• biomarkers
• diagnostic model
• hyperlipidemic acute pancreatitis
• lipidomics
• metabolic biomarkers

• Bureau of Science and Technology Nanchong Municipality

• UHPLC-MS
• anti-PD-1/PD-L1 inhibitors therapy
• hepatocellular carcinoma
• plasma metabolite
• prognosis

• Carcinoma, Hepatocellular/drug therapy
• Carcinoma, Hepatocellular/blood
• Carcinoma, Hepatocellular/immunology
• Humans
• Liver Neoplasms/drug therapy
• Liver Neoplasms/immunology
• Liver Neoplasms/blood
• Chromatography, High Pressure Liquid/methods
• Male
• Immune Checkpoint Inhibitors/therapeutic use
• Biomarkers, Tumor/blood
• B7-H1 Antigen/antagonists & inhibitors
• B7-H1 Antigen/blood
• Female
• Middle Aged
• Programmed Cell Death 1 Receptor/antagonists & inhibitors
• Mass Spectrometry/methods
• Aged
• Metabolomics/methods
• Glycerophospholipids/blood

• National Natural Science Foundation of China

• Hepatocellular carcinoma
• Metabolome
• Serum
• UHPLC-MS/MS

• Humans
• Carcinoma, Hepatocellular
• Liver Neoplasms/metabolism
• Tandem Mass Spectrometry/methods
• Metabolomics/methods
• Biomarkers
• Liver Cirrhosis
• Biomarkers, Tumor

• Biomarkers
• Diagnosis
• Hepatocellular carcinoma
• Multi-omics
• Prognosis
• Urine testing

• artificial intelligence
• biomarkers
• deep learning
• hepatocellular carcinoma
• liver cancer
• machine learning

• Biomarkers
• Biomarkers, Tumor
• Carcinoma, Hepatocellular/diagnosis
• Humans
• Liver Cirrhosis
• Liver Neoplasms
• Metabolomics/methods

• Department of Health
• BB/N016847/1 Biotechnology and Biological Sciences Research Council

• metabolic pathways summary
• metabolomics
• metabolomics analysis tools
• multi-omics integration algorithms

• National Science Foundation of China
• 2020 Li Ka Shing Foundation Cross-Disciplinary Research Grant

• Hepatocellular carcinoma
• Metabolomics
• Molecular mechanism
• Progressive biomarkers

• Animals
• Biomarkers, Tumor/analysis
• Biomarkers, Tumor/blood
• Biomarkers, Tumor/metabolism
• Biomarkers, Tumor/urine
• Carcinoma, Hepatocellular/chemically induced
• Carcinoma, Hepatocellular/metabolism
• Carcinoma, Hepatocellular/pathology
• Diethylnitrosamine/toxicity
• Liver Neoplasms/chemically induced
• Liver Neoplasms/metabolism
• Liver Neoplasms/pathology
• Male
• Metabolic Networks and Pathways
• Metabolomics/methods
• Multivariate Analysis
• Proton Magnetic Resonance Spectroscopy/methods
• ROC Curve
• Rats, Sprague-Dawley
• Reproducibility of Results
• Rats

• 2016JD05 Base Program of Joint training graduate student of Shanxi Province

• diagnosis and prognosis
• hcc
• metabolomics
• systematic review

• Biomarkers/blood
• Biomarkers/metabolism
• Biomarkers, Tumor
• Carcinoma, Hepatocellular/diagnosis
• Humans
• Liver Neoplasms/diagnosis
• Metabolomics
• Prognosis

• Dementia
• Panax Ginseng
• Urine metabolomics
• Vital energy deficiency

Several widely recognized metabolites play a role in regulating the pathophysiological processes of various disorders. Nonetheless, the lack of effective biomarkers for the early diagnosis of polycystic ovarian syndrome (PCOS) and premature ovarian failure (POF) has led to the discovery of serum-based metabolic biomarkers for these disorders. We aimed to identify various differentially expressed metabolites (DEMs) through serum-based metabolic profiling in patients with PCOS and POF and in healthy individuals by using liquid chromatography–mass spectrometry analysis. Furthermore, heatmap clustering, correlation, and Z-score analyses were performed to identify the top DEMs. Kyoto Encyclopedia of Genes and Genomes enriched pathways of DEMs were determined using metabolite-based databases. Moreover, the clinical significance of these DEMs was evaluated on the basis of area under the receiver operating characteristic curve. Significantly dysregulated expressions of several metabolites were observed in the intergroup comparisons of the PCOS, POF, and healthy control groups. Furthermore, 6 DEMs were most frequently observed among the three groups. The expressions of these DEMs were not only directly correlated but also exhibited potential significance in patients with PCOS and POF. Novel metabolites with up/downregulated expressions can be discovered in patients with PCOS and POF using serum-based metabolomics; these metabolites show good diagnostic performance and can act as effective biomarkers for the early detection of PCOS and POF. Furthermore, these metabolites might be involved in the pathophysiological mechanisms of PCOS and POF via interplay with corresponding genes.

