GC-MS-based metabolomics approach to diagnose depression in hepatitis B virus-infected patients with middle or old age.
ABSTRACT: Depression is concomitantly presented in hepatitis B virus (HBV)-infected patients (HB). However, there is still no objective method to diagnose HBV-infected patients with depression (dHB). Therefore, in this study, a gas chromatography-mass spectrometry (GC-MS)-based metabolomic approach was employed to profile urine samples from 59 dHB and 52 HB (the training set) in order to identify urinary metabolite biomarkers for dHB. Then, 41 dHB and 35 HB (the testing set) were used to independently validate the diagnostic generalizability of these biomarkers. In total, 13 differential metabolites responsible for the discrimination between dHB and HB were identified. These differential urinary metabolites belonged mainly to Lipid metabolism and Amino acid metabolism. A panel consisting of six urinary metabolite biomarkers (ethanolamine, azelaic acid, histidine, threitol, 2,4-dihydroxypyrimidine and levulinic acid) was identified. This panel was capable of distinguishing dHB from HB with an area under the receiver operating characteristic curve (AUC) of 0.986 in the training set. Moreover, this panel could classify blinded samples from the testing set with an AUC of 0.933. These findings indicated that the GC-MS-based metabolomics approach could be a useful tool in the clinical diagnosis of dHB, and the identified biomarkers were helpful for future developing an objective diagnostic method for dHB.
Project description:ObjectiveDepression could make the treatment outcome worse. However, up to now, no objective methods were developed to diagnose depression in hepatitis B virus (HBV)-infected patients. Therefore, the dual metabolomic platforms were used here to identify potential biomarkers for diagnosing HBV-infected patients with depression (dHB).MethodsBoth gas chromatography-mass spectrometry-based and nuclear magnetic resonance-based metabolomic platforms were used to conduct urine metabolic profiling of dHB subjects and HBV-infected patients without depression (HB). Orthogonal partial least-squares discriminant analysis was used to identify the differential metabolites between dHB subjects and HB subjects, and the step-wise logistic regression analysis was used to identify potential biomarkers.ResultsIn total, 21 important metabolites responsible for distinguishing dHB subjects from HB subjects were identified. Meanwhile, seven potential biomarkers (?-ydroxyisobutyric acid, hippuric acid, azelaic acid, ?isobutyric acid, malonic acid, levulinic acid, and ?phenylacetylglycine) were viewed as potential biomarkers. The simplified biomarker panel consisting of these seven metabolites had an excellent diagnostic performance in discriminating dHB subjects from HB subjects. Moreover, this panel could yield a higher accuracy in separating dHB subjects from HB subjects than our previous panels (identified by single metabolomic platform) did.ConclusionThese results suggested that the dual metabolomic platforms could yield a better urinary biomarker panel for dHB subjects than any single metabolomic platform did, and our results could be helpful for developing an objective method in future to diagnose depression in HBV-infected patients.
Project description:Major depressive disorder (MDD) is a widespread and debilitating mental disorder. However, there are no biomarkers available to aid in the diagnosis of this disorder. In this study, a nuclear magnetic resonance spectroscopy-based metabonomic approach was employed to profile urine samples from 82 first-episode drug-naïve depressed subjects and 82 healthy controls (the training set) in order to identify urinary metabolite biomarkers for MDD. Then, 44 unselected depressed subjects and 52 healthy controls (the test set) were used to independently validate the diagnostic generalizability of these biomarkers. A panel of five urinary metabolite biomarkers-malonate, formate, N-methylnicotinamide, m-hydroxyphenylacetate, and alanine-was identified. This panel was capable of distinguishing depressed subjects from healthy controls with an area under the receiver operating characteristic curve (AUC) of 0.81 in the training set. Moreover, this panel could classify blinded samples from the test set with an AUC of 0.89. These findings demonstrate that this urinary metabolite biomarker panel can aid in the future development of a urine-based diagnostic test for MDD.
