Dataset Information

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

ACCESS DATA

Similar Datasets

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:<p>Surveillance for hepatocellular carcinoma (HCC) with biannual ultrasound and serum alpha-fetoprotein, is recommended in patients with cirrhosis. However, its effectiveness for the detection of early-stage tumors is limited due to inadequate risk stratification and suboptimal performance of the current screening modalities. We developed a multi-center prospective cohort of patients with cirrhosis undergoing surveillance with contrast-enhanced MRI, and applied global untargeted metabolomics to 612 longitudinal samples collected from 203 patients. Among them, 37 developed HCC during follow-up. Linear mixed-effects models identified 150 metabolites among the 1263 detected, with significant abundance changes in serum samples collected prior to HCC diagnosis (Cases) compared to serum samples collected from patients who didn’t develop HCC during follow-up (Controls). The largest increases were observed for tauro-conjugated bile acids and gamma-glutamyl amino acids. The largest decreases were observed for derivatives of the bile acid deoxycholate, androgenic steroids, and acyl cholines. Using logistic regression analysis, 7 amino acids including serine and alanine, had stronger associations with HCC risk than AFP. The strongest protective effects were observed for N-acetylglycine and glycerophosphorylcholine (GPC). Analysis of serum samples collected after HCC treatment identified a reverse phenotype for metabolites associated with metabolism of amino acids, bile acids and purine & pyrimidine. Random forest machine learning algorithm using the 150 metabolites together with age, gender, PNPLA3 rs738409 and TMS6SF2 rs58542926 SNPs, identified 18 variables giving optimal model performance. Among them, N-acetylglycine, palmitoloelylcholine, GPC and alanine had better AUCs than AFP in discriminating between Cases and Controls. When restricting Cases to serum samples collected within 1 year prior to HCC diagnosis (Cases-12M), additional metabolites were identified, including several microbiota-derived metabolites. Random forest machine learning algorithm identified 20 top variables giving optimal model performance. The combination of the top 6 variables (AFP, 6-bromotryptophan, N-acetylglycine, salicyluric glucuronide, cortisol 21-sulfate and age) had good performance in discriminating Cases-12M from Controls (AUC=0.88 [0.82-0.94]). Finally, 28 of the 150 metabolites distinguished Cases with LIRAD-3 hepatic lesions who developed HCC during follow-up (Cases-LR3) from Controls with LIRAD-3 lesions who did not develop HCC during follow-up (Controls-LR3). Remarkably, the abundance of 7 acyl-cholines, GPC and GPC-related lysophospholipids were strongly reduced in Cases-LR3 compared to Controls-LR3. Altogether, this comprehensive study identified N-acetylglycine, amino acids, bile acids and choline-derived metabolites as main biomarkers of HCC risk. This study also revealed an important contribution of microbiota-derived metabolites to HCC development.</p>

Dataset Information

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure