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.

Cell membrane chromatography (CMC) derived from pathological tissues is ideal for screening specific components acting on specific diseases from complex medicines owing to the maximum simulation of in vivo drug-receptor interactions. However, there are no pathological tissue-derived CMC models that have ever been developed, as well as no visualized affinity comparison of potential active components between normal and pathological CMC columns. In this study, a novel comparative normal/failing rat myocardium CMC analysis system based on online column selection and comprehensive two-dimensional (2D) chromatography/monolithic column/time-of-flight mass spectrometry was developed for parallel comparison of the chromatographic behaviors on both normal and pathological CMC columns, as well as rapid screening of the specific therapeutic agents that counteract doxorubicin (DOX)-induced heart failure from Acontium carmichaeli (Fuzi). In total, 16 potential active alkaloid components with similar structures in Fuzi were retained on both normal and failing myocardium CMC models. Most of them had obvious decreases of affinities on failing myocardium CMC compared with normal CMC model except for four components, talatizamine (TALA), 14-acetyl-TALA, hetisine, and 14-benzoylneoline. One compound TALA with the highest affinity was isolated for further in vitro pharmacodynamic validation and target identification to validate the screen results. Voltage-dependent K+ channel was confirmed as a binding target of TALA and 14-acetyl-TALA with high affinities. The online high throughput comparative CMC analysis method is suitable for screening specific active components from herbal medicines by increasing the specificity of screened results and can also be applied to other biological chromatography models.

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