Weighted gene co-expression network analysis for excavation of Hub genes related to the development of breast cancer_West China Medical Journal

Authors：

ZHANG Yuanxin , DU Zhenggui ,  LI Hongjiang

Department of Breast Surgery, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, P. R. China;

Corresponding author：

LI Hongjiang, Email: lihongjiang@sohu.com

Keywords：

Weighted gene co-expression network analysis; Breast cancer; Tumor; Development

DOI：

10.7507/1002-0179.201904162

Video：

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Objective To explore the key modules and Hub genes related to the development of breast cancer from the level of gene network, and to verify whether these Hub genes have breast cancer specificity.Methods The key modules for the development of breast cancer were screened by weighted gene co-expression network analysis (WGCNA). The gene annotation database Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to enrich the function of the modules, exploring the Hub genes having the highest correlation between the development of breast cancer. Simultaneously, we analyzed the relationship between Hub genes and tumor collection unit.Results WGCNA defined 10 co-expression modules, of which the blue module was the key module related to the development of breast cancer and other malignant tumors. The genes included in this module were significantly enriched in pathways such as the Cell Cycle pathway (KEGG ID: cfa04110), the Viral Oncogenic pathway (KEGG ID: hsa05203), Cancer pathway (KEGG ID: hsa05200), and Systemic Lupus Erythematosus (KEGG ID: hsa05322). The top eight Hub genes were finally extracted from the blue module including NUSAP1, FOXM1, KIF20A, BIRC5, TOP2A, RRM2, CEP55, and ASPM. Among them, NUSAP1, KIF20A, TOP2A, CEP55 and ASPM were also closely related to the occurrence and development of tumor collection unit.Conclusion WGCNA can screen for key modules and Hub genes which are biologically relevant to the clinical features of our interest, and the Hub genes have no breast cancer specificity participating in breast cancer development .

Citation： ZHANG Yuanxin, DU Zhenggui, LI Hongjiang. Weighted gene co-expression network analysis for excavation of Hub genes related to the development of breast cancer. West China Medical Journal, 2020, 35(9): 1074-1081. doi: 10.7507/1002-0179.201904162 Copy

