Evidence map›Paper›PMID 38472225›Full record

ArticleScientific data2024

Single-cell integrative analysis reveals consensus cancer cell states and clinical relevance in breast cancer.

Lin Pang, Fengyu Xiang, Huan Yang, Xinyue Shen, Ming Fang, Ran Li, Yongjin Long, Jiali Li, Yonghuan Yu, Bo Pang

Open access · goldAbstract read
In one paragraph

Article in Scientific data, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
4.0field-weighted citation impact, top 6% 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

14 citing papers in PubMed, 17 citations in OpenAlex.

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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

10 authors at 1 institution in 1 country.

Lin Pang *College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China. panglin@hrbmu.edu.cn.ORCID 0000-0002-7171-6386
Fengyu Xiang *College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.ORCID 0009-0007-7182-5463
Huan Yang *College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
Xinyue ShenCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
Ming FangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
Ran LiCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
Yongjin LongCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
Jiali LiCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
Yonghuan YuCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
Bo PangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China. pangbo@ems.hrbmu.edu.cn.ORCID 0000-0002-0323-5484
Harbin Medical University · CN

Funding

National Natural Science Foundation of China (National Science Foundation of China) Nos. 82173321
6 · The paper itself

Abstract

High heterogeneity and complex interactions of malignant cells in breast cancer has been recognized as a driver of cancer progression and therapeutic failure. However, complete understanding of common cancer cell states and their underlying driver factors remain scarce and challenging. Here, we revealed seven consensus cancer cell states recurring cross patients by integrative analysis of single-cell RNA sequencing data of breast cancer. The distinct biological functions, the subtype-specific distribution, the potential cells of origin and the interrelation of consensus cancer cell states were systematically elucidated and validated in multiple independent datasets. We further uncovered the internal regulons and external cell components in tumor microenvironments, which contribute to the consensus cancer cell states. Using the state-specific signature, we also inferred the abundance of cells with each consensus cancer cell state by deconvolution of large breast cancer RNA-seq cohorts, revealing the association of immune-related state with better survival. Our study provides new insights for the cancer cell state composition and potential therapeutic strategies of breast cancer.

Indexed as

Breast NeoplasmsSingle-Cell AnalysisClinical RelevanceDatasets as TopicFemaleHumansSequence Analysis, RNATumor Microenvironment

Identifiers

PMID38472225
PMCPMC10933444
OpenAlexW4392682815

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.