肝细胞癌、肝硬化患者血清中代谢物组研究

采用核磁共振方法检测肝硬化、肝细胞癌(简称肝癌)患者和健康人血清中代谢物,研究3组血清代谢物组的差异.利用偏最小二乘法-判别式分析(partial least square-discriminant analysis, PLS-DA)对NMR谱数据进行模式识别分析,探讨利用基于1H NMR代谢组学技术诊断肝癌的可行性.结果表明,与健康人相比,肝硬化、肝癌患者血清中脂质(低密度脂蛋白和极低密度脂蛋白)、胆碱、乙酰乙酸等含量减少,谷氨酰胺、丙酮酸、苯丙氨酸、酪氨酸等含量增加.PLS-DA分析结果显示肝癌患者可与健康人、肝硬化患者鉴别开来,肝癌诊断灵敏度达921%,假阳性率为5.7%,优于血清甲胎蛋白(alpha-fetoprotein, AFP)检测.研究结果表明,基于1H NMR代谢组学技术结合PLS-DA的方法具有灵敏、准确、重复性好等优点,有助于肝癌早期诊断.     

Study of urinary steroid hormone disorders: difference between hepatocellular carcinoma in early stage and cirrhosis

Hepatocellular carcinoma (HCC) is the fifth most common cancer in the world. Discovery of novel biomarkers for early HCC from other liver diseases such as cirrhosis is of great clinical benefit. In this study, a novel steroid hormone metabolomic method based on liquid chromatography–mass spectrometry combined with logistic regression analysis was applied to study the steroid hormone disorders and to screen potential urinary steroid hormone biomarkers of early HCC. Thirty-six urinary steroid hormones were detected and quantified in healthy controls, cirrhotic patients, and early HCC patients. Heat map analysis and multivariate statistical analysis suggested severe disorders of steroid hormone network and holistically decreased urinary steroid hormone pattern in cirrhotic and early HCC patients. Logistic regression analysis reveals that a panel of two urinary steroid hormones (epitestosterone and allotetrahydrocortisol) displayed excellent diagnostic capability for distinguishing early HCC from cirrhosis with area under the curve (AUC)?=?0.938 of receiver operating characteristic (ROC) analysis. These results help to overcome the disadvantage of lower sensitivity and specificity of alpha-fetoprotein for distinguishing early HCC from cirrhosis. Our work shows that steroid hormone metabolomics is a promising biomarker tool for biomarker study of early HCC.

Chromatographic determination of some biomarkers of liver cirrhosis and hepatocellular carcinoma in Egyptian patients

Metabolomics has been shown to be an effective tool for disease diagnosis, biomarker screening and characterization of biological pathways. A total of 140 subjects were included in this study; urine metabolomes of patients with liver cirrhosis (LC, n = 40), patients with hepatocellular carcinoma (HCC; n = 55) and healthy male subjects (n = 45) as a control group were studied. Gas chromatography/mass spectrometry‐based urine metabolomics profiles were investigated for all participants. Diagnostic models were constructed with a combination of marker metabolites, using principal components analysis and receiver operator characteristic curves. A total of 57 peaks could be auto‐identified of which 13 marker metabolites (glycine, serine, threonine, proline, urea, phosphate, pyrimidine, arabinose, xylitol, hippuric acid, citric acid, xylonic acid and glycerol) were responsible for the separation of HCC group from healthy subjects. Also, eight markers metabolites (glycine, serine, threonine, proline, citric acid, urea, xylitol and arabinose) showed significant differences between the LC group and healthy subjects. No significant difference was detected between HCC and LC groups regarding all these metabolites. Metabolomic profile using GC–MS established an optimized diagnostic model to discriminate between HCC patients and healthy subjects; also it could be useful for diagnosis of LC patients. However, it failed to differentiate between HCC and LC patients.     

