Identification of Prognostic Candidate Genes in Breast Cancer by Integrated Bioinformatic Analysis

Journal of Clinical Medicine
Charles C N WangMing-Feng Hou

Abstract

Breast cancer is one of the most common malignancies. However, the molecular mechanisms underlying its pathogenesis remain to be elucidated. The present study aimed to identify the potential prognostic marker genes associated with the progression of breast cancer. Weighted gene coexpression network analysis was used to construct free-scale gene coexpression networks, evaluate the associations between the gene sets and clinical features, and identify candidate biomarkers. The gene expression profiles of GSE48213 were selected from the Gene Expression Omnibus database. RNA-seq data and clinical information on breast cancer from The Cancer Genome Atlas were used for validation. Four modules were identified from the gene coexpression network, one of which was found to be significantly associated with patient survival time. The expression status of 28 genes formed the black module (basal); 18 genes, dark red module (claudin-low); nine genes, brown module (luminal), and seven genes, midnight blue module (nonmalignant). These modules were clustered into two groups according to significant difference in survival time between the groups. Therefore, based on betweenness centrality, we identified TXN and ANXA2 in the nonmalignant module, ...Continue Reading

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Citations

Jun 5, 2020·Circulation Research·Christoph D RauYibin Wang
May 28, 2020·DNA and Cell Biology·Yun-Hua XuGuo Wang
Jun 10, 2020·Cancer Cell International·Purong ZhangQing Lu
Jun 2, 2020·World Journal of Gastroenterology : WJG·Li-Wei QiuHai-Jian Zhang
Mar 7, 2021·Antioxidants·Sandra FerreiraAna S Fernandes
May 1, 2021·Diagnostics·Mario Ollero, Dil Sahali
Aug 20, 2021·Frontiers in Genetics·Christina Y Yu, Antonina Mitrofanova

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Datasets Mentioned

BETA
GSE48213

Software Mentioned

Ensembl Gene
WGCNA
DynamicTreeCut
Ensembl
GSEA
R
FunRich
R package tidygraph
FunRich functional enrichment
biomaRt

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