Evidence of DNA methylation heterogeneity and epipolymorphism in kidney cancer tissue samples.

Clear cell renal cell carcinoma (ccRCC) is characterised by significant genetic heterogeneity, which has diagnostic and prognostic implications. Very limited evidence is available regarding DNA methylation heterogeneity. We therefore generate sequence level DNA methylation data on 136 multi-region tumour and normal kidney tissue from 18 ccRCC patients, along with matched whole exome sequencing (85 samples) and gene expression (47 samples) data on a subset of samples.

CHAMP delivers accurate taxonomic profiles of the prokaryotes, eukaryotes, and bacteriophages in the human microbiome.

Introduction: Accurate taxonomic profiling of the human microbiome composition is crucial for linking microbial species to health outcomes. Therefore, we created the Clinical Microbiomics Human Microbiome Profiler (CHAMP), a comprehensive tool designed for the profiling of prokaryotes, eukaryotes, and viruses across all body sites.

Methods: CHAMP uses a reference database derived from 30,382 human microbiome samples, covering 6,567 prokaryotic and 244 eukaryotic species, as well as 64,003 viruses.

Species Abundance in the Gut Is Linked to Subclinical Coronary Atherosclerosis in 8973 Participants From the SCAPIS Cohort.

Background: Gut microbiota have been implicated in atherosclerotic disease, but their relation with subclinical coronary atherosclerosis is unclear. This study aimed to identify associations between the gut microbiome and computed tomography-based measures of coronary atherosclerosis and to explore relevant clinical correlates.

Methods: We conducted a cross-sectional study of 8973 participants (50 to 65 years of age) without overt atherosclerotic disease from the population-based SCAPIS (Swedish Cardiopulmonary Bioimage Study).

Accurate detection of benign and malignant renal tumor subtypes with MethylBoostER: An epigenetic marker-driven learning framework.

Current gold standard diagnostic strategies are unable to accurately differentiate malignant from benign small renal masses preoperatively; consequently, 20% of patients undergo unnecessary surgery. Devising a more confident presurgical diagnosis is key to improving treatment decision-making. We therefore developed MethylBoostER, a machine learning model leveraging DNA methylation data from 1228 tissue samples, to classify pathological subtypes of renal tumors (benign oncocytoma, clear cell, papillary, and chromophobe RCC) and normal kidney.