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Journal of Cancer Research and Clinical Oncology

, Volume 145, Issue 9, pp 2383–2396 | Cite as

Co-expression network analysis identified candidate biomarkers in association with progression and prognosis of breast cancer

  • Qiang Zhou
  • Jiangbo Ren
  • Jinxuan Hou
  • Gang Wang
  • Lingao Ju
  • Yu XiaoEmail author
  • Yan GongEmail author
Original Article – Clinical Oncology

Abstract

Purpose

Breast cancer is one of the most common malignancies among females, and its prognosis is affected by a complex network of gene interactions. Weighted gene co-expression network analysis was used to construct free-scale gene co-expression networks and to identify potential biomarkers for breast cancer progression.

Methods

The gene expression profiles of GSE42568 were downloaded from the Gene Expression Omnibus database. RNA-sequencing data and clinical information of breast cancer from TCGA were used for validation.

Results

A total of ten modules were established by the average linkage hierarchical clustering. We identified 58 network hub genes in the significant module (R2 = 0.44) and 6 hub genes (AGO2, CDC20, CDCA5, MCM10, MYBL2, and TTK), which were significantly correlated with prognosis. Receiver-operating characteristic curve validated that the mRNA levels of these six genes exhibited excellent diagnostic efficiency in the test data set of GSE42568. RNA-sequencing data from TCGA showed that the expression levels of these six genes were higher in triple-negative tumors. One-way ANOVA suggested that these six genes were upregulated at more advanced stages. The results of independent sample t test indicated that MCM10 and TTK were associated with tumor size, and that AGO2, CDC20, CDCA5, MCM10, and MYBL2 were overexpressed in lymph-node positive breast cancer.

Conclusions

AGO2, CDC20, CDCA5, MCM10, MYBL2, and TTK were identified as candidate biomarkers for further basic and clinical research on breast cancer based on co-expression analysis.

Keywords

Breast cancer Weighted gene co-expression network analysis Prognosis Gene Expression Omnibus Biomarker 

Notes

Acknowledgements

The excellent technical assistance of Jianing Tang is gratefully acknowledged.

Funding

This study was supported by National Natural Science Foundation of China (Grant No. 81800429), the Fundamental Research Funds for the Central Universities (Grant No. 2042018kf0065), Health Commission of Hubei Province Scientific Research Project (Grant No. WJ2019Q047), and Zhongnan Hospital of Wuhan University Science, Technology and Innovation Seed Fund (Grant Nos. znpy2017001 and znpy2018028).

Compliance with ethical standards

Conflict of interest

The authors declare no conflicts of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Supplementary material

432_2019_2974_MOESM1_ESM.docx (3.9 mb)
Supplementary material 1 (DOCX 3965 kb)

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of UrologyZhongnan Hospital of Wuhan UniversityWuhanChina
  2. 2.Department of Biological RepositoriesZhongnan Hospital of Wuhan UniversityWuhanChina
  3. 3.Department of Thyroid and Breast SurgeryZhongnan Hospital of Wuhan UniversityWuhanChina
  4. 4.Human Genetics Resource Preservation Center of Hubei ProvinceZhongnan Hospital of Wuhan UniversityWuhanChina
  5. 5.Laboratory of Precision MedicineZhongnan Hospital of Wuhan UniversityWuhanChina

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