Results Of Partial Least Squares Discriminant Analysis Research Articles

Utilizing serum metabolomics for assessing postoperative efficacy and monitoring recurrence in gastric cancer patients

Objective(1) This study aims to identify distinct serum metabolites in gastric cancer patients compared to healthy individuals, providing valuable insights into postoperative efficacy evaluation and monitoring of gastric cancer recurrence; (2) Methods: Serum samples were collected from 15 healthy individuals, 16 gastric cancer patients before surgery, 3 months after surgery, 6 months after surgery, and 15 gastric cancer recurrence patients. T-test and analysis of variance (ANOVA) were performed to screen 489 differential metabolites between the preoperative group and the healthy control group. Based on the level of the above metabolites in the recurrence, preoperative, three-month postoperative, and six-month postoperative groups, we further selected 18 significant differential metabolites by ANOVA and partial least squares discriminant analysis (PLS-DA). The result of hierarchical clustering analysis about the above metabolites showed that the samples were regrouped into the tumor-bearing group (comprising the original recurrence and preoperative groups) and the tumor-free group (comprising the original three-month postoperative and six-month postoperative groups). Based on the results of PLS-DA, 7 differential metabolites (VIP > 1.0) were further selected to distinguish the tumor-bearing group and the tumor-free group. Finally, the results of hierarchical clustering analysis showed that these 7 metabolites could well identify gastric cancer recurrence; (3) Results: Lysophosphatidic acids, triglycerides, lysine, and sphingosine-1-phosphate were significantly elevated in the three-month postoperative, six-month postoperative, and healthy control groups, compared to the preoperative and recurrence groups. Conversely, phosphatidylcholine, oxidized ceramide, and phosphatidylglycerol were significantly reduced in the three-month postoperative, six-month postoperative, and healthy control groups compared to the preoperative and recurrence groups. However, these substances did not show significant differences between the preoperative and recurrence groups, nor between the three-month postoperative, six-month postoperative, and healthy control groups; (4) Conclusions: Our findings demonstrate the presence of distinct metabolites in the serum of gastric cancer patients compared to healthy individuals. Lysophosphatidic acid, triglycerides, lysine, sphingosine-1-phosphate, phosphatidylcholine, oxidized ceramide, and phosphatidylglycerol hold potential as biomarkers for evaluating postoperative efficacy and monitoring recurrence in gastric cancer patients. These metabolites exhibit varying concentrations across different sample categories.

Multivariate statistical analysis of tropane alkaloids in Anisodus tanguticus (Maxim.) Pascher from different regions to trace geographical origins

Abstract Anisodus tanguticus (Maxim.) Pascher is an important Tibetan folk medicine and the source of tropane alkaloids (TAs) grown in Qinghai-Tibet Plateau. There are marked differences in quality of A. tanguticus from geographic areas. The aim of present research was to establish a method for the quantitative analysis of TAs coupled with chemometrics analysis to trace geographical origins. Qualitative analysis of TAs in A. tanguticus was carried out using ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry and quantitative analysis of TAs in different plant organs from different geographical origin was achieved. Contents of TAs were subjected to the principal component analysis, and orthogonal partial least-squares discriminant analysis. The contents of the three marker compounds (anisodamine, anisodine and atropine) in the roots and acrial parts of A. tanguticus were positive correlated and varied significantly from different geographical origins. Principal component analysis, and orthogonal partial least-squares discriminant analysis results showed excellent discrimination between different geographical origin of A. tanguticus. This study could provide comprehensive evaluation and further utilization of A. tanguticus resources.

