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A six‑gene support vector machine classifier contributes to the diagnosis of pediatric septic shock

  • Authors:
    • Guoli Long
    • Chen Yang

  • View Affiliations

  • Published online on: January 23, 2020     https://doi.org/10.3892/mmr.2020.10959
  • Pages: 1561-1571

  • Copyright: © Long et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Septic shock is induced by an uncontrolled inflammatory immune response to pathogens and the survival rate of patients with pediatric septic shock (PSS) is particularly low, with a mortality rate of 25‑50%. The present study explored the mechanisms of PSS using four microarray datasets (GSE26378, GSE26440, GSE13904 and GSE4607) that were obtained from the Gene Expression Omnibus database. Based on the MetaDE package, the consistently differentially expressed genes (DEGs) in the four datasets were screened. Using the WGCNA package, the disease‑associated modules and genes were identified. Subsequently, the optimal feature genes were further selected using the caret package. Finally, a support vector machine (SVM) classifier based on the optimal feature genes was built using the e1071 package. Initially, there were 2,699 consistent DEGs across the four datasets. From the 10 significantly stable modules across the datasets, four stable modules (including the magenta, purple, turquoise and yellow modules), in which the consistent DEGs were significantly enriched (P<0.05), were further screened. Subsequently, six optimal feature genes (including cysteine rich transmembrane module containing 1, S100 calcium binding protein A9, solute carrier family 2 member 14, stomatin, uridine phosphorylase 1 and utrophin) were selected from the genes in the four stable modules. Additionally, an effective SVM classifier was constructed based on the six optimal genes. The SVM classifier based on the six optimal genes has the potential to be applied for PSS diagnosis. This may improve the accuracy of early PSS diagnosis and suggest possible molecular targets for interventions.
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March-2020
Volume 21 Issue 3

Print ISSN: 1791-2997
Online ISSN:1791-3004

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Spandidos Publications style
Long G and Long G: A six‑gene support vector machine classifier contributes to the diagnosis of pediatric septic shock. Mol Med Rep 21: 1561-1571, 2020
APA
Long, G., & Long, G. (2020). A six‑gene support vector machine classifier contributes to the diagnosis of pediatric septic shock. Molecular Medicine Reports, 21, 1561-1571. https://doi.org/10.3892/mmr.2020.10959
MLA
Long, G., Yang, C."A six‑gene support vector machine classifier contributes to the diagnosis of pediatric septic shock". Molecular Medicine Reports 21.3 (2020): 1561-1571.
Chicago
Long, G., Yang, C."A six‑gene support vector machine classifier contributes to the diagnosis of pediatric septic shock". Molecular Medicine Reports 21, no. 3 (2020): 1561-1571. https://doi.org/10.3892/mmr.2020.10959