The serum metabolic profiles of different Barcelona stages hepatocellular carcinoma associated with hepatitis B virus.

The present study aimed to explore the characteristic ions distinguishing different Barcelona stages in patients with hepatitis B virus (HBV)-associated hepatocellular carcinoma (HCC) using the ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) platform, and to evaluate their value in diagnosing and monitoring the progress of HCC. The serum was sampled from 20 healthy volunteers, 20 patients with HBV-induced cirrhosis and 75 patients with HBV-associated HCC of different BCLC stages. Samples were all examined using UPLC-MS. Principal components analysis (PCA) and the orthogonal partial least squares discriminant analysis (OPLS-DA) model were constructed to determine potential biomarkers. Then, the independent sample-nonparametric test was used to perform the final screening for ion identification. Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic value of these ions. Serum metabolomic PCA and OPLS-DA models were established to diagnose different BCLC stages of HCC associated with HBV, with OPLS-DA model parameters (R2X=67.2%, R2Y=82%, Q2Y=61.1%). A total of 20 metabolites with statistically significant differences among groups were identified, primarily including amino acids, bile acid, fatty acid and phosphatidate. The area under the curve (AUC) of LysoPC [18:2 (9Z,12Z)], LysoPC (P-16:0), asparaginyl-proline and vaccenic acid in the comparison between HCC and cirrhosis were all increased compared with that of AFP, indicating a more improved diagnosis ability. Furthermore, the AUC of L-aspartyl-4-phosphate and LysoPC [20:5 (5Z,8Z,11Z,14Z,17Z)] in the stage A vs. B comparison were increased compared with that of AFP, but were decreased in the comparison between stage B and C. The present study succeeded in screening metabolic ions that reflect the progress of HCC with high diagnostic value. Thus, the identified ions may serve a role in clinically diagnosing HBV-associated HCC and monitoring the development of the disease.

Cloning and expression of special F protein from human liver.

AIM: To clone human liver special F protein and to express it in a prokaryotic system. METHODS: Total RNA was isolated from human liver tissue and first-strand cDNA was reverse transcribed using the PCR reverse primer. Following this, cDNA of the F protein was ligated into the clone vector pUCm-T. The segment of F protein's cDNA was subcloned into the expression vector pET-15b and transformed into E. coli BL21 (DE3) pLyss. Isopropy-beta-D-thiogalactoside (IPTG) was then used to induce expression of the target protein. RESULTS: The cDNA clone of human liver special F protein (1134bp) was successfully produced, with the cDNA sequence being published in Gene-bank: DQ188836. We confirmed the expression of F protein by Western blot with a molecular weight of 43 kDa. The expressed protein accounted for 40% of the total protein extracted. CONCLUSION: F protein expresses cDNA clone in a prokaryotic system, which offers a relatively simple way of producing sufficient quantities of F protein and contributes to understanding the principal biological functions of this protein.

Serum IgG4 and IgG for the diagnosis of autoimmune pancreatitis: A systematic review with meta-analysis.

BACKGROUND AND OBJECTIVE: The correct diagnosis of autoimmune pancreatitis (AIP) is a clinical challenge. Emerging published data on the accuracy of serum IgG4 and IgG for diagnosing AIP are inconsistent. This study was performed to better elucidate the accuracy of serum IgG4 and IgG in diagnosing AIP. METHODS: A comprehensive literature search of PubMed, Web of Science, EMBASE, the Cochrane Library and some other databases was conducted before October 2014. The methodological quality of each study was assessed according to the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) checklist. Random-effects model was used to summarize the sensitivity, specificity and other measures of accuracy. RESULTS: Fifteen studies on IgG4 and 8 studies on IgG were included. The summary estimates for serum IgG4 in distinguishing AIP from the overall controls, pancreatic cancer and ordinary chronic pancreatitis were as follows: sensitivity 0.74 (0.70-0.77), 0.73 (0.69-0.77) and 0.76 (0.72-0.80), respectively, specificity, 0.94 (0.93-0.95), 0.93 (0.91-0.95) and 0.96 (0.95-0.97), respectively. The summary estimates for serum IgG in distinguishing AIP from the overall controls and pancreatic cancer were as follows: sensitivity, 0.53 (0.47-0.59) and 0.51 (0.44-0.57), respectively, specificity, 0.87 (0.85-0.89) and 0.94 (0.91-0.96), respectively. The area under the curve (AUC) of serum IgG in distinguishing AIP from ordinary chronic pancreatitis was 0.657. CONCLUSIONS: Both serum IgG4 and IgG have high specificity and relatively low sensitivity for diagnosing AIP. Besides, they are useful for distinguishing AIP from pancreatic cancer and ordinary chronic pancreatitis. To better elucidate the usefulness of serum IgG4 and IgG, further studies are needed.

Plasma Metabolic Profile Determination in Young ST-segment Elevation Myocardial Infarction Patients with Ischemia and Reperfusion: Ultra-performance Liquid Chromatography and Mass Spectrometry for Pathway Analysis.

