Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1242686 | ENA |
REPOSITORIES: ENA
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Project description:As a non-invasive blood testing, the detection of cell-free DNA (cfDNA) methylation in plasma is raising increasing interest due to its diagnostic and biology applications. Although extensively used in cfDNA methylation analysis, bisulfite sequencing is less cost-effective. Through enriching methylated cfDNA fragments with MeDIP followed by deep sequencing, we aimed to characterize cfDNA methylome in cancer patients. In this study, we investigated the cfDNA methylation patterns in lung cancer patients by MeDIP-seq. MEDIPS package was used for the identification of differentially methylated regions (DMRs) between patients and normal ones. Overall, we identified 330 differentially methylated regions (DMRs) in gene promoter regions, 33 hypermethylation and 297 hypomethylation respectively, by comparing lung cancer patients and healthy individuals as controls. The 33 hypermethylation regions represent 32 genes. Some of the genes had been previously reported to be associated with lung cancers, such as GAS7, AQP10, HLF, CHRNA9 and HOPX. Taken together, our study provided an alternative method of cfDNA methylation analysis in lung cancer patients with potential clinical applications.
2019-03-31 | E-MTAB-7163 | biostudies-arrayexpress
Project description:Nipple aspriate fluid (NAF) were obtained from 7 women including 3 breast cancer patients. Cell-free DNA (cfDNA) were isolated and bisulfite sequencing using Illumina HiSeq X Ten platform.
2023-09-26 | GSE238014 | GEO
Project description:Breast cancers of the luminal B subtype are frequent tumors with high proliferation and poor prognosis. Epigenetic alterations have been found in breast tumors and in biological fluids. We aimed to profile the cell-free DNA (cfDNA) methylome of metastatic luminal B breast cancer (LBBC) patients using an epigenomic approach to discover potential noninvasive biomarkers. Plasma cfDNA was analyzed using the Infinium MethylationEpic array (EPIC array) in a cohort of 14 women, including metastatic LBBC patients and healthy controls.
2022-11-16 | GSE214344 | GEO
Project description:We used a highly sensitive nano-5hmC-Seal method and profiled the genome-wide distribution of 5-hydroxymethylcytosine (5hmC) in plasma cell-free DNA (cfDNA) from 384 patients with bladder, breast, colorectal, kidney, lung, or prostate cancer and 221 controls. We used machine learning and developed plasma cfDNA 5hmC signatures that are highly sensitive for cancer detection and cancer origin determination. We also identified genes and signaling pathways with aberrant DNA hydroxymethylation in six cancers.
2022-09-23 | GSE202988 | GEO
Project description:Nucleosomes are the basic unit of packaging of eukaryotic chromatin, and nucleosome positioning can differ substantially between cell types. Here, we sequence 14.5 billion plasma-borne cell-free DNA (cfDNA) fragments (700-fold coverage) to generate genome-wide maps of in vivo nucleosome occupancy. We identify 13 million local maxima of nucleosome protection, spanning 2.53 gigabases (Gb) of the human genome, whose positions and spacings correlate with nuclear architecture, gene structure and gene expression. We further show that short cfDNA fragments - poorly recovered by standard protocols - directly footprint the in vivo occupancy of DNA-bound transcription factors such as CTCF. The sequence composition of cfDNA has previously been used to noninvasively monitor cancer, pregnancy and organ transplantation, but a key limitation of this paradigm is its dependence on genotypic differences to distinguish between contributing tissues. We show that nucleosome spacing in gene bodies and cis-regulatory elements, inferred from cfDNA in healthy individuals, correlates most strongly with transcriptional and epigenetic features of lymphoid and myeloid cells, consistent with hematopoietic cell death as the normal source of cfDNA. We build on this observation to show how in vivo nucleosome footprints can be used to infer the cell types that contribute to circulating cfDNA in pathological states such as cancer. Because it does not rely on genotypic differences, this strategy may enable the noninvasive cfDNA-based monitoring of a much broader set of clinical conditions than is currently possible. Sequencing of cfDNA libraries from healthy individuals, pooled healthy individuals and individuals with disease for the identification of nucleosomes and protection from other DNA binding proteins.
