Hepatocellular carcinoma (HCC) is one of the most common malignant tumors in the world. However, current biomarkers that discriminate HCC from liver cirrhosis (LC) are important but are limited. More reliable biomarkers for HCC diagnosis are therefore needed. Serum from HCC patients, LC patients and healthy volunteers were analyzed using NMR and LC/MS-based approach in conjunction with random forest (RF) analysis to discriminate their serum metabolic profiles. Thirty-two potential biomarkers have been identified, and the feasibility of using these biomarkers for the diagnosis of HCC was evaluated, where 100% sensitivity was achieved in detecting HCC patients even with AFP values lower than 20 ng/mL. The metabolic alterations induced by HCC showed perturbations in synthesis of ketone bodies, citrate cycle, phospholipid metabolism, sphingolipid metabolism, fatty acid oxidation, amino acid catabolism and bile acid metabolism in HCC patients. Our results suggested that these potential biomarkers identified appeared to have diagnostic and/or prognostic values for HCC, which deserve to be further investigated. In addition, it also suggested that RF is a classification algorithm well suited for selection of biologically relevant features in metabolomics.Hepatocellular carcinoma (HCC) is the most common intraabdominal malignancies and the third leading cause of cancer-related death worldwide, with a <7% 5-year survival rate.

Cognitive impairment commonly accompanies clinical syndromes associated with stroke. The identification of laboratory markers of post-stroke cognitive impairment (PSCI) may help detect patients at increased risk of cognitive deterioration and determine the appropriate treatment regimes. A non-targeted metabolomics approach based on ultra-high performance liquid chromatography coupled with Q-TOF mass spectrometry was applied to study PSCI. The stroke patients were significantly distinguishable from the healthy subjects. Stroke patients could be well-stratified based on cognitive impairment. Several differential serum metabolites were further identified for post-stroke non-cognitive impairment (PSNCI) and PSCI patients, suggesting metabolic dysfunction in inflammation, neurotoxicity, bioenergetic homeostasis, oxidative stress, and apoptosis. In total, three serum metabolites (glutamine, kynurenine, and LysoPC(18:2)) were identified as candidate diagnostic biomarkers for PSCI, and their combined use yielded good diagnostic capacity for PSCI by receiver operating characteristic curves. The present metabolomics study provided a novel strategy for stratifying stroke patients with cognitive impairment using serum-based metabolite markers, which could be of great importance in understanding the pathological mechanisms and determining the appropriate treatment regimes of PSCI patients.

BackgroundDoxorubicin (DOX) is one of the most potent antitumor agents available; however, its clinical use is limited because of the risk of severe cardiotoxicity. Though numerous studies have ascribed DOX cardiomyopathy to specific cellular pathways, the precise mechanism remains obscure. Sini decoction (SND) is a well-known formula of Traditional Chinese Medicine (TCM) and is considered as efficient agents against DOX-induced cardiomyopathy. However, its action mechanisms are not well known due to its complex components.Methodology/Principal FindingsA tissue-targeted metabonomic method using gas chromatography–mass spectrometry was developed to characterize the metabolic profile of DOX-induced cardiomyopathy in mice. With Elastic Net for classification and selection of biomarkers, twenty-four metabolites corresponding to DOX-induced cardiomyopathy were screened out, primarily involving glycolysis, lipid metabolism, citrate cycle, and some amino acids metabolism. With these altered metabolic pathways as possible drug targets, we systematically analyzed the protective effect of TCM SND, which showed that SND administration could provide satisfactory effect on DOX-induced cardiomyopathy through partially regulating the perturbed metabolic pathways.Conclusions/SignificanceThe results of the present study not only gave rise to a systematic view of the development of DOX-induced cardiomyopathy but also provided the theoretical basis to prevent or modify expected damage.

BackgroundIn spite of great advances in target-oriented Western medicine for treating myocardial infarction (MI), it is still a leading cause of death in a worldwide epidemic. In contrast to Western medicine, Traditional Chinese medicine (TCM) uses a holistic and synergistic approach to restore the balance of Yin-Yang of body energy so the body's normal function can be restored. Sini decoction (SND) is a well-known formula of TCM which has been used to treat MI for many years. However, its holistic activity evaluation and mechanistic understanding are still lacking due to its complex components.Methodology/Principal FindingsA urinary metabonomic method based on nuclear magnetic resonance and ultra high-performance liquid chromatography coupled to mass spectrometry was developed to characterize MI-related metabolic profiles and delineate the effect of SND on MI. With Elastic Net for classification and selection of biomarkers, nineteen potential biomarkers in rat urine were screened out, primarily related to myocardial energy metabolism, including the glycolysis, citrate cycle, amino acid metabolism, purine metabolism and pyrimidine metabolism. With the altered metabolism pathways as possible drug targets, we systematically analyze the therapeutic effect of SND, which demonstrated that SND administration could provide satisfactory effect on MI through partially regulating the perturbed myocardial energy metabolism.Conclusions/SignificanceOur results showed that metabonomic approach offers a useful tool to identify MI-related biomarkers and provides a new methodological cue for systematically dissecting the underlying efficacies and mechanisms of TCM in treating MI.

