Quantitative metabolomic profiling of serum and urine in DSS-induced ulcerative colitis of mice by (1)H NMR spectroscopy

Quantitative profiling of a large number of metabolic compounds is a promising method to detect biomarkers in inflammatory bowel diseases (IBD), such as ulcerative colitis (UC). We induced an experimental form of UC in mice by treatment with dextran sulfate sodium (DSS) and characterized 53 serum and 69 urine metabolites by use of (1)H NMR spectroscopy and quantitative ("targeted") analysis to distinguish between diseased and healthy animals. Hierarchical multivariate orthogonal partial least-squares (OPLS) models were developed to detect and predict separation of control and DSS-treated mice. DSS treatment resulted in weight loss, colonic inflammation, and increase in myeloperoxidase activity. Metabolomic patterns generated from the OPLS data clearly separated DSS-treated from control mice with a slightly higher predictive power (Q(2)) for serum (0.73) than urine (0.71). During DSS colitis, creatine, carnitine, and methylamines increased in urine while in serum, maximal increases were observed for ketone bodies, hypoxanthine, and tryptophan. Antioxidant metabolites decreased in urine whereas in serum, glucose and Krebs cycle intermediates decreased strongly. Quantitative metabolic profiling of serum and urine thus discriminates between healthy and DSS-treated mice. Analysis of serum or urine seems to be equally powerful for detecting experimental colitis, and a combined analysis offers only a minor improvement.     

Metabolomics reveals metabolite changes of patients with pulmonary arterial hypertension in China

The specific mechanism of pulmonary arterial hypertension (PAH) remains elusive. The present study aimed to explore the underlying mechanism of PAH through the identity of novel biomarkers for PAH using metabolomics approach. Serum samples from 40 patients with idiopathic PAH (IPAH), 20 patients with congenital heart disease‐associated PAH (CHD‐PAH) and 20 healthy controls were collected and analysed by ultra‐high‐performance liquid chromatography coupled with high‐resolution mass spectrometry (UPLC‐HRMS). Orthogonal partial least square‐discriminate analysis (OPLS‐DA) was applied to screen potential biomarkers. These results were validated in monocrotaline (MCT)‐induced PAH rat model. The OPLS‐DA model was successful in screening distinct metabolite signatures which distinguished IPAH and CHD‐PAH patients from healthy controls, respectively (26 and 15 metabolites). Unbiased analysis from OPLS‐DA identified 31 metabolites from PAH patients which were differentially regulated compared to the healthy controls. Our analysis showed dysregulation of the different metabolic pathways, including lipid metabolism, glucose metabolism, amino acid metabolism and phospholipid metabolism pathways in PAH patients compared to their healthy counterpart. Among these metabolites from dysregulated metabolic pathways, a panel of metabolites from lipid metabolism and fatty acid oxidation (lysophosphatidylcholine, phosphatidylcholine, perillic acid, palmitoleic acid, N‐acetylcholine‐d ‐sphingomyelin, oleic acid, palmitic acid and 2‐Octenoylcarnitine metabolites) were found to have a close association with PAH. The results from the analysis of both real‐time quantitative PCR and Western blot showed that expression of LDHA, CD36, FASN, PDK1 GLUT1 and CPT‐1 in right heart/lung were significantly up‐regulated in MCT group than the control group.     

Metabolic signatures of lung cancer in biofluids: NMR-based metabonomics of urine

In this study, 1H NMR-based metabonomics has been applied, for the first time to our knowledge, to investigate lung cancer metabolic signatures in urine, aiming at assessing the diagnostic potential of this approach and gaining novel insights into lung cancer metabolism and systemic effects. Urine samples from lung cancer patients (n = 71) and a control healthy group (n = 54) were analyzed by high resolution 1H NMR (500 MHz), and their spectral profiles subjected to multivariate statistics, namely, Principal Component Analysis (PCA), Partial Least Squares Discriminant Analysis (PLS-DA), and Orthogonal Projections to Latent Structures (OPLS)-DA. Very good discrimination between cancer and control groups was achieved by multivariate modeling of urinary profiles. By Monte Carlo Cross Validation, the classification model showed 93% sensitivity, 94% specificity and an overall classification rate of 93.5%. The possible confounding influence of other factors, namely, gender and age, have also been modeled and found to have much lower predictive power than the presence of the disease. Moreover, smoking habits were found not to have a dominating influence over class discrimination. The main metabolites contributing to this discrimination, as highlighted by multivariate analysis and confirmed by spectral integration, were hippurate and trigonelline (reduced in patients), and β-hydroxyisovalerate, α-hydroxyisobutyrate, N-acetylglutamine, and creatinine (elevated in patients relatively to controls). These results show the valuable potential of NMR-based metabonomics for finding putative biomarkers of lung cancer in urine, collected in a minimally invasive way, which may have important diagnostic impact, provided that these metabolites are found to be specifically disease-related.     

Serum amino acid profiles and their alterations in colorectal cancer

Mass spectrometry-based serum metabolic profiling is a promising tool to analyse complex cancer associated metabolic alterations, which may broaden our pathophysiological understanding of the disease and may function as a source of new cancer-associated biomarkers. Highly standardized serum samples of patients suffering from colon cancer (n?=?59) and controls (n?=?58) were collected at the University Hospital Leipzig. We based our investigations on amino acid screening profiles using electrospray tandem-mass spectrometry. Metabolic profiles were evaluated using the Analyst 1.4.2 software. General, comparative and equivalence statistics were performed by R 2.12.2. 11 out of 26 serum amino acid concentrations were significantly different between colorectal cancer patients and healthy controls. We found a model including CEA, glycine, and tyrosine as best discriminating and superior to CEA alone with an AUROC of 0.878 (95% CI 0.815-0.941). Our serum metabolic profiling in colon cancer revealed multiple significant disease-associated alterations in the amino acid profile with promising diagnostic power. Further large-scale studies are necessary to elucidate the potential of our model also to discriminate between cancer and potential differential diagnoses. In conclusion, serum glycine and tyrosine in combination with CEA are superior to CEA for the discrimination between colorectal cancer patients and controls.     

Multivariate Data Analysis as a Semi-quantitative Tool for Interpretive Evaluation of Comparability or Equivalence of Aerodynamic Particle Size Distribution Profiles

The purpose of this article is to investigate the performance of multivariate data analysis, especially orthogonal partial least square (OPLS) analysis, as a semi-quantitative tool to evaluate the comparability or equivalence of aerodynamic particle size distribution (APSD) profiles of orally inhaled and nasal drug products (OINDP). Monte Carlo simulation was employed to reconstitute APSD profiles based on 55 realistic scenarios proposed by the Product Quality Research Institute (PQRI) working group. OPLS analyses with different data pretreatment methods were performed on each of the reconstituted profiles. Compared to unit-variance scaling, equivalence determined based on OPLS analysis with Pareto scaling was shown to be more consistent with the working group assessment. Chi-square statistics was employed to compare the performance of OPLS analysis (Pareto scaling) with that of the combination test (i.e., chi-square ratio statistics and population bioequivalence test for impactor-sized mass) in terms of achieving greater consistency with the working group evaluation. A p value of 0.036 suggested that OPLS analysis with Pareto scaling may be more predictive than the combination test with respect to consistency. Furthermore, OPLS analysis may also be employed to analyze part of the APSD profiles that contribute to the calculation of the mass median aerodynamic diameter. Our results show that OPLS analysis performed on partial deposition sites do not interfere with the performance on all deposition sites.     

A metabolic discrimination model for nasopharyngeal carcinoma and its potential role in the therapeutic evaluation of radiotherapy

Nasopharyngeal carcinoma (NPC) is one of the most common malignancies in Southeast Asia and radiotherapy or radiotherapy, in combination with chemotherapy is the primary treatment strategy. In this study, we adopted a metabolomic method to investigate the metabolic disorders in NPC and evaluate the effect of radiotherapy on metabolic profile alterations in NPC patients. To generate the NPC metabolic profiles, 402 serum samples were collected from 100 newly-diagnosed NPC patients and 100 healthy volunteers. Based on gas chromatography–mass spectrometry (GC–MS) metabolomics coupled with partial least squares-discriminant analysis, a NPC discrimination model was constructed with a sensitivity of 88 % (88/100) and a specificity of 92 % (92/100). Seven metabolites, including glucose, linoleic acid, stearic acid, arachidonic acid, proline, β-hydroxy butyrate and glycerol 1-hexadecanoate, were identified as contributing mostly to the discrimination of NPC serum from healthy controls. To validate if the model can be applied for therapeutic evaluation, 202 serum samples were collected from 20 patients receiving standard radiotherapy for up to a 3-year follow-up period. The metabolic footprints of 20 NPC patients treated with standard radiotherapy are visually presented. Based on the footprint trends of the sera samples in irradiation-treated NPC patients who were gradually closer to healthy controls or not, patients were divided into positive and negative groups, respectively. The coincident rate of the trends of metabolic footprints to the actual clinical prognosis trend was approximately 80 %. This study demonstrates that a GC–MS-based metabolic profiling approach as a novel strategy may be capable to delineating the potential of metabolite alterations in discrimination and therapeutic evaluation of NPC patients.     

Esophageal cancer metabolite biomarkers detected by LC-MS and NMR methods

Background

Esophageal adenocarcinoma (EAC) is a rarely curable disease and is rapidly rising worldwide in incidence. Barret''s esophagus (BE) and high-grade dysplasia (HGD) are considered major risk factors for invasive adenocarcinoma. In the current study, unbiased global metabolic profiling methods were applied to serum samples from patients with EAC, BE and HGD, and healthy individuals, in order to identify metabolite based biomarkers associated with the early stages of EAC with the goal of improving prognostication.

Methodology/Principal Findings

Serum metabolite profiles from patients with EAC (n = 67), BE (n = 3), HGD (n = 9) and healthy volunteers (n = 34) were obtained using high performance liquid chromatography-mass spectrometry (LC-MS) methods. Twelve metabolites differed significantly (p<0.05) between EAC patients and healthy controls. A partial least-squares discriminant analysis (PLS-DA) model had good accuracy with the area under the receiver operative characteristic curve (AUROC) of 0.82. However, when the results of LC-MS were combined with 8 metabolites detected by nuclear magnetic resonance (NMR) in a previous study, the combination of NMR and MS detected metabolites provided a much superior performance, with AUROC = 0.95. Further, mean values of 12 of these metabolites varied consistently from healthy controls to the high-risk individuals (BE and HGD patients) and EAC subjects. Altered metabolic pathways including a number of amino acid pathways and energy metabolism were identified based on altered levels of numerous metabolites.

Conclusions/Significance

Metabolic profiles derived from the combination of LC-MS and NMR methods readily distinguish EAC patients and potentially promise important routes to understanding the carcinogenesis and detecting the cancer. Differences in the metabolic profiles between high-risk individuals and the EAC indicate the possibility of identifying the patients at risk much earlier to the development of the cancer.     

A comparison of mammography and ultrasonography in the evaluation of breast masses

Aim: Aim of the study is to evaluate breast masses using mammography (MG) and ultrasonography (USG) independently and in combination. Materials and methods: Our study group consisted of 62 female patients, with breast symptoms such as palpable lumps, pain in the breast and nipple discharge who were examined prospectively over a period of 6 months. All 62 patients were examined by both MG and USG independently. Fine needle aspiration cytology (FNAC) or core cut biopsy was done according to the findings of MG and USG and then the results were correlated with each modality finding. Results: According to this study MG showed an efficiency of 81.8 % compared to 95.5 % for USG in detecting fibrocystic mastitis. However their combined approach resulted in 100 %. In the case of fibroadenomas, MG showed 75 % efficiency and USG only 35 % and the combination resulting in 93.7 %. For carcinomas, MG had an efficiency of 77.8 % and USG 55.6 %, but the combination had an efficiency of 98.1 %. Overall, the histopathological results when correlated with each modality finding showed that MG had an efficiency of only 77.4 % and USG only 69.8 % when used alone in detecting these lesions of the breast compared to an efficiency of 98.1 % obtained by their combined approach. In our study, we showed that there was no significant difference in sensitivity between MG and USG (p = 0.3768) but there was significant difference in MG alone and MG-USG combination (p = 0.0015) and USG alone and USG-MG combination (p = 0.0001). Conclusion: Our study confirmed that combined MG and USG had higher sensitivity rate than the sensitivity rate observed for either single modality. The diagnostic accuracy for carcinomas of the breast appear to improve when MG was combined with USG, even in cases which showed no evidence of microcalcification or other signs of abnormalities. Our study implies that, USG may be the only viable modality in pregnant and lactating women as it does not involve ionizing radiation and also in dense breast tissue, as density is a limiting factor for MG.     

