ArticleOncology letters2017
Prognostic genes of breast cancer revealed by gene co-expression network analysis.
Article in Oncology letters, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Genome-wide search algorithms for identifying dynamic gene co-expression via Bayesian variable selection.Statistics in medicine · 2023Article
- In silico analysis of differentially expressed-aberrantly methylated genes in breast cancer for prognostic and therapeutic targets.Clinical and experimental medicine · 2023Article
- Subtype-specific network organization of molecular complexes in breast cancer.Journal of biosciences · 2023Article
- Eight hub genes as potential biomarkers for breast cancer diagnosis and prognosis: A TCGA-based study.World journal of clinical oncology · 2022Article
- Comparative Analysis of Gene Correlation Networks of Breast Cancer Patients Based on Mutations in TP53.Biomolecules · 2022Article
- A Novel IGLC2 Gene Linked With Prognosis of Triple-Negative Breast Cancer.Frontiers in oncology · 2021Article
- Integrated analysis of co-expression and ceRNA network identifies five lncRNAs as prognostic markers for breast cancer.Journal of cellular and molecular medicine · 2019Article
- Identification of key modules and prognostic markers in adrenocortical carcinoma by weighted gene co-expression network analysis.Oncology letters · 2019Article
- PPWD1 is associated with the occurrence of postmenopausal osteoporosis as determined by weighted gene co‑expression network analysis.Molecular medicine reports · 2019Article
- Article
- Phylostratigraphic analysis of gene co-expression network reveals the evolution of functional modules for ovarian cancer.Scientific reports · 2019Article
- Integrated Bioinformatics Data Analysis Reveals Prognostic Significance Of SIDT1 In Triple-Negative Breast Cancer.OncoTargets and therapy · 2019Article
- Prognostic Genes of Breast Cancer Identified by Gene Co-expression Network Analysis.Frontiers in oncology · 2018Article
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Authors and funding
6 authors.
Funding
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Abstract
The aim of the present study was to identify genes that may serve as markers for breast cancer prognosis by constructing a gene co-expression network and mining modules associated with survival. Two gene expression datasets of breast cancer were downloaded from ArrayExpress and genes from these datasets with a coefficient of variation >0.5 were selected and underwent functional enrichment analysis with the Database for Annotation, Visualization and Integration Discovery. Gene co-expression networks were constructed with the WGCNA package in R. Modules were identified from the network via cluster analysis. Cox regression was conducted to analyze survival rates. A total of 2,669 genes were selected, and functional enrichment analysis of them revealed that they were mainly associated with the immune response, cell proliferation, cell differentiation and cell adhesion. Seven modules were identified from the gene co-expression network, one of which was found to be significantly associated with patient survival time. Expression status of 144 genes from this module was used to cluster patient samples into two groups, with a significant difference in survival time revealed between these groups. These genes were involved in the cell cycle and tumor protein p53 signaling pathway. The top 10 hub genes were identified in the module. The findings of the present study could advance the understanding of the molecular pathogenesis of breast cancer.
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