Methane emissions and rumen metabolite concentrations in cattle fed two different silages

In this study, 18 animals were fed two forage-based diets: red clover (RC) and grass silage (GS), in a crossover-design experiment in which methane (CH4) emissions were recorded in respiration chambers. Rumen samples obtained through naso-gastric sampling tubes were analysed by NMR. Methane yield (g/kg DM) was significantly lower from animals fed RC (17.8 ± 3.17) compared to GS (21.2 ± 4.61) p = 0.008. In total 42 metabolites were identified, 6 showing significant differences between diets (acetate, propionate, butyrate, valerate, 3-phenylopropionate, and 2-hydroxyvalerate). Partial least squares discriminant analysis (PLS-DA) was used to assess which metabolites were more important to distinguish between diets and partial least squares (PLS) regressions were used to assess which metabolites were more strongly associated with the variation in CH4 emissions. Acetate, butyrate and propionate along with dimethylamine were important for the distinction between diets according to the PLS-DA results. PLS regression revealed that diet and dry matter intake are key factors to explain CH4 variation when included in the model. Additionally, PLS was conducted within diet, revealing that the association between metabolites and CH4 emissions can be conditioned by diet. These results provide new insights into the methylotrophic methanogenic pathway, confirming that metabolite profiles change according to diet composition, with consequences for CH4 emissions.

Species identification of teeth of human and non-human

Species identification is very important in forensic science case. However, the existing methods in forensic practice to identifying the species of bone and teeth are not objective, accurate or brief enough. We have reported the classification of bone species by Fourier transform infrared (FT-IR) and chemometric methods. Here we further use this method to realize the rapid detection of teeth species. 50 teeth samples from human and non-human (bovine, dog, rat, rabbit) were used in this study. Uncontrolled environment conditions were set to simulate real forensic casework. Teeth sample were prepared by grinding powder and pressed into KBr tablet, then the spectral data were collected. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were used in the study. The internal and external validations of PLS-DA results were 97.1% and 93.3% accuracy, respectively. The results illustrate that FT-IR spectroscopy be used as a practical tool to identify species of unknown teeth.

Identification of Cordyceps cicadae and Tolypocladium dujiaolongae based on ITS sequences and chemical pattern recognition method

Based on ITS sequences, the molecular identification of Cordyceps cicadae and Tolypocladium dujiaolongae was carried out, and high-performance liquid chromatography(HPLC) fingerprint combined with chemical pattern recognition method was established to differentiate C. cicadae from its adulterant T. dujiaolongae. The genomic DNA from 10 batches of C. cicadae and five batches of T. dujiaolongae was extracted, and ITS sequences were amplified by PCR and sequenced. The stable differential sites of these two species were compared and the phylogenetic tree was constructed via MEGA 7.0. HPLC was used to establish the fingerprints of C. cicadae and T. dujiaolongae, and similarity evaluation, cluster analysis(CA), principal component analysis(PCA), and partial least squares discriminant analysis(PLS-DA) were applied to investigate the chemical pattern recognition. The result showed that the sources of these two species were different, and there were 115 stable differential sites in ITS sequences of C. cicadae and T. dujiao-longae. The phylogenetic tree could distinguish them effectively. HPLC fingerprints of 18 batches of C. cicadae and 5 batches of T. dujiaolongae were established. The results of CA, PCA, and PLS-DA were consistent, which could distinguish them well, indicating that there were great differences in chemical components between C. cicadae and T. dujiaolongae. The results of PLS-DA showed that six components such as uridine, guanosine, adenosine, and N~6-(2-hydroxyethyl) adenosine were the main differential markers of the two species. ITS sequences and HPLC fingerprint combined with the chemical pattern recognition method can serve as the identification and differentiation methods for C. cicadae and T. dujiaolongae.

Non-target protein analysis of samples from wastewater treatment plants using the regions of interest-multivariate curve resolution (ROIMCR) chemometrics method

A new methodology is proposed for the non-target protein analysis of samples from wastewater treatment plants (WWTP). Proteins captured using polymeric (polycaprolactonediol) probes placed along different sites of a WWTP were extracted, digested and analyzed by LC-MS/MS. Selection of the Regions of Interest (ROI) from the raw MS data allowed their storage with the most relevant information present in the analyzed wastewater samples. Application of the Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) chemometrics method to the ROI MS data resolved the elution profiles and spectral fingerprints of the components present in the analyzed samples. Partial Least Squares-Discriminant Analysis (PLS-DA) of the peak heights of the elution profiles of these components could distinguish which of them changed more significantly along the different steps of the WWTP. Principal Component Analysis (PCA) of ROI data, and of ROIMCR and PLS-DA results allowed the clustering of the samples according to the different WWTP sites. A clear improvement in their discrimination was observed when PCA was applied to the results of ROIMCR and PLS-DA, compared with the results obtained considering only the unprocessed ROI data. Finally, when ROIMCR and PLS-DA results were analyzed using Proteome Discoverer, 281, 314, and 58 peptides were respectively identified at the three sampling sites in the case of ROIMCR, and 137, 123, and 31 peptides in the case of PLS-DA. From these peptides, a total number of 38 proteins are postulated to be present in the WWTP analyzed samples.

