Evidence map›Paper›PMID 39315182›Full record

ArticleHeliyon2024

Copper metabolism-related signature for prognosis prediction and MMP13 served as malignant factor for breast cancer.

Chaojie Han, Zhangyang Feng, Yingjian Wang, Mengsi Hu, Shoufang Xu, Feiyu Jiang, Yetao Han, Zhiwei Liu, Yunsen Li

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–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

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

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Review
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.

Chaojie HanInstitutes of Biology and Medical Sciences, Soochow University, 333 East Ganjiang Road, Suzhou, Jiangsu, 215127, China.
Zhangyang FengInstitutes of Biology and Medical Sciences, Soochow University, 333 East Ganjiang Road, Suzhou, Jiangsu, 215127, China.
Yingjian WangDepartment of Blood Transfusion, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, 3 East Qingchun Road, Hangzhou, Zhejiang, 310016, China.
Mengsi HuDepartment of Blood Transfusion, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, 3 East Qingchun Road, Hangzhou, Zhejiang, 310016, China.
Shoufang XuDepartment of Blood Transfusion, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, 3 East Qingchun Road, Hangzhou, Zhejiang, 310016, China.
Feiyu JiangDepartment of Blood Transfusion, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, 3 East Qingchun Road, Hangzhou, Zhejiang, 310016, China.
Yetao HanDepartment of Blood Transfusion, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, 3 East Qingchun Road, Hangzhou, Zhejiang, 310016, China.
Zhiwei LiuDepartment of Blood Transfusion, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, 3 East Qingchun Road, Hangzhou, Zhejiang, 310016, China.
Yunsen LiInstitutes of Biology and Medical Sciences, Soochow University, 333 East Ganjiang Road, Suzhou, Jiangsu, 215127, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: To comprehensively analyze the copper metabolism in Breast cancer, we established a prognostic signature for breast cancer (BC) related to copper metabolism. Methods: Copper metabolism-related genes were sourced from previous literatures and were selected by the Univariate Cox regression. Cu-enrichment scores were calculated via ssGSEA. Differentially expressed genes were identified with limma between high and low Cu-enrichment scores group, then we used the Random Survival Forest and LASSO to build the CuScore for BC. Kaplan-Meier analysis, ROC curves, and Cox regression were used to evaluate CuScore. Genomic mutations were analyzed with GISTIC. Immune cells were examined using ESTIMATE, ssGSEA and TIMER. Enrichment analysis used clusterProfiler and GSVA. The GDSC database and oncoPredict package analyzed chemotherapeutic sensitivity. MMP13 was selected for in vitro assays. Results: Four copper metabolism-related genes (UBE2D2, SLC31A1, ATP7A, and MAPK1) with prognostic value were identified. Higher expression levels of these genes were associated with higher Cu-enrichment scores, a factor of malignancy in breast cancer. Among 115 differentially expressed genes, 19 prognostic genes were identified, with three (CEACAM5, MMP13, and CRISP3) highlighted by Random Survival Forest and LASSO. Higher CuScores correlated with worse prognoses and were effective in predicting breast cancer outcomes. CuScore and metastasis were independent prognostic factors. Tumor-infiltrating immune cells were associated with lower CuScores. GO-GSEA analysis indicated six immune-related pathways might be regulated by CuScore. Patients with higher CuScores had lower TMB and were more sensitive to Sapitinib and LCL161, while those with lower CuScores might respond better to anti-PD1 therapy. High MMP13 expression in breast cancer was linked to malignancy, affecting cell proliferation and migration. Conclusion: The identified copper metabolism-related gene signature has the potential to predict prognosis and guide clinical treatment for BC. Among these genes, MMP13 may act as a malignant factor in BC.

Indexed as

Breast cancerCopper metabolismMMP13PrognosisTreatment

Identifiers

PMID39315182
PMCPMC11417231

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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.