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Metabolomics biomarkers of hepatocellular carcinoma in a prospective cohort of patients with cirrhosis

ABSTRACT:

BACKGROUND & AIMS: The effectiveness of surveillance for hepatocellular carcinoma (HCC) in patients with cirrhosis is limited, due to inadequate risk stratification and suboptimal performance of current screening modalities.

METHODS: We developed a multicenter prospective cohort of patients with cirrhosis undergoing surveillance with MRI and applied global untargeted metabolomics to 612 longitudinal serum samples from 203 patients. Among them, 37 developed HCC during follow-up.

RESULTS: We identified 150 metabolites with significant abundance changes in samples collected prior to HCC (Cases) compared to samples from patients who did not develop HCC (Controls). Tauro-conjugated bile acids and gamma-glutamyl amino acids were increased, while acyl-cholines and deoxycholate derivatives were decreased. Seven amino acids including serine and alanine had strong associations with HCC risk, while strong protective effects were observed for N-acetylglycine and glycerophosphorylcholine. Machine learning using the 150 metabolites, age, gender, and PNPLA3 and TMS6SF2 single nucleotide polymorphisms, identified 15 variables giving optimal performance. Among them, N-acetylglycine had the highest AUC in discriminating Cases and Controls. When restricting Cases to samples collected within 1 year prior to HCC (Cases-12M), additional metabolites including microbiota-derived metabolites were identified. The combination of the top six variables identified by machine learning (alpha-fetoprotein, 6-bromotryptophan, N-acetylglycine, salicyluric glucuronide, testosterone sulfate and age) had good performance in discriminating Cases-12M from Controls (AUC 0.88, 95% CI 0.83-0.93). Finally, 23 metabolites distinguished Cases with LI-RADS-3 lesions from Controls with LI-RADS-3 lesions, with reduced abundance of acyl-cholines and glycerophosphorylcholine-related lysophospholipids in Cases.

CONCLUSIONS: This study identified N-acetylglycine, amino acids, bile acids and choline-derived metabolites as biomarkers of HCC risk, and microbiota-derived metabolites as contributors to HCC development.

INSTRUMENT(S): Q Exactive

SUBMITTER: Laura Beretta

PROVIDER: MTBLS8764 | MetaboLights | 2024-05-09

REPOSITORIES: MetaboLights

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			Action	DRS
	MTBLS8764	Other
	FILES	Other
	a_MTBLS8764_Method1_POS_1_metabolite_profiling_mass_spectrometry.txt	Txt
	a_MTBLS8764_Method2_POS_2_metabolite_profiling_mass_spectrometry.txt	Txt
	a_MTBLS8764_Method3_NEG_1_metabolite_profiling_mass_spectrometry.txt	Txt

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Project description:MicroRNAs (miRNAs) are abundant in the circulation and play a central role in diverse biological processes; they may be useful for early diagnosis of hepatocellular carcinoma (HCC). We conducted a two-phase, case-control study (20 pairs for the discovery set and 49 pairs for the validation set) to test the hypothesis that genome-wide dysregulation of circulating miRNAs differentiate HCC cases from controls. Taqman low density arrays were used to examine genome-wide miRNA expression for the discovery set, and quantitative RT-PCR was used to validate candidate miRNAs for both discovery and validation sets. Sixty-six miRNAs were found to be significantly over-expressed in plasma of HCC cases compared to controls after adjusting for false discovery rate (p<0.05). A volcano plot indicated that 7 miRNAs had greater than 2-fold case-control differences with p<0.01. Four significant miRNAs (miR-150, miR-30c, miR-483-5p and miR-520b) detectable in all samples with varied expression levels were further validated in a validation set. MiR-483-5p was statistically significantly over-expressed in HCC cases compared with controls (3.20 vs. 0.82, p<0.0001). HCC risk factors and clinic-pathological characteristics did not influence miR-483-5p expression. The combination of plasma miR-483-5p level and HCV status can significantly differentiate HCC cases from controls with an AUC of 0.908 (p<0.0001). The sensitivity and specificity were, respectively, 75.5% and 89.8%. These preliminary results suggest the importance of dysregulated circulating miR-483-5p as a potential HCC biomarker. Confirmation of aberrant expression of miR-483-5p in a large prospective HCC study will provide support for its application to HCC detection. A hospital-based HCC case-control study including 20 HCC patients and 20 controls is conducted in Columbia University Medical Center (CUMC), which is approved by the Institutional Review Board. Cases were newly diagnosed HCC patients who were treated in the Hepatobiliary Oncology Clinics, CUMC, and examined pathologically. Controls were recruited from volunteers through the Research Recruitment and Minority Outreach (RRMO) core of Herbert Irving Comprehensive Cancer Center (HICCC). Flyers were placed at strategic locations around CUMC where hospital visitors and employees frequent or were handed out at inreach events at the hospital or at outreach events in the community. Interested participants were directed to contact the trained recruitment staff from the RRMO and given further information about participation. Interested participants were excluded from the control group if diagnosed for any kind of cancer or liver disease. Eligible controls were asked to fill out the same demographic and epidemiological questionnaire as HCC cases. In the current study, controls were matched with HCC cases on age (M-BM-15 yrs), gender (male/female) and ethnicities (Caucasian/Hispanic/African-American/Asian).