Quality evaluation of Cabernet Sauvignon wines in different vintages by1H nuclear magnetic resonance-based metabolomics

AbstractA proton nuclear magnetic resonance (NMR)-based metabolomic study was used to characterize 2009, 2010, 2011, and 2012 vintages of Cabernet Sauvignon wines from Ningxia, which were vinified using the same fermentation technique. The pattern recognition methods of principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and orthogonal PLS-DA (OPLS-DA) clearly distinguished between the different vintages of wine driven by the following metabolites: valine, 2,3-butanediol, ethyl acetate, proline, succinic acid, lactic acid, acetic acid, glycerol, gallic acid, and choline. The PLS-DA loading plots also differentiated among the metabolites of different vintages. In the 2009 vintage wines, we found the highest levels of gallic acid, valine, proline, and 2,3-butanediol. The 2011 vintage wines contained the highest levels of lactic acid, and the highest levels of ethyl acetate, succinic acid, glycerol, and choline were observed in the 2012 vintage wines. We selected eight metabolites from the1H NMR spectra that were quantified according to their peak areas, and the concentrations were in agreement with the results of PLS-DA and OPLS-DA analyses.

Comparison of chemical and fatty acid composition of green coffee bean (Coffea arabica L.) from different geographical origins

This study aimed to quantitatively screen and compare the chemical compounds including protein, lipid, sucrose, total phenolics (TPC), and total titratable acidity (TTA) along with the fatty acid profile of green coffee beans from various geographical origins (China, Indonesia, Ethiopia, Kenya, Guatemala, Honduras, Brazil, and Colombia). Meanwhile, partial least squares discriminant analysis (PLS-DA) was used to investigate whether the analyzed parameters’ data can be successfully applied to differentiate the geographical origins of green coffee beans. The levels of protein, lipid, sucrose, TTA, and TPC in all samples were in the range of 13.06–15.98 g 100 g−1, 12.88–16.29 g 100 g−1, 7.30–11.45 g 100 g−1, 231.43–361.21 mL 0.1 mol L−1 NaOH 100 g−1, and 37.56–50.57 mg GAE g−1, respectively. The fatty acid composition was dominated by C18:2, C16:0, C18:1, and C18:0. The major fatty acid group was unsaturated fatty acids (USFA), accounting for 51.66–54.28% of total fatty acids. Statistical analysis showed significant differences in the chemical and fatty acid composition of green coffee beans from eight geographical origins (P＜0.05). Moreover, the PLS-DA results revealed excellent discrimination between the different geographical origins of green coffee beans, highlighting lipid, C24:0, C22:0, C18:3, C17:0, C18:0, C20:0, C16:0, and protein as discriminatory features.

Insights into the Role of 2-Methyl-3-furanthiol and 2-Furfurylthiol as Markers for the Differentiation of Chinese Light, Strong, and Soy Sauce Aroma Types of Baijiu.

The sensory impacts of two thiols, 2-methyl-3-furanthiol (MFT) and 2-furfurylthiol (FFT), in Chinese soy sauce aroma-type Baijiu (SSB), strong aroma-type Baijiu (STB), and light aroma-type Baijiu (LTB) (liquor) were evaluated and combined with partial least squares discriminant analysis (PLS-DA) to differentiate Chinese Baijiu. The flavor dilution factors of these two thiols ranged from 81 to 6561, and quantitative results showed that MFT and FFT were significantly more abundant (p < 0.001) in SSB than in STB and LTB. The determined odor activity values (OAVs) suggest that MFT (OAV: 34-121) and FFT (OAV: 11-103) contribute significantly to the overall aroma profiles of LTB and STB. Interestingly, the OAVs of these two thiols were high (256-263) and did not significantly differ (p > 0.05) in SSB. Notably, hierarchical cluster analysis and PLS-DA results revealed that these compounds can be used to differentiate Chinese LTB, STB, and SSB. According to their prominent organoleptic and distinguishing roles, these two thiols can be regarded as flavor markers for SSB.

