pancreatic cancer ( pc ) 
 is one of the most fatal cancers , with a 
 5 year survival of less than 5% .
an estimated 
 46  420 new cases of pc are expected in the u.s . in 2014 as 
 well as 39  590 deaths from this disease .
a major hurdle toward improving clinical outcome of pc 
 is the lack of diagnostic biomarkers at early stages of the disease .
given the high mortality associated with pc , 
 novel and cost - effective biomarkers to improve treatment and survival 
 outcomes of pc patients are urgently needed . to date , the only 
 treatment that provides significant survival 
 benefit is surgical resection , but only 2025% of patients 
 are diagnosed at early disease stages when resection is appropriate .
the clinical symptoms of pc are usually vague 
 and nonspecific until progression to advanced stages has occurred . 

attempts to reduce pc deaths have therefore relied 
 greatly on early cancer detection and treatment , generally through 
 imaging examination , such as magnetic resonance imaging ( mri ) , computed 
 tomography , endoscopic retrograde cholangiopancreatography ( ercp ) , 
 or endoscopic ultrasound ( eu )
. however , the specificity and sensitivity 
 of these modalities are not adequate for tumors of less than 2 cm 
 in diameter .
the traditional tumor marker ca19 - 9 , 
 the sensitivity of which can reach 80% for pc , is unsuitable for early 
 detection of pc due to low sensitivity for patients at resectable 
 stages and especially because of its weak 
 specificity .
numerous efforts have been 
 made in the search for pc biomarkers during recent decades , and , as 
 a result , tumor - specific growth factor ( tsgf ) , ca242 , mic-1 , platelet factor 4 , peanut 
 agglutinin ( pna)-binding glycoprotein , cell adhesion molecule 17.1 , and serum 
 immune signatures found by affinity proteomics have been identified as candidate biomarkers .
unfortunately , 
 these biomarkers display low sensitivity for resectable disease , and 
 their accuracy for detecting resectable stage cancer has not been 
 evaluated .
metabonomics , a new member of omics technologies 
 that quantitatively 
 measures altered metabolites resulting from pathophysiological changes , 
 is rapidly becoming a discovery tool for new diagnostic and prognostic 
 biomarkers of human diseases .
we have previously shown the 
 use of metabonomics for diagnosis and evaluation of pathologic conditions 
 of various cancers .
recently , metabonomics studies of pc have identified biomarkers 
 in plasma or tissue that differentiate pc from controls .
however , these studies have 
 had relatively small sample sizes and a small number of early stage 
 or resectable cancers . in this study , we used a combination 
 of liquid chromatography  time - of - flight 
 mass spectrometry ( lc  tofms ) and gas chromatography  time - of - flight 
 mass spectrometry ( gc  tofms ) to profile plasma metabolites 
 of pc patients and controls from the u.s . and china .
the aim of this 
 study was to identify plasma metabolites as potential markers for 
 early detection of pc and to test the diagnostic performance of these 
 markers .
blood samples 
 used in this study 
 were from 100 pc patients and 100 age- and gender - matched population 
 controls in connecticut ( ct ) , usa , and from 100 pc patients and 100 
 similarly matched population controls from shanghai ( sh ) , china ( table 1 ) .
the pc patients were newly diagnosed with pancreatic 
 ductal adenocarcinoma and were not recurrent or on any medication 
 prior to sample collection .
patient characteristics , staging of the 
 disease , and other parameters are shown in table 1 .
these studies were approved by the state of connecticut department 
 of public health as well as by the institutional review boards of 
 30 connecticut hospitals ( ct ) , the institutional human subjects review 
 board of the shanghai cancer institute ( sh ) , and the human investigation 
 committee of yale university ( ct ) and shanghai cancer institute ( sh ) . 

blood specimens 
 were collected 
 from all participants and returned on ice to our laboratories within 
 2 h of collection for blood processing .
tofms 
 were used for the metabonomic profiling of all samples in the study . 