BACKGROUND: This study was to establish a disease differentiation model for ST-segment elevation myocardial infarction (STEMI) youth patients experiencing ischemia and reperfusion via ultra-performance liquid chromatography and mass spectrometry (UPLC/MS) platform, which searches for closely related characteristic metabolites and metabolic pathways to evaluate their predictive value in the prognosis after discharge. METHODS: Forty-seven consecutive STEMI patients (23 patients under 45 years of age, referred to here as "youth," and 24 "elderly" patients) and 48 healthy control group members (24 youth, 24 elderly) were registered prospectively. The youth patients were required to provide a second blood draw during a follow-up visit one year after morbidity (n = 22, one lost). Characteristic metabolites and relative metabolic pathways were screened via UPLC/MS platform base on the Kyoto encyclopedia of genes and genomes (KEGG) and Human Metabolome Database. Receiver operating characteristic (ROC) curves were drawn to evaluate the predictive value of characteristic metabolites in the prognosis after discharge. RESULTS: We successfully established an orthogonal partial least squares discriminated analysis model (R2X = 71.2%, R2Y = 79.6%, and Q2 = 55.9%) and screened out 24 ions; the sphingolipid metabolism pathway showed the most drastic change. The ROC curve analysis showed that ceramide [Cer(d18:0/16:0), Cer(t18:0/12:0)] and sphinganine in the sphingolipid pathway have high sensitivity and specificity on the prognosis related to major adverse cardiovascular events after youth patients were discharged. The area under curve (AUC) was 0.671, 0.750, and 0.711, respectively. A follow-up validation one year after morbidity showed corresponding AUC of 0.778, 0.833, and 0.806. CONCLUSIONS: By analyzing the plasma metabolism of myocardial infarction patients, we successfully established a model that can distinguish two different factors simultaneously: pathological conditions and age. Sphingolipid metabolism is the top most altered pathway in young STEMI patients and as such may represent a valuable prognostic factor and potential therapeutic target.

Analysis of serum metabolic profile by ultra-performance liquid chromatography-mass spectrometry for biomarkers discovery: application in a pilot study to discriminate patients with tuberculosis.

BACKGROUND: Tuberculosis (TB) is a chronic wasting inflammatory disease characterized by multisystem involvement, which can cause metabolic derangements in afflicted patients. Metabolic signatures have been exploited in the study of several diseases. However, the serum that is successfully used in TB diagnosis on the basis of metabolic profiling is not by much. METHODS: Orthogonal partial least-squares discriminant analysis was capable of distinguishing TB patients from both healthy subjects and patients with conditions other than TB. Therefore, TB-specific metabolic profiling was established. Clusters of potential biomarkers for differentiating TB active from non-TB diseases were identified using Mann-Whitney U-test. Multiple logistic regression analysis of metabolites was calculated to determine the suitable biomarker group that allows the efficient differentiation of patients with TB active from the control subjects. RESULTS: From among 271 participants, 12 metabolites were found to contribute to the distinction between the TB active group and the control groups. These metabolites were mainly involved in the metabolic pathways of the following three biomolecules: Fatty acids, amino acids, and lipids. The receiver operating characteristic curves of 3D, 7D, and 11D-phytanic acid, behenic acid, and threoninyl-γ-glutamate exhibited excellent efficiency with area under the curve (AUC) values of 0.904 (95% confidence interval [CI]: 0863-0.944), 0.93 (95% CI: 0.893-0.966), and 0.964 (95% CI: 00.941-0.988), respectively. The largest and smallest resulting AUCs were 0.964 and 0.720, indicating that these biomarkers may be involved in the disease mechanisms. The combination of lysophosphatidylcholine (18:0), behenic acid, threoninyl-γ-glutamate, and presqualene diphosphate was used to represent the most suitable biomarker group for the differentiation of patients with TB active from the control subjects, with an AUC value of 0.991. CONCLUSION: The metabolic analysis results identified new serum biomarkers that can distinguish TB from non-TB diseases. The metabolomics-based analysis provides specific insights into the biology of TB and may offer new avenues for TB diagnosis.

Human liver tissue metabolic profiling research on hepatitis B virus-related hepatocellular carcinoma.

AIM: To select characteristic endogenous metabolites in hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) patients and to identify their molecular mechanism and potential clinical value. METHODS: An ultra performance liquid chromatography and linear trap quadrupole-Orbitrap XL-mass spectrometry platform was used to analyze endogenous metabolites in the homogenate of central tumor tissue, adjacent tissue and distant tissue obtained from 10 HBV-related HCC patients. After pretreatment with Mzmine software, including peak detection, alignment and normalization, the acquired data were treated with Simca-P+software to establish multivariate statistical analysis based on a pattern recognition technique and characteristic metabolites highly correlated with changing trends in metabolic profiling were selected and further identified. RESULTS: Based on data acquired using Mzmine software, a principal component analysis model (R2X = 66.9%, Q2 = 21.7%) with 6 principal components and an orthogonal partial least squares discriminant analysis model (R2X = 76.5%, R2Y = 93.7%, Q2 = 68.7%) with 2 predicted principal components and 5 orthogonal principal components were established in the three tissue groups. Forty-nine ions were selected, 33 ions passed the 2 related samples nonparametric test (P < 0.05) and 14 of these were further identified as characteristic metabolites that showed significant differences in levels between the central tumor tissue group and distant tumor tissue group, including 9 metabolites (L-phenylalanine, glycerophosphocholine, lysophosphatidylcholines, lysophosphatidylethanolamines and chenodeoxycholic acid glycine conjugate) which had been reported as serum metabolite biomarkers for HCC diagnosis in previous research, and 5 metabolites (beta-sitosterol, quinaldic acid, arachidyl carnitine, tetradecanal, and oleamide) which had not been reported before. CONCLUSION: Characteristic metabolites and metabolic pathways highly related to HCC pathogenesis and progression are identified through metabolic profiling analysis of HCC tissue homogenates.