Project description:Circulating microRNAs (miRNA) are biomarkers for several neoplastic diseases, including hepatocellular carcinoma (HCC). We performed a literature search, followed by experimental screening and validation in order to establish a miRNA panel in combination with the assessment of alpha-fetoprotein (AFP) levels and to evaluate its performance in HCC diagnostics.Expression of miRNAs was quantified by quantitative PCR (qPCR) in 406 serum samples from 118 Vietnamese patients with hepatitis B (HBV)-related HCC, 69 patients with HBV-related liver cirrhosis (LC), 100 chronic hepatitis B (CHB) patients and 119 healthy controls (HC).Three miRNAs (mir-21, mir-122, mir-192) were expressed differentially among the studied subgroups and positively correlated with AFP levels. The individual miRNAs mir-21, mir-122, mir192 or the triplex miRNA panel showed high diagnostic accuracy for HCC (HCC vs. CHB, AUC = 0.906; HCC vs. CHB+LC, AUC = 0.81; HCC vs. CHB+LC+HC, AUC = 0.854). When AFP levels were ?20ng/ml, the triplex miRNA panel still was accurate in distinguishing HCC from the other conditions (CHB, AUC = 0.922; CHB+LC, AUC = 0.836; CHB+LC+HC, AUC = 0.862). When AFP levels were used in combination with the triplex miRNA panel, the diagnostic performance was significantly improved in discriminating HCC from the other groups (LC, AUC = 0.887; CHB, AUC = 0.948; CHB+LC, AUC = 0.887).The three miRNAs mir-21, mir-122, mir-192, together with AFP, are biomarkers that may be applied to improve diagnostics of HCC in HBV patients, especially in HBV-related LC patients with normal AFP levels or HCC patients with small tumor sizes.
Project description:Telomerase reverse-transcriptase (TERT) gene promoter mutations in circulating cell-free DNA (cfDNA) as well as the levels of circulating microRNA-122 (miR-122) have been reported as potential noninvasive biomarkers for several. This study evaluates the diagnostic performance of potent biomarker-based panels composing of serological AFP, miR-122 and circulating TERT promoter mutations for screening HBV-related HCC. TERT promoter mutations (C228T and C250T) and miR-122 expression were assessed in the plasma samples from 249 patients with HBV-related liver diseases by nested PCR and qRT-PCR assays, respectively. The diagnostic values of TERT promoter mutations, miR-122 expression and biomarker-based panels were assessed by computation of the area under the curve (AUC). Nested-PCR assays were optimized to detect C228T and C250T mutations in TERT promoter with detection limit of 1%. The common hotspot C228T was observed in 22 HCC cases. The triple combinatory panel (AFP@TERT@miR-122) acquired the best diagnostic value to distinguish HCC from CHB (AUC?=?0.98), LC (AUC?=?0.88) or non-HCC (LC?+?CHB, AUC?=?0.94) compared to the performance of double combinations or single biomarkers, respectively. Notably, among patients with AFP levels?20?ng/?l, the double combination panel (TERT@miR-122) retains satisfactory diagnostic performance in discriminating HCC from the others (HCC vs. CHB, AUC?=?0.96; HCC vs. LC, AUC?=?0.88, HCC vs. non-HCC, AUC?=?0.94). The triple combination panel AFP@TERT@miR-122 shows a better diagnostic performance for screening HCC in HBV patients, regardless of AFP levels. The newly established panels can be a potential application in clinical practice in Vietnamese setting.
Project description:The human urinary proteome provides an assessment of kidney injury with specific biomarkers for different kidney injury phenotypes. In an effort to fully map and decipher changes in the urine proteome and peptidome after kidney transplantation, renal allograft biopsy matched urine samples were collected from 396 kidney transplant recipients. Centralized and blinded histology data from paired graft biopsies was used to classify urine samples into diagnostic categories of acute rejection, chronic allograft nephropathy, BK virus nephritis, and stable graft. A total of 245 urine samples were analyzed by liquid chromatography-mass spectrometry using isobaric Tags for Relative and Absolute Quantitation (iTRAQ) reagents. From a group of over 900 proteins identified in transplant injury, a set of 131 peptides were assessed by selected reaction monitoring for their significance in accurately segregating organ injury causation and pathology in an independent cohort of 151 urine samples. Ultimately, a minimal set of 35 proteins were identified for their ability to segregate the 3 major transplant injury clinical groups, comprising the final panel of 11 urinary peptides for acute rejection (93% area under the curve [AUC]), 12 urinary peptides for chronic allograft nephropathy (99% AUC), and 12 urinary peptides for BK virus nephritis (83% AUC). Thus, urinary proteome discovery and targeted validation can identify urine protein panels for rapid and noninvasive differentiation of different causes of kidney transplant injury, without the requirement of an invasive biopsy.
