Identification of key genes associated with sepsis patients infected by staphylococcus aureus through weighted gene co-expression network analysis.

Han Wu, Haoting Chen, Junjie Wang, Shaohua Yin, Jiaqian Huang, Zhiqiang Wang, Xiaojie Zhang, Minghua Wang
Author Information
  1. Han Wu: Department of Biochemistry and Molecular Biology, Medical College, Soochow University Suzhou, China.
  2. Haoting Chen: Department of Biological Sciences, Xi'an Jiaotong-Liverpool University Suzhou, China.
  3. Junjie Wang: Department of Biochemistry and Molecular Biology, Medical College, Soochow University Suzhou, China.
  4. Shaohua Yin: Department of Biochemistry and Molecular Biology, Medical College, Soochow University Suzhou, China.
  5. Jiaqian Huang: Department of Biochemistry and Molecular Biology, Medical College, Soochow University Suzhou, China.
  6. Zhiqiang Wang: Department of Biochemistry and Molecular Biology, Medical College, Soochow University Suzhou, China.
  7. Xiaojie Zhang: Department of Experimental Center, Medical College, Soochow University Suzhou, China.
  8. Minghua Wang: Department of Biochemistry and Molecular Biology, Medical College, Soochow University Suzhou, China.

Abstract

The prevention and treatment of staphylococcus aureus septicemia is one of the thorniest problems in modern medicine. However, as the underlying pathogenesis of sepsis is still unclear, there is currently no golden standard for clinical diagnosis. In this study, we used GSE33341 dataset for differentially expressed gene (DEG) analysis and screened out 857 differentially expressed genes associated with staphylococcus aureus infection. The module having the highest correlation with clinical features of sepsis was screened by weighted gene co-expression network analysis (WGCNA). The genes in the selected module and the differentially expressed genes were represented in Venn diagram, and 59 pathogenic genes at the intersection were obtained. GO and KEGG analysis showed that these genes were mainly related to aerobic respiration, cellular stress response, mitochondrial electron transport, mitochondrial transport, oxidative phosphorylation. Kaplan-Meier was used to analyze the influence of the top 10 key genes on the prognosis of sepsis patients. The results showed that the high expression of NDUFA4, NDUFB3, COX7A2, ATP5J and COX7C was significantly correlated with the poor overall survival (OS) in patients with bacterial sepsis. These findings may potentially provide a reference for the diagnosis and treatment of bacterial septicemia.

Keywords

References

  1. Bioinformation. 2015 Apr 30;11(4):207-16 [PMID: 26124562]
  2. EMBO Mol Med. 2018 Aug;10(8): [PMID: 29976786]
  3. J Crit Care. 2012 Jun;27(3):314.e1-11 [PMID: 21798705]
  4. Biomed Res Int. 2020 Jan 23;2020:6140728 [PMID: 32047813]
  5. Infect Control Hosp Epidemiol. 2020 Apr;41(4):425-429 [PMID: 31973783]
  6. BMC Pediatr. 2017 Feb 1;17(1):44 [PMID: 28143490]
  7. Expert Rev Clin Immunol. 2014 Oct;10(10):1349-56 [PMID: 25142036]
  8. Anasthesiol Intensivmed Notfallmed Schmerzther. 1998 Feb;33(2):77-87 [PMID: 9558431]
  9. Nat Commun. 2019 Apr 3;10(1):1523 [PMID: 30944313]
  10. Eur Rev Med Pharmacol Sci. 2017 Feb;21(3):549-553 [PMID: 28239813]
  11. Biol Trace Elem Res. 2017 Nov;180(1):63-69 [PMID: 28261761]
  12. J Cell Mol Med. 2020 Nov;24(21):12258-12271 [PMID: 32951280]
  13. Int Heart J. 2021 May 29;62(3):636-646 [PMID: 33994501]
  14. Curr Opin Infect Dis. 2019 Oct;32(5):497-504 [PMID: 31335441]
  15. Eur Rev Med Pharmacol Sci. 2018 Jun;22(11):3553-3569 [PMID: 29917210]
  16. Gene. 2006 Mar 15;369:35-44 [PMID: 16309857]
  17. Methods Mol Biol. 2010;594:57-72 [PMID: 20072909]
  18. Front Bioeng Biotechnol. 2020 May 05;8:390 [PMID: 32432096]
  19. Crit Care. 2017 Aug 14;21(1):211 [PMID: 28807042]
  20. Cureus. 2018 Oct 30;10(10):e3521 [PMID: 30648056]
  21. J Surg Res. 2015 May 15;195(2):568-79 [PMID: 25769491]
  22. N Engl J Med. 2015 Apr 23;372(17):1629-38 [PMID: 25776936]
  23. Antioxid Redox Signal. 2014 Mar 1;20(7):1126-67 [PMID: 23991888]
  24. Crit Care Resusc. 2013 Jun;15(2):103-9 [PMID: 23931041]

Word Cloud

Created with Highcharts 10.0.0genessepsisanalysisstaphylococcusaureusgenedifferentiallyexpressedweightednetworkpatientstreatmentsepticemiaclinicaldiagnosisusedscreenedassociatedmoduleco-expressionshowedmitochondrialtransportkeysurvivalbacterialpreventiononethorniestproblemsmodernmedicineHoweverunderlyingpathogenesisstillunclearcurrentlygoldenstandardstudyGSE33341datasetDEG857infectionhighestcorrelationfeaturesWGCNAselectedrepresentedVenndiagram59pathogenicintersectionobtainedGOKEGGmainlyrelatedaerobicrespirationcellularstressresponseelectronoxidativephosphorylationKaplan-Meieranalyzeinfluencetop10prognosisresultshighexpressionNDUFA4NDUFB3COX7A2ATP5JCOX7CsignificantlycorrelatedpooroverallOSfindingsmaypotentiallyprovidereferenceIdentificationinfectedBacterialdifferentialcoexpression

Similar Articles

Cited By