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Loci-specific differences in blood DNA methylation in HBV-negative populations at risk for hepatocellular carcinoma development - post-diagnostic HCC blood samples

ABSTRACT: Genome wide DNA methylation profiling of blood samples collected from patients after diagnosis with hepatocellular carcinoma (HCC) (cases) vs. blood samples collected from healthy individuals without family history of cancer (controls). The Illumina Infinium 450K Human DNA methylation Beadchip v1.2 was used to obtain DNA methylation profiles across approximately 450,000 CpGs in human samples corresponding to cases (post-diagnostic HCC) and controls. Samples included 24 cases and 24 controls. Cases were matched with controls on gender, age, ethnicity, hepatitis C infection, and diabetes. The presence or absence of HCC in our study was determined based on the AASLD criteria.

ORGANISM(S): Homo sapiens

PROVIDER: GSE113409 | GEO | 2022/01/31

REPOSITORIES: GEO

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Project description:Background: Fetal alcohol spectrum disorder (FASD) is a developmental disorder that manifests through a range of cognitive, adaptive, physiological, and neurobiological deficits resulting from prenatal alcohol exposure. Although the North American prevalence is currently estimated at 2-5%, FASD has proven difficult to identify in the absence of the overt physical features characteristic of fetal alcohol syndrome. As interventions may have the greatest impact at an early age, accurate biomarkers are needed to identify children at risk for FASD. Building on our previous work identifying distinct DNA methylation patterns in children and adolescents with FASD, we have attempted to validate these associations in a different clinical cohort and to use our DNA methylation signature to develop a possible epigenetic predictor of FASD. Methods: Genome-wide DNA methylation patterns were analyzed using the Illumina HumanMethylation450 array in the buccal epithelial cells of a cohort of 48 individuals aged 3.5-18 (24 FASD cases, 24 controls). The DNA methylation predictor of FASD was built using a stochastic gradient boosting model on our previously published dataset FASD cases and controls (GSE80261). The predictor was tested on the current dataset and an independent dataset of 48 autism spectrum disorder cases and 48 controls (GSE50759). Results: We validated findings from our previous study that identified a DNA methylation signature of FASD, replicating the altered DNA methylation levels of 161/648 CpGs in this independent cohort, which may represent a robust signature of FASD in the epigenome. We also generated a predictive model of FASD using machine learning in a subset of our previously published cohort of 179 samples (83 FASD cases, 96 controls), which was tested in this novel cohort of 48 samples and resulted in a moderately accurate predictor of FASD status. Upon testing the algorithm in an independent cohort of individuals with autism spectrum disorder, we did not detect any bias towards autism, sex, age, or ethnicity. Conclusion: These findings further support the association of FASD with distinct DNA methylation patterns, while providing a possible entry point towards the development of epigenetic biomarkers of FASD.

Project description: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.

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Loci-specific differences in blood DNA methylation in HBV-negative populations at risk for hepatocellular carcinoma development - post-diagnostic HCC blood samples

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