Evidence map›Paper›PMID 33499770›Full record

ArticleTechnology in cancer research & treatment

Key Genes and Prognostic Analysis in HER2+ Breast Cancer.

Yujie Weng, Wei Liang, Yucheng Ji, Zhongxian Li, Rong Jia, Ying Liang, Pengfei Ning, Yingqi Xu

Open access · goldAbstract read
In one paragraph

Article in Technology in cancer research & treatment. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
1.9field-weighted citation impact, top 14% 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

16 citing papers in PubMed, 28 citations in OpenAlex.

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  13. Survivin expression as a prognostic marker for breast cancer.Revista da Associacao Medica Brasileira (1992) · 2023
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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

8 authors at 2 institutions in 1 country.

Yujie WengCollege of Computer and Information, Inner Mongolia Medical University, Hohhot, Inner Mongolia Autonomous Region, China.
Wei LiangCollege of Computer and Information, Inner Mongolia Medical University, Hohhot, Inner Mongolia Autonomous Region, China.
Yucheng JiCollege of Computer and Information, Inner Mongolia Medical University, Hohhot, Inner Mongolia Autonomous Region, China.
Zhongxian LiCollege of Computer and Information, Inner Mongolia Medical University, Hohhot, Inner Mongolia Autonomous Region, China.
Rong JiaCollege of Computer and Information, Inner Mongolia Medical University, Hohhot, Inner Mongolia Autonomous Region, China.
Ying LiangCollege of Computer and Information, Inner Mongolia Medical University, Hohhot, Inner Mongolia Autonomous Region, China.
Pengfei NingCollege of Computer and Information, Inner Mongolia Medical University, Hohhot, Inner Mongolia Autonomous Region, China.
Yingqi XuCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province, China.ORCID 0000-0002-5426-8147
Inner Mongolia Medical University · CNHarbin Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Human epidermal growth factor 2 (HER2)+ breast cancer is considered the most dangerous type of breast cancers. Herein, we used bioinformatics methods to identify potential key genes in HER2+ breast cancer to enable its diagnosis, treatment, and prognosis prediction. Datasets of HER2+ breast cancer and normal tissue samples retrieved from Gene Expression Omnibus and The Cancer Genome Atlas databases were subjected to analysis for differentially expressed genes using R software. The identified differentially expressed genes were subjected to gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses followed by construction of protein-protein interaction networks using the STRING database to identify key genes. The genes were further validated via survival and differential gene expression analyses. We identified 97 upregulated and 106 downregulated genes that were primarily associated with processes such as mitosis, protein kinase activity, cell cycle, and the p53 signaling pathway. Visualization of the protein-protein interaction network identified 10 key genes (

Indexed as

Gene Expression Regulation, NeoplasticBiomarkers, TumorBreast NeoplasmsComputational BiologyErb-b2 Receptor Tyrosine KinasesFemaleGene Expression ProfilingGene OntologyGenomicsHumansPrognosisProtein Interaction MappingROC CurveSurvival AnalysisTranscriptomeBiomarkers, TumorERBB2 protein, humanErb-b2 Receptor Tyrosine KinasesbioinformaticsHER2+ breast cancerkey geneprognosis-related genetherapeutic targetTPX2

Identifiers

PMID33499770
PMCPMC7844453
OpenAlexW3122953404

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.