the profiling procedure ( sample preparation , metabolite separation 
 and detection , metabonomic data preprocessing , metabolite annotation , 
 and statistical analysis for biomarker identification ) was performed 
 following our previously published protocols with minor modifications .
quality control ( qc ) samples , 
 which were prepared by mixing equal amounts of plasma from all subject 
 samples , were used to control intra- and interbatch variability .
details of plasma 
 sample preparation and lc / gc  ms analysis are provided in the supporting information .
the metabonomic data obtained 
 were normalized using internal standard p - chlorophenylalanine 
 and calibrated using qc samples .
tofms 
 and lc  tofms data sets were combined and exported to simca - p+ 
 12.0 software ( umetrics , ume , sweden ) for multivariate statistical 
 analysis .
orthogonal partial least squares - discriminant 
 analysis ( opls - da ) were performed to discriminate between pc patients 
 and controls . on the basis of a threshold of variable importance in 
 the projection ( vip , value > 1 ) from the 7-fold cross - validated 
 opls - da 
 model ,
a panel of metabolites responsible for the difference in the 
 metabolic profiles of patients and controls was obtained .
in addition 
 to the multivariate statistical method , student s t - test was also applied to measure the significance of each metabolite . 

the resultant p values for all metabolites were subsequently 
 adjusted to account for multiple testing by a false discovery rate 
 ( fdr ) method .
metabolites with both multivariate 
 and univariate statistical significance ( vip > 1 and p < 0.05 ) were considered to be potential markers capable of differentiating 
 pc from controls .
the corresponding fold change was calculated to 
 show how these selected differential metabolites varied in the cancer 
 samples relative to the controls . altered metabolic pathways in pc 
 were analyzed by means of the quantitative enrichment analysis ( qea ) 
 algorithm represented in the metabolite set enrichment analysis ( msea ) 
 method .
receiver operating characteristic ( roc ) curve analysis 
 and binary logistic regression were conducted using spss software 
 ( ibm spss statistics 19 , usa ) following our previously published data 
 analysis protocols .
briefly , a logistic 
 regression model constructed using the binary outcome of pc and control 
 as dependent variables was used to determine the best combination 
 of plasma markers for pc prediction .
the forward stepwise regression , 
 the procedure to select the strongest variables ( metabolites ) until 
 there are no more significant predictors in the data set , was used 
 for potential biomarker selection .
the wald test was used to assess 
 significance in logistic regression , and this test assigns a p value to each metabolite to assess significance .
roc curves 
 for the logistic regression model were plotted with the fitted probabilities 
 from the established model as possible cut - points for the computation 
 of sensitivity and specificity .
demographic , 
 lifestyle , and clinical information of the study subjects is listed 
 in table 1 .
patients and controls were well - matched 
 for age and gender within each study site .
, 202 metabolites were identified 
 ( supporting information table s1 ) from 
 the detected spectral features of samples ; of these , 109 metabolites 
 ( 53.7% , 70 metabolites from gc  ms and 39 from lc 
ms ) 
 were validated with reference standards , whereas the others were annotated 
 by comparing with available databases including the nist library and 
 the human metabolome database ( hmdb ) . a one - predictive component and 
 two - orthogonal component opls - da model ( r2x = 0.170 , r2y(cum ) = 0.757 , 
 q2(cum ) = 0.565 )
was constructed with satisfactory discriminating 
 ability using the metabonomics data of the 202 identified plasma metabolites 
 in ct samples ( figure 1a ) .
similarly , a one - predictive 
 component and four - orthogonal component opls - da model ( r2x = 0.258 , 
 r2y(cum ) = 0.880 , q2(cum ) = 0.679 ) was constructed with satisfactory 
 discriminating ability using the metabonomics data of the 202 identified 
 plasma metabolites in sh samples ( figure 1b ) . 