Autoantibody signature in human ductal pancreatic adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy characterized by rapid progression, invasiveness, and resistance to treatment. It is the fourth leading cause of cancer death with a 2% 5-year survival rate. Biomarkers for its early detection are lacking. This study was designed to use a proteomics-based approach as a means of identifying antigens that elicit a humoral response in PDAC patients. Antibodies against PDAC-associated antigens are useful for early cancer diagnosis and therapy. Proteins from PDAC cell lines were separated by 2-DE, and the serum IgG reactivity of 70 PDAC patients, 40 healthy subjects (HS), 30 non-PDAC tumor patients, and 15 chronic pancreatitis (CP) patients was tested by Western blot analysis. Spots specifically recognized by PDAC sera and revealed by mass spectrometry corresponded to metabolic enzymes or cytoskeletal proteins. Most were up-regulated in PDAC tissues. Thus, it seems that metabolic enzymes and cytoskeletal proteins are specific targets of the humoral response during PDAC. The results of further studies of these serological-defined antigens could be of diagnostic and therapeutic significance in PDAC.     

Metabonomic profiles discriminate hepatocellular carcinoma from liver cirrhosis by ultraperformance liquid chromatography-mass spectrometry

Hepatocellular carcinoma (HCC) is the fifth most common cancer worldwide and usually develops in patients with liver cirrhosis (LC). Biomarkers that discriminate HCC from LC are important but are limited. In the present study, an ultraperformance liquid chromatography-mass spectrometry (UPLC-MS)-based metabonomics approach was used to characterize serum profiles from HCC (n = 82), LC (n = 48), and healthy subjects (n = 90), and the accuracy of UPLC-MS profiles and alpha-fetoprotein (AFP) levels were compared for their use in HCC diagnosis. By multivariate data and receiver operating characteristic curves analysis, metabolic profiles were capable of discriminating not only patients from the controls but also HCC from LC with 100% sensitivity and specificity. Thirteen potential biomarkers were identified and suggested that there were significant disturbances of key metabolic pathways, such as organic acids, phospholipids, fatty acids, bile acids, and gut flora metabolism, in HCC patients. Canavaninosuccinate was first identified as a metabolite that exhibited a significant decrease in LC and an increase in HCC. In addition, glycochenodeoxycholic acid was suggested to be an important indicator for HCC diagnosis and disease prognosis. UPLC-MS signatures, alone or in combination with AFP levels, could be an efficient and convenient tool for early diagnosis and screening of HCC in high-risk populations.     

Revealing the metabonomic variation of EC using 1H-NMR spectroscopy and its association with the clinicopathological characteristics

In this study, (1)H NMR-based metabonomics has been applied to investigate esophageal cancer metabolic signatures in plasma and urine, purpose of assessing the diagnostic potential of this approach and gaining novel insights into esophageal cancer metabolism and systemic effects. Plasma and urine samples from esophageal cancer patients (n = 108) and a control healthy group (n = 40) were analyzed by Nuclear Magnetic Resonance (NMR) spectroscopy (600 MHz), and their spectral profiles subjected to Orthogonal Projections to Latent Structures (OPLS-DA) for multivariate statistics. Potential metabolic biomarkers were identified using data base comparisons used for examining the significance of metabolites. Compared to healthy controls, esophageal cancer plasma had higher levels of dimethylamine, α-glucose, β-glucose, citric acid, together with lower levels of Leucine, alanine, isoleucine, valine, glycoprotein, lactate, acetone, acetate, choline, isobutyrate, unsaturated lipid, VLDL, LDL, 1-methylhistidine; Compared to healthy controls, esophageal cancer urine had higher levels of Mannitol, glutamate, γ-propalanine, phenylalanine, acetate, allantoin, pyruvate, tyrosine, β-glucose and guinolinate, together with lower levels of N-acetylcysteine, valine, dihydrothymine, hippurate, methylguanidine, 1-methylnicotin- amide and Citric acid; Very good discrimination between cancer and control groups was achieved by multivariate modeling of plasma and urinary profiles. (1)H NMR-based metabolite profiling analysis was shown to be an effective approach to differentiating between patients with EC and healthy subjects. Good sensitivity and selectivity were shown by using the metabolite markers discovered to predict the classification of samples from the healthy control group and the patients with the disease. Plasma and urine metabolic profiling may have potential for early diagnosis of EC and may enhance our understanding of its mechanisms.     