Normalization using a tagged-internal standard assay for analysis of antibody arrays and the evaluation of serological biomarkers for liver disease

For minimizing systemic experimental variation in the analysis of antibody array data, we developed a novel median-centered/IgM-tagged-internal standard (TIS) assay normalization using median-centering and TIS assay-based determination of serum IgM concentrations. We evaluated five normalization methods by analyzing correlation coefficients and coefficients of variation for six serum proteins using human serum samples from normal controls (n = 25) and patients with liver cirrhosis (n = 25) or hepatocellular carcinoma (HCC; n = 29). Median-centered normalization improved correlation coefficients, while IgM-based normalizations improved coefficients of variation. The TIS assay was more efficient, economical, and reproducible for determining IgM concentrations than enzyme-linked immunosorbent assay. Additionally, we normalized antibody array data for six serum proteins using the median-centered/IgM-TIS assay, and evaluated serum biomarkers through distribution analysis of normalized fluorescence intensities and receiver operating characteristic analyses for the diagnosis of liver cirrhosis and HCC. Apolipoprotein A-1 and a combination of alpha-fetoprotein and C-reactive protein were determined to be potential serological biomarkers for liver cirrhosis and HCC, respectively. Thus, median-centered/IgM-TIS assay normalization is a useful approach for analyzing antibody array data and evaluating serological biomarkers for the diagnosis of liver disease or cancers.     

Utilization of metabolomics to identify serum biomarkers for hepatocellular carcinoma in patients with liver cirrhosis

Characterizing the metabolic changes pertaining to hepatocellular carcinoma (HCC) in patients with liver cirrhosis is believed to contribute towards early detection, treatment, and understanding of the molecular mechanisms of HCC. In this study, we compare metabolite levels in sera of 78 HCC cases with 184 cirrhotic controls by using ultra performance liquid chromatography coupled with a hybrid quadrupole time-of-flight mass spectrometry (UPLC–QTOF MS). Following data preprocessing, the most relevant ions in distinguishing HCC cases from patients with cirrhosis are selected by parametric and non-parametric statistical methods. Putative metabolite identifications for these ions are obtained through mass-based database search. Verification of the identities of selected metabolites is conducted by comparing their MS/MS fragmentation patterns and retention time with those from authentic compounds. Quantitation of these metabolites is performed in a subset of the serum samples (10 HCC and 10 cirrhosis) using isotope dilution by selected reaction monitoring (SRM) on triple quadrupole linear ion trap (QqQLIT) and triple quadrupole (QqQ) mass spectrometers. The results of this analysis confirm that metabolites involved in sphingolipid metabolism and phospholipid catabolism such as sphingosine-1-phosphate (S-1-P) and lysophosphatidylcholine (lysoPC 17:0) are up-regulated in sera of HCC vs. those with liver cirrhosis. Down-regulated metabolites include those involved in bile acid biosynthesis (specifically cholesterol metabolism) such as glycochenodeoxycholic acid 3-sulfate (3-sulfo-GCDCA), glycocholic acid (GCA), glycodeoxycholic acid (GDCA), taurocholic acid (TCA), and taurochenodeoxycholate (TCDCA). These results provide useful insights into HCC biomarker discovery utilizing metabolomics as an efficient and cost-effective platform. Our work shows that metabolomic profiling is a promising tool to identify candidate metabolic biomarkers for early detection of HCC cases in high risk population of cirrhotic patients.     

Phospholipidomic identification of potential plasma biomarkers associated with type 2 diabetes mellitus and diabetic nephropathy

