Detection of Significant Pneumococcal Meningitis Biomarkers by Ego Network
- 91 Downloads
To identify significant biomarkers for detection of pneumococcal meningitis based on ego network.
Based on the gene expression data of pneumococcal meningitis and global protein-protein interactions (PPIs) data recruited from open access databases, the authors constructed a differential co-expression network (DCN) to identify pneumococcal meningitis biomarkers in a network view. Here EgoNet algorithm was employed to screen the significant ego networks that could accurately distinguish pneumococcal meningitis from healthy controls, by sequentially seeking ego genes, searching candidate ego networks, refinement of candidate ego networks and significance analysis to identify ego networks. Finally, the functional inference of the ego networks was performed to identify significant pathways for pneumococcal meningitis.
By differential co-expression analysis, the authors constructed the DCN that covered 1809 genes and 3689 interactions. From the DCN, a total of 90 ego genes were identified. Starting from these ego genes, three significant ego networks (Module 19, Module 70 and Module 71) that could predict clinical outcomes for pneumococcal meningitis were identified by EgoNet algorithm, and the corresponding ego genes were GMNN, MAD2L1 and TPX2, respectively. Pathway analysis showed that these three ego networks were related to CDT1 association with the CDC6:ORC:origin complex, inactivation of APC/C via direct inhibition of the APC/C complex pathway, and DNA strand elongation, respectively.
The authors successfully screened three significant ego modules which could accurately predict the clinical outcomes for pneumococcal meningitis and might play important roles in host response to pathogen infection in pneumococcal meningitis.
KeywordsPneumococcal meningitis EgoNet algorithm Ego gene Pathway Differential co-expression network
QW: Conception and design, drafting of the manuscript; ZL: Acquisition of data, statistical analysis, administrative technical or material support; LZ: Analysis and interpretation of data, supervision, literature review; HZ: Critical revision of the manuscript for important intellectual content and will act as guarantor for the paper.
Compliance with Ethical Standards
Conflict of Interest
Source of Funding
This research was supported by Department of Pediatrics, Orthopaedics and Neurology, Jiyang Public Hospital.
- 8.Yang R, Bai Y, Qin Z, Yu T. EgoNet: identification of human disease ego-network modules. BMC Genomics. 2014;15:314.Google Scholar
- 9.Irwin AD, Marriage F, Mankhambo LA, et al. Novel biomarker combination improves the diagnosis of serious bacterial infections in Malawian children. BMC Med Genomics. 2012;5:13.Google Scholar
- 14.Benesty J, Chen J, Huang Y, Cohen I. Pearson correlation coefficient. In: Cohen I, Huang Y, Chen J, Benesty J, editors. Noise reduction in speech processing. Berlin: Springer; 2009. p. 1–4.Google Scholar
- 18.Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Royal Stat Soc Series B (Methodological). 1995;57:289–300.Google Scholar
- 22.Pinheiro DML, Fontes FL, de Oliveira AHS, et al. Polymorphisms in DNA repair gene XRCC1 (Arg194Trp) and (Arg399Gln) and their role in the susceptibility of bacterial meningitis. J Meningitis. 2016;1:105.Google Scholar