Discovery of candidate serum proteomic and metabolomic biomarkers in ankylosing spondylitis

Ankylosing Spondylitis (AS) is a common inflammatory rheumatic disease with a predilection for the axial skeleton, affecting 0.2% of the population. Current diagnostic criteria rely on a composite of clinical and radiological changes, with a mean time to diagnosis of 5 to 10 years. In this study we employed nano liquid-chromatography mass spectrometry analysis to detect and quantify proteins and small compounds including endogenous peptides and metabolites in serum from 18 AS patients and nine healthy individuals. We identified a total of 316 proteins in serum, of which 22 showed significant up- or down-regulation (p < 0.05) in AS patients. Receiver operating characteristic analysis of combined levels of serum amyloid P component and inter-α-trypsin inhibitor heavy chain 1 revealed high diagnostic value for Ankylosing Spondylitis (area under the curve = 0.98). We also depleted individual sera of proteins to analyze endogenous peptides and metabolic compounds. We detected more than 7000 molecular features in patients and healthy individuals. Quantitative MS analysis revealed compound profiles that correlate with the clinical assessment of disease activity. One molecular feature identified as a Vitamin D3 metabolite-(23S,25R)-25-hydroxyvitamin D3 26,23-peroxylactone-was down-regulated in AS. The ratio of this vitamin D metabolite versus vitamin D binding protein serum levels was also altered in AS as compared with controls. These changes may contribute to pathological skeletal changes in AS. Our study is the first example of an integration of proteomic and metabolomic techniques to find new biomarker candidates for the diagnosis of Ankylosing Spondylitis.     

Statistically integrated metabonomic-proteomic studies on a human prostate cancer xenograft model in mice

A novel statistically integrated proteometabonomic method has been developed and applied to a human tumor xenograft mouse model of prostate cancer. Parallel 2D-DIGE proteomic and 1H NMR metabolic profile data were collected on blood plasma from mice implanted with a prostate cancer (PC-3) xenograft and from matched control animals. To interpret the xenograft-induced differences in plasma profiles, multivariate statistical algorithms including orthogonal projection to latent structure (OPLS) were applied to generate models characterizing the disease profile. Two approaches to integrating metabonomic data matrices are presented based on OPLS algorithms to provide a framework for generating models relating to the specific and common sources of variation in the metabolite concentrations and protein abundances that can be directly related to the disease model. Multiple correlations between metabolites and proteins were found, including associations between serotransferrin precursor and both tyrosine and 3-D-hydroxybutyrate. Additionally, a correlation between decreased concentration of tyrosine and increased presence of gelsolin was also observed. This approach can provide enhanced recovery of combination candidate biomarkers across multi-omic platforms, thus, enhancing understanding of in vivo model systems studied by multiple omic technologies.     

Statistical multivariate metabolite profiling for aiding biomarker pattern detection and mechanistic interpretations in GC/MS based metabolomics

A strategy for robust and reliable mechanistic statistical modelling of metabolic responses in relation to drug induced toxicity is presented. The suggested approach addresses two cases commonly occurring within metabonomic toxicology studies, namely; 1) A pre-defined hypothesis about the biological mechanism exists and 2) No such hypothesis exists. GC/MS data from a liver toxicity study consisting of rat urine from control rats and rats exposed to a proprietary AstraZeneca compound were resolved by means of hierarchical multivariate curve resolution (H-MCR) generating 287 resolved chromatographic profiles with corresponding mass spectra. Filtering according to significance in relation to drug exposure rendered in 210 compound profiles, which were subjected to further statistical analysis following correction to account for the control variation over time. These dose related metabolite traces were then used as new observations in the subsequent analyses. For case 1, a multivariate approach, named Target Batch Analysis, based on OPLS regression was applied to correlate all metabolite traces to one or more key metabolites involved in the pre-defined hypothesis. For case 2, principal component analysis (PCA) was combined with hierarchical cluster analysis (HCA) to create a robust and interpretable framework for unbiased mechanistic screening. Both the Target Batch Analysis and the unbiased approach were cross-verified using the other method to ensure that the results did match in terms of detected metabolite traces. This was also the case, implying that this is a working concept for clustering of metabolites in relation to their toxicity induced dynamic profiles regardless if there is a pre-existing hypothesis or not. For each of the methods the detected metabolites were subjected to identification by means of data base comparison as well as verification in the raw data. The proposed strategy should be seen as a general approach for facilitating mechanistic modelling and interpretations in metabolomic studies.     