Comparative of functional components, antioxidant and α-glucosidase inhibition activities between Choerospondias axillaris fruit peel vinegar and apple vinegar

Based on the idea of plant metabolomics, ultra performance liquid chromatography-quadrupole time-of-flight mass spectrometry(UPLC-Q-TOF-MS/MS) was used to compare the chemical composition between 6 batches of fruit vinegar brewed from Choerospondias axillaris fruit peel and 6 batches of apple vinegar purchased from 3 companies. Antioxidant and α-glucosidase inhibition activities were also tested in vitro. A total of 43 compounds were identified by reference substance, liquid chromatography-mass spectrometry(LC-MS/MS) fragmentation information or literature data. A total of 40 compounds were identified in the C. axillaris fruit peel vinegar. A total of 16 compounds were identified in apple vinegar. There were 13 common ingredients including organic acids and esters such as citric acid, 2-isopropyl malic acid, and triethyl citrate. The results of partial leastsquares-discriminant analysis(PLS-DA) indicated that they had 33 significantly different compounds such as proanthocyanidin oligomer, quercetin-3-O-rhamnoside and heptadecanoic acid. The proanthocyanidins and flavonoid glycosides in C. axillaris peel vinegar were more abundant than apple vinegar, so it had better health function than ordinary fruit vinegar. The results showed that C. axillaris fruit peel vinegar had stronger antioxidant and α-glucosidase inhibition activities in vitro. The vinegar brewed from waste C. axillaris fruit peel had more chemical ingredients than the apple vinegar. C. axillaris fruit peel vinegar had better biological activity and health function, so it had good development prospect. This study provided the scientific evidence for exploiting the C. axillaris fruit peel into high value-added products. It also provided ideas for the comprehensive development and utilization of similar Chinese medicine waste.

Predictors of fatal outcomes in pedestrian accidents in Tabriz Metropolis of Iran: Application of PLS-DA method

Objectives: Road traffic deaths in walking pedestrians are a global public health problem. Considering that in Iran pedestrians have a high proportion of deaths caused by traffic accidents, the objective of the present study was to investigate mortality rate and related factors of fatal injury in pedestrian crashes in Tabriz Metropolis of Iran as the largest and most populous city of the northwest of Iran.Methods: The design of this study is case-control based on police and Forensic Medicine Organization data. All registered fatal pedestrian crashes in Tabriz Metropolis from 2014 to 2015 (146 cases) were included in the study as the case group. Also, 292 pedestrians (the ratio of cases to controls was 1:2) with non-fatal crashes were considered as the control group. Due to high dimensional data and multicollinearity issue, Partial least squares discriminant analysis (PLS-DA) was used for data analysis. Importance of the variables was determined by the VIP (Variable Importance in the Projection) index. Performance of the model was assessed by using training and test set validation method. The area under the ROC curve (AUC) and classification error rates were calculated for the test set. R software version 3.5.1 (mixOmcs packages) was used for data analysis.Results: According to the results of PLS-DA, the most important variables related to fatal outcome in pedestrian crashes with VIP > 1 were: pedestrian age (positive effect), type of vehicle (light machinery with a negative effect), kind of vehicle plate (private plate with a negative effect), season of accident occurrence (winter season with a positive effect), type of driver’s licenses (Class A with a positive effect), pedestrian gender (male with a positive effect) and Fault of Pedestrian (At-fault with a positive effect). The overall accuracy for the fitted model and AUC were 0.77 and 0.79, respectively.Conclusions: The results show that predictors of a fatal outcome in pedestrian accidents in Tabriz can be attributed to the pedestrian characteristics (which notably account for differences in vulnerability in case of an accident), the car and driver features, and weather (which may all notably influence the amount of energy involved in the collision, through the car mass, speed, and conditions delaying the braking response or reducing the braking effectiveness). Regarding the statistical method, the PLS-DA is a powerful method which can be used to analyze high dimension data with multicollinearity issue.