pc patients from both ct and sh sample sets could be separated from 
 their control counterparts .
metabolic profiles depicted by opls - da scores 
 plots of lctofms 
 and gctofms spectral data ( 202 metabolites ) from ( a ) ct plasma 
 samples , ( b ) sh plasma samples , and ( c ) 3d opls - da scores plot of 
 plasma metabolic profiles of pc patients and controls from ct and 
 sh . using the vip values ( vip > 
 1 ) derived from the opls - da model and 
 the p values ( p < 0.05 ) , 65 differentially 
 expressed metabolites in the ct set and 62 in the sh set were obtained , 
 among which 31 metabolites were the same and were significantly altered 
 in the same direction ( table 2 ) .
pc patients 
 can be discriminated from control subjects with the 31 differential 
 metabolites identified both in ct and sh samples , as evidenced by 
 a 3d opls - da scores plot of plasma metabolic profiles of pc patients 
 and controls from ct and sh shown in figure 1c .
variable 
 importance in the projection 
 ( vip ) was obtained from opls - da with a threshold of 1.0 . fold change ( fc )
was obtained by 
 comparing those metabolites in the pc group to the control group ; 
 fc with a value > 1 indicates a relatively higher concentration 
 present 
 in the pc group , whereas a value < 1 indicates a relatively lower 
 concentration compared to the control group .
the 31 significantly altered plasma metabolites in 
 both ct and 
 sh patients ( adjusted p < 0.05 , supporting information figure s1a ) include amino acids , carbohydrates , 
 lipids , nucleosides , organic acids , aromatic heteropolycyclic compounds , 
 aliphatic acyclic compounds , and aliphatic heteromonocyclic compounds 
 ( table 2 and supporting 
 information figure s1a ) .
thirty six metabolic pathways were 
 found to be dysregulated in pc based on the analysis of the qea algorithm 
 of the msea method ( bonferroni - corrected p < 0.05 , supporting information figure s1b ) . to evaluate the potential utility of plasma metabolites 
 for the discrimination between pc patients and control subjects
, we 
 developed a logistic regression model based on the 31 validated biomarkers 
 from the ct set . through a forward stepwise analysis , we identified 
 glutamate , choline , 1,5-anhydro - d - glucitol , betaine , and 
 methylguanidine as being the best predictors of disease status in 
 the regression model ( table 3 ) . using these 
 metabolites , we established a regression model as follows : p values were calculated 
 using the wald test .
next , 
 we generated roc curves to assess the potential usefulness 
 of plasma metabolite signatures as noninvasive biomarkers for the 
 diagnosis of pc .
our roc analyses revealed that plasma metabolite 
 biomarkers were robust in discriminating patients with pc from controls 
 in ct , with an area under the curve ( auc ) value of 0.943 ( 95%
ci = 
 0.9080.977 ) ( figure 2a ) . using a cutoff 
 value of 0.3598 ,
the sensitivity , specificity , and positive and negative 
 predictive values are given in table 4 .
( a ) roc curve 
 analysis for the predictive power of combined plasma 
 biomarkers for distinguishing pc from controls in the ct set .
the 
 final logistic model included five plasma biomarkers : glutamate , choline , 
 1,5-anhydro - d - glucitol , betaine , and methylguanidine .
( b ) 
 roc curve analysis for the predictive power of combined plasma biomarkers 
 for distinguishing pc from control in the sh set . at the cutoff value 
 determined in the ct set
, plasma metabolite biomarkers yielded an 
 auc value of 0.835 ( 95% ci , 0.7770.893 ) with 77.4% sensitivity 
 and 75.8% in discriminating pc from controls .
( c ) plots of the diagnostic 
 values of the constructed diagnostic model and tumor marker levels 
 in 100 pc patients and 100 controls in the ct set and 100 pc patients 
 and 100 controls in the sh set , according to disease stage .
third , the parameters obtained from the ct set were 
 used to predict 
 the probability of pc diagnosis in the sh set .
similarly , the roc 
 curve was constructed with the predicted probability for the sh set 
 and is shown in figure 2b .
at the same cutoff value of 0.3598 , its sensitivity and 
 specificity values were still substantial ( table 4 ) .
the predictive values obtained from this classification 
 method in the ct and sh samples are also shown in scatter plots in 
 figure 2c . to investigate whether the 
 pc - associated plasma metabolite signatures 
 can differentiate between patients and controls independently of possible 
 confounding risk factors ,
the pc - associated plasma metabolite 
 signatures remained significant ( p < 0.001 ) after 
 adjustment for bmi and use of tobacco and alcohol ( supporting information table s2 ) .
genetic alterations enable cancer 
 cells to reprogram metabolism to meet increased energy demands for 
 cell proliferation and to survive in hypoxic and nutrient - deprived 
 tumor microenvironments . in this
regard , 
 a better understanding of metabolic dysregulation in pc is important 
 and necessary .
metabonomics allows for global assessment of 
 the cellular metabolic state within the context of the immediate environment , 
 taking into account genetic regulation , altered kinetic activity of 
 enzymes , and changes in inflammatory and stress levels . in this study , we used combined lc 
the combination of different analytical platforms takes advantage 
 of complementary analytical outcomes and , therefore , provides an unrivaled 
 number of identified metabolites for explaining biological variations 
 associated with pathophysiological conditions and obtains cross - validated results as well .
we also included two 
 groups of subjects with different ethnic backgrounds , from usa and 
 china , to identify and validate the metabolite biomarkers .
although 
 there are ethnic differences in metabonomic profiles between ct and 
 sh subjects ( figure 1c ) , pc patients can still 
 be readily discriminated from the controls .
the opls - da models 
 derived from the current metabonomic analysis 
 were able to differentiate between pc and controls in both the ct 
 and sh cohorts , highlighting the diagnostic potential of this noninvasive 
 analytical approach .
our results also demonstrated the potential role 
 of metabolite biomarkers in the early detection of pc , which is supported 
 by the markedly high auc values of 0.943 and 0.835 from comparisons 
 between pc patients and controls ( sensitivity = 97.7 and 77.4% ; specificity 
 = 83.1 and 75.8% ) in the ct and sh cohorts , respectively .
even more 
 important for potential early diagnosis of pc , our metabonomics - based 
 diagnostic model was sensitive at detecting early stage pc in the 
 models that were adjusted for bmi and history of smoking and drinking .
the pathogenesis of pancreatic disease 
 can cause significant decreases 
 in plasma levels of amino acids , fatty acids , aliphatic acyclic compounds , 
 and aromatic heteropolycyclic compounds ( valine , glutamine , proline , 
 tryptophan , monoisobutyl phthalic acid , propionylcarnitine , urea , 
 and uric acid ) and increases in glutamic acid and glycocholic acid , 
 which have been demonstrated by other research groups that have found 
 alterations in plasma / serum metabolites similar to ours .
it is well - known that uptake 
 and catabolism of amino acids and fatty acids are enhanced to support 
 rapid cell proliferation in cancer tissues and that these changes may be explained as a result of the enhanced 
 usage in tumors ( supporting information figures 
 s2 and s3 ) .
metabolic pathway analysis also suggests that glycine / serine / threonine / methionine 
 metabolism ( supporting information figure s2 ) , glutamate pathway ( supporting information 
 figure s3 ) , tyrosine metabolism ( supporting 
 information figure s4 ) , tca cycle ( supporting 
 information figure s5 ) , choline metabolism ( supporting information figures s2 and s6 ) , and bile acid metabolism 
 ( supporting information figure s7 ) are 
 markedly altered .
pc may result from a mutation in either the 
 exocrine or endocrine 
 function of the pancreas .
therefore , 
 there is a possibility that the decreases in their plasma metabolite 
 levels also reflect malnutrition .
consistent with results found by 
 kobayashi and colleagues , we observed 
 that plasma levels of 1,5-anhydro - d - glucitol were significantly 
 reduced in pc patients compared to controls ( table 2 ) .
1,5-anhydro - d - glucitol is reported to be a biomarker 
 of short - term glycemic control , and decreased 
 plasma levels of 1,5-anhydro - d - glucitol suggest the presence 
 of hyperglycemia and glycosuria .
these results indicate some impairment 
 of glucose tolerance in these patients because of pancreatic insufficiency . 

