Genome-wide discovery and validation of diagnostic DNA methylation-based biomarkers for hepatocellular cancer detection in circulating cell free DNA
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA531089 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Background & Aims. Hepatocellular carcinoma (HCC), the most prevalent form of liver cancer, is growing in incidence but treatment options remain limited, particularly for late stage disease. As liver cirrhosis is the principal risk state for HCC development, markers to detect early HCC within this patient population are urgently needed. Perturbation of epigenetic marks, such as DNA methylation (5mC), is a hallmark of human cancers, including HCC. Identification of regions with consistently altered 5mC levels in circulating cell free DNA (cfDNA) during progression from cirrhosis to HCC could therefore serve as markers for development of minimally-invasive screens of early HCC diagnosis and surveillance. Methods. To discover DNA methylation derived biomarkers of HCC in the background of liver cirrhosis, we profiled genome-wide 5mC landscapes in patient cfDNA using the Infinium HumanMethylation450k BeadChip Array. We further linked these findings to primary tissue data available from TCGA and other public sources. Using biological and statistical frameworks, we selected CpGs that robustly differentiated cirrhosis from HCC in primary tissue and cfDNA followed by validation in an additional independent cohort. Results. We identified CpGs that segregate patients with cirrhosis, from patients with HCC within a cirrhotic liver background, through genome-wide analysis of cfDNA 5mC landscapes. Lasso regression analysis pinpointed a panel of probes in our discovery cohort that were validated in two independent datasets. A panel of five CpGs (cg04645914, cg06215569, cg23663760, cg13781744, and cg07610777) yielded AUROCs of 0.9525, 0.9714, and 0.9528 in cfDNA discovery and tissue validation cohorts 1 and 2, respectively. Conclusions. 5mC markers derived from cfDNA robustly identify HCC within a cirrhotic liver background indicating that further validation is warranted. Our finding that 5mC markers derived from primary tissue did not perform well in cfDNA, compared to those identified directly from cfDNA, reveals potential advantages of starting with cfDNA to discover high performing markers for liquid biopsy development.
2019-08-16 | GSE129374 | GEO
Project description:Hepatocellular carcinoma (HCC), the most prevalent form of liver cancer, is growing in incidence but treatment options remain limited, particularly for late stage disease. As liver cirrhosis is the principal risk state for HCC development, markers to detect early HCC within this patient population are urgently needed. Perturbation of epigenetic marks, such as DNA methylation (5mC), is a hallmark of human cancers, including HCC. Identification of regions with consistently altered 5mC levels in circulating cell free DNA (cfDNA) during progression from cirrhosis to HCC could therefore serve as markers for development of minimally-invasive screens of early HCC diagnosis and surveillance. Methods: To discover DNA methylation derived biomarkers of HCC in the background of liver cirrhosis, we profiled genome-wide 5mC landscapes in patient cfDNA using the Infinium HumanMethylation450k BeadChip Array. We further linked these findings to primary tissue data available from TCGA and other public sources. Using biological and statistical frameworks, we selected CpGs that robustly differentiated cirrhosis from HCC in primary tissue and cfDNA followed by validation in an additional independent cohort. Results: We identified CpGs that segregate patients with cirrhosis, from patients with HCC within a cirrhotic liver background, through genome-wide analysis of cfDNA 5mC landscapes. Lasso regression analysis pinpointed a panel of probes in our discovery cohort that were validated in two independent datasets. A panel of five CpGs (cg04645914, cg06215569, cg23663760, cg13781744, and cg07610777) yielded area under the receiver operating characteristic (AUROC) curves of 0.9525, 0.9714, and 0.9528 in cfDNA discovery and tissue validation cohorts 1 and 2, respectively. Validation of a 5-marker panel created from combining hypermethylated and hypomethylated CpGs in an independent cfDNA set by bisulfite pyrosequencing yielded an AUROC of 0.956, compared to the discovery AUROC of 0.996. Conclusion: Our finding that 5mC markers derived from primary tissue did not perform well in cfDNA, compared to those identified directly from cfDNA, reveals potential advantages of starting with cfDNA to discover high performing markers for liquid biopsy development.
