Identifying critical state of breast cancer cell differentiation based on landscape dynamic network biomarkers_Journal of Biomedical Engineering

Authors：

ZHAO Hongqian ,  GAO Jie

Jiangnan University, School of Science, Wuxi, Jiangsu 214122, P.R.China;

Corresponding author：

GAO Jie, Email: gaojie@jiangnan.edu.cn

Keywords：

breast cancer; landscape dynamic network biomarkers; critical state; early warning

DOI：

10.7507/1001-5515.201908013

Video：

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Breast cancer is a malignant tumor with the highest morbidity and mortality in female in recent years, and it is a complex disease that affects human health. Studies have shown that dynamic network biomarkers (DNB) can effectively identify critical states at which complex diseases such as breast cancer change from a normal state to a disease state. However, the traditional DNB method requires data from multiple samples in the same disease state, which is usually unachievable in clinical diagnosis. This paper quantitatively analyzes the time series data of MCF-7 breast cancer cells and finds the DNB module of a single sample in the time series based on landscape DNB (L-DNB) method. Then, a comprehensive index is constructed to detect its early warning signals to determine the critical state of breast cancer cell differentiation. The results of this study may be of great significance for the prevention and early diagnosis of breast cancer. It is expected that this paper can provide references for the related research of breast cancer.

Citation： ZHAO Hongqian, GAO Jie. Identifying critical state of breast cancer cell differentiation based on landscape dynamic network biomarkers. Journal of Biomedical Engineering, 2020, 37(2): 304-310. doi: 10.7507/1001-5515.201908013 Copy

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6.	Liu Rui, Yu Xiangtian, Liu Xiaoping, et al. Identifying critical transitions of complex diseases based on a single sample. Bioinformatics, 2014, 30(11): 1579-1586.
7.	Liu Xiaoping, Xiao Chang, Leng Siyang, et al. Detection for disease tipping points by landscape dynamic network biomarkers. National Science Review, 2019, 6(4): 775-785.
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10.	Liu Xiaoping, Wang Yuetong, Ji Hongbin, et al. Personalized characterization of diseases using sample-specific networks. Nucleic Acids Res, 2016, 44(22): e164.
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12.	Ogata H, Goto S, Fujibuchi Wataru, et al. Computation with the KEGG pathway database. Biosystems, 1998, 47(1/2): 119-128.
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15.	Foulkes W D, Ghadirian P, Akbari M R, et al. Identification of a novel truncating PALB2 mutation and analysis of its contribution to early-onset breast cancer in French-Canadian women. Breast Cancer Research, 2007, 9(6): 83-91.
16.	Southey M C, Teo Z L, Dowty J G, et al. A PALB2 mutation associated with high risk of breast cancer. Breast Cancer Res, 2010, 12(6): R109.
17.	Mehra R, Varambally S, Lei Ding, et al. Identification of GATA3 as a breast cancer prognostic marker by global gene expression meta-analysis. Cancer Res, 2005, 65(24): 11259-11264.
18.	Daly R J, Binder M D, Sutherland R L. Overexpression of the Grb2 gene in human breast cancer cell lines. Oncogene, 1994, 9(9): 2723-2727.
19.	Fang Cheng, Li Juanjuan, Deng Tong, et al. Actinin-4 as a diagnostic biomarker in serum of breast cancer patients. Medical Science Monitor, 2019, 25: 3298-3302.
20.	Xin Lian, Yu Jiao, Yu Yang, et al. CrkL regulates SDF-1-induced breast cancer biology through balancing Erk1/2 and PI3K/Akt pathways. Medical Oncology, 2015, 32(1): 411-418.
21.	Nagai M A, Fregnani J H, Netto M M, et al. Down-regulation of PHLDA1 gene expression is associated with breast cancer progression. Breast Cancer Res Treat, 2007, 106(1): 49-56.
22.	Birts C N, Harding R, Soosaipillai G, et al. Expression of CtBP family protein isoforms in breast cancer and their role in chemoresistance. Biology of the Cell, 2011, 103(1): 1-19.
23.	Zhao Zheng, Song Zhangjun, Liao Zijun, et al. PKM2 promotes stemness of breast cancer cell by through Wnt/β-catenin pathway. Tumor Biology, 2016, 37(3): 4223-4234.
24.	Zhang Kun, Zhou Jiaojiao, Zhu Xuan, et al. Germline mutations of PALB2 gene in a sequential series of Chinese patients with breast cancer. Breast Cancer Res Treat, 2017, 166(3): 865-873.