• metabolic biomarkers
• polycystic ovarian syndrome
• premature ovarian failure

Metabolic disorders have attracted increasing attention from scientists who conduct research on various tumours, especially hepatocellular carcinoma (HCC). The purpose of this study was to assess the prognostic significance of metabolism in HCC.

The expression profiles of metabolism-related genes (MRGs) of 349 surviving HCC patients were extracted from The Cancer Genome Atlas (TCGA) database. Subsequently, a series of biomedical computational algorithms were used to identify a seven-MRG signature as a prognostic model. GSEA indicated the function and pathway enrichment of these MRGs. Then, drug sensitivity analysis was used to identify the hub gene, which was tested using IHC staining.

A total of 420 differential MRGs and 116 differentially expressed transcription factors (TFs) were identified in HCC patients based on data from the TCGA database. The GO and KEGG enrichment analyses indicated that metabolic disturbance might be involved in the development of HCC. LASSO regression analysis was used to construct a seven-MRG signature (DHDH, ENO1, G6PD, LPCAT1, PDE6D, PIGU and PPAT) that could predict the prognosis of HCC patients. GSEA revealed the functional and pathway enrichment of these seven MRGs. Then, drug sensitivity analysis indicated that G6PD might play a key role in the prognosis of HCC by promoting chemoresistance. Finally, we used IHC staining to demonstrate the relationship between G6PD expression levels and clinical parameters in HCC patients.

The results of this study provide a potential method for predicting the prognosis of HCC patients and avenues for further studies of HCC metabolism. Moreover, the function of G6PD may play a key role in the development and progression of HCC.

• The Cancer Genome Atlas
• bioinformatical analysis
• hepatocellular carcinoma
• metabolic-genomic landscape
• prognostic index

La_0.8Sr_0.2CrO₃ (0.2LSCO) thin films were prepared via the RF sputtering method to fabricate thin-film thermocouples (TFTCs), and post-annealing processes were employed to optimize their properties to sense high temperatures. The XRD patterns of the 0.2LSCO thin films showed a pure phase, and their crystallinities increased with the post-annealing temperature from 800 °C to 1000 °C, while some impurity phases of Cr₂O₃ and SrCr₂O₇ were observed above 1000 °C. The surface images indicated that the grain size increased first and then decreased, and the maximum size was 0.71 μm at 1100 °C. The cross-sectional images showed that the thickness of the 0.2LSCO thin films decreased significantly above 1000 °C, which was mainly due to the evaporation of Sr²⁺ and Cr³⁺. At the same time, the maximum conductivity was achieved for the film annealed at 1000 °C, which was 6.25 × 10⁻² S/cm. When the thin films post-annealed at different temperatures were coupled with Pt reference electrodes to form TFTCs, the trend of output voltage to first increase and then decrease was observed, and the maximum average Seebeck coefficient of 167.8 µV/°C was obtained for the 0.2LSCO thin film post-annealed at 1100 °C. Through post-annealing optimization, the best post-annealing temperature was 1000 °C, which made the 0.2LSCO thin film more stable to monitor the temperatures of turbine engines for a long period of time.

• La0.8Sr0.2CrO3
• high-temperature sensing
• post-annealing
• thin-film thermocouple

Cancers often loose the enzyme of folate metabolism ALDH1L1. We proposed that such loss is advantageous for the malignant tumor growth and tested this hypothesis in mice proficient or deficient (gene knockout) in ALDH1L1 expression. Liver cancer in both groups was induced by injection of chemical carcinogen diethylnitrosamine. While the number of tumors observed in ALDH1L1 proficient and deficient mice was similar, tumors grew faster and to a larger size in the knockout mice. We conclude that the ALDH1L1 loss promotes liver tumor growth without affecting tumor initiation or multiplicity. Accelerated growth of tumors lacking the enzyme was linked to several metabolic pathways, which are beneficial for rapid proliferation.

Cytosolic 10-formyltetrahydrofolate dehydrogenase (ALDH1L1) is commonly downregulated in human cancers through promoter methylation. We proposed that ALDH1L1 loss promotes malignant tumor growth. Here, we investigated the effect of the Aldh1l1 mouse knockout (Aldh1l1^−/−) on hepatocellular carcinoma using a chemical carcinogenesis model. Fifteen-day-old male Aldh1l1 knockout mice and their wild-type littermate controls (Aldh1l1^+/+) were injected intraperitoneally with 20 μg/g body weight of DEN (diethylnitrosamine). Mice were sacrificed 10, 20, 28, and 36 weeks post-DEN injection, and livers were examined for tumor multiplicity and size. We observed that while tumor multiplicity did not differ between Aldh1l1^−/− and Aldh1l1^+/+ animals, larger tumors grew in Aldh1l1^−/− compared to Aldh1l1^+/+ mice at 28 and 36 weeks. Profound differences between Aldh1l1^−/− and Aldh1l1^+/+ mice in the expression of inflammation-related genes were seen at 10 and 20 weeks. Of note, large tumors from wild-type mice showed a strong decrease of ALDH1L1 protein at 36 weeks. Metabolomic analysis of liver tissues at 20 weeks showed stronger differences in Aldh1l1^+/+ versus Aldh1l1^−/− metabotypes than at 10 weeks, which underscores metabolic pathways that respond to DEN in an ALDH1L1-dependent manner. Our study indicates that Aldh1l1 knockout promoted liver tumor growth without affecting tumor initiation or multiplicity.

• ALDH1L1
• diethylnitrosamine (DEN) carcinogenesis
• folate metabolism
• liver cancer
• metabolomics
• mouse knockout

• P30 DK056350 NIDDK NIH HHS
• R01 CA095030 NCI NIH HHS

• aging
• biomarkers
• machine learning
• metabolic trajectories
• time-series

• Aging/metabolism
• Aging/urine
• Algorithms
• Animals
• Biomarkers/urine
• Body Weight
• Feeding Behavior
• Machine Learning
• Metabolome
• Metabolomics
• Neoplasms/urine
• Rats, Wistar
• Time Factors
• Rats

In recent years, the advantages of artificial intelligence (AI) in data processing and model analysis have emerged in the medical field, enabled by computer technology developments and the integration of multiple disciplines. The application of AI in the medical field has gradually deepened and broadened. Among them, the development of clinical medicine intelligent decision-making is the fastest. The advantage of clinical medicine intelligent decision-making is to make the diagnosis faster and more accurate on the basis of certain information. Urine detection technologies, such as urine proteomics, urine metabolomics, and urine RNomics, have developed rapidly with the advancements in omics and medical tests. Advances in urine testing have made it possible to obtain a wealth of information from easily accessible urine. However, it has always been a problem to extract effective information from this information and use it. AI technology provides the possibility to process and use the information in urine. AI, combined with urine detection, not only provides new possibilities for precise and individual diagnosis and disease treatment, but also helps promote non-invasive diagnosis and treatment. This article reviews the research and applications of AI combined with urine detection for disease diagnosis and treatment and discusses its existing problems and future development.

• Artificial intelligence (AI)
• diagnosis
• omics
• urine detection

The huge prognostic difference between early and late stage hepatocellular carcinoma (HCC) is a challenging diagnostic problem. Alpha-fetoprotein is the mostly widely used biomarker for HCC used in the clinic, however it’s sensitivity and specificity of is not optimal. The development and application of multiple biotechnologies, including next generation sequencing, multiple “omics” data, that include genomics, epigenomics, transcriptomics, proteomics, metabolomics, metagenomics has been used for HCC diagnostic biomarker screening. Effective biomarkers/panels/models have been identified and validated at different clinical levels. A large proportion of these have a good diagnostic performance for HCC, especially for early HCC. In this article, we reviewed the various HCC biomarkers derived from “omics” data and discussed the advantages and disadvantages for diagnosis HCC.

