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pmid:30254986

Prognostic Genes of Breast Cancer Identified by Gene Co-expression Network Analysis.

Jianing Tang · Deguang Kong · Qiuxia Cui · Kun Wang · Dan Zhang · Yan Gong · Gaosong Wu

Frontiers in oncology · 2018 doi:10.3389/fonc.2018.00374

Paper

complete

Experiment

complete

Data

partial

Analysis

partial

Execution

absent

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Record details sci_version · id · kind · revision · content_hash · schema · created_at · updated_at · generator
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pmid:30254986
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science-record
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2026-11-01T15:40:37Z
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2026-11-01T21:21:40Z
generator object · 2 fields
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title
Prognostic Genes of Breast Cancer Identified by Gene Co-expression Network Analysis.
doi
10.3389/fonc.2018.00374
pmid
30254986
authors array · 7 items
[0]
Jianing Tang
[1]
Deguang Kong
[2]
Qiuxia Cui
[3]
Kun Wang
[4]
Dan Zhang
[5]
Yan Gong
[6]
Gaosong Wu
journal
Frontiers in oncology
year
2018
abstract
Breast cancer is one of the most common malignancies. The molecular mechanisms of its pathogenesis are still to be investigated. The aim of this study was to identify the potential genes associated with the progression of breast cancer. Weighted gene co-expression network analysis (WGCNA) was used to construct free-scale gene co-expression networks to explore the associations between gene sets and clinical features, and to identify candidate biomarkers. The gene expression profiles of GSE1561 were selected from the Gene Expression Omnibus (GEO) database. RNA-seq data and clinical information of breast cancer from TCGA were used for validation. A total of 18 modules were identified via the average linkage hierarchical clustering. In the significant module (R2 = 0.48), 42 network hub genes were identified. Based on the Cancer Genome Atlas (TCGA) data, 5 hub genes (CCNB2, FBXO5, KIF4A, MCM10, and TPX2) were correlated with poor prognosis. Receiver operating characteristic (ROC) curve validated that the mRNA levels of these 5 genes exhibited excellent diagnostic efficiency for normal and tumor tissues. In addition, the protein levels of these 5 genes were also significantly higher in tumor tissues compared with normal tissues. Among them, CCNB2, KIF4A, and TPX2 were further upregulated in advanced tumor stage. In conclusion, 5 candidate biomarkers were identified for further basic and clinical research on breast cancer with co-expression network analysis.
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Weighted gene co-expression network analysis (WGCNA) was used to construct free-scale gene co-expression networks to explore the associations between gene sets and clinical features, and to identify candidate biomarkers.
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sections[0] "Abstract" paragraph 1
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Weighted gene co-expression network analysis (WGCNA) was used to construct free-scale gene co-expression networks to explore the associations between gene sets and clinical features, and to identify candidate biomarkers.
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The gene expression profiles of GSE1561 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE1561) submitted by Richard Iggo et al. was downloaded from the Gene Expression Omnibus (GEO) database.
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sections[3] "Data procession" paragraph 2
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The gene expression profiles of GSE1561 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE1561) submitted by Richard Iggo et al. was downloaded from the Gene Expression Omnibus (GEO) database.
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Homo sapiens
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EORTC 10994 clinical trial
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2026-11-01T15:39:54Z
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Weighted gene co-expression network analysis (WGCNA) was used to construct free-scale gene co-expression networks to explore the associations between gene sets and clinical features, and to identify candidate biomarkers.
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Automated Assays Laravel application and simulated adapters
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Weighted gene co-expression network analysis (WGCNA) was used to construct free-scale gene co-expression networks to explore the associations between gene sets and clinical features, and to identify candidate biomarkers. The gene expression profiles of GSE1561 were selected from the Gene Expression Omnibus (GEO) database. RNA-seq data and clinical information of breast cancer from TCGA were used for validation. A total of 18 modules were identified via the average linkage hierarchical clustering. In the significant module (R2 = 0.48), 42 network hub genes were identified. Based on the Cancer Genome Atlas (TCGA) data, 5 hub genes (CCNB2, FBXO5, KIF4A, MCM10, and TPX2) were correlated with poor prognosis. Receiver operating characteristic (ROC) curve validated that the mRNA levels of these 5 genes exhibited excellent diagnostic efficiency for normal and tumor tissues. 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