Background: Esophageal cancer (ECa) is the 7th most incident cancer and the 6th leading cause of cancer-related death. Most patients are diagnosed with locally advanced or metastatic disease, enduring poor survival. Biomarkers enabling early cancer detection may improve patient management, treatment effectiveness, and survival, are urgently needed. In this context, epigenetic-based biomarkers such as DNA methylation are potential candidates.
Methods: Herein, we sought to identify and validate DNA methylation-based biomarkers for early detection and prediction of response to therapy in ECa patients. Promoter methylation levels were assessed in a series of treatment-naïve ECa, post-neoadjuvant treatment ECa, and normal esophagus tissues, using quantitative methylation-specific PCR for COL14A1, GPX3, and ZNF569.
Results: ZNF569 methylation (ZNF569me) levels significantly differed between ECa and normal samples (p < 0.001). Moreover, COL14A1 methylation (COL14A1me) and GPX3 methylation (GPX3me) levels discriminated adenocarcinomas and squamous cell carcinomas, respectively, from normal samples (p = 0.002 and p = 0.009, respectively). COL14A1me & ZNF569me accurately identified adenocarcinomas (82.29%) whereas GPX3me & ZNF569me identified squamous cell carcinomas with 81.73% accuracy. Furthermore, ZNF569me and GPX3me levels significantly differed between normal and pre-treated ECa.
Conclusion: The biomarker potential of a specific panel of methylated genes for ECa was confirmed. These might prove useful for early detection and might allow for the identification of minimal residual disease after adjuvant therapy.
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http://dx.doi.org/10.1186/s40364-020-00248-7 | DOI Listing |
Epigenomics
December 2024
Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia.
Aims: Clustering algorithms have been widely applied to tumor DNA methylation datasets to define methylation-based cancer subtypes. This study aimed to evaluate the agreement between subtypes obtained from common clustering strategies.
Materials & Methods: We used tumor DNA methylation data from 409 women with breast cancer from the Melbourne Collaborative Cohort Study (MCCS) and 781 breast tumors from The Cancer Genome Atlas (TCGA).
Clin Epigenetics
December 2024
Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310022, Zhejiang, China.
Background: Pancreatic adenocarcinoma (PDAC) exhibits a complex microenvironment with diverse cell populations influencing patient prognosis. Single-cell RNA sequencing (scRNA-seq) was used to identify prognosis-related cell types, and DNA methylation (DNAm)-based models were developed to predict outcomes based on their cellular characteristics.
Methods: We integrated scRNA-seq, bulk data, and clinical information to identify key cell populations associated with prognosis.
Am J Hum Genet
December 2024
Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK.
Exploring the molecular correlates of metabolic health measures may identify their shared and unique biological processes and pathways. Molecular proxies of these traits may also provide a more objective approach to their measurement. Here, DNA methylation (DNAm) data were used in epigenome-wide association studies (EWASs) and for training epigenetic scores (EpiScores) of six metabolic traits: body mass index (BMI), body fat percentage, waist-hip ratio, and blood-based measures of glucose, high-density lipoprotein cholesterol, and total cholesterol in >17,000 volunteers from the Generation Scotland (GS) cohort.
View Article and Find Full Text PDFBackground: People living with the human immunodeficiency virus (HIV) are at a greater risk of developing hepatocellular carcinoma (HCC), potentially due to the stimulation of inflammation by HIV infection. Inflammation-related DNA methylation signatures obtained in liquid biopsy, such as circulating cell-free DNA (cfDNA), may serve as promising minimally invasive biomarkers that can inform diagnosis of HCC.
Methods: Using data from 249 individuals with HIV (114 individuals with normal liver conditions, 69 with fibrosis, 30 with cirrhosis, and 36 with HCC), we constructed a cfDNA methylation-based inflammation score (inflammation-DNAm score) based on 54 CpGs previously associated with circulating C-reactive protein concentrations.
Genome Biol
December 2024
School of Basic Medical Sciences, Suzhou Medical College, Soochow University, Suzhou, 215123, China.
Background: Plasma cell-free DNA (cfDNA) is derived from cellular death in various tissues. Investigating the tissue origin of cfDNA through cell type deconvolution, we can detect changes in tissue homeostasis that occur during disease progression or in response to treatment. Consequently, cfDNA has emerged as a valuable noninvasive biomarker for disease detection and treatment monitoring.
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