Serum miR-518e-5p is a potential biomarker for secondary imatinib-resistant gastrointestinal stromal tumor

Gastrointestinal stromal tumors (GISTs) are the most common mesenchymal tumor of the intestinal tract. Imatinib is used as first-line therapy for GIST patients; however, secondary imatinib resistance poses a significant clinical challenge. Here, we analyzed serum miRNA expression profiles to identify specific serum miRNAs that could be used as early diagnostic markers. Candidate miRNAs were validated using Taqman quantitative PCR with serum samples from secondary imatinib-resistant GIST patients (n?=?39), imatinib-sensitive GIST patients (n?=?37), and healthy controls (n?=?28). Serum miR-518e-5p and miR-548e levels were higher in secondary imatinib-resistant GIST than imatinib-sensitive GIST patients or healthy controls (P?<?0.0001). However, ROC analysis indicated that only miR-518e-5p could distinguish imatinib-resistant GIST. To discriminate imatinib-resistant from imatinib-sensitive GIST patients, the AUC for serum miR-518e-5p was 0.9938, with 99.8% sensitivity and 82.1% specificity. Serum miR-518e-5p could also discriminate imatinib-resistant GIST patients from healthy controls with 99.9% sensitivity and 97.4% specificity. These data indicate that serum miR-518e-5p is a potentially promising non-invasive biomarker for early detection and diagnosis of secondary imatinib-resistant GIST.     

癌胚抗原蛋白芯片的研发及其在肝癌检测中的评价

目的:研究建立一种简便、快速、特异性高、低成本的检测血清中癌胚抗原(CEA)浓度的蛋白芯片,并通过检测肝细胞肝癌(HCC)对其进行评价。方法:采用双抗体夹心法,制备能够形成捕获抗体-抗原-检测抗体的"三明治"结构的蛋白芯片检测血清中CEA浓度。通过用该蛋白芯片检测50例CEA阳性HCC患者血清和56例健康人血清,对其进行盲法验证。结果:以CEA5 ng/m L为阳性判定标准,得出CEA蛋白芯片的灵敏度为92%(46/50),特异度为100%(56/56)。受试者工作特征(ROC)曲线分析显示该蛋白芯片检测出血清CEA的ROC曲线下面积(AUC)为0.960,与0.5相比差异有统计学意义(P0.001),其判定CEA阳性的准确性较高。结论:成功建立检测血清中CEA浓度的蛋白芯片,为下一步研发多种标志物联合检测HCC的蛋白芯片提供候选血清标志物。     

Cholesterol and its esters as serum biomarkers in malignant obstructive jaundice: a single step 1H NMR metabolomic approach

The paucity of biomarkers for malignant obstructive jaundice results in formidable morbidity and mortality rates. Therefore, alternative diagnostic measures are required for improved clinical interpretation and better peri-operative management of patients. In the present study, ¹H NMR-based metabolomic approach has been applied to investigate serum and bile based metabolic biomarkers in benign and malignant causes of obstructive jaundice (OBJ). Serum and bile specimens from benign OBJ patients (n = 28), malignant OBJ patients (n = 36) and serum of healthy controls (n = 57) were analysed by ¹H NMR spectroscopy. Quantitation of eight serum metabolites (isobutyrate, lactate, alanine, acetone, glutamine, creatine, threonine and 1-methylhistidine) was carried out. A newer and rapid single step NMR based semi-quantitative ratio analysis of serum total cholesterol (tCho), cholesterol (Chol) and cholesterol ester (CE) were performed in deuterated dimethyl sulfoxide-d₆. In bile, total bile acids, cholesterol, phosphatidylcholine, glycerophosphatidylcholine and urea were quantified. The effect of benign and malignant OBJ on small metabolites and lipids were statistically analysed by Mann–Whitney U test and multivariate discriminant function analysis. It was found that malignancy could be differentiated from benign cases of OBJ with a correct classification of 85.7 % when eight serum metabolites in combination with ratios of serum cholesterol were analysed. Significant alterations in serum tCho, Chol, CE and serum metabolites may have potential for early and differential non-invasive diagnosis of malignant and benign OBJ cases. It will further augment the novel insights of local and systemic effects in OBJ patients.     