| S-EPMC6831291 | biostudies-literature
Project description:Background: Gastric cancer (GC) remains the third leading cause of cancer death worldwide due to the absence of sensitive and specific biomarkers for its early detection. 5-hydroxymethylcytosine (5hmC)-enriched gene profiles and regions show tissue-specific and tumor specific, and cell-free DNA (cfDNA) 5hmC modification feature sequencing provides a robust tool for gastric cancer detection.Methods: A matched case‒control study design with 50 gastric cancer patients and 50 controls was performed to sequence the 5hmC modification of cfDNA. Significantly differential 5hmC modification genes were identified to construct a gastric cancer diagnostic model. The data set from Gene Expression Omnibus consisted of 61 gastric cancer patients, 90 healthy individuals and 22 benign gastric disease controls was used as an external testing set to test the robustness of the diagnostic model. Results: The vast majority of 5hmC peaks were distributed in the gene body, accounting for more than 90% of the total 5hmC peaks in both the GC and control groups. The diagnostic model was developed based on five different 5hmC modification genes, FBXL7, PDE3A, TPO, SNTG2 and SXBP5. The model could clearly distinguish gastric cancer patients from controls in the training (AUC=0.95, sensitivity=88.6%, specificity=94.3%), validation (AUC=0.87, sensitivity=73.3%, specificity=93.3%) and testing (AUC=0.90, sensitivity=81.9%, specificity=90.2%) sets. The predicted risk scores of the controls were significantly lower than those of GC patients in our own data (P<0.001) or GEO external testing data (P<0.001), but no significant difference was observed between different TNM stage groups (P=0.09 and 0.66). Furthermore, the risk scores of healthy and benign gastric disease controls in the testing set were also not different (P=0.10). Conclusions: The characteristics of 5hmC in cfDNA are specific to GC patients, and the diagnostic model constructed by the 5hmC features of five genes could effectively identify GC patients.
2024-12-30 | GSE246110 | GEO
Project description:Development of high resolution/accurate mass liquid chromatography-coupled tandem mass spectrometry (LC-MS/MS) methodology enables the characterization of covalently modified DNA induced by interaction with genotoxic agents in complex biological samples. Constant neutral loss monitoring of 2´-deoxyribose or the nucleobases using data-dependent acquisition represents a powerful approach for the unbiased detection of DNA modifications (adducts). The lack of available bioinformatics tools necessitates manual processing of acquired spectral data and hampers high throughput application of these techniques. To address this limitation, we present an automated workflow for the detection and curation of putative DNA adducts by using diagnostic fragmentation filtering of LC-MS/MS experiments within the open-source software MZmine. The workflow utilizes a new feature detection algorithm, DFBuilder, which employs diagnostic fragmentation filtering using a user-defined list of fragmentation patterns to reproducibly generate feature lists for precursor ions of interest. The DFBuilder feature detection approach readily fits into a complete small molecule discovery workflow and drastically reduces the processing time associated with analyzing DNA adductomics results. We validate our workflow using a mixture of authentic DNA adduct standards and demonstrate the effectiveness of our approach by reproducing and expanding the results of a previously published study of colibactin-induced DNA adducts. The reported workflow serves as a technique to assess the diagnostic potential of novel fragmentation pattern combinations for the unbiased detection of chemical classes of interest.
