Metabonomic analysis of hepatitis B virus-induced liver failure: identification of potential diagnostic biomarkers by fuzzy support vector machine

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Abstract

Hepatitis B virus (HBV)-induced liver failure is an emergent liver disease leading to high mortality. The severity of liver failure may be reflected by the profile of some metabolites. This study assessed the potential of using metabolites as biomarkers for liver failure by identifying metabolites with good discriminative performance for its phenotype. The serum samples from 24 HBV-induced liver failure patients and 23 healthy volunteers were collected and analyzed by gas chromatography-mass spectrometry (GC-MS) to generate metabolite profiles. The 24 patients were further grouped into two classes according to the severity of liver failure. Twenty-five commensal peaks in all metabolite profiles were extracted, and the relative area values of these peaks were used as features for each sample. Three algorithms, F-test, k-nearest neighbor (KNN) and fuzzy support vector machine (FSVM) combined with exhaustive search (ES), were employed to identify a subset of metabolites (biomarkers) that best predict liver failure. Based on the achieved experimental dataset, 93.62% predictive accuracy by 6 features was selected with FSVM-ES and three key metabolites, glyceric acid, cis-aconitic acid and citric acid, are identified as potential diagnostic biomarkers.

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