Evidence map›Paper›PMID 37974462›Full record

ArticleCell cycle (Georgetown, Tex.)2023

Multi-omics integration analysis unveils heterogeneity in breast cancer at the individual level.

Zhangxiang Zhao, Tongzhu Jin, Bo Chen, Qi Dong, Mingyue Liu, Jiayu Guo, Xiaoying Song, Yawei Li, Tingting Chen, Huiming Han and 2 more

Open access · hybridAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cell cycle (Georgetown, Tex.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
0.9field-weighted citation impact, top 20% 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

5 citing papers in PubMed, 1 synthesis or guideline pooled it, 4 citations in OpenAlex.

  1. Pooled it
  2. Review
  3. Review
  4. Article
  5. 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

12 authors at 2 institutions in 1 country.

Zhangxiang ZhaoThe Sino-Russian Medical Research Center of Jinan University, The Institute of Chronic Disease of Jinan University, The First Affiliated Hospital of Jinan University, Guangzhou, China.
Tongzhu JinDepartment of Pharmacology (State-Province Key Laboratories of Biomedicine-Pharmaceutics of China, Key Laboratory of Cardiovascular Research, Ministry of Education), College of Pharmacy, Harbin Medical University, Harbin, China.
Bo ChenDepartment of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Qi DongDepartment of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Mingyue LiuDepartment of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Jiayu GuoDepartment of Pharmacology (State-Province Key Laboratories of Biomedicine-Pharmaceutics of China, Key Laboratory of Cardiovascular Research, Ministry of Education), College of Pharmacy, Harbin Medical University, Harbin, China.
Xiaoying SongDepartment of Pharmacology (State-Province Key Laboratories of Biomedicine-Pharmaceutics of China, Key Laboratory of Cardiovascular Research, Ministry of Education), College of Pharmacy, Harbin Medical University, Harbin, China.
Yawei LiDepartment of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Tingting ChenDepartment of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Huiming HanDepartment of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Haihai LiangThe Sino-Russian Medical Research Center of Jinan University, The Institute of Chronic Disease of Jinan University, The First Affiliated Hospital of Jinan University, Guangzhou, China.
Yunyan GuDepartment of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Harbin Medical University · CNFirst Affiliated Hospital of Jinan University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Identifying robust breast cancer subtypes will help to reveal the cancer heterogeneity. However, previous breast cancer subtypes were based on population-level quantitative gene expression, which is affected by batch effects and cannot be applied to individuals. We detected differential gene expression, genomic, and epigenomic alterations to identify driver differential expression at the individual level. The individual driver differential expression reflected the breast cancer patients' heterogeneity and revealed four subtypes. Mesenchymal subtype as the most aggressive subtype harbored deletion and downregulated expression of genes in chromosome 11q23 region. Specifically, silencing of the

Indexed as

Breast NeoplasmsEpigenomicsFemaleGene Expression ProfilingGenomicsHumansMultiomicsTumor MicroenvironmentBreast cancerindividual differential expressionmulti-omics

Identifiers

PMID37974462
PMCPMC10730166
OpenAlexW4388771744

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.