Evidence map›Paper›PMID 41427984›Full record

ArticleDiscover oncology2025

Novel common target genes for breast cancer and colorectal cancer: a Mendelian randomization and spatial transcriptomics study.

Pengyu Miao, Zhaokai Zhou, Ling Zhang, Xufeng Huang, Zhengrui Li, Shouxin Wei, András Hajdu

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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. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Pengyu Miao *Southwest Medical University, Luzhou, Sichuan, China.
Zhaokai Zhou *The Second Xiangya Hospital of Central South University, Changsha, 410011, China.
Ling ZhangShanghai Jiao Tong University School of Medicine, Shanghai, China.
Xufeng HuangDepartment of Data Visualization, Faculty of Informatics, University of Debrecen, Debrecen, Hungary. huangxufeng@mailbox.unideb.hu.
Zhengrui LiShanghai Jiao Tong University School of Medicine, Shanghai, China. lzr_0108@sjtu.edu.cn.
Shouxin WeiDepartment of Gastrointestinal Surgery, Suining Central Hospital, Suining, China. 1079656665@qq.com.
András HajduDepartment of Data Visualization, Faculty of Informatics, University of Debrecen, Debrecen, Hungary.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionBreast and colorectal cancer are a major global public health problem. Breast cancer is one of the most common cancers worldwide. Colorectal cancer is the third most common cancer and the second most common cause of tumor death worldwide. Central memory T (TCM) cells are closely related to the development of tumors and important targets for immunotherapy. Therefore, identifying the common signaling molecules of these two diseases in TCM cells can improve our understanding of these diseases and lead to the development of therapies that can be effective for treating both.

methodsSingle-cell RNA (scRNA) data of breast cancer (GSE161529) and colorectal cancer (GSE222300) patients was downloaded from the GEO database. The data were normalized and dimension reduced, then different T cell subsets were identified and differential gene expression analysis of central memory CD8 + T cells was conducted. Mendelian randomization analysis, reverse causality detection, and co-localization analysis was performed to explore the relationship between differentially-expressed genes and the disease. Quasi-temporal analysis and metabolic analysis was done using scRNA sequencing technology and further analysis of gene expression and metabolism in spatial transcriptomes. Finally, the degree of association between drug target genes was analyzed by protein-protein interaction (PPI) analysis.

resultsOur analysis identified four genes (ZFP36L2, CKS1B, PTTG1, and ITGAE) that were associated with risk of both breast and colorectal cancer. In the pseudotime analysis, we found that the expression levels of CKS1B and PTTG1 decreased over time (p < 0.05) while ZFP36L2 and ITGAE increased over time (p < 0.05). In the metabolic analysis, these four genes were closely associated with the cysteine and methionine metabolism pathways, which was corroborated in the spatial transcription analysis. Finally, the PPI analysis among the drug target genes identified an interaction between PTTG1 and CKS1B genes.

conclusionThis study reports that the ZFP36L2, CKS1B, PTTG1, and ITGAE genes could potentially influence breast cancer and colorectal cancer development via TCM CD8 + T cells. These four genes are putative common markers for diagnosis, treatment, and monitoring tumor response to therapies.

Indexed as

Breast cancerColorectal cancerMendelian randomizationSingle-cell RNA sequencingSpatial transcriptomics

Identifiers

PMID41427984
PMCPMC12722630

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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.