A LC-MS based method, which utilizes both reversed-performance (RP) chromatography and hydrophilic interaction chromatography (HILIC) separations, has been carried out in conjunction with multivariate data analysis to discriminate the global serum profiles of renal cell carcinoma (RCC) patients and healthy controls. The HILIC was found necessary for a comprehensive serum metabonomic profiling as well as RP separation. The feasibility of using serum metabonomics for the diagnosis and staging of RCC has been evaluated. One-hundred percent sensitivity in detection has been achieved, and a satisfactory clustering between the early stage and advanced-stage patients is observed. The results suggest that the combination of LC-MS analysis with multivariate statistical analysis can be used for RCC diagnosis and has potential in the staging of RCC. The MS/MS experiments have been carried out to identify the biomarker patterns that made great contribution to the discrimination. As a result, 30 potential biomarkers for RCC are identified. It is possible that the current biomarker patterns are not unique to RCC but just the result of any malignancy disease. To further elucidate the pathophysiology of RCC, related metabolic pathways have been studied. RCC is found to be closely related to disturbed phospholipid catabolism, sphingolipid metabolism, phenylalanine metabolism, tryptophan metabolism, fatty acid beta-oxidation, cholesterol metabolism, and arachidonic acid metabolism.

The purpose of this study was to use metabonomic profiling to identify a potential specific biomarker pattern in urine as a noninvasive bladder cancer (BC) detection strategy. A liquid chromatography-mass spectrometry based method, which utilized both reversed phase liquid chromatography and hydrophilic interaction chromatography separations, was performed, followed by multivariate data analysis to discriminate the global urine profiles of 27 BC patients and 32 healthy controls. Data from both columns were combined, and this combination proved to be effective and reliable for partial least squares-discriminant analysis. Following a critical selection criterion, several metabolites showing significant differences in expression levels were detected. Receiver operating characteristic analysis was used for the evaluation of potential biomarkers. Carnitine C9:1 and component I, were combined as a biomarker pattern, with a sensitivity and specificity up to 92.6% and 96.9%, respectively, for all patients and 90.5% and 96.9%, respectively for lowgrade BC patients. Metabolic pathways of component I and carnitine C9:1 are discussed. These results indicate that metabonomics is a practicable tool for BC diagnosis given its high efficacy and economization. The combined biomarker pattern showed better performance than single metabolite in discriminating bladder cancer

Bladder cancer (BC) and kidney cancer (KC) are the first two commonly occurring genitourinary cancers in China. In this study, a comprehensive LC-MS-based method, which utilizes both reversed phase liquid chromatography (RPLC) and hydrophilic interaction chromatography (HILIC) separations, has been carried out in conjunction with multivariate data analysis to discriminate the global serum profiles of BC, KC, and noncancer controls. An independent test set consisting of different patients has been used to objectively evaluate the predictive ability of the analysis platform. Excellent sensitivity and specificity have been achieved in detection of KC and BC. The results suggest that serum metabolic profiling could be used for different types of genitourinary cancer diagnosis. Furthermore, cancer type-specific biomarkers were found through a critical selection criterion. As a result, eicosatrienol, azaprostanoic acid, docosatrienol, retinol, and 14'-apo-beta-carotenal  were found as specific biomarkers for BC; and PE(P-16:0e/0:0), glycerophosphorylcholine, ganglioside GM3 (d18:1/22:1), C17 sphinganine, and SM(d18:0/16:1(9Z)) were found as specific biomarkers for KC. Receiver operating characteristic (ROC) analysis was used for the preliminary evaluation of the biomarkers. These biomarkers have great potential to be used in the clinical diagnosis after further rigorous assessment.

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strategy provides methodological reference for interpreting the optimization of core components group and inference of molecular mechanism of formula in the treatment of complex diseases in TCM.

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Complex disease is a cascade process which is associated with functional abnormalities in multiple proteins and protein-protein interaction (PPI) networks. One drug one target has not been able to perfectly intervene complex diseases. Increasing evidences show that Chinese herb formula usually treats complex diseases in the form of multi-components and multi-targets. The key step to elucidate the underlying mechanism of formula in traditional Chinese medicine (TCM) is to optimize and capture the important components in the formula. At present, there are several formula optimization models based on network pharmacology has been proposed. Most of these models focus on the 2D/3D similarity of chemical structure of drug components and ignore the functional optimization space based on relationship between pathogenetic genes and drug targets. How to select the key group of effective components (KGEC) from the formula of TCM based on the optimal space which link pathogenic genes and drug targets is a bottleneck problem in network pharmacology. To address this issue, we designed a novel network pharmacological model, which takes Lang Chuang Wan (LCW) treatment of systemic lupus erythematosus (SLE) as the case. We used the weighted gene regulatory network and active components targets network to construct disease-targets-components network, after filtering through the network attribute degree, the optimization space and effective proteins were obtained. And then the KGEC was selected by using contribution index (CI) model based on knapsack algorithm. The results show that the enriched pathways of effective proteins we selected can cover 96% of the pathogenetic genes enriched pathways. After reverse analysis of effective proteins and optimization with CI index model, KGEC with 82 components were obtained, and 105 enriched pathways of KGEC targets were consistent with enriched pathways of pathogenic genes (80.15%). Finally, the key components in KGEC of LCW were evaluated by in vitro experiments. These results indicate that the proposed model with good accuracy in screening the KGEC in the formula of TCM, which provides reference for the optimization and mechanism analysis of the formula in TCM.