Evidence map›Paper›PMID 38326441›Full record

ArticleScientific reports2024

Identification and validation of a copper homeostasis-related gene signature for the predicting prognosis of breast cancer patients via integrated bioinformatics analysis.

Yi Li, Xiuxian Wei, Yuning Wang, Wenzhuo Wang, Cuntai Zhang, Deguang Kong, Yu Liu

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact, top 99% 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

1 citing paper in PubMed, 0 citations in OpenAlex.

  1. Article
4 · The record

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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

7 authors at 3 institutions in 1 country.

Yi LiDepartment of Geriatrics, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Building 6, 1095 Jiefang Avenue, Wuhan, 430030, People's Republic of China.
Xiuxian WeiDepartment of Geriatrics, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Building 6, 1095 Jiefang Avenue, Wuhan, 430030, People's Republic of China.
Yuning WangDepartment of Geriatrics, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Building 6, 1095 Jiefang Avenue, Wuhan, 430030, People's Republic of China.
Wenzhuo WangDepartment of Geriatrics, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Building 6, 1095 Jiefang Avenue, Wuhan, 430030, People's Republic of China.
Cuntai ZhangDepartment of Geriatrics, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Building 6, 1095 Jiefang Avenue, Wuhan, 430030, People's Republic of China.
Deguang KongDepartment of Breast and Thyroid Surgery, Renmin Hospital of Wuhan University, 238 Ziyang Road, Wuhan, 430060, People's Republic of China. kongdeguang08@126.com.
Yu LiuDepartment of Geriatrics, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Building 6, 1095 Jiefang Avenue, Wuhan, 430030, People's Republic of China. liuyu2012@tjh.tjmu.edu.cn.
Tongji Hospital · CNHuazhong University of Science and Technology · CNRenmin Hospital of Wuhan University · CN

Funding

National Natural Science Foundation of China NO.81801386National Natural Science Foundation of China NO.82103671the Knowledge Innovation Program of Wuhan-Shuguang Project NO.2022020801020452
6 · The paper itself

Abstract

The prognostic value of copper homeostasis-related genes in breast cancer (BC) remains largely unexplored. We analyzed copper homeostasis-related gene profiles within The Cancer Genome Atlas Program breast cancer cohorts and performed correlation analysis to explore the relationship between copper homeostasis-related mRNAs (chrmRNA) and lncRNAs. Based on these results, we developed a gene signature-based risk assessment model to predict BC patient outcomes using Cox regression analysis and a nomogram, which was further validated in a cohort of 72 BC patients. Using the gene set enrichment analysis, we identified 139 chrmRNAs and 16 core mRNAs via the Protein-Protein Interaction network. Additionally, our copper homeostasis-related lncRNAs (chrlncRNAs) (PINK1.AS, OIP5.AS1, HID.AS1, and MAPT.AS1) were evaluated as gene signatures of the predictive model. Kaplan-Meier survival analysis revealed that patients with a high-risk gene signature had significantly poorer clinical outcomes. Receiver operating characteristic curves showed that the prognostic value of the chrlncRNAs model reached 0.795 after ten years. Principal component analysis demonstrated the capability of the model to distinguish between low- and high-risk BC patients based on the gene signature. Using the pRRophetic package, we screened out 24 anticancer drugs that exhibited a significant relationship with the predictive model. Notably, we observed higher expression levels of the four chrlncRNAs in tumor tissues than in the adjacent normal tissues. The correlation between our model and the clinical characteristics of patients with BC highlights the potential of chrlncRNAs for predicting tumor progression. This novel gene signature not only predicts the prognosis of patients with BC but also suggests that targeting copper homeostasis may be a viable treatment strategy.

Indexed as

Breast NeoplasmsRNA, Long NoncodingComputational BiologyCopperFemaleHumansPrognosisRNA, MessengerCopperRNA, Long NoncodingRNA, MessengerBreast cancerCopper homeostasisImmune infiltrationlncRNAsTumor microenvironment

Identifiers

PMID38326441
PMCPMC10850146
OpenAlexW4391602309

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