2015-11-04 | E-GEOD-71378 | biostudies-arrayexpress
Project description:Cell free DNA (cfDNA) in human plasma carries abundant information on physiological condition, especially in cancer patients such as esophageal cancer. Next-generation sequencing (NGS) as a rapidly developed technology could decode the information effectively. As a key step of NGS, using existing methods to construct cfDNA sequencing libraries is limited by several shortcomings. In this study, we developed a new NGS library construction method for highly degraded DNA, cfDNA as example, based on Single strand Adaptor Library Preparation (SALP). With the high ligation efficiency of single strand adaptor (SSA) which overhangs 3 random bases at 3' end of a double-strand DNA, the new method could construct the sequencing library with high sensitivity without using specific enzymes except T4 DNA ligase and Taq polymerase. With the special designed barcode T adaptor (BTA), multiple libraries constructed from different samples can be amplified in unbiased strategy and facility to compare. Using this method, this study successfully sequenced and compared totally 20 cfDNA samples derived from esophageal cancer patients and healthy people in whole genome scale. This study also compared the chromatin state between cancer patients and healthy people using cfDNA, identified the significant difference between different health condition. Our findings extend the application of cfDNA beyond the analysis with degraded DNA fragments itself, to the transcription regulation level, which also provide an important clue for biopsy of esophageal cancer and other diseases.
2019-04-12 | GSE116345 | GEO
Project description:Aims: Cancer is an important public health problem worldwide. In recent years, methods based on the analysis of plasma cfDNA which is as an emerging technology have been largely explored for noninvasive prenatal testing and cancer liquid biopsy. We inferred both epigenetic and genetic biomarkers of esophageal cancer (ESCA) in cfDNA with adapted SALP-seq. Materials & methods: Next Generation Sequencing libraries of cfDNA samples from ESCA patients and healthy people were constructed by using SALP-seq. We performed bioinformatics analysis on our sequencing data to find cancer-specific biomarkers. Results & conclusion: 54 important regulatory elements of ESCA, 49 epigenetic and 37 genetic ESCA-specific genes were inferred in cfDNA with SALP-seq, which may ultimately contribute to the development of effective diagnostic and therapeutic approaches for ESCA.
2022-08-28 | GSE136541 | GEO
Project description:The early detection of tissue and organ damage associated with autoimmune diseases (AID) has been identified as key to improve long-term survival, but non-invasive biomarkers are lacking. Elevated cell-free DNA (cfDNA) levels have been observed in AID and inflammatory bowel disease (IBD), prompting interest to use cfDNA as a potential non-invasive diagnostic and prognostic biomarker. Despite these known disease-related changes in concentration, it remains impossible to identify AID and IBD patients through cfDNA analysis alone. By using unsupervised clustering on large sets of shallow whole-genome sequencing (sWGS) cfDNA data, we uncover AID- and IBD-specific genome-wide patterns in plasma cfDNA in both the obstetric and general AID and IBD populations. Supervised learning of the genome-wide patterns allows AID prediction with 50% sensitivity at 95% specificity. Importantly, the method can identify pregnant women with AID during routine non-invasive prenatal screening. Since AID pregnancies have an increased risk of severe complications, early recognition or detection of new onset AID can redirect pregnancy management and limit potential adverse events. This method opens up new avenues for screening, diagnosis and monitoring of AID and IBD.
2022-09-14 | E-MTAB-11607 | biostudies-arrayexpress
Project description:Cell-free DNA (cfDNA) contains a composite map of the epigenomes of its cells-of-origin. Tissue-specific TF binding inferred from cfDNA could enable us to track disease states in humans in a non-invasive manner. Here, we present a method to identify the subset of genome-wide transcription factor binding sites that are protected in plasma. We map binding at tissue-specific sites of constitutive factor CTCF and tissue-specific factors PU.1, LYL1, ER, and FOXA1 in plasma cfDNA. Our method also captures the chromatin structure around the factor-bound sites in their cells-of-origin. We define pure tumor TF signatures in an in vivo model by applying our method to estrogen receptor-positive (ER+) breast cancer xenografts. The tumor-specific cfDNA protections of ER-α and FOXA1 reflect TF-specific accessibility across human breast tumors, demonstrating our ability to capture tumor TF binding in plasma. By modeling cfDNA from cancer donors as a mixture of healthy plasma and pure tumor signatures, we can identify tumor TF binding in humans. Thus, our method enables non-invasive mapping of the regulatory phenotype of cancer in humans.
2021-05-24 | GSE171434 | GEO
Project description:Cell-free DNAs (cfDNAs) are DNA fragments found in blood, originating mainly from immune cells in healthy individuals and from both immune and cancer cells in cancer patients. While cancer-derived cfDNAs carry mutations, they also retain epigenetic features such as DNA methylation and nucleosome positioning. In this study, we examine nucleosome enrichment patterns in cfDNAs from breast and pancreatic cancer patients and find significant enrichment at open chromatin regions. Differential enrichment is observed not only at cancer cell type specific ATAC-seq peaks but also at CD4? T cell specific peaks, suggesting both tumor- and immune-derived contributions to the cfDNA signal. To leverage these patterns, we apply an interpretable machine learning model (XGBoost) trained on cell type specific open chromatin regions. This approach improves cancer detection accuracy and highlights key genomic loci associated with the disease state. Our pipeline provides a robust and interpretable framework for cfDNA-based cancer detection.
2025-09-08 | GSE279542 | GEO