Hepatocellular carcinoma (HCC) is the third most lethal cancer worldwide. The lack of effective biomarkers for the early detection of HCC results in unsatisfactory curative treatments. Here, metabolite biomarkers were identified and validated for HCC diagnosis. A total of 1,448 subjects, including healthy controls and patients with chronic hepatitis B virus infection, liver cirrhosis, and HCC, were recruited from multiple centers in China. Liquid chromatography–mass spectrometry–based metabolomics methods were used to characterize the subjects' serum metabolic profiles and to screen and validate the HCC biomarkers. A serum metabolite biomarker panel including phenylalanyl‐tryptophan and glycocholate was defined. This panel had a higher diagnostic performance than did α‐fetoprotein (AFP) in differentiating HCC from a high‐risk population of cirrhosis, such as an area under the receiver‐operating characteristic curve of 0.930, 0.892, and 0.807 for the panel versus 0.657, 0.725, and 0.650 for AFP in the discovery set, test set, and cohort 1 of the validation set, respectively. In the nested case–control study, this panel had high sensitivity (range 80.0%‐70.3%) to detect preclinical HCC, and its combination with AFP provided better risk prediction of preclinical HCC before clinical diagnosis. Besides, this panel showed a larger area under the receiver‐operating characteristic curve than did AFP (0.866 versus 0.682) to distinguish small HCC, and 80.6% of the AFP false‐negative patients with HCC were correctly diagnosed using this panel in the test set, which was corroborated by the validation set. The specificity and biological relevance of the identified biomarkers were further evaluated using sera from another two cancers and HCC tissue specimens, respectively. Conclusion: The discovered and validated serum metabolite biomarker panel exhibits good diagnostic performance for the early detection of HCC from at‐risk populations. (Hepatology 2018;67:662‐675).

Background: Currently available prognostic tools and focused therapeutic methods result in unsatisfactory treatment of gastric cancer (GC). A deeper understanding of human epidermal growth factor receptor 2 (HER2)-coexpressed metabolic pathways may offer novel insights into tumour-intrinsic precision medicine.Methods: The integrated multi-omics strategies (including transcriptomics, proteomics and metabolomics) were applied to develop a novel metabolic classifier for gastric cancer. We integrated TCGA-STAD cohort (375 GC samples and 56753 genes) and TCPA-STAD cohort (392 GC samples and 218 proteins), and rated them as transcriptomics and proteomics data, resepectively. 224 matched blood samples of GC patients and healthy individuals were collected to carry out untargeted metabolomics analysis.Results: In this study, pan-cancer analysis highlighted the crucial role of ERBB2 in the immune microenvironment and metabolic remodelling. In addition, the metabolic landscape of GC indicated that alanine, aspartate and glutamate (AAG) metabolism was significantly associated with the prevalence and progression of GC. Weighted metabolite correlation network analysis revealed that glycolysis/gluconeogenesis (GG) and AAG metabolism served as HER2-coexpressed metabolic pathways. Consensus clustering was used to stratify patients with GC into four subtypes with different metabolic characteristics (i.e. quiescent, GG, AAG and mixed subtypes). The GG subtype was characterised by a lower level of ERBB2 expression, a higher proportion of the inflammatory phenotype and the worst prognosis. However, contradictory features were found in the mixed subtype with the best prognosis. The GG and mixed subtypes were found to be highly sensitive to chemotherapy, whereas the quiescent and AAG subtypes were more likely to benefit from immunotherapy.Conclusions: Transcriptomic and proteomic analyses highlighted the close association of HER-2 level with the immune status and metabolic features of patients with GC. Metabolomics analysis highlighted the co-expressed relationship between alanine, aspartate and glutamate and glycolysis/gluconeogenesis metabolisms and HER2 level in GC. The novel integrated multi-omics strategy used in this study may facilitate the development of a more tailored approach to GC therapy.

