Evidence map›Paper›PMID 40198525›Full record

ArticleDiscover oncology2025

Causal relationship between breast cancer and acute myeloid leukemia based on two-sample bidirectional Mendelian randomization and transcriptome overlap analysis.

Xin'an Pan, Cuihan Huang, Xinyi Bai, Fei Li

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Article in Discover oncology, 2025. 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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5 · Who and what money

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4 authors.

Xin'an Pan *Department of Hematology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, No. 17 Yongwai Zheng Street, East Lake District, Nanchang City, 330006, Jiangxi Province, China.
Cuihan Huang *The First Clinical Medical College of Nanchang University, Xuefu Road, Nanchang, 330006, Jiangxi, China.
Xinyi BaiSchool of Public, Health of Nanchang University, Xuefu Road, Nanchang, 330006, Jiangxi, China.
Fei LiDepartment of Hematology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, No. 17 Yongwai Zheng Street, East Lake District, Nanchang City, 330006, Jiangxi Province, China. yx021021@sina.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundBreast cancer is the most prevalent malignancy and the leading cause of cancer-related deaths among women worldwide. Several case reports have shown that some breast cancer patients subsequently develop acute myeloid leukemia (AML) within a short period. However, the causal relationship and pathogenic mechanisms between breast cancer and AML remain incompletely understood.

methodsMendelian randomization (MR) analyses were conducted to explore the bidirectional causal relationships between breast cancer and AML. Additionally, we applied the Bayesian Weighted Mendelian Randomization (BWMR) approach to validate the results of the MR analysis. Subsequently, we utilized RNA-seq data from various sources to explore the potential molecular signaling pathways between breast cancer and AML.

resultsBoth IVW method and BWMR approach demonstrated that data from three distinct sources consistently indicated breast cancer as a risk factor for AML, with all sources showing statistically significant results (all P < 0.05, Odds Ratios [ORs] > 1). Bioinformatic analyses suggested that extracellular vesicle functions and p53 signaling pathway may mediate molecular links between breast cancer and AML. Using machine learning, we identified 8 genes with high diagnostic efficacy for predicting the occurrence of AML in breast cancer patients.

conclusionsMR analyses indicated a causal relationship between breast cancer and AML. Additionally, transcriptome analysis offered a theoretical basis for understanding the potential mechanisms and therapeutic targets of AML in breast cancer patients.

Indexed as

Acute myeloid leukemiaBreast cancerMendelian randomizationTranscriptome overlap analysis

Identifiers

PMID40198525
PMCPMC11979033

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