Mixomics analysis of breast cancer: Long non-coding RNA linc01561 acts as ceRNA involved in the progression of breast cancer.

This study aimed at finding the long non-coding RNA (lncRNA), miRNA and mRNA which played critical roles in breast cancer (BrCa) by using mixOmics R package. The BrCa dataset were obtained from TCGA and then analyzed using "DESeq2" R package. Multivariate analyses were performed with the "mixOmics" R package and the first component of the stacked partial least-Squares discriminant analysis results were used for searching the interested lncRNA, miRNA and mRNA. qRT-PCR was applied to identify the bioinformatics results in four BrCa cell lines (MCF7, BT-20, ZR-75-1, and MX-1) and the breast epithelial cell line MCF-10 A. Then cells (MCF-1 and MX-1) were transfected with si-linc01561, miR-145-5p mimics and si-MMP11 to further investigate the effects of linc01561, miR-145-5p and MMP11 on the BrCa cells proliferation and apoptosis. MixOmics results showed that linc01561, miR-145-5p and MMP11 might play important roles in BrCa. qRT-PCR results identified that in BrCa cell lines, linc01561 and MMP11 were higher expressed while miR-145-5p was lower expressed compared with those in epithelial cell line. The linc01561 inhibition elevated miR-145-5p expression and then suppressed MMP11 expression. Moreover, linc01561 inhibition suppressed the BrCa cells proliferation and promoted the apoptosis, which was realized by up-regulating expression of miR-145-5p and down-regulating expression of MMP11. In summary, the findings of this study, based on ceRNA theory, combining the research foundation of miR-145-5p and MMP11, and taking linc01561 as a new study point, provide new insight into molecular-level reversing proliferation and apoptosis of BrCa.

Study on serum markers of prostate cancer by nuclear magnetic resonance-based metabolomics

Objective To investigate the clinical value of serum metabolomic profile of prostate cancer using nuclear magnetic resonance-based metabolomics. Methods The retrospective case control study was adopted.The clinical data of 31 patients with prostate cancer, 28 patients of prostatic hyperplasia and 31 healthy volunteers were enrolled in this study from May 2016 to May 2017 at the first affiliated hospital of Xinjiang medical university. In PCa group, the mean age was 66.3 years old, ranging 53-80 years old. In BPH group, the mean age was 59.3 years old, ranging 46-75 years old. In volunteer group, the mean age was 47.8 years old, ranging 35-62 years old.. The serum of the 3 groups was measured by 1H-NMR spectroscopy. Multivariate statistical analysis was used to analyze the serum differential metabolism of the 3 groups, including principal components analysis(PCA), partial least squares discriminant analysis(PLS-DA)and orthogonal partial least squares discriminant analysis(OPLS-DA). Results The multivariate statistical analysis of PCA that the rate of the first principal component 1(PC1) was 53.24%, the second principal component 2(PC2) was 25.31% and the cumulative contribution rate was 78.55%.Results of PLS-DA showed that partial data overlap of the three groups, but the separation trend was appeared.The variance of X(R2X) and Y(R2Y) matrixes and predictive value Q2 were 0.67, 0.60, and 0.42.The results of OPLS-DA showed that the difference among the PCa group and BPH group, healthy group were obvious. The separation trend were appeared and the differential metabolites could be screened effectively. The R2X、R2Y and Q2 was 0.24, 0.57, 0.21 and 0.30, 0.65, 0.36. 26 different serum metabolites were detected in the 3 groups, including citric acid, arginine, threonine, citrulline, glutamine, lactic acid, alanine, unsaturated fats, glycoprotein etc. Conclusions Compared with BPH group and healthy group, the serum of prostate cancer patients showed significant differences in metabolism. Nuclear magnetic resonance metabolomics analysis can effectively distinguish these serum metabolic differences. Key words: Prostate cancer; Nuclear magnetic resonance; Serum biomarkers; Metabolomics; Diagnosis