Early dysbiosis in the gut microbiota may contribute to the severity of acute pancreatitis (AP), however, a comprehensive understanding of the gut microbiome, potential pathobionts, and host metabolome in individuals with AP remains elusive. Hence, we employed fecal whole-metagenome shotgun sequencing in 82 AP patients and 115 matched healthy controls, complemented by untargeted serum metabolome and lipidome profiling in a subset of participants. Analyses of the gut microbiome in AP patients revealed reduced diversity, disrupted microbial functions, and altered abundance of 77 species, influenced by both etiology and severity. AP-enriched species, mostly potential pathobionts, correlated positively with host liver function and serum lipid indicators. Conversely, many AP-depleted species were short-chain fatty acid producers. Gut microflora changes were accompanied by shifts in the serum metabolome and lipidome. Specifically, certain gut species, like enriched Bilophila wadsworthia and depleted Bifidobacterium spp., appeared to contribute to elevated triglyceride levels in biliary or hyperlipidemic AP patients. Through culturing and whole-genome sequencing of bacterial isolates, we identified virulence factors and clinically relevant antibiotic resistance in patient-derived strains, suggesting a predisposition to opportunistic infections. Finally, our study demonstrated that gavage of specific pathobionts could exacerbate pancreatitis in a caerulein-treated mouse model. In conclusion, our comprehensive analysis sheds light on the gut microbiome and serum metabolome in AP, elucidating the role of pathobionts in disease progression. These insights offer valuable perspectives for etiologic diagnosis, prevention, and intervention in AP and related conditions.

Early diagnosis of hepatocellular carcinoma (HCC) remains challenging to date. Characteristic metabolic deregulations of HCC may enable novel biomarkers discovery for early diagnosis. A capillary electrophoresis-time of flight mass spectrometry (CE-TOF/MS)-based metabolomics approach was performed to discover and validate potential biomarkers for HCC from the diethylnitrosamine-induced rat hepatocarcinogenesis model to human subjects. Time series sera from the animal model were evaluated using multivariate and univariate analyses to reveal dynamic metabolic changes. Two independent human cohorts (populations I and II) containing 122 human serum specimens were enrolled for validations. A novel biomarker pattern of ratio creatine/betaine which reflects the balance of methylation was identified. This biomarker pattern achieved effective classification of pre-HCC and HCC stages in animal model. It was still effective in the diagnosis of HCC from high-risk patients with cirrhotic nodules, achieving AUC values of 0.865 and 0.905 for two validation cohorts, respectively. The diagnosis of small HCC from cirrhosis with an AUC of 0.928 highlighted the potential for early diagnosis. This ratio biomarker can also improve the diagnostic performance of α-fetoprotein (AFP). This study demonstrates the efficacy of present strategy for biomarker discovery and the potential of metabolomics approach to provide novel insights for disease study.

Osteoporosis is becoming a highly prevalent disease in a large proportion of the global aged population. Serum metabolite markers may be important for the treatment and early prevention of osteoporosis. Serum samples from 32 osteoporosis and 32 controls were analyzed by untargeted metabolomics and lipidomic approaches performed on an ultra-high performance liquid chromatography and high-resolution mass spectrometry (UHPLC-HRMS) system. To find systemic disturbance of osteoporosis, weighted gene correlation network analysis (WGCNA) and statistical methods were employed for data-mining. Then, an in-depth targeted method was utilized to determine potential markers from the family of key metabolites. As a result, 1,241 metabolites were identified from untargeted methods and WGCNA indicated that lipids metabolism is deregulated and glycerol phospholipids, sphingolipids, fatty acids, and bile acids (BA) are majorly affected. As key metabolites of lipids metabolism, 66 bile acids were scanned and 49 compounds were quantified by a targeted method. Interestingly, hyocholic acids (HCA) were found to play essential roles during the occurrence of osteoporosis and may be potential markers. These metabolites may be new therapeutic or diagnosis targets for the screening or treatment of osteoporosis. Quantified measurement of potential markers also enables the establishment of diagnostic models for the following translational research in the clinic.