Type 2 diabetes mellitus (T2DM) and its attendant complications, such as diabetic nephropathy (DN), impose a significant societal and economic burden. The investigation of discovering potential biomarkers for T2DM and DN will facilitate the prediction and prevention of diabetes. Phospholipids (PLs) and their metabolisms are closely allied to nosogenesis and aggravation of T2DM and DN. The aim of this study is to characterize the human plasma phospholipids in T2DM and DN to identify potential biomarkers of T2DM and DN. Normal phase liquid chromatography coupled with time of flight mass spectrometry (NPLC-TOF/MS) was applied to the plasma phospholipids metabolic profiling of T2DM and DN. The plasma samples from control (n = 30), T2DM subjects (n = 30), and DN subjects (n = 52) were collected and analyzed. The significant difference in metabolic profiling was observed between healthy control group and DM group as well as between control group and DN group by the help of partial least squares discriminant analysis (PLS-DA). PLS-DA and one-way analysis of variance (ANOVA) were successfully used to screen out potential biomarkers from complex mass spectrometry data. The identification of molecular components of potential biomarkers was performed on Ion trap-MS/MS. An external standard method was applied to quantitative analysis of potential biomarkers. As a result, 18 compounds in 7 PL classes with significant regulation in patients compared with healthy controls were regarded as potential biomarkers for T2DM or DN. Among them, 3 DM-specific biomarkers, 8 DN-specific biomarkers and 7 common biomarkers to DM and DN were identified. Ultimately, 2 novel biomarkers, i.e., PI C18:0/22:6 and SM dC18:0/20:2, can be used to discriminate healthy individuals, T2DM cases and DN cases from each other group.     

Serum metabolomics reveals the deregulation of fatty acids metabolism in hepatocellular carcinoma and chronic liver diseases

Patients with chronic liver diseases (CLD) including chronic hepatitis B and hepatic cirrhosis (CIR) are the major high-risk population of hepatocellular carcinoma (HCC). The differential diagnosis between CLD and HCC is a challenge. This work aims to study the related metabolic deregulations in HCC and CLD to promote the discovery of the differential metabolites for distinguishing the different liver diseases. Serum metabolic profiling analysis from patients with CLD and HCC was performed using a liquid chromatography–mass spectrometry system. The acquired large amount of metabolic information was processed with the random forest–recursive feature elimination method to discover important metabolic changes. It was found that long-chain acylcarnitines accumulated, whereas free carnitine, medium and short-chain acylcarnitines decreased with the severity of the non-malignant liver diseases, accompanied with corresponding alterations of enzyme activities. However, the general changing extent was smaller in HCC than in CIR, possibly due to the special energy-consumption mechanism of tumor cells. These observations may help to understand the mechanism of HCC occurrence and progression on the metabolic level and provide information for the identification of early and differential metabolic markers for HCC.     

Identification of molecular markers for the oncogenic differentiation of hepatocellular carcinoma

The aim of this study was to identify molecular markers associated with oncogenic differentiation in hepatocellular carcinoma (HCC). Using an unsupervised clustering method with a cDNA microarray, HCC (T) gene expression profiles and corresponding non-tumor tissues (NT) from 40 patients were analyzed. Of total 217 genes, 72 were expressed preferentially in HCC tissues. Among 186 differentially regulated genes, there were molecular chaperone and tumor suppressor gene clusters in the Edmondson grades I and II (GI/II) subclass compared with the liver cirrhosis (LC) subclass. The Edmondson grades III and IV (GIII/IV) subclass with a poor survival (P=0.0133) contained 122 differentially regulated genes with a cluster containing various metastasis- and invasion-related genes compared with the GI/II subclass. Immunohistochemical analysis revealed that ANXA2, one of the 72 genes preferentially expressed in HCC, was over-expressed in the sinusoidal endothelium and in malignant hepatocytes in HCC. The genes identified in the HCC subclasses will be useful molecular markers for the genesis and progression of HCC. In addition, ANXA2 might be a novel marker for tumor angiogenesis in HCC.     

Metabolomic approach to Alzheimer’s disease diagnosis based on mass spectrometry

Alzheimer??s disease is the most common neurodegenerative disease, but there is still no cure and early diagnosis remains very difficult. For this reason, the discovery of new biomarkers is of great importance. The application of metabolomics is emerging in this field, based on the use of mass spectrometry as a technique of analysis. In this work, blood serum samples (from Alzheimer??s disease patients and healthy controls) were analysed by mass spectrometry in order to search for potential metabolomic biomarkers. The application of multivariate statistical tools (PLS-DA) enabled us to discriminate between groups. In addition, some phosphatidylcholine compounds were identified as markers of the disease.     