Grouping marian plums harvested at different times by transmittance near-infrared spectroscopy

Marian plum (Bouea burmanica Griff.) 'Toon Klaow' is one of Thailand's favorite fruits. Marian plum's edible quality depends strongly on its harvest time. This study investigated a non-destructive technique for classifying marian plums according to their harvest time after the day that their blossom set. The non-destructive technique used was transmittance mode, short wavelength near-infrared (SW-NIR) spectroscopy in the wavelength range 660-960 nm. Marian plum samples (n=110) were harvested at 62, 65, 68 and 73 days after flowering. The soluble solids content (SSC) and titratable acid (TA) were determined accurately by standard methods. SW-NIR spectra of these samples were obtained and analyzed by principle component analysis (PCA) and partial least squares discriminant analysis (PLS-DA). The following spectral pretreatments were applied, standard normal variate (SNV), smoothing (Savitsky-Golay), and first derivative, in order to obtain optimal grouping results. The PC1 and PC2 score plot of the PCA could not clearly separate some of the classified groups. For the results of PLS-DA, its cross-validated grouping accuracy was R=0.91 and RMSECV=1.28; hence, it can be concluded that SW-NIR spectroscopy has good potential for determining the harvest time of marian plums.

Exploring the mechanism of Jieduquyuziyin prescription on systemic lupus erythematosus by GC-MS-based urine metabolomics.

A urine metabolomics method based on gas chromatography-mass spectrometry was developed in order to investigate the metabolite characteristics of systemic lupus erythematosus (SLE) and the therapeutic effects of Jieduquyuziyin prescription. The urinary metabolic profiles in urine specimens of the SLE model mice (MRL/lpr) group, prednisone acetate-treated SLE mouse group, Jieduquyuziyin prescription-treated SLE mouse group, and control group (C57BL/6 J) after the administration were analyzed by gas chromatography-mass spectrometry. These metabolic profiles were then processed by multivariate analysis, in particular Mass Profiler Professional, SIMCA-P and partial least-squares discriminant analysis. According to the partial least-squares discriminant analysis results, the SLE model group and the control group were obviously separated, indicating that the incidence of SLE had a greater impact on the metabolic network, and the SLE model group had significant difference compared with the control group in urine metabolites. Eleven differential metabolites were identified to be related to SLE, and the results of differential metabolite identification showed that the metabolites were mainly related to energy metabolism and amino acid metabolism pathway. These results can provide an experimental basis for further exploring the mechanism of traditional Chinese medicine in the treatment of SLE.

Differentiation of Crataegus spp. guided by nuclear magnetic resonance spectrometry with chemometric analyses

For compliance with US Current Good Manufacturing Practice regulations for dietary supplements, manufacturers must provide identity of source plant material. Despite the popularity of hawthorn as a dietary supplement, relatively little is known about the comparative phytochemistry of different hawthorn species, and in particular North American hawthorns. The combination of NMR spectrometry with chemometric analyses offers an innovative approach to differentiating hawthorn species and exploring the phytochemistry. Two European and two North American species, harvested from a farm trial in late summer 2008, were analyzed by standard 1D 1H and J-resolved (JRES) experiments. The data were preprocessed and modelled by principal component analysis (PCA). A supervised model was then generated by partial least squares-discriminant analysis (PLS-DA) for classification and evaluated by cross validation. Supervised random forests models were constructed from the dataset to explore the potential of machine learning for identification of unique patterns across species. 1D 1H NMR data yielded increased differentiation over the JRES data. The random forests results correlated with PLS-DA results and outperformed PLS-DA in classification accuracy. In all of these analyses differentiation of the Crataegus spp. was best achieved by focusing on the NMR spectral region that contains signals unique to plant phenolic compounds. Identification of potentially significant metabolites for differentiation between species was approached using univariate techniques including significance analysis of microarrays and Kruskall-Wallis tests.

