ArticleMethods in molecular biology (Clifton, N.J.)2026
Using Epigenetic Data to Deconvolute Immune Cells in Cancer from Blood Samples.
Article in Methods in molecular biology (Clifton, N.J.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
DNA methylation plays a crucial role in regulating gene expression and is a hallmark of epigenetic dysregulation in human tumors. High-throughput DNA methylation profiling can unravel intricate patterns in cancer. Moreover, understanding immune cell dynamics is essential for comprehending cancer progression and treatment response. Using DNA methylation data in immune cells, we can apply deconvolution algorithms estimate proportions of major immune cell types, providing insights into immune status and its implications in cancer. Functional analysis can identify specific overrepresented or underrepresented immune cell subsets, potentially uncovering novel biomarkers or therapeutic targets. This pipeline presents a detailed workflow in RStudio for DNA methylation studies and immune cell deconvolution, enhancing reproducibility and efficiency. The workflow integrates preprocessing, analysis, and visualization steps, facilitating robust inference of cell-type proportions from DNA methylation data.
Indexed as
Identifiers
41028269What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.