Evidence map›Paper›PMID 40621063›Full record

ArticleComputational and structural biotechnology journal2025

BCMA: An integrative and versatile database for multi-scale and multi-omics molecular atlas of breast cancer.

Wenbo Guo, Zikang Yin, Qinglin Mei, Lianshuo Li, Yonghui Gong, Xinqi Li, Wei Zhang, Wenjie Lei, Bingqiang Liu, Lin Hou and 2 more

Abstract read
In one paragraph

Article in Computational and structural biotechnology journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Wenbo GuoMOE Key Lab of Bioinformatics, BNRIST Bioinformatics Division, Department of Automation, Tsinghua University, Beijing, China.
Zikang YinMOE Key Lab of Bioinformatics, BNRIST Bioinformatics Division, Department of Automation, Tsinghua University, Beijing, China.
Qinglin MeiMOE Key Lab of Bioinformatics, BNRIST Bioinformatics Division, Department of Automation, Tsinghua University, Beijing, China.
Lianshuo LiState Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China.
Yonghui GongHainan Zhongke Data Source Technology Co., Ltd., Hainan, China.
Xinqi LiMOE Key Lab of Bioinformatics, BNRIST Bioinformatics Division, Department of Automation, Tsinghua University, Beijing, China.
Wei ZhangDepartment of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, Shandong, China.
Wenjie LeiHainan Zhongke Data Source Technology Co., Ltd., Hainan, China.
Bingqiang LiuSchool of Mathematics, Shandong University, Jinan, China.
Lin HouDepartment of Statistics and Data Science, Tsinghua University, Beijing, China.
Mei YangDepartment of Breast Surgery, Zhujiang Hospital of Southern Medical University (the Second Clinical Medical College), Guangzhou, China.
Jin GuMOE Key Lab of Bioinformatics, BNRIST Bioinformatics Division, Department of Automation, Tsinghua University, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Breast cancer (BC) is one of the most common cancer types among women worldwide. Understanding the complex molecular and cellular characteristics of BC is crucial for advancing precision treatment. To enable more reliable and reproducible biological discoveries, it is critical to collect molecular data from diverse BC cohorts and establish an integrative, versatile analysis platform. Here, we present BCMA (Breast Cancer Molecular Atlas, http://lifeome.net/database/bcma/), a multi-scale, multi-omics BC database that encompasses 6 bulk multi-omics datasets and 9 single-cell transcriptomics datasets, collectively covering 5424 cases and 236,363 cells. The BCMA systemically characterizes the molecular features of BC, including gene mutations, copy number alterations, RNA expression, miRNA expression, DNA methylation, as well as clinical phenotypes and cell heterogeneity. Meanwhile, a user-friendly interface for gene-centered search is provided, achieving the clinical information statistics, genomic events analysis, differential multi-omics feature identification, functional enrichment analysis, survival analysis, co-expression analysis, as well as single-cell gene expression profiling and cell type annotation. This platform holds great potential to enhance the understanding of molecular characteristics underlying BC and to facilitate the identification of disease-associated biomarkers.

Indexed as

Breast cancerDatabaseMolecular atlasMulti-omicsSingle-cell

Identifiers

PMID40621063
PMCPMC12226376

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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.