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1. Ferlay J, Colombet M, Soerjomataram I, et al. Estimating the global cancer incidence and mortality in 2018: GLOBOCAN sources and methods. Int J Cancer, 2019, 144(8): 1941-1953.
2. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2017. CA CancerJ Clin, 2017, 67(1): 7-30.
3. Lauby-Secretan B, Scoccianti C, Loomis D, et al. Body fatness and cancer--viewpoint of the IARC working group. N Engl J Med, 2016, 375(8): 794-798.
4. de Roon M, May AM, McTiernan A, et al. Effect of exercise and/or reduced calorie dietary interventions on breast cancer-related endogenous sex hormones in healthy postmenopausal women. Breast Cancer Res, 2018, 20(1): 81.
5. Weng YI, Huang TH, Yan PS. Methylated DNA immunoprecipitation and microarray-based analysis: detection of DNA methylation in breast cancer cell lines. Methods Mol Biol, 2009, 590: 165-176.
6. Fleming AM, Ding Y, Burrows CJ. Oxidative DNA damage is epigenetic by regulating gene transcription via base excision repair. Proc Natl Acad Sci USA, 2017, 114(10): 2604-2609.
7. Jasin M. Homologous repair of DNA damage and tumorigenesis: the BRCA connection. Oncogene, 2002, 21(58): 8981-8993.
8. Eidsaa M, Stubbs L, Almaas E. Comparative analysis of weighted gene co-expression networks in human and mouse. PLoS One, 2017, 12(11): e0187611.
9. Yang CW, Wang SF, Yang XL, et al. Identification of gene expression models for laryngeal squamous cell carcinoma using co-expression network analysis. Medicine (Baltimore), 2018, 97(7): e9738.
10. Wan Q, Tang J, Han Y, et al. Co-expression modules construction by WGCNA and identify potential prognostic markers of uveal melanoma. Exp Eye Res, 2018, 166: 13-20.
11. Miao L, Yin RX, Pan SL, et al. Weighted gene co-expression network analysis identifies specific modules and hub genes related to hyperlipidemia. Cell Physiol Biochem, 2018, 48(3): 1151-1163.
12. Tian F, Zhao J, Fan X, et al. Weighted gene co-expression network analysis in identification of metastasis-related genes of lung squamous cell carcinoma based on the cancer genome atlas database. J Thorac Dis, 2017, 9(1): 42-53.
13. Yu K, Ganesan K, Tan LK, et al. A precisely regulated gene expression cassette potently modulates metastasis and survival in multiple solid cancers. PLoS Genet, 2008, 4(7): e1000129.
14. 吴斌, 沈自尹. 基因芯片表达谱数据的预处理分析. 中国生物化学与分子生物学报, 2006, 22(4): 272-277.
15. Bolstad BM, Irizarry RA, Astrand M, et al. A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics, 2003, 19(2): 185-193.
16. Barabási AL, Dezső Z, Ravasz E, et al. Scale-free and hierarchical structures in complex networks. AIP Conference Proceedings, 2003, 66(1).
17. Barabási AL, Bonabeau E. Scale-free networks. Sci Am, 2003, 288(5): 60-69.
18. Ravasz E, Somera AL, Mongru DA, et al. Hierarchical organization of modularity in metabolic networks. Science, 2002, 5586(297): 1551-1555.
19. Segal E, Shapira M, Regev A, et al. Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data. Nat Genet, 2003, 34(2): 166-176.
20. Dong J, Horvath S. Understanding network concepts in modules. BMC Syst Biol, 2007, 1: 24.
21. Ashburner M, Ball CA, Blake JA, et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet, 2000, 25(1): 25-29.
22. Kanehisa M, Furumichi M, Tanabe M. KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res, 2017, 45(D1): D353-D361.
23. Najafi M, Farhood B, Mortezaee K. Extracellular matrix (ECM) stiffness and degradation as cancer drivers. J Cell Biochem, 2019, 120(3): 2782-2790.
24. Komemi O, Shochet GE, Pomeranz M, et al. Placenta-conditioned extracellular matrix (ECM) activates breast cancer cell survival mechanisms: a key for future distant metastases. Int J Cancer, 2019, 144(7): 1633-1644.
25. Wagner M, Koyasu S. Cancer immunoediting by innate lymphoid cells. Trends Immunol, 2019, 40(5): 415-430.
26. Batlle E, Massagué J. Transforming growth factor-β signaling in immunity and cancer. Immunity, 2019, 50(4): 924-940.
27. Brandt J, Borgquist S, Almquist M, et al. Thyroid function and survival following breast cancer. Br J Surg, 2016, 103(12): 1649-1657.
28. Brandt J, Borgquist S, Manjer J. Prospectively measured thyroid hormones and thyroid peroxidase antibodies in relation to risk of different breast cancer subgroups: a malmö diet and cancer study. Cancer Causes Control, 2015, 26(8): 1093-1104.
29. Davis PJ, Mousa SA, Cody V, et al. Small molecule hormone or hormone-like ligands of integrin αVβ3: implications for cancer cell behavior. Horm Cancer, 2013, 4(6): 335-342.
30. Moeller LC, Führer D. Thyroid hormone, thyroid hormone receptors, and cancer: a clinical perspective. Endocr Relat Cancer, 2013, 20(2): R19-R29.
31. Ferreira E, da Silva AE, Serakides R. Ehrlich tumor as model to study artificial hyperthyroidism influence on breast cancer. Pathol Res Pract, 2007, 203(1): 39-44.
32. Gago-Dominguez M, Castelao JE. Role of lipid peroxidation and oxidative stress in the association between thyroid diseases and breast cancer. Crit Rev Oncol Hematol, 2008, 68(2): 107-114.

West China Medical Journal

Weighted gene co-expression network analysis for excavation of Hub genes related to the development of breast cancer

Abstract Full text Figures/Tables Video References Cited by

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