Project description:This study was designed to analyze urinary proteins associated with ovarian cancer (OC) and investigate the potential urinary biomarker panel to predict malignancy in women with pelvic masses. We analyzed 23 biomarkers in urine samples obtained from 295 patients with pelvic masses scheduled for surgery. The concentration of urinary biomarkers was quantitatively assessed by the xMAP bead-based multiplexed immunoassay. To identify the performance of each biomarker in predicting cancer over benign tumors, we used a repeated leave-group-out cross-validation strategy. The prediction models using multimarkers were evaluated to develop a urinary ovarian cancer panel. After the exclusion of 12 borderline tumors, the urinary concentration of 17 biomarkers exhibited significant differences between 158 OCs and 125 benign tumors. Human epididymis protein 4 (HE4), vascular cell adhesion molecule (VCAM), and transthyretin (TTR) were the top three biomarkers representing a higher concentration in OC. HE4 demonstrated the highest performance in all samples withOC(mean area under the receiver operating characteristic curve (AUC) 0.822, 95% CI: 0.772-0.869), whereas TTR showed the highest efficacy in early-stage OC (AUC 0.789, 95% CI: 0.714-0.856). Overall, HE4 was the most informative biomarker, followed by creatinine, carcinoembryonic antigen (CEA), neural cell adhesion molecule (NCAM), and TTR using the least absolute shrinkage and selection operator (LASSO) regression models. A multimarker panel consisting of HE4, creatinine, CEA, and TTR presented the best performance with 93.7% sensitivity (SN) at 70.6% specificity (SP) to predict OC over the benign tumor. This panel performed well regardless of disease status and demonstrated an improved performance by including menopausal status. In conclusion, the urinary biomarker panel with HE4, creatinine, CEA, and TTR provided promising efficacy in predicting OC over benign tumors in women with pelvic masses. It was also a non-invasive and easily available diagnostic tool.
Project description:Bipolar disorder (BD) is a debilitating mental disorder that cannot be diagnosed by objective laboratory-based modalities. Our previous studies have independently used nuclear magnetic resonance (NMR)-based and gas chromatography-mass spectrometry (GC-MS)-based metabonomic methods to characterize the urinary metabolic profiles of BD subjects and healthy controls (HC). However, the combined application of NMR spectroscopy and GC-MS may identify a more comprehensive metabolite panel than any single metabonomic platform alone. Therefore, here we applied a dual platform (NMR spectroscopy and GC-MS) that generated a panel of five metabolite biomarkers for BD-four GC-MS-derived metabolites and one NMR-derived metabolite. This composite biomarker panel could effectively discriminate BD subjects from HC, achieving an area under receiver operating characteristic curve (AUC) values of 0.974 in a training set and 0.964 in a test set. Moreover, the diagnostic performance of this panel was significantly superior to the previous single platform-derived metabolite panels. Thus, the urinary biomarker panel identified here shows promise as an effective diagnostic tool for BD. These findings also demonstrate the complementary nature of NMR spectroscopy and GC-MS for metabonomic analysis, suggesting that the combination of NMR spectroscopy and GC-MS can identify a more comprehensive metabolite panel than applying each platform in isolation.
Project description:N(6)-(2-Hydroxy-3-buten-1-yl)-2'-deoxyadenosine (N(6)-HB-dA I) and N(6),N(6)-(2,3-dihydroxybutan-1,4-diyl)-2'-deoxyadenosine (N(6),N(6)-DHB-dA) are exocyclic DNA adducts formed upon alkylation of the N(6) position of adenine in DNA by epoxide metabolites of 1,3-butadiene (BD), a common industrial and environmental chemical classified as a human and animal carcinogen. Since the N(6)-H atom of adenine is required for Watson-Crick hydrogen bonding with thymine, N(6)-alkylation can prevent adenine from normal pairing with thymine, potentially compromising the accuracy of DNA replication. To evaluate the ability of BD-derived N(6)-alkyladenine lesions to induce mutations, synthetic oligodeoxynucleotides containing site-specific (S)-N(6)-HB-dA I and (R,R)-N(6),N(6)-DHB-dA adducts were subjected to in vitro translesion synthesis in the presence of human DNA polymerases ?, ?, ?, and ?. While (S)-N(6)-HB-dA I was readily bypassed by all four enzymes, only polymerases ? and ? were able to carry out DNA synthesis past (R,R)-N(6),N(6)-DHB-dA. Steady-state kinetic analyses indicated that all four DNA polymerases preferentially incorporated the correct base (T) opposite (S)-N(6)-HB-dA I. In contrast, hPol ? was completely blocked by (R,R)-N(6),N(6)-DHB-dA, while hPol ? and ? inserted A, G, C, or T opposite the adduct with similar frequency. HPLC-ESI-MS/MS analysis of primer extension products confirmed that while translesion synthesis past (S)-N(6)-HB-dA I was mostly error-free, replication of DNA containing (R,R)-N(6),N(6)-DHB-dA induced significant numbers of A, C, and G insertions and small deletions. These results indicate that singly substituted (S)-N(6)-HB-dA I lesions are not miscoding, but that exocyclic (R,R)-N(6),N(6)-DHB-dA adducts are strongly mispairing, probably due to their inability to form stable Watson-Crick pairs with dT.
