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An Integrated Systems Biology and Network-Based Approaches to Identify Novel Biomarkers in Breast Cancer Cell Lines Using Gene Expression Data

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Abstract

Breast cancer is the most common cause of death in women worldwide. Approximately 5%–10% of instances are attributed to mutations acquired from the parents. Therefore, it is highly recommended to design more potential drugs and drug targets to eradicate such complex diseases. Network-based gene expression profiling is a suggested tool for discovering drug targets by incorporating various factors such as disease states, intensities based on gene expression as well as protein–protein interactions. To find prospective biomarkers in breast cancer, we first identified differentially expressed genes (DEGs) statistical methods p-value and false discovery rate were initially used. Of the total 82 DEGs, 67 were upregulated while the remaining 17 were downregulated. Sub-modules and hub genes include VEGFA with the highest degree, followed by 15 CCND1 and CXCL8 with 12-degree score was found. The survival analysis revealed that all the hub genes have important role in the development and progression of breast cancer. Enrichment analysis revealed that most of these genes are involved in signaling pathways and in the extracellular spaces. We also identified transcription factors and kinases, which regulate proteins in the DEGs PPI. Finally, drugs for each hub genes were identified. These results further expanded the knowledge regarding important biomarkers in breast cancer.

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Funding

Dong-Qing Wei is supported by the grants from the Key Research Area Grant 2016YFA0501703 of the Ministry of Science and Technology of China, the National Natural Science Foundation of China (Contract no. 61832019, 61503244), the Natural Science Foundation of Henan Province (162300410060) and Joint Research Funds for Medical and Engineering and Scientific Research at Shanghai Jiao Tong University (YG2017ZD14). The computations were partially performed at the Center for High-Performance Computing, Shanghai Jiao Tong University.

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Authors and Affiliations

  1. Department of Bioinformatics and Biological Statistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, People’s Republic of China

    Abbas Khan, Muhammad Junaid, Wang Heng & Dong-Qing Wei

  2. Peng Cheng Laboratory, Vanke Cloud City Phase I Building 8, Xili Street, Nashan District, Shenzhen, 518055, Guangdong, People’s Republic of China

    Dong-Qing Wei

  3. Joint Laboratory of International Cooperation in Metabolic and Developmental Sciences, Ministry of Education, Xidan, People’s Republic of China

    Dong-Qing Wei

  4. Laboratory of Animal and Human Physiology, Department of Animal Sciences, Quaid-I-Azam University, Islamabad, 45320, Pakistan

    Zainab Rehman & Abdul Aziz Khan

  5. Department of Biochemistry, Faculty of Biological Sciences, Quaid-I-Azam University, Islamabad, 45320, Pakistan

    Huma Farooque Hashmi

  6. Centre for Biotechnology and Microbiology, University of Swat, Mingora, Swat, Khyber Pakhtunkhwa, Pakistan

    Abrar Mohammad Sayaf & Syed Shujait Ali

  7. School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan, China

    Fakhr Ul Hassan

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  1. Abbas Khan
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  2. Zainab Rehman
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  3. Huma Farooque Hashmi
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  4. Abdul Aziz Khan
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  5. Muhammad Junaid
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  7. Syed Shujait Ali
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  8. Fakhr Ul Hassan
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  9. Wang Heng
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  10. Dong-Qing Wei
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Contributions

AK, AAK, ZR and HFH conceptualized the methodology. AK, AAK and HFH did the analysis. AK, AS, SSA, FH and DQW wrote the manuscript. DQW supervised the study. All the authors approved the manuscript.

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Correspondence to Wang Heng or Dong-Qing Wei.

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Khan, A., Rehman, Z., Hashmi, H.F. et al. An Integrated Systems Biology and Network-Based Approaches to Identify Novel Biomarkers in Breast Cancer Cell Lines Using Gene Expression Data. Interdiscip Sci Comput Life Sci 12, 155–168 (2020). https://doi.org/10.1007/s12539-020-00360-0

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