Metabonomic Profiling of Serum and Urine by 1H NMR-Based Spectroscopy Discriminates Patients with Chronic Obstructive Pulmonary Disease and Healthy Individuals

Chronic obstructive pulmonary disease (COPD) has seriously impacted the health of individuals and populations. In this study, proton nuclear magnetic resonance (¹H NMR)-based metabonomics combined with multivariate pattern recognition analysis was applied to investigate the metabolic signatures of patients with COPD. Serum and urine samples were collected from COPD patients (n = 32) and healthy controls (n = 21), respectively. Samples were analyzed by high resolution ¹H NMR (600 MHz), and the obtained spectral profiles were then subjected to multivariate data analysis. Consistent metabolic differences have been found in serum as well as in urine samples from COPD patients and healthy controls. Compared to healthy controls, COPD patients displayed decreased lipoprotein and amino acids, including branched-chain amino acids (BCAAs), and increased glycerolphosphocholine in serum. Moreover, metabolic differences in urine were more significant than in serum. Decreased urinary 1-methylnicotinamide, creatinine and lactate have been discovered in COPD patients in comparison with healthy controls. Conversely, acetate, ketone bodies, carnosine, m-hydroxyphenylacetate, phenylacetyglycine, pyruvate and α-ketoglutarate exhibited enhanced expression levels in COPD patients relative to healthy subjects. Our results illustrate the potential application of NMR-based metabonomics in early diagnosis and understanding the mechanisms of COPD.     

Plasma-free amino acid profiling of cervical cancer and cervical intraepithelial neoplasia patients and its application for early detection

In this study, plasma-free amino acid profiles were used to investigate pre-cancerous cervical intraepithelial neoplasia (CIN) and cervical squamous cell carcinoma (CSCC) metabolic signatures in plasma. Additionally, the diagnostic potential of these profiles was assessed, as well as their ability to provide novel insight into CSCC metabolism and systemic effects. Plasma samples from CIN patients (n = 26), CSCC patients (n = 22), and a control healthy group (n = 35) were analyzed by high-performance liquid chromatography, and their spectral profiles were subjected to the t test for statistical significance. Potential metabolic biomarkers were identified using database comparisons that examine the significance of metabolites. Compared with healthy controls, patients with CIN and CSCC demonstrated lower levels of plasma amino acids; plasma levels of arginine and threonine were increased in CIN patients but were decreased in cervical cancer patients. Additionally, the levels of a larger group of amino acids (aspartate, glutamate, asparagine, serine, glycine, histidine, taurine, tyrosine, valine, methionine, lysine, isoleucine, leucine, and phenylalanine) were gradually reduced from CIN to invasive cancer. These findings suggest that plasma-free amino acid profiling has great potential for improving cancer screening and diagnosis and for understanding disease pathogenesis. Plasma-free amino acid profiles may have the potential be used to determine cancer diagnoses in the early stage from a single blood sample and may enhance our understanding of its mechanisms.     

Combined experimental and statistical strategy for mass spectrometry based serum protein profiling for diagnosis of breast cancer: a case-control study

Serum protein profiling by mass spectrometry is a promising method for early detection of cancer. We have implemented a combined strategy based on matrix-assisted laser desorption ionization mass spectrometry (MALDI MS) and statistical data analysis for serum protein profiling and applied it in a well-described breast cancer case-control study. A rigorous sample collection protocol ensured high quality specimen and reduced bias from preanalytical factors. Preoperative serum samples obtained from 48 breast cancer patients and 28 controls were used to generate MALDI MS protein profiles. A total of nine mass spectrometric protein profiles were obtained for each serum sample. A total of 533 common peaks were defined and represented a 'reference protein profile'. Among these 533 common peaks, we identified 72 peaks exhibiting statistically significant intensity differences ( p < 0.01) between cases and controls. A diagnostic rule based on these 72 mass values was constructed and exhibited a cross-validated sensitivity and specificity of approximately 85% for the detection of breast cancer. With this method, it was possible to distinguish early stage cancers from controls without major loss of sensitivity and specificity. We conclude that optimized serum sample handling and mass spectrometry data acquisition strategies in combination with statistical analysis provide a viable platform for serum protein profiling in cancer diagnosis.