Evidence map›Paper›PMID 39614330›Full record

ArticleBreast cancer research : BCR2024

Uncovering immune cell-associated genes in breast cancer: based on summary data-based Mendelian randomized analysis and colocalization study.

Jingyang Liu, Wen Sun, Ning Li, Haibin Li, Lijuan Wu, Huan Yi, Jianguang Ji, Deqiang Zheng

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Breast cancer research : BCR, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

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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

8 authors.

Jingyang Liu *Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China.
Wen Sun *Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China.
Ning LiDepartment of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China.
Haibin LiDepartment of Cardiac Surgery, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China.
Lijuan WuDepartment of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China.
Huan Yi *Department of Gynecologic Oncology, Fujian Maternity and Child Health Hospital College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Medical University, Fuzhou, Fujian, China. yi.huan@fjmu.edu.cn.
Jianguang Ji *Faculty of Health Science, University of Macau, Taipa, Macao SAR, China. Jianguangji@um.edu.mo.
Deqiang Zheng *Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China. dqzheng@ccmu.edu.cn.

Funding

Beijing Municipal Health System Special Funds of High-Level Medical Personnel Construction 2022-3-042Swedish Research Council 2021-01187
6 · The paper itself

Abstract

backgroundBreast cancer, which is the most prevalent form of cancer among women globally, encompasses various subtypes that demand distinct treatment approaches. The tumor microenvironment and immune response are of crucial significance in the development and progression of breast cancer. Nevertheless, there has been scant evidence concerning the genes within breast cancer - specific immune cells.

methodsWe utilized summary data-based Mendelian randomization (SMR) to identify genes associated with breast cancer by utilizing expression quantitative trait loci (eQTL) datasets for 14 different immune cell types and genome-wide association studies (GWAS) for overall breast cancer and its subtypes. Furthermore, colocalization analysis was carried out to evaluate whether the observed association in SMR analyses is influenced by the same causal variant. Replication analysis and bulk RNA sequencing (bulkRNA-seq) analysis were employed to validate promising immune genes as potential drug targets.

resultsAfter correcting for the rate of false discovery, we discovered a total of 17 genes in 9 immune cell types that were significantly associated with overall breast cancer and its subtypes. The genes KCNN4, L3MBTL3, ZBTB38, MDM4, and TNFSF10 were identified in overall breast cancer and its subtypes. Colocalization analyses provided robust evidence in support of these associations. Notably, the KCNN4 gene in non-classical MONOcytes (MONOnc) was further validated through replication analysis and bulkRNA-seq analysis.

conclusionIn summary, our research has revealed a repertoire of genes within diverse immune cells associated with breast cancer. KCNN4 gene in non-classical MONOcytes (MONOnc) exhibited a negative association with overall breast cancer and its subtypes, which was identified as a potential drug target for breast cancer, opening up new avenues for therapeutic interventions.

Indexed as

Breast NeoplasmsGenome-Wide Association StudyMendelian Randomization AnalysisQuantitative Trait LociBiomarkers, TumorFemaleGene Expression Regulation, NeoplasticGenetic Predisposition to DiseaseHumansPolymorphism, Single NucleotideTumor MicroenvironmentBiomarkers, TumorBreast cancerBulkRNA-seqDrug targetImmune cellMendelian randomizationscRNA-seq

Identifiers

PMID39614330
PMCPMC11606077

What Socratic holds

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LicenceCC BY-NC-ND
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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 Socratic graph.