An approach for the identification of multiple components in traditional Chinese medicine injections (TCMIs) using a combination of hydrophilic interaction chromatography (HILIC) and reversed-phase liquid chromatography (RPLC) coupled with time-of-flight mass spectrometry (TOFMS) was developed for the quality control of Guanxinning injection (GXNI), a widely used TCMI, composed of Salvia miltiorrhiza and Ligusticum Chuanxiong. A total of 50 compounds from five compound classes, including saccharides, amino acids, organic acids, phenolic acids and phthalides, were identified or tentatively characterized on the basis of accurate mass measurements and subsequent TOFMS product ions. Six groups of isomers of phenolic acids and saccharides were tentatively distinguished. It was observed that the ESI-TOFMS fragmentation behavior of phthalides was different in negative and positive ion mode, and the fragmentation pathways were tentatively elucidated using structurally-relevant product ions. Several highly polar constituents were characterized for the first time from GXNI by HILIC/TOFMS. In addition, all the constituents identified from GXNI were further assigned in the two individual crude drugs. The integrated strategy has provided a powerful approach for the separation and identification of the multiple components in GXNI, and it has also assisted in the establishment of methods for the comprehensive safety and quality evaluation of TCMIs.

BackgroundHypothyroidism is a chronic condition of endocrine disorder and its precise molecular mechanism remains obscure. In spite of certain efficacy of thyroid hormone replacement therapy in treating hypothyroidism, it often results in other side effects because of its over-replacement, so it is still urgent to discover new modes of treatment for hypothyroidism. Sini decoction (SND) is a well-known formula of Traditional Chinese Medicine (TCM) and is considered as efficient agents against hypothyroidism. However, its holistic effect assessment and mechanistic understanding are still lacking due to its complex components.Methodology/Principal FindingsA urinary metabonomic method based on ultra performance liquid chromatography coupled to mass spectrometry was employed to explore global metabolic characters of hypothyroidism. Three typical hypothyroidism models (methimazole-, propylthiouracil- and thyroidectomy-induced hypothyroidism) were applied to elucidate the molecular mechanism of hypothyroidism. 17, 21, 19 potential biomarkers were identified with these three hypothyroidism models respectively, primarily involved in energy metabolism, amino acid metabolism, sphingolipid metabolism and purine metabolism. In order to avert the interference of drug interaction between the antithyroid drugs and SND, the thyroidectomy-induced hypothyroidism model was further used to systematically assess the therapeutic efficacy of SND on hypothyroidism. A time-dependent recovery tendency was observed in SND-treated group from the beginning of model to the end of treatment, suggesting that SND exerted a recovery effect on hypothyroidism in a time-dependent manner through partially regulating the perturbed metabolic pathways.Conclusions/SignificanceOur results showed that the metabonomic approach is instrumental to understand the pathophysiology of hypothyroidism and offers a valuable tool for systematically studying the therapeutic effects of SND on hypothyroidism.

An ultra performance liquid chromatography coupled to mass spectrometry-based metabonomic approach, which utilizes both reversed-performance (RP) chromatography and hydrophilic interaction chromatography (HILIC) separations, has been developed to characterize the global serum metabolic profile associated with myocardial infarction (MI). The HILIC was found necessary for a comprehensive serum metabonomic profiling, providing complementary information to RP chromatography. By combining with partial least squares discriminant analysis, 21 potential biomarkers in rat serum were identified. To further elucidate the pathophysiology of MI, related metabolic pathways have been studied. It was found that MI was closely related to disturbed sphingolipid metabolism, phospholipid catabolism, fatty acid transportation and metabolism, tryptophan metabolism, branched-chain amino acids metabolism, phenylalanine metabolism, and arginine and proline metabolism. With the presented metabonomic method, we systematically analyzed the therapeutic effects of Traditional Chinese Medicine Sini decoction (SND). The results demonstrated that SND administration could provide satisfactory effects on MI through partially regulating the perturbed metabolic pathways.

To address the shortcomings of cystoscopy and urine cytology for detecting and grading bladder cancer (BC), ultrahigh performance liquid chromatography (UHPLC) coupled with Q-TOF mass spectrometry in conjunction with univariate and multivariate statistical analyses was employed as an alternative method for the diagnosis of BC. A series of differential serum metabolites were further identified for low-grade(LG) and high-grade(HG) BC patients, suggesting metabolic dysfunction in malignant proliferation, immune escape, differentiation, apoptosis and invasion of cancer cells in BC patients. In total, three serum metabolites including inosine, acetyl-N-formyl-5-methoxykynurenamine and PS(O-18:0/0:0) were selected by binary logistic regression analysis, and receiver operating characteristic (ROC) test based on their combined use for HG BC showed that the area under the curve (AUC) was 0.961 in the discovery set and 0.950 in the validation set when compared to LG BC. Likewise, this composite biomarker panel can also differentiate LG BC from healthy controls with the AUC of 0.993 and 0.991 in the discovery and validation set, respectively. This finding suggested that this composite serum metabolite signature was a promising and less invasive classifier for probing and grading BC, which deserved to be further investigated in larger samples.