2021-01-25 | ST001661 | MetabolomicsWorkbench
Project description:Background: The lack of highly sensitive and specific diagnostic biomarkers is a major contributor to the poor outcomes of patients with hepatocellular carcinoma (HCC). We sought to develop a non-invasive diagnostic approach using circulating cell-free DNA (cfDNA) for the early detection of HCC. Methods: Applying the 5hmC-Seal technique, we obtained genome-wide 5-hydroxymethylcytosines (5hmC) in cfDNA samples from 2,554 Chinese subjects: 1,204 HCC patients, 392 patients with chronic hepatitis B virus infection (CHB) or liver cirrhosis (LC), and 958 healthy individuals and patients with benign liver lesions. A diagnostic model for early HCC was developed through case-control analyses using the elastic net regularization for feature selection. Results: The 5hmC-Seal data from HCC patients showed a genome-wide distribution enriched with liver-derived enhancer marks. We developed a 32-gene diagnostic model that accurately distinguished early HCC (stage 0/A) based on the Barcelona Clinic Liver Cancer (BCLC) staging system from non-HCC (validation set: AUC = 88.4%; 95% CI, 85.8-91.1%), showing superior performance over α-fetoprotein (AFP). Besides detecting patients with early stage or small tumors (e.g., ≤ 2.0 cm) from non-HCC, the 5hmC model showed high capacity for distinguishing early HCC from high risk subjects with CHB or LC history (validation set: AUC = 84.6%; 95% CI, 80.6-88.7%), also significantly outperforming AFP. Furthermore, the 5hmC diagnostic model appeared to be independent from potential confounders (e.g., smoking/alcohol intake history). Conclusions: We have developed and validated a non-invasive approach with clinical application potential for the early detection of HCC that are still surgically resectable in high risk individuals.
2019-08-07 | GSE112679 | GEO
Project description:This study was conducted with the primary objective of assessing the performance of cfDNA methylation in the detection of colorectal cancer (CRC). Five tumor tissue, 20 peripheral blood leucocyte, and 169 cfDNA samples were collected for whole-genome bisulfite sequencing (WGBS) analysis. Bioinformatic analysis was conducted to identify differentially methylated regions (DMRs) and their functional characteristics. Quantitative methylation-specific PCR (qMSP) was used to validate the methylation levels of DMRs in the tissues and leucocytes. cfDNA samples from CRC patients and healthy controls were used to evaluate the performance of the DMR analysis. WGBS analysis revealed a decrease in DNA methylation levels in the CpG context in CRC tumor tissues compared with adjacent normal tissues. A total of 132 DMRs in cfDNA were identified as potential markers for diagnosing CRC. In a cohort of 95 CRC patients and 74 healthy controls, a combination of the three DMRs (DAB1, PPP2R5C, and FAM19A5) yielded an AUC of 0.763, achieving 64.21% sensitivity and 78.38% specificity in discriminating CRC patients from healthy controls. This study provides insights into DNA methylation patterns in CRC and identifies a set of DMRs in cfDNA with potential diagnostic value for CRC. These DMRs hold promise as biomarkers for CRC detection, offering promise for non-invasive CRC diagnosis. Further research is warranted to validate these findings in larger cohorts.
| S-EPMC11353097 | biostudies-literature
Project description:Background. Gliomas, especially the high-grade glioblastomas (GBM), are highly aggressive tumors in the central nervous system (CNS) with dismal clinical outcomes. Effective biomarkers, which are not currently available, may improve clinical outcomes through early detection. We sought to develop a non-invasive diagnostic approach for gliomas based on 5-hydroxymethylcytosines (5hmC) in circulating cell-free DNA (cfDNA). Methods. We obtained genome-wide 5hmC profiles using the 5hmC-Seal technique in cfDNA samples from 111 prospectively enrolled patients with gliomas and 111 age-, gender-matched healthy individuals, which were split into a training set and a validation set. Integrated models comprised of 5hmC levels summarized for gene bodies, long non-coding RNAs (lncRNAs), cis-regulatory elements, and repetitive elements were developed using the elastic net regularization under a case-control design. Results. The integrated 5hmC-based models differentiated healthy individuals from gliomas (AUC [area under the curve] = 84%; 95% confidence interval [CI], 74-93%), GBM patients (AUC = 84%; 95% CI, 74-94%), WHO II-III glioma patients (AUC = 86%; 95% CI, 76-96%), regardless of IDH1 (encoding isocitrate dehydrogenase) mutation status or other glioma-related pathological features such as TERT, TP53 in the validation set. Furthermore, the 5hmC biomarkers in cfDNA showed the potential as an independent indicator from IDH1 mutation status and worked in synergy with IDH1 mutation to distinguish GBM from WHO II-III gliomas. Exploration of the 5hmC biomarkers for gliomas revealed relevance to glioma biology. Conclusions. The 5hmC-Seal in cfDNA offers the promise as a non-invasive approach for effective detection of gliomas in a screening program.
