism Tyrosine metabolism Gut flora metabolism Gut flora metabolism Gut flora metabolism Gut flora metabolism Pyrimidine metabolism Pyrimidine metabolism Vitamin Others Others Others Others Others Others Others Others Others Others Others Others Others Others

a Variable importance in the projection (VIP) was obtained from OPLS-DA with a threshold of 1.0. bp values were calculated from student’s t test. cp values were calculated from Wilcoxon-Mann−Whitney test. dFold change was calculated from the arithmetic mean values of each group. Fold change with a positive value indicates a relatively higher concentration present in CRC patients while a negative value means a relatively lower concentration as compared to the healthy controls. G, GC−TOFMS. L, UPLC−QTOFMS.

aminobutyrate in the training set which reflect several key metabolic processesTCA cycle, fatty acid metabolism, tryptophan metabolism and gut flora metabolism were provided in Figure 2B−E, demonstrating fluctuations in single metabolites in CRC patients. The AUCs of ROC for single metabolites are provided in Supplementary Table S1, Supporting Information. Six representative metabolites with characteristic expression levels among different CRC stages were shown in Figure 3.

0.993, sensitivity of 97.5% and specificity of 97.6% for the training set, and an AUC of 0.998, sensitivity of 97.5% and specificity of 100% for the testing set. This result is consistent with our previously published CRC metabonomic studies, where several key metabolic pathways including glycolysis and TCA cycle, urea cycle, tryptophan metabolism, fatty acid metabolism, polyamine metabolism and gut microflora were altered in association with CRC morbidity.17,18 Due to the use of more advanced analytical platform, GC-TOFMS and UPLCQTOFMS, in this study, a greater number of important metabolites in the above pathways were captured, which constitutes a more distinct metabolic footprint of CRC. In addition, the CRC subjects involved in this study were in earlier pathological stages, with 70% at TNM-I and -II stages, compared to 70% of subjects at TNM-II and -III stages in the last study.17,18 As a result, there are differences among metabolite markers discovered in the two studies. Intermediates in glycolysis and TCA cycle were differentially expressed, as shown in Supplementary Table S2, Supporting

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DISCUSSION In this study, we were able to discriminate all of the 101 CRC patients including 24 TNM-I stage patients from the 103 healthy controls in an OPLS-DA analysis of urinary metabolites, in which 35 metabolites were identified as markers of CRC patients. The panel of 35 urinary metabolites, particularly, citrate, hippurate, p-cresol, 2-aminobutyrate, myristate, putrescine, and kynurenate, were able to discriminate CRC subjects from their healthy counterparts, with an AUC of 1358



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Figure 2. (A) ROC curve analysis of the ability of urinary metabolites including citrate, hippurate, p-cresol, 2-aminobutyrate, myristate, putrescine, and kynurenate to discriminate between samples from CRC patients (n = 61) and healthy controls (n = 62). The area under the curve (AUC) was 0.993 or 0.998 for the training set (blue line) or the testing set (red line), respectively. (B−E) Box plots of citrate, kynurenate, 2-aminobutyrate, and hippurate in the training set, correlated with disturbed metabolic pathwaysTCA cycle, tryptophan metabolism and gut flora metabolism.

Information, presumably as a result of “Warburg effect” in cancer cells. Increased lactate was repeatedly observed in CRC tissue metabolomic studies10,11,13,25 and also in our previous serum analysis.17 In this study, more intermediates such as fumarate and aconitate were found significantly altered as shown in Figure 4A, Table 2 and Supplementary Table S2, Supporting Information. The findings with decreased levels of citrate and isocitrate are consistent with our previous urinary metabonomic study.18 However, succinate was elevated in urine, which is consistent with the observation in precancerous colon lesion model but different from the results in human subjects in our previous study.18 The increased level of succinate was also observed in CRC tissue research, with increased succinnyl-CoA, succinate, fumarate and malate accompanied by decreased citrate, and aconitate.13 This

observation can be explained by the anaplerotic reactions in TCA cycle in those patients with relatively early stages, where glutamine was extensively used to replenish TCA cycle intermediates in cancer cells through α-ketoglutarate (α-KG) and oxaloacetate (OAA) to maintain a stable supply source for fatty acid biosynthesis, along with the active glycolysis providing the major carbon.26 More recent studies revealed that serine synthesis is also extensively involved in the production of α-KG to replenish TCA cycle, and that approximately 50% of the glutamine-derived α-KG used in the TCA cycle comes from the serine synthesis pathway in cells with high phosphoglycerate dehydrogenase expression.27,28 Intermediates from gut microbial-host co-metabolism, which are linked to choline metabolism and phenylalanine, tryptophan and tyrosine metabolism, are significantly altered in urinary 1359



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Figure 3. Bar charts of mean intensity of six representative metabolites in urine samples of CRC patients with different stages and healthy controls.