1. Hong Wei, Dong Erdan. The past, present and future of breast cancer research in China. Cancer Lett, 2014, 351(1): 1-5.
2. Fan Lei, Strasser-Weippl K, Li Junjie, et al. Breast cancer in China. Lancet Oncol, 2014, 15(7): e279-e289.
3. Liu Rui, Li Meiyi, Liu Zhiping, et al. Identifying critical transitions and their leading biomolecular networks in complex diseases. Sci Rep, 2012, 2(1): 813-821.
4. Liu Rui, Wang Xiangdong, Aihara K, et al. Early diagnosis of complex diseases by molecular biomarkers, network biomarkers, and dynamical network biomarkers. Med Res Rev, 2014, 34(3): 455-478.
5. Chen Luonan, Liu Rui, Liu Zhiping, et al. Detecting early-warning signals for sudden deterioration of complex diseases by dynamical network biomarkers. Sci Rep, 2012, 2(1): 342-349.
6. Liu Rui, Yu Xiangtian, Liu Xiaoping, et al. Identifying critical transitions of complex diseases based on a single sample. Bioinformatics, 2014, 30(11): 1579-1586.
7. Liu Xiaoping, Xiao Chang, Leng Siyang, et al. Detection for disease tipping points by landscape dynamic network biomarkers. National Science Review, 2019, 6(4): 775-785.
8. Chen Pei, Liu Rui, Chen Luonan, et al. Identifying critical differentiation state of MCF-7 cells for breast cancer by dynamical network biomarkers. Front Genet, 2015, 6: 252-258.
9. Saeki Y, Endo T, Ide K, et al. Ligand-specific sequential regulation of transcription factors for differentiation of MCF-7 cells. BMC Genomics, 2009, 10(1): 545.
10. Liu Xiaoping, Wang Yuetong, Ji Hongbin, et al. Personalized characterization of diseases using sample-specific networks. Nucleic Acids Res, 2016, 44(22): e164.
11. Liu Xiaoping, Chang Xiao, Liu Rui, et al. Quantifying critical states of complex diseases using single-sample dynamic network biomarkers. PLoS Comput Biol, 2017, 13(7): e1005633.
12. Ogata H, Goto S, Fujibuchi Wataru, et al. Computation with the KEGG pathway database. Biosystems, 1998, 47(1/2): 119-128.
13. Osaki M, Oshimura M, Ito H. PI3K-Akt pathway: its functions and alterations in human cancer. Apoptosis, 2004, 9(6): 667-676.
14. El-Deiry W S, Tokino T, Velculescu V E, et al. WAF1, a potential mediator of p53 tumor suppression. Cell, 1993, 75(4): 817-825.
15. Foulkes W D, Ghadirian P, Akbari M R, et al. Identification of a novel truncating PALB2 mutation and analysis of its contribution to early-onset breast cancer in French-Canadian women. Breast Cancer Research, 2007, 9(6): 83-91.
16. Southey M C, Teo Z L, Dowty J G, et al. A PALB2 mutation associated with high risk of breast cancer. Breast Cancer Res, 2010, 12(6): R109.
17. Mehra R, Varambally S, Lei Ding, et al. Identification of GATA3 as a breast cancer prognostic marker by global gene expression meta-analysis. Cancer Res, 2005, 65(24): 11259-11264.
18. Daly R J, Binder M D, Sutherland R L. Overexpression of the Grb2 gene in human breast cancer cell lines. Oncogene, 1994, 9(9): 2723-2727.
19. Fang Cheng, Li Juanjuan, Deng Tong, et al. Actinin-4 as a diagnostic biomarker in serum of breast cancer patients. Medical Science Monitor, 2019, 25: 3298-3302.
20. Xin Lian, Yu Jiao, Yu Yang, et al. CrkL regulates SDF-1-induced breast cancer biology through balancing Erk1/2 and PI3K/Akt pathways. Medical Oncology, 2015, 32(1): 411-418.
21. Nagai M A, Fregnani J H, Netto M M, et al. Down-regulation of PHLDA1 gene expression is associated with breast cancer progression. Breast Cancer Res Treat, 2007, 106(1): 49-56.
22. Birts C N, Harding R, Soosaipillai G, et al. Expression of CtBP family protein isoforms in breast cancer and their role in chemoresistance. Biology of the Cell, 2011, 103(1): 1-19.
23. Zhao Zheng, Song Zhangjun, Liao Zijun, et al. PKM2 promotes stemness of breast cancer cell by through Wnt/β-catenin pathway. Tumor Biology, 2016, 37(3): 4223-4234.
24. Zhang Kun, Zhou Jiaojiao, Zhu Xuan, et al. Germline mutations of PALB2 gene in a sequential series of Chinese patients with breast cancer. Breast Cancer Res Treat, 2017, 166(3): 865-873.

Journal of Biomedical Engineering

Identifying critical state of breast cancer cell differentiation based on landscape dynamic network biomarkers

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