glutamate has been implicated in tumorigenesis through activation 
 of alpha - amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid ( ampa ) 
 receptors ( ampar ) , and glutamate concentration plays a key role in 
 pc cell invasion and migration .
our observation 
 of a significantly elevated plasma level of glutamate is in accordance 
 with serum analyses of pc by bathe et al .
choline - containing 
 metabolites have already been chosen as biomarkers in various carcinoma 
 studies and have been reported to be 
 decreased in pc .
choline deficiency can 
 also produce severe acute pancreatitis in animal models . in our results , reduced betaine enhanced the 
 discrimination of pc from controls .
betaine donates methyl groups 
 for remethylation of homocysteine to methionine and dimethylglycine , 
 which support proper liver and pancreatic function , cellular replication , 
 and detoxification reactions . because choline is a precursor of betaine
, 
 the depletion of both betaine and choline in pc may be interrelated 
 ( supporting information figures s2 and s6 ) .
it has been reported that urinary levels of methylguanidine are 
 significantly increased in chronic pancreatitis patients , yet this metabolite has not been studied in 
 pc to date .
glycocholic acid was not selected in the panel of 
 markers ( glutamate , 
 choline , 1,5-anhydro - d - glucitol , betaine , and methylguanidine ) 
 for the prediction of pc using the forward stepwise regression method . 
 because its level is remarkably increased in pc patients ( 3.65-fold 
 increase in the ct set and 7.35-fold increase in the sh set ) , we added 
 glycocholic acid manually into the logistic regression model , and 
 as a result , there is no statistical significance for glycocholic 
 acid in the model ( p = 0.229 ) ( supporting information table s3 ) .
although our current 
 assay may stimulate the development of tools 
 for early diagnosis of pc , there are a number of limitations to consider . 

