Evidence map›Paper›PMID 38259456›Full record

ArticleFrontiers in immunology2023

A zinc metabolism-related gene signature for predicting prognosis and characteristics of breast cancer.

Jinghui Hong, Mengxin Li, Yichang Chen, Ye Du, Dong Song

Open access · goldAbstract read
In one paragraph

Article in Frontiers in immunology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
1.2field-weighted citation impact, top 25% of its field
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

8 citing papers in PubMed, 4 citations in OpenAlex.

  1. Bioinformatics Analysis of the Diagnostic Value of Copper and Zinc Metabolism-Related Genes in Major Depressive Disorder: An In Silico Multi-Cohort Study.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2026
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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

5 authors at 2 institutions in 1 country.

Jinghui HongDepartment of Breast Surgery, General Surgery Center, The First Hospital of Jilin University, Changchun, Jilin, China.
Mengxin LiDepartment of Breast Surgery, General Surgery Center, The First Hospital of Jilin University, Changchun, Jilin, China.
Yichang ChenDepartment of Breast Surgery, General Surgery Center, The First Hospital of Jilin University, Changchun, Jilin, China.
Ye DuDepartment of Breast Surgery, General Surgery Center, The First Hospital of Jilin University, Changchun, Jilin, China.
Dong SongDepartment of Breast Surgery, General Surgery Center, The First Hospital of Jilin University, Changchun, Jilin, China.
First Hospital of Jilin University · CNJilin University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Breast cancer is one of the most serious and prevalent malignancies. Zinc is commonly known to play a crucial role in the development and progression of breast cancer; however, the detailed mechanisms underlying this role are not well understood. This study aimed to develop a zinc metabolism-related gene (ZMRG) signature based on a multi-database study to predict patient prognosis and investigate the relationship between drug therapy response and immune enrichment. Methods: Data for breast cancer samples from The Cancer Genome Atlas and Gene Expression Omnibus databases were screened for zinc metabolism-related genes using the Molecular Signature Database. Cox and Least Absolute Shrinkage and Selection Operator regressions were performed to construct a ZMRG signature. To assess the predictive performance of the gene signature, Kaplan-Meier analysis and receiver operating characteristic curves were used. Additionally, we utilised single-sample gene set enrichment analysis, the Tumour Immune Estimation Resource, the Genomics of Drug Sensitivity in Cancer database, and the Cancer Therapeutics Response Portal to investigate the association between the tumour microenvironment and drug sensitivity. Quantitative PCR was used to assess the expression of each gene in the signature in breast cancer cell lines and patient samples. Results: Five ZMRGs were identified (ATP7B, BGLAP, P2RX4, SLC39A11, and TH) and a risk profile was constructed for each. Two risk groups, high- and low-risk, were identified in this way, and the high-risk score subgroups were found to have worse prognosis. This risk profile was validated using the GSE42568 dataset. Tumour microenvironment and drug sensitivity analyses showed that the expression of these five ZMRGs was significantly associated with immune response. The high-risk group showed substantial immune cell infiltration and enrichment of immune pathways, and patients were more sensitive to drugs commonly used in breast cancer. Conclusion: The ZMRG signature represents a new prognostic predictor for patients with breast cancer, and may also provide new insights into individualised treatment of breast cancer.

Indexed as

Breast NeoplasmsDatabases, ChemicalDatabases, FactualFemaleHumansPrognosisTumor MicroenvironmentZincZincbreast cancerprognosissignaturetumour microenvironmentzinc metabolism

Identifiers

PMID38259456
PMCPMC10800782
OpenAlexW4390666376

What OpenQuestion holds

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LicenceCC BY
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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 OpenQuestion graph.