2021-05-30 | GSE132118 | GEO
Project description:DNA modifications such as 5-methylcytosines (5mC) and 5-hydroxymethylcytosines (5hmC) are epigenetic marks known to affect global gene expression in mammals. Given their prevalence in the human genome, close correlation with gene expression, and high chemical stability, these DNA epigenetic marks could serve as ideal biomarkers for cancer diagnosis. Taking advantage of a highly sensitive and selective chemical labeling technology, we report here genome-wide 5hmC profiling in circulating cell-free DNA (cfDNA) and paired tumor/adjacent tissues collected from a cohort of 90 healthy individuals and 260 patients recently diagnosed with colorectal, gastric, pancreatic, liver, or thyroid cancer. 5hmC was mainly distributed in transcriptionally active regions coincident with open chromatin and permissive histone modifications. Robust cancer-specific epigenetic signatures in cfDNA were identified in different cancers. 5hmC-based biomarkers demonstrated highly accurate predictive value for patients with colorectal and gastric cancers versus healthy controls, superior to conventional biomarkers, and comparable to epigenetic biomarkers from tissue biopsies. This new strategy could lead to the development of an effective blood-based, minimally-invasive cancer diagnosis and prognosis approach.
2017-09-05 | GSE89570 | GEO
Project description:BackgroundPlasma cell-free DNA (cfDNA) methylation has shown promising results in the early detection of multiple cancers recently. Here, we conducted a study to investigate the performance of cfDNA methylation in the early detection of esophageal cancer (ESCA).MethodsSpecific methylation markers for ESCA were identified and optimized based on esophageal tumor and paired adjacent tissues (n = 24). Age-matched participants with ESCA (n = 85), benign esophageal diseases (n = 10), and healthy controls (n = 125) were randomized into the training and test sets to develop a classifier to differentiate ESCA from healthy controls and benign esophageal disease. The classifier was further validated in an independent plasma cohort of ESCA patients (n = 83) and healthy controls (n = 98).ResultsIn total, 921 differentially methylated regions (DMRs) between tumor and adjacent tissues were identified. The early detection classifier based on those DMRs was first developed and tested in plasma samples, discriminating ESCA patients from benign and healthy controls with a sensitivity of 76.2% (60.5-87.9%) and a specificity of 94.1% (85.7-98.4%) in the test set. The performance of the classifier was consistent irrespective of sex, age, and pathological diagnosis (P > 0.05). In the independent plasma validation cohort, similar performance was observed with a sensitivity of 74.7% (64.0-83.6%) and a specificity of 95.9% (89.9-98.9%). Sensitivity for stage 0-II was 58.8% (44.2-72.4%).ConclusionWe demonstrated that the cfDNA methylation patterns could distinguish ESCAs from healthy individuals and benign esophageal diseases with promising sensitivity and specificity. Further prospective evaluation of the classifier in the early detection of ESCAs in high-risk individuals is warranted.
| S-EPMC8513367 | biostudies-literature
Project description:Sepsis remains a diagnostic challenge with no gold-standard test. Urine provides a readily available, non-invasive biofluid with significant diagnostic potential. Urinary gene expression has been previously used for diagnosis and prognosis of urological malignancies and transplant allograft rejections, but remains unutilized for sepsis diagnosis. In this study, the authors use urinary gene expression profiles to both diagnose sepsis and characterize its pathophysiology. By using differential expression augmented with machine learning ensembles, the authors identify a collection of cellular mRNA from 239 genes in patient urine which show exceptional power in classifying septic patients from those with chronic systemic disease in both internal and independent external validation cohorts. Functional analysis indexes the disrupted biological pathways in early sepsis and additionally reveals key molecular networks driving its pathogenesis. This study serves a pioneering step towards expanding the clinical potential of urinary molecular profiles for application to systemic diseases.
2020-02-20 | GSE112098 | GEO