Home Liver Research Plasma phenylalanine and glutamine concentrations correlate with subsequent hepatocellular carcinoma occurrence in liver cirrhosis patients: an exploratory study

Plasma phenylalanine and glutamine concentrations correlate with subsequent hepatocellular carcinoma occurrence in liver cirrhosis patients: an exploratory study

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Plasma phenylalanine and glutamine concentrations correlate with subsequent hepatocellular carcinoma occurrence in liver cirrhosis patients: an exploratory study

Patients

This study was approved by the institutional review board of Chang Gung Memorial Hospital, Taiwan and conducted according to the principles in the declaration of Helsinki. A cohort of 475 consecutive HCC-naïve liver cirrhosis patients were recruited from three medical centers, the Keelung, Linko and Kaohsiung branches of Chang Gung Memorial Hospitals, which were located respectively in the northern, central-north and southern regions of Taiwan. The cirrhosis was diagnosed by either liver biopsy, or ultrasound imaging in conjunction with the detection of esophageal varices using endoscopy, or the transient elastography (Fibroscan; Echosens, France) measurements greater than 12 kilopascal (kPa). All patients were above 18 years old and have given informed consent. Peripheral bloods for the metabolomics study were collected between January, 2013 and August, 2014. These patients were then regularly followed in outpatient clinics every 3 months. Ultrasound survey were performed regularly until HCC was diagnosed. The end of follow-up is 2017/2/28. During the study period, all HBV patients have achieved sustained virological response. Patients with HCV were viremic.

Baseline clinical information such as age, gender, etiology (HBV, HCV), and aspartate Aminotransferase (AST), alanine Aminotransferase (ALT), AST/ALT and the Fibrosis 4 score (FIB-4)6 were retrieved from chart records. Patients were prospectively followed until the occurrence of HCC or the end of follow-up. A total of 39 patients developed HCC during the follow up time. The peripheral blood samples were centrifuged for the separation of plasma, which were then stored in − 20 °C until further analysis.

Untargeted metabolomics using NMR

The plasma sample (350 μl) was mixed with 350 μl of plasma buffer solution (75 mM Na2HPO4, 0.08% TSP, 2 mM NaN3, 20% D2O), and 600 μl of the supernatant was transferred to NMR tubes for data acquisition.

1H NMR spectra were acquired on a Bruker Avance III HD 600 MHz NMR spectrometer with a 5 mm inverse triple resonance CryoProbe (1H/13C/15N) (Bruker Biospin GmbH, Rheinstetten, Germany). The spectra were acquired by Carr-Purcell-Meiboom-Gill spin-echo (CPMG) pulse sequence at 310 K, and broad signals from proteins were attenuated by the 80 ms T2 relaxation time. The spectrum was collected with a spectral width of 12,019.23 Hz and 72 k data points and then acquisitions were accumulated 64 times. All NMR spectra were phased and baseline-corrected and then referenced to the doublet of 1H-α-glucose at 5.23 ppm by using Topspin software (version 3.2.2; Bruker Biospin GmbH, Rheinstetten, Germany)13. We chose CPMG pulse as a compromise of efficiency and effectiveness for the current study. The utilization of NOESY, PURGE, PROJECT pulses and other technique remain our future research14.

Each 1H NMR spectrum (in the range of 9.5–0.5 ppm, excluding the water region) from plasma was segmented into 0.01 ppm with equal widths, and normalized to the reference by AMIX (version 3.9.14; Bruker Biospin GmbH, Rheinstetten, Germany). The resulting data sets were analyzed by SIMCA-P+ (version 13.0; Umetrics, Umea, Sweden), and all data were Pareto-scaled for multivariate statistical analysis. Resonant frequencies of each metabolite were referred from an in-house library, Chenomx NMR Sutie 7.1 (Chenomx, Edomonton, Canada), or HMDB (https://www.hmdb.ca/)15. More technical details can be found in literature13.

Ultra-performance liquid chromatography (UPLC)-based amino acid measurement

The plasma samples (100 µl) were precipitated by adding an equal volume (100 µl) of 10% sulfosalicylic acid containing an internal standard (norvaline 200 µM)16. The 20 µl of the supernatant was mixed with 60 µl borate buffer (pH 8.8) and then the derivatization was activated by adding 20 µl of 10 mM AQC in acetonitrile. After 10 min reaction time, the reaction was disrupted by mixing with an equal volume of Eluent A (20 mM ammonium formate/0.6% Formic acid/1% acetonitrile) and analyzed using the ACQUITY UPLC System. The AQC derivatization reagent was obtained from the Waters Corporation (Milford, MA, USA)17.

The Waters ACQUITY UPLC System (Waters crop., Milford, USA) consisted of a Binary Solvent Manager (BSM), a Sample Manager fitted with a 10-µl loop, and a Tunable UV (TUV) detector. The system was controlled, and the data was collected using Empower 2 software. The separations were performed on a 2.1 × 100 mm ACQUITY BEH C18 column at 60 °C and flow rate of 0.70 ml/min, and the detection was set at 260 nm using a sampling rate of 20 points/s. The mobile phase was 20 mM ammonium formate/0.6% formic acid/1% acetonitrile in water (Eluent A) and in acetonitrile (Eluent B)15.

Data visualization and analysis

Clinical variables were compared using Welch’s t test (a.k.a. unequal variances t-statistics), Mann–Whitney U test and χ2 test, where the obtained P values smaller than 0.05 were considered statistical significance. The result of NMR exploration was presented using a scatter plot of the Welch’s t statistics and the 1H chemical shifts. Welch’s t-test were performed on patients with or without the occurrence of HCC during the follow-up. Cox regressions were used for univariate and multivariate analysis of clinical and metabolic variables for their correlation with the time to HCC. Cumulative incidence of HCC of different patient strata were compared using log-rank tests. The IBM SPSS software version 20 (IBM, Armonk, NY) was used. The Box-and-Whisker plots were produced by the R statistical package. The HCC risk models were constructed by the multivariate combination of variables using the generalized iterative modelling method (GIM). This algorithm can identify optimum polynomial combinations of variables with respect to the fitness function18,19, which in this research is the likelihood function as in the Cox regression. The software code of GIM (capable of doing the time-to-event analysis) can be downloaded at the following website (https://github.com/khliang/GIM).

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