first , the identified markers used to build the logistic regression 
 model were selected from both the ct and sh data sets .
therefore , 
 the marker panel needs to be further validated with a new independent 
 sample set .
we did not have access to subjects with chronic pancreatitis 
 and thus our marker panel needs to be examined in such patients to 
 see how much they differ from pc patients .
third , recent metabolomics 
 studies of pc have provided metabolite biomarkers that can differentiate pc patients from controls . however , whether 
 the identified metabolite biomarkers are specific to pc , rather than 
 involving other malignancies or inflammatory disease in general , is 
 not clear .
future metabonomics studies need to evaluate the specificity 
 of metabolite biomarkers for pc versus other malignancies .
fourth , 
 we observed the impact of only two ethnic backgrounds on the performance 
 of diagnostic markers .
a new sample set with more diverse ethnic backgrounds 
 would be useful to increase generalizability .
in addition , the sensitivity 
 and specificity of this metabolite panel needs to be compared with 
 the performance of existing markers , such as ca19 - 9 , in future studies .
in summary , we identified a panel of five plasma metabolite markers 
 of pc and developed a diagnostic model using logistic regression analysis 
 of the biomarker panel .
additional studies are still necessary for further evaluation and 
 validation of the biomarkers identified in the current study
. however , 
 this novel approach holds potential to improve patient prognosis by 
 early detection of pc , when it may still be at a resectable stage .
 patients 
 with pancreatic cancer ( pc ) are usually diagnosed at late 
 stages , when the disease is nearly incurable . 
 sensitive and specific 
 markers are critical for supporting diagnostic and therapeutic strategies . 
 
 the aim of this study was to use a metabonomics approach to identify 
 potential plasma biomarkers that can be further developed for early 
 detection of pc . in this study , plasma metabolites of newly diagnosed 
 pc patients ( n = 100 ) and age- and gender - matched 
 controls ( n = 100 ) from connecticut ( ct ) , usa , and 
 the same number of cases and controls from shanghai ( sh ) , china , were 
 profiled using combined gas and liquid chromatography mass spectrometry . 
 
 the metabolites consistently expressed in both ct and sh samples were 
 used to identify potential markers , and the diagnostic performance 
 of the candidate markers was tested in two sample sets . 
 a diagnostic 
 model was constructed using a panel of five metabolites including 
 glutamate , choline , 1,5-anhydro - d - glucitol , betaine , and 
 methylguanidine , which robustly distinguished pc patients in ct from 
 controls with high sensitivity ( 97.7% ) and specificity ( 83.1% ) ( area 
 under the receiver operating characteristic curve [ auc ] = 0.943 , 95% 
 confidence interval [ ci ] = 0.9080.977 ) . 
 this panel of metabolites 
 was then tested with the sh data set , yielding satisfactory accuracy 
 ( auc = 0.835 ; 95% ci = 0.7770.893 ) , with a sensitivity of 
 77.4% and specificity of 75.8% . 
 this model achieved a sensitivity 
 of 84.8% in the pc patients at stages 0 , 1 , and 2 in ct and 77.4% 
 in the pc patients at stages 1 and 2 in sh . 
 plasma metabolic signatures 
 show promise as biomarkers for early detection of pc . 
Introduction
Materials and Methods
Results
Discussion
Conclusions