• Hepatocellular carcinoma
• Diagnosis
• Circulating tumor cells
• Exosomes
• Circulating tumor DNA
• RNA
• Metabolomics
• Protein

• Biomarkers, Tumor/analysis
• Biomarkers, Tumor/genetics
• Biomarkers, Tumor/metabolism
• Carcinoma, Hepatocellular/diagnosis
• Carcinoma, Hepatocellular/genetics
• Carcinoma, Hepatocellular/metabolism
• Clinical Trials as Topic
• Computational Biology/methods
• Early Detection of Cancer/methods
• High-Throughput Screening Assays/methods
• Humans
• Liver Neoplasms/diagnosis
• Liver Neoplasms/genetics
• Liver Neoplasms/metabolism
• Predictive Value of Tests
• Sensitivity and Specificity

Chronic hepatic disease can present a diagnostic challenge with different etiologies being associated with similar clinical and laboratory findings. The histopathological assessment of a liver biopsy specimen is usually required in order to make a definitive diagnosis and the availability of non-invasive prognostic biomarkers is limited. The emerging science of metabolomics is used to detect changes in endogenous low molecular weight metabolites in biological samples and offers the possibility of identifying noninvasive markers of disease. The objective of this study was to investigate differences in the urine metabolome between healthy dogs, dogs with chronic hepatitis, dogs with hepatocellular carcinoma, and dogs with a congenital portosystemic shunt. Stored urine samples from 10 healthy dogs, 10 dogs with chronic hepatitis, 6 dogs with hepatocellular carcinoma, and 5 dogs with a congenital portosystemic shunt were analyzed. The urine metabolome was analyzed by gas chromatography–quadrupole time of flight mass spectrometry and 220 known metabolites were identified. Principal component analysis and heat dendrogram plots of the metabolomics data showed clustering between groups. Random forest analysis showed differences in the abundance of various metabolites including putrescine, gluconic acid, sorbitol, and valine. Based on univariate statistics, 37 metabolites were significantly different between groups. In, conclusion, the urine metabolome varies between healthy dogs, dogs with chronic hepatitis, dogs with hepatocellular carcinoma, and dogs with a congenital portosystemic shunt. Further targeted assessment of these metabolites is needed to assess their diagnostic utility.

• age-related macular degeneration
• metabolomics
• nuclear magnetic resonance spectroscopy
• urine

• Aged
• Aged, 80 and over
• Biomarkers/urine
• Body Fluids/metabolism
• Female
• Humans
• Macular Degeneration/diagnostic imaging
• Macular Degeneration/pathology
• Macular Degeneration/urine
• Magnetic Resonance Imaging
• Magnetic Resonance Spectroscopy
• Male
• Metabolomics
• Middle Aged

• K01 HL146980 NHLBI NIH HHS
• R01 EY030088 NEI NIH HHS

• DNA methylation
• hepatocellular carcinoma
• machine learning

• aflatoxin
• dairy cow
• metabolomics
• plasma

• Aflatoxin B1/pharmacology
• Animal Feed
• Animals
• Biomarkers/blood
• Cattle
• Clay
• Diet/veterinary
• Eating
• Ethanol/blood
• Female
• Metabolomics
• Proton Magnetic Resonance Spectroscopy
• Saccharomyces cerevisiae
• Sequestering Agents/pharmacology

Metabolites detectible in human biofluids are attractive biomarkers for the diagnosis of early Lyme disease (ELD), a vector-borne infectious disease. Urine represents an easily obtained clinical sample that can be applied for diagnostic purposes. However, few studies have explored urine for biomarkers of ELD. In this study, metabolomics approaches were applied to evaluate small molecule metabolites in urine from patients with ELD (n = 14), infectious mononucleosis (n = 14) and healthy controls (n = 14). Metabolic biosignatures for ELD versus healthy controls and ELD versus infectious mononucleosis were generated using untargeted metabolomics. Pathway analyses and metabolite identification revealed the dysregulation of several metabolic processes in ELD as compared to healthy controls or mononucleosis, including metabolism of tryptophan. Linear discriminant analyses demonstrated that individual metabolic biosignatures can correctly discriminate ELD from the other patient groups with accuracies of 71 to 100%. These data provide proof-of-concept for use of urine metabolites as biomarkers for diagnostic classification of ELD.

