Evidence map›Paper›PMID 42326801›Full record

ArticlemedRxiv : the preprint server for health sciences2026

Deconvolution-based cell-type specific DNA methylation-wide and transcriptome-wide association studies identify risk CpG sites and genes associated with colorectal cancer risk.

Qing Li, Lili Xu, Jifeng Wang, Chao Li, Wanqing Wen, Xiang Shu, Yaohua Yang, Xiao-Ou Shu, Qiuyin Cai, Jirong Long and 8 more

Abstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

18 authors.

Qing LiDivision of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA.
Lili XuDivision of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA.
Jifeng WangDivision of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA.
Chao LiDivision of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA.
Wanqing WenDivision of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA.
Xiang ShuDepartment of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Yaohua YangDepartment of Genome Sciences, UVA Comprehensive Cancer Center, School of Medicine, University of Virginia, Charlottesville, VA, USA.
Xiao-Ou ShuDivision of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA.ORCID 0000-0002-3815-7172
Qiuyin CaiDivision of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA.
Jirong LongDivision of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA.
Bhuminder SinghEpithelial Biology Center and Department of Cell and Developmental Biology, Vanderbilt University Medical Center, Nashville, TN, USA.
Ken S LauEpithelial Biology Center and Department of Cell and Developmental Biology, Vanderbilt University Medical Center, Nashville, TN, USA.
Zhijun YinDepartment of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA.
Graham CaseyDepartment of Genome Sciences, UVA Comprehensive Cancer Center, School of Medicine, University of Virginia, Charlottesville, VA, USA.
Mingyang SongDepartments of Epidemiology and Nutrition, Harvard TH Chan School of Public Health, Boston, MA, USA.
Ulrike PetersDivision of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA.
Wei ZhengDivision of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA.
Xingyi GuoDivision of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA.

Funding

Leveraging Omics and Electronic Health Records Data to study Colorectal Adenoma genetics and Drug RepurposingR01CA297582 · NCI · VANDERBILT UNIVERSITY MEDICAL CENTER · PI GUO, XINGYI, YIN, ZHIJUN · 2025 to 2025
$4.2M
Uncovering colorectal cancer etiology and biology by integrating proteomics with other omics dataR01CA269589 · NCI · VANDERBILT UNIVERSITY MEDICAL CENTER · PI Xingyi Guo, Wei Zheng · 2023 to 2026
$2.9M
Harnessing DNA methylation in peripheral blood for improved colorectal cancer preventionR01CA285851 · NCI · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · PI Marios Giannakis, Mingyang Song · 2024 to 2026
$2.0M
NCI NIH HHS R01 CA269589NCI NIH HHS R01 CA285851NCI NIH HHS R01 CA297582
6 · The paper itself

Abstract

Bulk tissue-based DNA methylation-wide (MWAS) and transcriptome-wide association studies (TWAS) have identified CpG sites and genes associated with colorectal cancer (CRC) risk, but do not account for cellular heterogeneity. To address this, we developed a deconvolution-informed framework to infer cell-type specific DNA methylation and gene expression profiles from bulk normal colon tissues using reference single-cell epigenomic and transcriptomic datasets. We performed cell-type specific MWAS (ctMWAS) using deconvoluted DNA methylation data from 293 normal colon samples and conducted cell-type specific TWAS (ctTWAS) using deconvoluted gene expression data from 707 normal colon samples. Genetically predicted methylation and expression models were integrated with CRC GWAS summary statistics (78,473 cases and 107,143 controls) to identify risk-associated CpG sites and genes. Through ctMWAS, ctTWAS, and colocalization analyses, we identified 178 high-confidence cell-type-specific CpG sites and 68 genes associated with CRC across 26 previously unreported GWAS loci. Through additional integrative methylation-gene analysis, we prioritized 132 candidate risk genes, the majority of which were supported by multi-omics evidence and stage-specific dysregulation across the adenoma-carcinoma and serrated-carcinoma progression pathways. Pathway enrichment analyses implicated pathways involved in DNA double-strand break repair, TP53 regulation, TGF-β signaling, and innate immune responses. Among prioritized genes, 14 were identified as putative druggable targets linked to 90 FDA-approved or clinical-stage drugs. Experimental validation supports an oncogenic role for

Indexed as

Cell-type specific analysisColorectal cancerCpG methylationctMWASctTWASSF3A3Susceptibility genes

Identifiers

PMID42326801
PMCPMC13278294

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Registered trials

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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.