Evidence map›Paper›PMID 36428940›Full record

ArticleDiagnostics (Basel, Switzerland)2022

The Analysis of Relevant Gene Networks Based on Driver Genes in Breast Cancer.

Luxuan Qu, Zhiqiong Wang, Hao Zhang, Zhongyang Wang, Caigang Liu, Wei Qian, Junchang Xin

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Diagnostics (Basel, Switzerland), 2022. 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

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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

7 authors.

Luxuan QuSchool of Computer Science and Engineering, Northeastern University, Shenyang 110169, China.ORCID 0000-0001-8452-2743
Zhiqiong WangCollege of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China.
Hao ZhangDepartment of Breast Surgery, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang 110042, China.
Zhongyang WangSchool of Computer Science and Engineering, Northeastern University, Shenyang 110169, China.ORCID 0000-0002-9956-795X
Caigang LiuDepartment of Breast Surgery, Shengjing Hospital of China Medical University, Shenyang 110004, China.ORCID 0000-0003-3729-2839
Wei QianCollege of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China.
Junchang XinSchool of Computer Science and Engineering, Northeastern University, Shenyang 110169, China.ORCID 0000-0003-2077-8269

Funding

Fundamental Research Funds for the Central Universities of China N2019007Fundamental Research Funds for the Central Universities of China N2104001Fundamental Research Funds for the Central Universities of China N2116016Fundamental Research Funds for the Central Universities of China N2224001-10National Natural Science Foundation of China 62072089
6 · The paper itself

Abstract

backgroundThe occurrence and development of breast cancer has a strong correlation with a person's genetics. Therefore, it is important to analyze the genetic factors of breast cancer for future development of potential targeted therapies from the genetic level.

methodsIn this study, we complete an analysis of the relevant protein-protein interaction network relating to breast cancer. This includes three steps, which are breast cancer-relevant genes selection using mutual information method, protein-protein interaction network reconstruction based on the STRING database, and vital genes calculating by nodes centrality analysis.

resultsThe 230 breast cancer-relevant genes were chosen in gene selection to reconstruct the protein-protein interaction network and some vital genes were calculated by node centrality analyses. Node centrality analyses conducted with the top 10 and top 20 values of each metric found 19 and 39 statistically vital genes, respectively. In order to prove the biological significance of these vital genes, we carried out the survival analysis and DNA methylation analysis, inquired about the prognosis in other cancer tissues and the RNA expression level in breast cancer. The results all proved the validity of the selected genes.

conclusionsThese genes could provide a valuable reference in clinical treatment among breast cancer patients.

Indexed as

breast cancercentrality analysismutual informationprotein–protein interactionsurvival analysis

Identifiers

PMID36428940
PMCPMC9689550

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