• Biotin
• Carcinoma, Hepatocellular
• Enzyme-Linked Immunosorbent Assay
• Glycocholic Acid/analysis
• Glycocholic Acid/urine
• Humans
• Reproducibility of Results
• Single-Chain Antibodies/chemistry

• P42 ES004699 NIEHS NIH HHS

Hepatocellular carcinoma (HCC) is the third most common cause of death from cancer, with increasing prevalence worldwide. The mortality rate of HCC is similar to its incidence rate, which reflects its poor prognosis. At present, the diagnosis of HCC is still mostly dependent on invasive biopsy, imaging methods, and serum α-fetoprotein (AFP) testing. Because of the asymptomatic nature of early HCC, biopsy and imaging methods usually detect HCC at the middle–late stages. AFP has limited sensitivity and specificity, as many other nonmalignant liver diseases can also result in a very high serum level of AFP. Therefore, better biomarkers with higher sensitivity and specificity at earlier stages are greatly needed. Since metabolic reprogramming is an essential hallmark of cancer and the liver is the metabolic hub of living systems, it is useful to investigate HCC from a metabolic perspective. As a noninvasive and nondestructive approach, metabolomics provides holistic information on dynamically metabolic responses of living systems to both endogenous and exogenous factors. Therefore, it would be conducive to apply metabolomics in investigating HCC. In this review, we summarize recent metabolomic studies on HCC cellular, animal, and clinicopathologic models with attention to metabolomics as a biomarker in cancer diagnosis. Recent applications of metabolomics with respect to therapeutic and prognostic evaluation of HCC are also covered, with emphasis on the potential of treatment by drugs from natural products. In the last section, the current challenges and trends of future development of metabolomics on HCC are discussed. Overall, metabolomics provides us with novel insight into the diagnosis, prognosis, and therapeutic evaluation of HCC.

• biomarker
• diagnosis and therapy
• hepatocellular carcinoma
• metabolomics

Metabolomics has been shown to be an effective tool for biomarker screening and pathway characterization and disease diagnosis. Metabolic characteristics of hepatocellular carcinoma (HCC) may enable the discovery of novel biomarkers for its diagnosis. In this work, metabolomics was used to investigate metabolic alterations of HCC patients. Plasma samples from HCC patients and age-matched healthy controls were investigated using high resolution ultrahigh performance liquid chromatography-mass spectrometry and metabolic differences were analyzed using pattern recognition methods. 23 distinguishable metabolites were identified. The altered metabolic pathways were associated with arginine and proline metabolism, glycine, serine and threonine metabolism, steroid hormone biosynthesis, starch and sucrose metabolism, etc. To demonstrate the utility of plasma biomarkers for the diagnosis of HCC, five metabolites comprising deoxycholic acid 3-glucuronide, 6-hydroxymelatonin glucuronide, 4-methoxycinnamic acid, 11b-hydroxyprogesterone and 4-hydroxyretinoic acid were selected as candidate biomarkers. These metabolites that contributed to the combined model could significantly increase the diagnostic performance of HCC. It has proved to be a powerful tool in the discovery of new biomarkers for disease detection and suggest that panels of metabolites may be valuable to translate our findings to clinically useful diagnostic tests.

Metabolomics has been shown to be an effective tool for biomarker screening and pathway characterization and disease diagnosis.

• 1-methylhistamine
• biodosimetry
• metabolomics analysis
• urine

• Animals
• Fourier Analysis
• Gamma Rays
• Male
• Metabolome/radiation effects
• Metabolomics/methods
• Mice
• Molecular Weight
• Radiation
• Spectrometry, Mass, Electrospray Ionization/methods
• Tandem Mass Spectrometry
• Time Factors
• Urine/chemistry

Multi-dimensional omics could be helpful to interpret the underlying mechanisms of disease.

Metabolomics has emerged as an essential tool for studying metabolic processes, stratification of patients, as well as illuminating the fundamental metabolic alterations in disease onset, progression, or response to therapeutic intervention. Metabolomics materialized within the pharmaceutical industry as a standalone assay in toxicology and disease pathology and eventually evolved towards aiding in drug discovery and pre-clinical studies via supporting pharmacokinetic and pharmacodynamic characterization of a drug or a candidate. Recent progress in the field is illustrated by coining of the new term—Pharmacometabolomics. Integration of data from metabolomics with large-scale omics along with clinical, molecular, environmental and behavioral analysis has demonstrated the enhanced utility of deconstructing the complexity of health, disease, and pharmaceutical intervention(s), which further highlight it as an essential component of systems medicine. This review presents the current state and trend of metabolomics applications in pharmaceutical development, and highlights the importance and potential of clinical metabolomics as an essential part of multi-omics protocols that are directed towards shaping precision medicine and population health.

• biomarkers
• drug
• mass spectrometry
• metabolomics
• patients
• pharmaceutical