Serum exosomal microRNAs as novel biomarkers for hepatocellular carcinoma

Recent studies have shown that circulating microRNAs are a potential biomarker in various types of malignancies. The aim of this study was to investigate the feasibility of using serum exosomal microRNAs as novel serological biomarkers for hepatocellular carcinoma (HCC) in patients with chronic hepatitis B (CHB). We measured the serum exosomal microRNAs and serum circulating microRNAs in patients with CHB (n=20), liver cirrhosis (LC) (n=20) and HCC (n=20). Serum exosomal microRNA was extracted from 500 μl of serum using an Exosome RNA Isolation kit. The expression levels of microRNAs were quantified by real-time PCR. The expression levels of selected microRNAs were normalized to Caenorhabditis elegans microRNA (Cel-miR-39). The serum levels of exosomal miR-18a, miR-221, miR-222 and miR-224 were significantly higher in patients with HCC than those with CHB or LC (P<0.05). Further, the serum levels of exosomal miR-101, miR-106b, miR-122 and miR-195 were lower in patients with HCC than in patients with CHB (P=0.014, P<0.001, P<0.001 and P<0.001, respectively). There was no significant difference in the levels of miR-21 and miR-93 among the three groups. Additionally, the serum levels of circulating microRNAs showed a smaller difference between HCC and either CHB or LC. This study suggests that serum exosomal microRNAs may be used as novel serological biomarkers for HCC.     

Metabonomics study of liver cancer based on ultra performance liquid chromatography coupled to mass spectrometry with HILIC and RPLC separations

In this study, urinary metabolites from liver cancer patients and healthy volunteers were studied by a metabonomic method based on ultra performance liquid chromatography coupled to mass spectrometry. Both hydrophilic interaction chromatography (HILIC) and reversed-phase liquid chromatography (RPLC) were used to separate the urinary metabolites. Principle component analysis (PCA) and partial least squares to latent structure-discriminant analysis (PLS-DA) models were built to separate the healthy volunteers from the liver cancer patients and to find compounds that are expressed in significantly different amounts between the two populations. 21 metabolite ions were considered as potential biomarkers according to the Variable importance in the Project (VIP) value and S-plot. Compared with RPLC, a more sensitive and stable response can be recorded in HILIC mode due to the high content of organic solvent used. Moreover, the liver cancer group and the healthy volunteers can be better separated based on the data from the HILIC separation, which indicates that HILIC is suitable for urinary metabonomic analysis. In HILIC mode, several polar compounds related to arginine and proline metabolism, alanine and aspartate metabolism, lysine degradation, nicotinate and nicotinamide metabolism were found to be significantly changed in the concentrations of the two different populations: healthy and cancer. In contrast, in RPLC mode, these changed compounds are related to fatty acids oxidation.     

Myofibroblasts are important contributors to human hepatocellular carcinoma: Evidence for tumor promotion by proteome profiling

Liver cancer typically occurs with a background of chronic fibrosis, characterized by the accumulation of myofibroblast‐like cells. We performed 2D‐PAGE‐based comparative analyses with the aim to identify proteins expressed in human hepatocellular carcinoma (HCC) tissue but not in neighboring healthy liver tissue, and to make out which cell types are responsible for the expression of proteins most characteristic for HCC. LC‐MS/MS analysis of the most striking spots identified proteins that were mainly related to myofibroblast‐like cells. To gain more insights into the role of these cells in their contribution to HCC, we isolated myofibroblast‐like cells as well as hepatocytes, both derived from HCC tissues, and subjected them to proteome profiling based on shotgun experiments. Comparative analysis, also referring to proteome profiles of other cell types previously investigated by us, pointed again to a marked contribution of myofibroblast‐like cells to HCC. Intriguingly, secretome analysis of these cells identified several growth factors that may act as tumor promoters and several proteins that have been described as potential biomarkers for HCC including dickkopf‐1, connective tissue growth factor, and CXCL1. Other biomarker candidates presently identified in the secretome of myofibroblasts, including lipocalin‐1 and pappalysin‐1, may be selected for future clinical validation. The identification of myofibroblast‐like cells as important source of tumor‐promoters may open new avenues to therapeutic intervention by targeting these stroma cells in addition to the cancer cells.     

