Evidence map›Paper›PMID 41425594›Full record

ArticleFrontiers in immunology2025

Advancing breast cancer biomarkers: a centromere-related gene signature integrated with single-cell analysis for prognostic prediction.

Ye Lu, Shengbin Pei, Wenxiang Zhang, Zheng Qu, Xiangyu Wang, Siqing Liu, Hao Dong, Kan Yonemori, Yi Fang, Xiangyi Kong and 2 more

Abstract read
In one paragraph

Article in Frontiers in immunology, 2025. 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
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.

  1. Article
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

12 authors.

Ye Lu *Department of Breast Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Shengbin Pei *Department of Breast Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Wenxiang Zhang *Department of Breast Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Zheng Qu *Department of Breast Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Xiangyu WangDepartment of Breast Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Siqing LiuDepartment of Breast Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Hao DongDepartment of Oncoplastic Reconstructive and Aesthetic Breast Surgery, Plastic Surgery Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Kan YonemoriDepartment of Medical Oncology, National Cancer Center Hospital, Tokyo, Japan.
Yi FangDepartment of Breast Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Xiangyi KongDepartment of Breast Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Jing WangDepartment of Breast Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Jidong GaoDepartment of Breast Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Breast cancer (BC) is the most common malignancy among women and shows significant heterogeneity in its prognosis. Among the subtypes, triple-negative breast cancer (TNBC) has the poorest prognosis. Despite advancements in molecular stratification tools, such as Oncotype DX and MammaPrint, prognostic models based on chromosomal instability are still insufficient. The centromere protein (CENP) family, which plays a crucial role in maintaining genomic stability, is associated with tumor progression due to aberrant expression. Methods: In this study, we integrated multi-omics data, including RNA transcriptomic profiles and single-cell RNA sequencing, to identify gene modules linked to CENPA using weighted gene co-expression network analysis (WGCNA). We developed a prognostic model employing Cox regression and the LASSO algorithm. Validation was performed on independent cohorts, and the model's performance was tested by stratifying patients into high- and low-risk groups based on their five-year survival rates (p < 0.001). Results: The prognostic model effectively identified high- and low-risk patient groups, with the high-risk group showing significantly reduced five-year survival. Single-cell analysis revealed that CENPA-high subpopulations were enriched in proliferative tumor cells and were associated with an immunosuppressive tumor microenvironment. Conclusion: This study is the first to establish a CENP-based prognostic model for BC, offering novel biomarkers and potential therapeutic targets for personalized treatment. Additionally, the biological function of the key molecule MMP1 was validated through both

Indexed as

Biomarkers, TumorBreast NeoplasmsCentromereCentromere Protein ATranscriptomeTriple Negative Breast NeoplasmsFemaleGene Expression ProfilingGene Expression Regulation, NeoplasticGene Regulatory NetworksHumansPrognosisSingle-Cell AnalysisTumor MicroenvironmentBiomarkers, TumorCENPA protein, humanCentromere Protein Abreast cancerCENPsMMP1prognostic modelsingle-cell RNA sequencing

Identifiers

PMID41425594
PMCPMC12711834

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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.