Open Access

Identification of potential biomarkers of sepsis using bioinformatics analysis

  • Authors:
    • Yu-Xia Yang
    • Li Li
  • View Affiliations

  • Published online on: March 2, 2017     https://doi.org/10.3892/etm.2017.4178
  • Pages: 1689-1696
  • Copyright: © Yang et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Sepsis is defined as the systemic inflammatory response to infection and is one of the leading causes of mortality in critically ill patients. The goal of the present study is to elucidate the molecular mechanism of sepsis. Transcription profile data (GSE12624) were downloaded that had a total of 70 samples (36 sepsis samples and 34 non‑sepsis samples) from the Gene Expression Omnibus database. Protein‑protein interaction network analysis was conducted in order to comprehensively understand the interactions of genes in all samples. Hierarchical clustering and analysis of covariance (ANCOVA) global test were performed to identify the differentially expressed clusters in the networks, followed by function and pathway enrichment analyses. Finally, a support vector machine (SVM) was performed to classify the clusters, and 10‑fold cross‑validation method was performed to evaluate the classification results. A total of 7,672 genes were obtained after preprocessing of the mRNA expression profile data. The PPI network of genes under sepsis and non-sepsis status collected 1,996/2,147 genes and 2,645/2,783 interactions. Moreover, following the ANCOVA global test (P<0.05), 24 differentially expressed clusters with 12 clusters in septic and 12 clusters in non‑septic samples were identified. Finally, 207 biomarker genes, including CDC42, CSF3R, GCA, HMGB2, RHOG, SERPINB1, TYROBP SERPINA1, FCER1 G and S100P in the top six clusters, were collected using the SVM method. The SERPINA1, FCER1 G and S100P genes are thought to be potential biomarkers. Furthermore, Gene oncology terms, including the intracellular signaling cascade, regulation of programmed cell death, regulation of cell death, regulation of apoptosis and leukocyte activation may participate in sepsis.
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May-2017
Volume 13 Issue 5

Print ISSN: 1792-0981
Online ISSN:1792-1015

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Spandidos Publications style
Yang Y and Li L: Identification of potential biomarkers of sepsis using bioinformatics analysis. Exp Ther Med 13: 1689-1696, 2017.
APA
Yang, Y., & Li, L. (2017). Identification of potential biomarkers of sepsis using bioinformatics analysis. Experimental and Therapeutic Medicine, 13, 1689-1696. https://doi.org/10.3892/etm.2017.4178
MLA
Yang, Y., Li, L."Identification of potential biomarkers of sepsis using bioinformatics analysis". Experimental and Therapeutic Medicine 13.5 (2017): 1689-1696.
Chicago
Yang, Y., Li, L."Identification of potential biomarkers of sepsis using bioinformatics analysis". Experimental and Therapeutic Medicine 13, no. 5 (2017): 1689-1696. https://doi.org/10.3892/etm.2017.4178