Project description:Conventional markers of juvenile-onset systemic lupus erythematosus (JSLE) disease activity fail to adequately identify lupus nephritis (LN). While individual novel urine biomarkers are good at detecting LN flares, biomarker panels may improve diagnostic accuracy. The aim of this study was to assess the performance of a biomarker panel to identify active LN in two international JSLE cohorts.Novel urinary biomarkers, namely vascular cell adhesion molecule-1 (VCAM-1), monocyte chemoattractant protein 1 (MCP-1), lipocalin-like prostaglandin D synthase (LPGDS), transferrin (TF), ceruloplasmin, alpha-1-acid glycoprotein (AGP) and neutrophil gelatinase-associated lipocalin (NGAL), were quantified in a cross-sectional study that included participants of the UK JSLE Cohort Study (Cohort 1) and validated within the Einstein Lupus Cohort (Cohort 2). Binary logistic regression modelling and receiver operating characteristic curve analysis [area under the curve (AUC)] were used to identify and assess combinations of biomarkers for diagnostic accuracy.A total of 91 JSLE patients were recruited across both cohorts, of whom 31 (34 %) had active LN and 60 (66 %) had no LN. Urinary AGP, ceruloplasmin, VCAM-1, MCP-1 and LPGDS levels were significantly higher in those patients with active LN than in non-LN patients [all corrected p values (p c) <?0.05] across both cohorts. Urinary TF also differed between patient groups in Cohort 2 (p c?=?0.001). Within Cohort 1, the optimal biomarker panel included AGP, ceruloplasmin, LPGDS and TF (AUC 0.920 for active LN identification). These results were validated in Cohort 2, with the same markers resulting in the optimal urine biomarker panel (AUC 0.991).In two international JSLE cohorts, urinary AGP, ceruloplasmin, LPGDS and TF demonstrate an 'excellent' ability for accurately identifying active LN in children.
Project description:Recently, expression signatures of exosomal long non-coding RNAs (lncRNAs) have been proposed as potential non-invasive biomarkers for cancer detection. In this study, we aimed to develop a urinary exosome (UE)-derived lncRNA panel for diagnosis and recurrence prediction of bladder cancer (BC). Quantitative real-time polymerase chain reaction (qRT-PCR) was performed to screen and evaluate the expressions of eight candidate lncRNAs in a training set (208 urine samples) and a validation set (160 urine samples). A panel consisting of three differently expressed lncRNAs (MALAT1, PCAT-1 and SPRY4-IT1) was established for BC diagnosis in the training set, showing an area under the receiver-operating characteristic (ROC) curve (AUC) of 0.854. Subsequently, the performance of the panel was further verified with an AUC of 0.813 in the validation set, which was significantly higher than that of urine cytology (0.619). In addition, Kaplan-Meier analysis suggested that the up-regulation of PCAT-1 and MALAT1 was associated with poor recurrence-free survival (RFS) of non-muscle-invasive BC (NMIBC) (p?<?0.001 and p?=?0.002, respectively), and multivariate Cox proportional hazards regression analysis revealed that exosomal PCAT-1 overexpression was an independent prognostic factor for the RFS of NMIBC (p?=?0.018). Collectively, our findings indicated that UE-derived lncRNAs possessed considerable clinical value in the diagnosis and prognosis of BC.