Analysis of nuclear magnetic resonance-based metabonomics of pancreatic cancer

Objective To investigate the clinical value of serum metabonomic profile of pancreatic cancer using nuclear magnetic resonance (NMR)-based metabonomics. Methods The retrospective case-control study was adopted. The clinical data of 23 patients with pancreatic cancer (PC group) and 16 healthy volunteers (control group) who were admitted to the Fujian Medical University Union Hospital between December 2013 and December 2014 were collected. The serum of the 2 groups was measured by 1H NMR spectroscopy. Multivariate statistical analyses were performed to identify the characteristic metabolites in the 2 groups, including principal component analysis (PCA), partial least squares discriminant analysis(PLS-DA) and orthogonal partial least squares discriminant analysis (OPLS-DA). Observation indicators included: (1) multivariate statistical analysis of serum metabonomic profile, results of PCA, PLS-DA and OPLS-DA, (2) screening of metabolites. Measurement data with normal distribution were presented as ±s. The comparison between groups was evaluated with the t test. The count data were analyzed using the chi-square test. Results (1) The multivariate statistical analysis of serum metabonomic profile: results of PCA showed that expression rates of principal component 1 (PC1) and principal component 2 (PC2) to original data were 54.9% and 23.5%, with both cumulative contribution rate of 78.4%. Results of PLS-DA showed that the separative trend between PC group and control group was appeared, and variance of X and Y matrixes and predictive value were 0.254, 0.816 and 0.385. Results of OPLS-DA showed that the differences of samples between the 2 groups were further increased, and differential metabolites were screened according to the distinction of scores between the 2 groups, value of R2X, R2Y and Q2 was 0.254, 0.816 and 0.433. (2) Screening of metabolites: 35 serum metabolites were detected in the 2 groups. Compared with the control group, levels of 3-hydroxybuyarate, citrate, formate, glutamate, isoleucine, methionine and phenylalanine in the PC group were elevated (r=0.524, 0.511, 0.656, 0.566, 0.503, 0.498, 0.648, P<0.05), and levels of 3-methylhistidine, alanine, glutamine, LDL and VLDL in the PC group were decreased (r=-0.607, -0.508, -0.560, -0.568, -0.559, P<0.05). Conclusions Compared with healthy controls, several amino acids, citrate and lipoproteins demonstrate the metabolic differences in the serum of patients with pancreatic cancer. NMR based metabonomic profile technology can distinguish the difference of serum metabolites between patients with pancreatic cancer and healthy controls. NMR based metabonomic technology may be a promising method for the diagnosis of pancreatic cancer. Key words: Pancreatic neoplasms; Nuclear magnetic resonance; Metabonomics; Diagnosis; Serum biomarker

Chemotaxonomic Metabolite Profiling of 62 Indigenous Plant Species and Its Correlation with Bioactivities

Chemotaxonomic metabolite profiling of 62 indigenous Korean plant species was performed by ultrahigh performance liquid chromatography (UHPLC)-linear trap quadrupole-ion trap (LTQ-IT) mass spectrometry/mass spectrometry (MS/MS) combined with multivariate statistical analysis. In partial least squares discriminant analysis (PLS-DA), the 62 species clustered depending on their phylogenetic family, in particular, Aceraceae, Betulaceae, and Fagaceae were distinguished from Rosaceae, Fabaceae, and Asteraceae. Quinic acid, gallic acid, quercetin, quercetin derivatives, kaempferol, and kaempferol derivatives were identified as family-specific metabolites, and were found in relatively high concentrations in Aceraceae, Betulaceae, and Fagaceae. Fagaceae and Asteraceae were selected based on results of PLS-DA and bioactivities to determine the correlation between metabolic differences among plant families and bioactivities. Quinic acid, quercetin, kaempferol, quercetin derivatives, and kaempferol derivatives were found in higher concentrations in Fagaceae than in Asteraceae, and were positively correlated with antioxidant and tyrosinase inhibition activities. These results suggest that metabolite profiling was a useful tool for finding the different metabolic states of each plant family and understanding the correlation between metabolites and bioactivities in accordance with plant family.