Project description:Although hepatocellular carcinoma (HCC) has been subjected to continuous investigation and its symptoms are well-known, early stage diagnosis of this disease remains difficult and the survival rate after diagnosis is typically very low (3−5%). Early and accurate detection of metabolic changes in the sera of patients with liver cirrhosis can help improve the prognosis of HCC and lead to a better understanding of its mechanism at the molecular level, thus providing patients with in-time treatment of the disease. In this study, we compared metabolite levels in sera of 40 HCC patients and 49 cirrhosis patients from Egypt by using ultraperformance liquid chromatography coupled with quadrupole time-of-flight mass spectrometer (UPLC-QTOF MS). Following data preprocessing, the most relevant ions in distinguishing HCC cases from cirrhotic controls are selected by statistical methods. Putative metabolite identifications for these ions are obtained through mass-based database search. The identities of some of the putative identifications are verified by comparing their MS/MS fragmentation patterns and retention times with those from authentic compounds. Finally, the serum samples are reanalyzed for quantitation of selected metabolites as candidate biomarkers of HCC. This quantitation was performed using isotope dilution by selected reaction monitoring (SRM) on a triple quadrupole linear ion trap (QqQLIT) coupled to UPLC. Statistical analysis of the UPLC-QTOF data identified 274 monoisotopic ion masses with statistically significant differences in ion intensities between HCC cases and cirrhotic controls. Putative identifications were obtained for 158 ions by mass based search against databases. We verified the identities of selected putative identifications including glycholic acid (GCA), glycodeoxycholic acid (GDCA), 3β, 6β-dihydroxy-5β-cholan-24-oic acid, oleoyl carnitine, and Phe-Phe. SRM-based quantitation confirmed significant differences between HCC and cirrhotic controls in metabolite levels of bile acid metabolites, long chain carnitines and small peptide. Our study provides useful insight into appropriate experimental design and computational methods for serum biomarker discovery using LC−MS/MS based metabolomics. This study has led to the identification of candidate biomarkers with significant changes in metabolite levels between HCC cases and cirrhotic controls. This is the first MS-based metabolic biomarker discovery study on Egyptian subjects that led to the identification of candidate metabolites that discriminate early stage HCC from patients with liver cirrhosis.

Project description:Gene-expression profiles of hepatitis C-related, early-stage liver cirrhosis Background & Aims: Liver cirrhosis affects 1%M-bM-^HM-^R2% of population and is the major risk factor of hepatocellular carcinoma (HCC). Hepatitis C cirrhosis-related HCC is the most rapidly increasing cause of cancer death in the US. Non-invasive methods have been developed to identify patients with asymptomatic, early-stage cirrhosis, increasing the burden of HCC surveillance, but biomarkers are needed to identify patients with cirrhosis who are most in need of surveillance. We investigated whether a liver-derived 186-gene signature previously associated with outcomes of patients with HCC is prognostic for patients newly diagnosed with cirrhosis but without HCC. Methods: We performed gene expression profile analysis of formalin-fixed needle biopsies from the livers of 216 patients with hepatitis C-related early-stage (Child-Pugh class A) cirrhosis who were prospectively followed for a median of 10 years at an Italian center. We evaluated whether the 186-gene signature was associated with death, progression of cirrhosis, and development of HCC. Results: Fifty-five (25%), 101 (47%), and 60 (28%) patients were classified as having poor-, intermediate-, and good-prognosis signatures, respectively. In multivariable Cox regression modeling, the poor-prognosis signature was significantly associated with death (P=.004), progression to advanced cirrhosis (P<.001), and development of HCC (P=.009). The 10-year rates of survival were 63%, 74%, and 85% and the annual incidences of HCC were 5.8%, 2.2%, and 1.5% for patients with poor-, intermediate-, and good-prognosis signatures, respectively. Conclusions: A 186-gene signature used to predict outcomes of patients with HCC is also associated with outcomes of patients with hepatitis C-related early-stage cirrhosis. This signature might be used to identify patients with cirrhosis in most need of surveillance and strategies to prevent their development of HCC. 216 liver biopsy specimens

Project description:Characterizing the metabolic changes pertaining to hepatocellular carcinoma (HCC) in patients with liver cirrhosis is believed to contribute towards early detection, treatment, and understanding of the molecular mechanisms of HCC. In this study, we compare metabolite levels in sera of 78 HCC cases with 184 cirrhotic controls by using ultra performance liquid chromatography coupled with a hybrid quadrupole time-of-flight mass spectrometry (UPLC-QTOF MS). Following data preprocessing, the most relevant ions in distinguishing HCC cases from patients with cirrhosis are selected by parametric and non-parametric statistical methods. Putative metabolite identifications for these ions are obtained through mass-based database search. Verification of the identities of selected metabolites is conducted by comparing their MS/MS fragmentation patterns and retention time with those from authentic compounds. Quantitation of these metabolites is performed in a subset of the serum samples (10 HCC and 10 cirrhosis) using isotope dilution by selected reaction monitoring (SRM) on triple quadrupole linear ion trap (QqQLIT) and triple quadrupole (QqQ) mass spectrometers. The results of this analysis confirm that metabolites involved in sphingolipid metabolism and phospholipid catabolism such as sphingosine-1-phosphate (S-1-P) and lysophosphatidylcholine (lysoPC 17:0) are up-regulated in sera of HCC vs. those with liver cirrhosis. Down-regulated metabolites include those involved in bile acid biosynthesis (specifically cholesterol metabolism) such as glycochenodeoxycholic acid 3-sulfate (3-sulfo-GCDCA), glycocholic acid (GCA), glycodeoxycholic acid (GDCA), taurocholic acid (TCA), and taurochenodeoxycholate (TCDCA). These results provide useful insights into HCC biomarker discovery utilizing metabolomics as an efficient and cost-effective platform. Our work shows that metabolomic profiling is a promising tool to identify candidate metabolic biomarkers for early detection of HCC cases in high risk population of cirrhotic patients.

Project description:Introduction: Metabolomics provides simultaneous assessment of a broad range of metabolites that can potentially serve as indicators of the overall physiology status as well as the response to host and environmental stimuli. It has been broadly used for biomarker discovery and characterization of complex diseases such as cancer. The evaluation of the changes in the levels of metabolites in samples stratified by race could lead to the identification of more reliable biomarkers than those obtained through whole-population-based approaches. In this study, we used plasma samples collected from patients recruited at MedStar Georgetown University Hospital to investigate metabolites that may be associated with hepatocellular carcinoma (HCC) in a race-specific manner. Methods: Plasma samples were protein depleted using a solution composed of acetonitrile:isopropanol:water (3:3:2) containing a mixture of isotope-labeled internal standards. The extracted metabolites were trimethylsilyl derivatized prior to analysis by GC-MS. A quality control (QC) sample derived by pooling plasma from multiple subjects was run in between samples to assess reproducibility. A mixture of fatty acids methyl esters (FAMEs) and alkane standards was run for retention index calibration. The mixture of isotope-labeled internal standards was used to evaluate the reproducibility of the GC-MS data across multiple runs. Preliminary Data: Plasma samples collected from 40 HCC cases and 44 patients with liver cirrhosis were analyzed. The cirrhotic controls were frequency matched with the HCC cases by demographic variables. The participants included 19 African Americans (AA) and 50 European Americans (EA). The analysis targeted 46 metabolites for quantitative analysis by Agilent GC-qMS in selected ion monitoring (SIM) mode. The data were pre-processed by the Automated Mass Spectral Deconvolution and Identification System (AMDIS) for peak detection, deconvolution, and identification. The resulting peaks were aligned using Mass Profiler Professional (MPP) from Agilent Technologies. A LASSO regression model was applied to select metabolites with significant changes in HCC vs. cirrhosis in three groups: (1) AA and EA combined; (2) AA only; and (3) EA only. Also, metabolites that distinguish HCC cases from cirrhosis in the three groups were selected by considering only those subjects with Hepatitis C virus (HCV) infection. The performances of the metabolites selected by LASSO in each group were evaluated through a leave-one-out cross-validation. We identified race-specific metabolites that differentiated HCC cases from cirrhotic controls, yielding better area under the ROC curve compared to alpha-fetoprotein (AFP) - the serological marker widely used for the diagnosis of HCC. Novel Aspect: We identified race-associated metabolites that are significantly altered in HCC vs. cirrhosis, suggesting the potential role of race in HCC.

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Metabolomics biomarkers of hepatocellular carcinoma in a prospective cohort of patients with cirrhosis

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