Figure 4B, phenylalanine, tryptophan and tyrosine metabolic pathways are linked to gut flora metabolism. The downstream product of phenylalanine, such as p-cresol and hippurate, were found significantly decreased in CRC patients. These changes were different from the previous results with CRC patients but in agreement with the results obtained in precancerous rat colon model.18 This is presumably due to the fact that the CRC patients in this study were at earlier pathological stages relative to those in the previous study, and that the rat model was at precancerous development stage. As shown in Figure 3, the urinary concentrations of these metabolites altered at different pathological stages. Similar to the products of phenylalanine metabolism, intermediates in tryptophan and tyrosine metabolism, namely, indoleacetate, indole, homovanillate, phenol

metabolome (Figure 4B). Two out of 7 most important markers (citrate, hippurate, p-cresol, 2-aminobutyrate, myristate, putrescine, and kynurenate) are gut flora metabolites. The AUC of these markers was 0.993 and 0.998 in training and testing set, respectively, indicating a strong diagnostic power using gut flora related metabolites alone. The alteration of these metabolites suggests an abnormal gut ecosystem resulting from altered intestinal microbial composition characteristic to CRC. Our intestinal microbial community is an enormous and diverse ecosystem with known functions in nutrition, gut epithelial cell health, and innate immunity.29 Human intestinal microbes are implicated in the development of a number of metabolic phenotypes such as obesity and insulin resistance, as well as alterations in immune responses.30−33 As shown in 1360



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Figure 4. Disturbed metabolic pathways associated with CRC morbidity. (A) Glycolysis and TCA cycle, glutaminolysis, fatty acid synthesis, urea cycle, polyamine metabolism; (B) gut flora metabolites related to choline metabolism, phenylalanine, tryptophan and tyrosine metabolism. ★ Differentially expressed metabolites. ↑ Higher level in CRC patients compared to healthy controls. ↓ Lower level in CRC patients compared to healthy controls. Solid arrows indicate a single step reaction and dotted arrows indicate multistep reactions.

model.18 Polyamines, as mentioned in our previous urinary paper, may modulate the RNA expression of the cancer-related gene cyclooxygenase-2 (COX-2) through the polyaminedependent gene, eIF 5A.18,36,37 The elevated urinary excretion levels of putrescine suggest an association between CRC and enhanced COX-2 enzyme activity.18

were all decreased. TMAO, an oxidation product of trimethylamine (TMA) from choline metabolism,33,34 was also found depleted in the urine of CRC subjects. Intestinal microflora is involved in TMAO formation from dietary free choline35 and lipid phosphatidylcholine (also known as lecithin).33 While TMA and TMAO are generally regarded as nontoxic substances, they are of clinical interest because of their potential to form the carcinogen N-nitrosodimethylamine.35 The depletion of these metabolites constitutes a distinct urinary metabolic profile of CRC subjects (Figure 4) and supports that CRC is associated with an altered intestinal microbial composition. Consistent with our previous study, urea, ornithine and citrulline in urea cycle and uridine in pyrimidine metabolism were found decreased in CRC subjects.18 Putrescine, the polyamine produced from ornithine (Figure 4), was detected elevated in this study, similar to that in the precancerous rat

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CONCLUSIONS Overall, our findings confirm a distinct urinary metabolic profile of CRC patients characterized by altered levels of many metabolites derived from gut microbial-host co-metabolism as well as metabolites involved in TCA cycle, tryptophan metabolism, and polyamine metabolism. A panel of metabolite markers composed of 7 metabolites (citrate, hippurate, p-cresol, 2-aminobutyrate, myristate, putrescine, and kynurenate), was able to discriminate CRC subjects from their healthy counterparts, with an AUC of 0.993 and 0.998 for the training 1361



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set and the testing set, respectively. Our study highlight the significance of the distinct urinary metabolic profile of CRC, characterized by a panel of metabolites that can be further developed to be important diagnostic markers for the early detection of CRC in the future.

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ASSOCIATED CONTENT

S Supporting Information *

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

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AUTHOR INFORMATION

Corresponding Author

*Pingang He, Department of Chemistry, East China Normal University, Shanghai 200062, China. Email: [email protected]. edu.cn. Yan Zhang, Ministry of Education Key Laboratory of Systems Biomedicine, Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai 200240, China. Email: [email protected]. Wei Jia, Department of Nutrition, University of North Carolina at Greensboro, North Carolina Research Campus, Kannapolis, North Carolina 28081, USA. Phone: 704-250-5803. Fax: 704-250-5809. E-mail: w_ [email protected].

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ACKNOWLEDGMENTS This work was financially supported by the National Basic Research Program of China (2007CB914703). REFERENCES

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Distinct Urinary Metabolic Profile of Human Colorectal Cancer

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