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Article

Network‑based gene function inference method to predict optimal gene functions associated with fetal growth restriction

  • Authors:
    • Ke‑Jun Ye
    • Jie Dai
    • Ling‑Yun Liu
    • Meng‑Jia Peng
  • View Affiliations / Copyright

    Affiliations: Department of Gynaecology and Obstetrics, Ruian People's Hospital, Ruian, Zhejiang 325200, P.R. China, Department of Clinical Laboratory, Ruian People's Hospital, Ruian, Zhejiang 325200, P.R. China
  • Pages: 3003-3010
    |
    Published online on: June 29, 2018
       https://doi.org/10.3892/mmr.2018.9232
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Abstract

The guilt by association (GBA) principle has been widely used to predict gene functions, and a network‑based approach may enhance the confidence and stability of the analysis compared with focusing on individual genes. Fetal growth restriction (FGR), is the second primary cause of perinatal mortality. Therefore, the present study aimed to predict the optimal gene functions for FGR using a network‑based GBA method. The method was comprised of four parts: Identification of differentially‑expressed genes (DEGs) between patients with FGR and normal controls based on gene expression data; construction of a co‑expression network (CEN) dependent on DEGs, using the Spearman correlation coefficient algorithm; collection of gene ontology (GO) data on the basis of a known confirmed database and DEGs; and prediction of optimal gene functions using the GBA algorithm, for which the area under the receiver operating characteristic curve (AUC) was obtained for each GO term. A total of 115 DEGs and 109 GO terms were obtained for subsequent analysis. All DEGs were mapped to the CEN and formed 6,555 edges. The results of GBA algorithm demonstrated that 78 GO terms had a good classification performance with AUC >0.5. In particular, the AUC for 5 of the GO terms was >0.7, and these were defined as optimal gene functions, including defense response, immune system process, response to stress, cellular response to chemical stimulus and positive regulation of biological process. In conclusion, the results of the present study provided insights into the pathological mechanism underlying FGR, and provided potential biomarkers for early detection and targeted treatment of this disease. However, the interactions between the 5 GO terms remain unclear, and further studies are required.
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Copy and paste a formatted citation
Spandidos Publications style
Ye KJ, Dai J, Liu LY and Peng MJ: Network‑based gene function inference method to predict optimal gene functions associated with fetal growth restriction. Mol Med Rep 18: 3003-3010, 2018.
APA
Ye, K., Dai, J., Liu, L., & Peng, M. (2018). Network‑based gene function inference method to predict optimal gene functions associated with fetal growth restriction. Molecular Medicine Reports, 18, 3003-3010. https://doi.org/10.3892/mmr.2018.9232
MLA
Ye, K., Dai, J., Liu, L., Peng, M."Network‑based gene function inference method to predict optimal gene functions associated with fetal growth restriction". Molecular Medicine Reports 18.3 (2018): 3003-3010.
Chicago
Ye, K., Dai, J., Liu, L., Peng, M."Network‑based gene function inference method to predict optimal gene functions associated with fetal growth restriction". Molecular Medicine Reports 18, no. 3 (2018): 3003-3010. https://doi.org/10.3892/mmr.2018.9232
Copy and paste a formatted citation
x
Spandidos Publications style
Ye KJ, Dai J, Liu LY and Peng MJ: Network‑based gene function inference method to predict optimal gene functions associated with fetal growth restriction. Mol Med Rep 18: 3003-3010, 2018.
APA
Ye, K., Dai, J., Liu, L., & Peng, M. (2018). Network‑based gene function inference method to predict optimal gene functions associated with fetal growth restriction. Molecular Medicine Reports, 18, 3003-3010. https://doi.org/10.3892/mmr.2018.9232
MLA
Ye, K., Dai, J., Liu, L., Peng, M."Network‑based gene function inference method to predict optimal gene functions associated with fetal growth restriction". Molecular Medicine Reports 18.3 (2018): 3003-3010.
Chicago
Ye, K., Dai, J., Liu, L., Peng, M."Network‑based gene function inference method to predict optimal gene functions associated with fetal growth restriction". Molecular Medicine Reports 18, no. 3 (2018): 3003-3010. https://doi.org/10.3892/mmr.2018.9232
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