Evidence map›Paper›PMID 41967136›Full record

ArticleJournal of the National Cancer Institute2026

Identification of immune cell type-specific susceptibility genes in multiple cancers using transcriptome-wide association studies.

Fei Qin, Xing Hua, Xiaoyu Wang, Haoyu Zhang, Jiyeon Choi, Xiaohong R Yang, Tongwu Zhang, Mitchell J Machiela, Samuel Anyaso-Samuel, Maria Teresa Landi and 7 more

Abstract read
In one paragraph

Article in Journal of the National Cancer Institute, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

17 authors.

Fei QinDivision of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, United States.ORCID 0000-0003-3678-2879
Xing HuaDivision of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, United States.
Xiaoyu WangDivision of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, United States.ORCID 0009-0005-5143-6909
Haoyu ZhangDivision of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, United States.ORCID 0000-0001-6423-0444
Jiyeon ChoiDivision of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, United States.ORCID 0000-0002-0955-2384
Xiaohong R YangDivision of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, United States.ORCID 0000-0003-4451-8664
Tongwu ZhangDivision of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, United States.ORCID 0000-0003-2124-2706
Mitchell J MachielaDivision of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, United States.ORCID 0000-0001-6538-9705
Samuel Anyaso-SamuelDivision of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, United States.
Maria Teresa LandiDivision of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, United States.ORCID 0000-0003-4507-329X
Sonja I BerndtDivision of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, United States.
Mark P PurdueDivision of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, United States.
Demetrius AlbanesDivision of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, United States.ORCID 0000-0001-8330-4293
Bin ZhuDivision of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, United States.ORCID 0000-0003-0172-5516
Kevin M BrownDivision of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, United States.ORCID 0000-0002-8558-6711
Jianxin ShiDivision of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, United States.ORCID 0000-0001-8606-4707
Kai YuDivision of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, United States.ORCID 0000-0002-5337-137X

Funding

Intramural NIH HHS Z99 CA999999Intramural Research Program, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health
6 · The paper itself

Abstract

backgroundTranscriptome-wide association studies (TWAS) integrate gene expression and genome-wide association studies (GWAS) to identify disease susceptibility genes. Because gene expression varies substantially across cell types within tissues, cell type-specific prediction models may enhance the power of TWAS.

methodsWe conducted cell type-specific TWAS leveraging single-cell RNA sequencing data from the OneK1K cohort (14 immune cell types, 1.27 million cells) and GWAS summary statistics for 7 cancers (>290 000 cases in total). To improve prediction accuracy, we developed a modeling framework that incorporates shared gene expression effects across cell types.

resultsAt a false discovery rate of 5%, we identified 106 (Bonferroni 5%: 13) previously unreported loci for breast cancer, 51 (4) loci for prostate cancer, 11 (4) loci for lung cancer, 39 (5) loci for melanoma, 9 (1) loci for ovarian cancer, and 2 (1) loci for diffuse large B-cell lymphoma, with most genes exhibiting cell type specificity. Gene set analyses confirmed joint associations of unreported genes with breast and prostate cancer risk in UK Biobank data. Additional lung tissue single-cell RNA sequencing data with 113 individuals validated 18 of 32 (56.3%) statistically significant genes for lung cancer. Across cancers, 139 statistically significant genes were shared by at least 2 cancer types and were primarily enriched in specific immune cell types.

conclusionCell type-specific TWAS improve the identification of novel cancer susceptibility loci and provide insights into the immune landscape of cancer etiology.

Indexed as

Genetic Predisposition to DiseaseGenome-Wide Association StudyNeoplasmsTranscriptomeFemaleGene Expression ProfilingHumansMalePolymorphism, Single NucleotideSingle-Cell Gene Expression Analysis

Identifiers

PMID41967136
PMCPMC13277145

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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.