Dansylation isotope labeling liquid chromatography mass spectrometry for parallel profiling of human urinary and fecal submetabolomes

Human urine and feces can be non-invasively collected for metabolomics-based disease biomarker discovery research. Because urinary and fecal metabolomes are thought to be different, analysis of both biospecimens may generate a more comprehensive metabolomic profile that can be better related to the health state of an individual. Herein we describe a method of using differential chemical isotope labeling (CIL) liquid chromatography mass spectrometry (LC-MS) for parallel metabolomic profiling of urine and feces. Dansylation labeling was used to quantify the amine/phenol submetabolome changes among different samples based on ¹²C-labeling of individual samples and ¹³C-labeling of a pooled urine or pooled feces and subsequent analysis of the ¹³C-/¹²C-labeled mixture by LC-MS. The pooled urine and pooled feces are further differentially labeled, mixed and then analyzed by LC-MS in order to relate the metabolite concentrations of the common metabolites found in both biospecimens. This method offers a means of direct comparison of urinary and fecal submetabolomes. We evaluated the analytical performance and demonstrated the utility of this method in the analysis of urine and feces collected daily from three healthy individuals for 7 days. On average, 2534 ± 113 (n = 126) peak pairs or metabolites could be detected from a urine sample, while 2507 ± 77 (n = 63) peak pairs were detected from a fecal sample. In total, 5372 unique peak pairs were detected from all the samples combined; 3089 and 3012 pairs were found in urine and feces, respectively. These results reveal that the urine and fecal metabolomes are very different, thereby justifying the consideration of using both biospecimens to increase the probability of finding specific biomarkers of diseases. Furthermore, the CIL LC-MS method described can be used to perform parallel quantitative analysis of urine and feces, resulting in more complete coverage of the human metabolome.     

Exploratory Study Using Urinary Volatile Organic Compounds for the Detection of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) biomarkers are lacking in clinical practice. We therefore explored the pattern and composition of urinary volatile organic compounds (VOCs) in HCC patients. This was done in order to assess the feasibility of a potential non-invasive test for HCC, and to enhance our understanding of the disease. This pilot study recruited 58 participants, of whom 20 were HCC cases and 38 were non-HCC cases. The non-HCC cases included healthy individuals and patients with various stages of non-alcoholic fatty liver disease (NAFLD), including those with and without fibrosis. Urine was analysed using gas chromatography–ion mobility spectrometry (GC–IMS) and gas chromatography–time-of-flight mass spectrometry (GC–TOF-MS). GC–IMS was able to separate HCC from fibrotic cases with an area under the curve (AUC) of 0.97 (0.91–1.00), and from non-fibrotic cases with an AUC of 0.62 (0.48–0.76). For GC-TOF-MS, a subset of samples was analysed in which seven chemicals were identified and tentatively linked with HCC. These include 4-methyl-2,4-bis(p-hydroxyphenyl)pent-1-ene (2TMS derivative), 2-butanone, 2-hexanone, benzene, 1-ethyl-2-methyl-, 3-butene-1,2-diol, 1-(2-furanyl)-, bicyclo(4.1.0)heptane, 3,7,7-trimethyl-, [1S-(1a,3β,6a)]-, and sulpiride. Urinary VOC analysis using both GC–IMS and GC-TOF-MS proved to be a feasible method of identifying HCC cases, and was also able to enhance our understanding of HCC pathogenesis.     

Metabolite profiling analysis of hepatitis B virus–induced liver cirrhosis patients with minimal hepatic encephalopathy using gas chromatography-time-of-flight mass spectrometry and ultra-performance liquid chromatography-quadrupole-time-of-flight mass spectrometry

