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pmid:31106147

Overexpression of ASPM, CDC20, and TTK Confer a Poorer Prognosis in Breast Cancer Identified by Gene Co-expression Network Analysis.

Jianing Tang · Mengxin Lu · Qiuxia Cui · Dan Zhang · Deguang Kong · Xing Liao · Jiangbo Ren · Yan Gong · Gaosong Wu

Frontiers in oncology · 2019 doi:10.3389/fonc.2019.00310

Paper

complete

Experiment

partial

Data

partial

Analysis

partial

Execution

absent

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pmid:31106147
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title
Overexpression of ASPM, CDC20, and TTK Confer a Poorer Prognosis in Breast Cancer Identified by Gene Co-expression Network Analysis.
doi
10.3389/fonc.2019.00310
pmid
31106147
authors array · 9 items
[0]
Jianing Tang
[1]
Mengxin Lu
[2]
Qiuxia Cui
[3]
Dan Zhang
[4]
Deguang Kong
[5]
Xing Liao
[6]
Jiangbo Ren
[7]
Yan Gong
[8]
Gaosong Wu
journal
Frontiers in oncology
year
2019
abstract
Breast cancer is one of the most common malignancies among females, and its prognosis is affected by a complex network of gene interactions. In this study, we constructed free-scale gene co-expression networks using weighted gene co-expression network analysis (WGCNA). The gene expression profiles of GSE25055 were downloaded from the Gene Expression Omnibus (GEO) database to identify potential biomarkers associated with breast cancer progression. GSE42568 was downloaded for validation. A total of 9 modules were established via the average linkage hierarchical clustering. We identified 3 hub genes (ASPM, CDC20, and TTK) in the significant module (R 2 = 0.52), which were significantly correlated with poor prognosis both in test and validation datasets. In the datasets GSE25055 and GSE42568, higher expression levels of ASPM, CDC20, and TTK correlated with advanced tumor grades. Immunohistochemistry data from the Human Protein Atlas also demonstrated that their protein levels were higher in tumor samples. According to gene set enrichment analysis, 4 commonly enriched pathways were identified: cell cycle pathway, DNA replication pathway, homologous recombination pathway, and P53 signaling pathway. In addition, strong correlations were found among their expression levels. In conclusion, our WGCNA analysis identified candidate prognostic biomarkers for further basic and clinical researches.
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The gene expression profiles of GSE25055 submitted by Christos Hatzis were downloaded from the GEO database (https://www.ncbi.nlm.nih.gov/geo/).
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The gene expression profiles of GSE25055 submitted by Christos Hatzis were downloaded from the GEO database (https://www.ncbi.nlm.nih.gov/geo/).
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Discovery cohort for genomic predictor of response and survival following neoadjuvant taxane-anthracycline chemotherapy in breast cancer
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Survival analysis
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time_to_event_with_censoring
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Clustering
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Weighted gene co-expression network analysis (WGCNA)
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“Then WGCNA package in R was used to constructed gene co-expression network (302 samples were used).”
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candidate
Automated Assays Laravel application and simulated adapters
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GSE42568
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GSE42568
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[0]
The gene expression profiles of GSE25055 submitted by Christos Hatzis were downloaded from the GEO database (https://www.ncbi.nlm.nih.gov/geo/).
basis
The paper names GSE25055, GSE42568 in Gene Expression Omnibus (GEO) and the accession resolves in the repository. No dataset was downloaded and no licence was read.
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minimax
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PMID 31106147 full text
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GEO GSE25055
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d TTK correlated with advanced tumor grades. Immunohistochemistry data from the Human Protein Atlas also demon
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Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:31106147", "kind": "science-record", "revision": 8, "content_hash": "sha256:192a768ada62e4341ac246b77a11c7d6731e9a93380840250b9a3ce96ccf54a8", "created_at": "2026-11-01T15:40:45Z", "updated_at": "2026-11-01T22:00:33Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Overexpression of ASPM, CDC20, and TTK Confer a Poorer Prognosis in Breast Cancer Identified by Gene Co-expression Network Analysis.", "doi": "10.3389/fonc.2019.00310", "pmid": "31106147", "authors": ["Jianing Tang", "Mengxin Lu", "Qiuxia Cui", "Dan Zhang", "Deguang Kong", "Xing Liao", "Jiangbo Ren", "Yan Gong", "Gaosong Wu"], "journal": "Frontiers in oncology", "year": 2019, "abstract": "Breast cancer is one of the most common malignancies among females, and its prognosis is affected by a complex network of gene interactions. In this study, we constructed free-scale gene co-expression networks using weighted gene co-expression network analysis (WGCNA). The gene expression profiles of GSE25055 were downloaded from the Gene Expression Omnibus (GEO) database to identify potential biomarkers associated with breast cancer progression. GSE42568 was downloaded for validation. A total of 9 modules were established via the average linkage hierarchical clustering. We identified 3 hub genes (ASPM, CDC20, and TTK) in the significant module (R 2 = 0.52), which were significantly correlated with poor prognosis both in test and validation datasets. In the datasets GSE25055 and GSE42568, higher expression levels of ASPM, CDC20, and TTK correlated with advanced tumor grades. Immunohistochemistry data from the Human Protein Atlas also demonstrated that their protein levels were higher in tumor samples. According to gene set enrichment analysis, 4 commonly enriched pathways were identified: cell cycle pathway, DNA replication pathway, homologous recombination pathway, and P53 signaling pathway. In addition, strong correlations were found among their expression levels. 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