Evidence map›Paper›PMID 40500539›Full record

ArticleDiscover oncology2025

Mechanisms of organotropism in breast cancer and predicting metastasis to distant organs using deep learning.

Meizhu Xiao, Zhijin Fu, Yanjiao Li, Min Zhang, Denan Zhang, Lei Liu, Qing Jin, Xiujie Chen, Hongbo Xie

Abstract read
In one paragraph

Article in Discover oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

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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

2 citing papers in PubMed.

  1. Review
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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

9 authors.

Meizhu Xiao *Department of Pharmacogenomics, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, People's Republic of China.
Zhijin Fu *Department of Pharmacogenomics, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, People's Republic of China.
Yanjiao Li *Department of Pharmacogenomics, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, People's Republic of China.
Min ZhangDepartment of Pharmacogenomics, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, People's Republic of China.
Denan ZhangDepartment of Pharmacogenomics, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, People's Republic of China.
Lei LiuDepartment of Pharmacogenomics, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, People's Republic of China.
Qing JinDepartment of Pharmacogenomics, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, People's Republic of China.
Xiujie ChenDepartment of Pharmacogenomics, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, People's Republic of China. chenxiujie@ems.hrbmu.edu.cn.
Hongbo XieDepartment of Pharmacogenomics, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, People's Republic of China. xiehongbo@ems.hrbmu.edu.cn.

Funding

Heilongjiang Provincial Postdoctoral Science Foundation LBH-Q19144National Natural Science Foundation of China 61971166National Natural Science Foundation of China 62072144Postdoctoral Scientific Research Development Fund of Heilongjiang Province LBH-Q20159
6 · The paper itself

Abstract

backgroundMetastasis, the spread of cancer cells from the primary tumor to distant organs, is the leading cause of mortality in cancer patients. This process often exhibits a preference for specific organs, a phenomenon known as tumor organotropism. This study focuses on the organotropism of breast cancer and analyzes its genomic alterations following metastasis to four organs (bone, brain, liver, and lung). The research aims to explore the intrinsic characteristics of primary breast cancer and the interactions between tumor cells and the tumor microenvironment (TME) within these target organs. Building upon this foundation, we developed a deep learning model to identify organ-specific metastatic genes, providing insights into the molecular mechanisms of metastasis.

methodsTo investigate the mechanisms of organ-specific metastasis in breast cancer, we employed an integrative approach combining single-cell RNA sequencing, bulk RNA sequencing, ChIP-seq data, and deep learning techniques. Single-cell analysis provided detailed insights into cellular heterogeneity and microenvironment interactions at metastatic sites. Bulk RNA sequencing enabled the identification of gene expression patterns associated with metastatic propensity. A deep neural network (DNN) model was developed to analyze these complex datasets and identify key predictors of organ-specific metastasis.

resultsOur integrative analysis revealed distinct gene expression profiles and cellular compositions in metastatic lesions across different organs. We have identified that, regardless of the target organ, breast cancer metastasis critically depends on specific biological signaling pathways, including the MAPK signaling pathway, metabolic pathways, the PI3K-Akt signaling pathway, and the positive regulation of cell adhesion. Single-cell sequencing highlighted unique interactions between tumor cells and the microenvironment, which varied significantly depending on the metastatic site. Fibroblasts play a critical role in facilitating the colonization of breast cancer cells in metastatic organs. The deep learning models effectively identified key molecular signatures and pathways associated with organ-specific metastasis, providing insights into the metastatic process.

conclusionThe study underscores the importance of the tumor microenvironment in influencing breast cancer metastasis to distant organs. We also established a comprehensive framework for understanding the mechanisms driving organotropism metastasis in breast cancer. Additionally, we identified key genes and signaling pathways associated with organ-specific metastasis, providing insights that may inform future studies on risk assessment and potential therapeutic targets for metastatic breast cancer.

Indexed as

Breast cancerDeep neural networksMetastasisOrganotropism

Identifiers

PMID40500539
PMCPMC12158884

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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.