This study used gas chromatography-time-of-flight mass spectrometry (GC-TOFMS) and ultra-performance liquid chromatography-quadrupole TOFMS (UPLC-QTOFMS) metabonomic analytical techniques in combination with bioinformatics and pattern recognition analysis methods to analyze the serum metabolite profiling of hepatitis B virus (HBV)–induced liver cirrhosis patients with minimal hepatic encephalopathy (MHE), to find the specific biomarkers of MHE, to reveal the pathogenesis of MHE, and to determine a promising approach for early diagnosis of MHE. Serum samples of 100 normal controls (NC group), 29 HBV-induced liver cirrhosis patients with MHE (MHE group), and 24 HBV-induced liver cirrhosis patients without MHE [comprising 12 cases of compensated cirrhosis (CS group) and 12 cases of decompensated cirrhosis (DS group)] were collected and employed into GC-TOFMS and UPLC-QTOFMS platforms for serum metabolite detection; the outcome data were then analyzed using principal component analysis and orthogonal partial least squares-discriminant analysis (OPLS-DA). There were no significant differential metabolites between the NC group and the CS group. A series of key differential metabolites were detected. According to the variable influence in projection values and P-values, 60 small-molecule metabolites were considered to be dysregulated in the MHE group (compared to the NC group); 27 of these 60 dysregulated differential metabolites were considered to be the potential biomarkers (see Table 4, marked in bold); 66 small-molecule metabolites were considered to be dysregulated in the DS group (compared to the NC group); 34 of these 66 dysregulated differential metabolites were considered to be the potential biomarkers (see Table 5, marked in bold). According to the fold-change values, 9 of these 27 metabolites, namely valine, oxalic acid, erythro-sphingosine, 4,7,10,13,16,19-docosahexaenoic acid, isoleucine, allo-isoleucine, thyroxine, rac-octanoyl carnitine, and tocopherol (vitamin E), were downregulated in the MHE group (compared to the NC group); the other 18, namely adenine, glycochenodeoxycholic acid, fucose, allothreonine, glycohyocholic acid, glycoursodeoxycholic acid, tyrosine, taurocheno-deoxycholate, phenylalanine, 2-hydroxy-3-methyl-butanoic acid, hydroxyacetic acid, taurocholate, sorbitol, rhamnose, tauroursodeoxycholate, tolbutamide, pyroglutamic acid, and malic acid, were upregulated; 6 of these 34 metabolites were downregulated in the DS group (compared to the NC group), and the other 28 were upregulated, as shown in Table 5. (a) GC-TOFMS and UPLC-QTOFMS metabonomic analytical platforms can detect a range of metabolites in the serum; this might be of great help to study the pathogenesis of MHE and may provide a new approach for the early diagnosis of MHE. (b) Metabonomics analysis in combination with pattern recognition analysis might have great potential to distinguish the HBV-induced liver cirrhosis patients who have MHE from the normal healthy population and HBV-induced liver cirrhosis patients without MHE.     

Identifying critical states of hepatocellular carcinoma based on landscape dynamic network biomarkers

Hepatocellular carcinoma (HCC) is the major histological form of primary liver cancer. It has usually reached the disease state once the patient is diagnosed since there are no specific symptoms in the early stages of HCC. This fact increases the difficulty of curing HCC. Recently, quantities of evidence have shown that many mathematical methods (such as dynamic network biomarkers, DNB) can be used to detect critical states or tipping points of complex diseases. However, it is difficult to apply the DNB theory to the clinic since multiple samples are generally unavailable for individual patient. This paper constructs a novel method based on landscape dynamic network biomarkers (L-DNB), which aims to detect early warning signals from cirrhosis state to very advanced HCC state in individual patient. The selected dataset contains multiple samples for each HCC state. A score that indicates the disease characteristics is calculated for each sample by RNA-seq data, and several scores constitute a distribution in the same state. Quantifying the statistical characteristics of these distributions and determining that low-grade dysplastic and high-grade dysplastic are the critical states of HCC. These results can provide scientific advice for early warning indicators and optimal treatment time for HCC.     

膀胱癌血清及尿液代谢组学研究

应用代谢组学研究方法,对与膀胱癌(Bladder cancer,BC)发病相关的生物标志物进行筛选,采用液相色谱-电喷雾质谱(LC-ESI/MS)联用技术对20名膀胱癌患者与24名正常人的血清和尿液进行研究.多变量统计分析结果表明,膀胱癌患者和正常人聚类明显,血清和尿液中分别发现13个潜在标志物.其中,(2E,6E,8E)-二十二碳三烯-1-醇、7-((1S,2S)-2-(庚胺)环己基)庚酸和(11E,14E,17E)-三烯-二十碳-1-醇首次在血清中发现,有潜力成为膀胱癌诊断标志物.液相色谱-质谱联用结合多变量分析的代谢组学研究技术在膀胱癌诊断中展现出巨大潜力.     

Association between interleukin 6 promoter variants and chronic hepatitis B progression

Interleukin 6 (IL6) plays an essential role in the regulation of immune response to chronic disease. In this study, the three known single nucleotide polymorphisms (SNPs) in the IL6 promoter region were genotyped in a large chronic hepatitis B cohort to evaluate the effects of IL6 promoter variants. The single base extension method was used for this genotyping. Haplotypes were constructed by the three SNPs in IL6. Allele frequencies were compared for; i) patients with chronic hepatitis (CH) and chronic carriers vs. chronic hepatis patients with clinical evidence of liver cirrhosis (LC) (i.e., portal hypertension), ii) cirrhotic patients with hepatocellular carcinoma (HCC) vs. without HCC by logistic regression, and iii) with respect to the time intervals from the onset of infection to HCC. Results were analyzed by Cox relative hazard analysis on the assumption that all the patients were infected during early infancy. The frequencies of each SNP were 0.002 (IL6-597 G>A), 0.25 (IL6-572 C>G) and 0.002 (IL6-174 G>C), respectively, in the Korean population (n = 1,046). No significant associations were detected between IL6-572 C>G and chronic hepatitis B outcome in this study; i.e., LC occurrence on CH (OR = 0.16-1.27, P = 0.13- 0.71) and HCC occurrence on LC (OR = 1.04-1.23, P = 0.89-0.60) of heterozygotes and homozygotes for G allele in referent comparison to homozygotes for common allele (C/C genotype), and time interval to HCC (RH = 0.67-1.00; P = 0.14-0.99). In conclusion, there appeared to be no significant associations between IL6 promoter variants and disease outcome in chronic hepatitis B.     

Effect of paeonol on antioxidant and immune regulatory activity in hepatocellular carcinoma rats

The study investigated the immunity and antioxidant potential of paeonol by employing a hepatocellular carcinoma (HCC) rat model. Three doses of paeonol (20, 40, 60 mg/kg b.w. orally) were administrated to diethylnitrosamine (DEN)-induced HCC rats. Results showed that paeonol significantly reduced the serum AST, ALT, ALP, GGT, AFU and liver MDA levels, increased serum WBC, TP, ALB, A/G, TNF-α and IFN-γ and liver antioxidant enzymes activities (SOD, CAT, GSH-Px, GR) in HCC rats. Altogether, these results suggest that the paeonol could effectively decrease oxidative injury and improve immunity function in HCC rats.     

A serum metabolomic investigation on hepatocellular carcinoma patients by chemical derivatization followed by gas chromatography/mass spectrometry

The purpose of this study was to investigate the serum metabolic difference between hepatocellular carcinoma (HCC, n = 20) male patients and normal male subjects (n = 20). Serum metabolome was detected through chemical derivatization followed by gas chromatography/mass spectrometry (GC/MS). The acquired GC/MS data was analyzed by stepwise discriminant analysis (SDA) and support vector machine (SVM). The metabolites including butanoic acid, ethanimidic acid, glycerol, L-isoleucine, L-valine, aminomalonic acid, D-erythrose, hexadecanoic acid, octadecanoic acid, and 9,12-octadecadienoic acid in combination with each other gave the strongest segregation between the two groups. By applying these variables, our method provided a diagnostic model that could well discriminate between HCC patients and normal subjects. More importantly, the error count estimate for each group was 0%. The total classifying accuracy of the discriminant function tested by SVM 20-fold cross validation was 75%. This technique is different from traditional ones and appears to be a useful tool in the area of HCC diagnosis. Copyright (c) 2008 John Wiley & Sons, Ltd.