Potential Genes and Pathways of Neonatal Sepsis Based on Functional Gene Set Enrichment Analyses

Abstract

Background: Neonatal sepsis (NS) is considered as the most common cause of neonatal deaths that newborns suffer from. Although numerous studies focus on gene biomarkers of NS, the predictive value of the gene biomarkers is low. NS pathogenesis is still needed to be investigated.

Methods: After data preprocessing, we used KEGG enrichment method to identify the differentially expressed pathways between NS and normal controls. Then, functional principal component analysis (FPCA) was adopted to calculate gene values in NS. In order to further study the key signaling pathway of the NS, elastic-net regression model, Mann-Whitney U test, and coexpression network were used to estimate the weights of signaling pathway and hub genes.

Results: A total of 115 different pathways between NS and controls were first identified. FPCA made full use of time-series gene expression information and estimated F values of genes in the different pathways. The top 1000 genes were considered as the different genes and were further analyzed by elastic-net regression and MWU test. There were 7 key signaling pathways between the NS and controls, according to different sources. Among those genes involved in key pathways, 7 hub genes, PIK3CA, TGFBR2, CDKN1B, KRAS, E2F3, TRAF6, and CHUK, were determined based on the coexpression network. Most of them were cancer-related genes. PIK3CA was considered as the common marker, which is highly expressed in the lymphocyte group. Little was known about the correlation of PIK3CA with NS, which gives us a new enlightenment for NS study.

Conclusion: This research might provide the perspective information to explore the potential novel genes and pathways as NS therapy targets.

Figure 1

The distribution of F value of pathway genes. Time-series gene signatures data were analyzed by FPCA and each gene obtained an F value (x-coordinate, F value). Y-axis represents gene density. The genes were ranked in the order of F value, and the top 1000 of them were selected. The red line represents the threshold of top 1000 genes. (a) All Sources, (b) Blood Source, (c) Lymphocyte Source, and (d) Monocyte Source.

Figure 2

Sum weights of 115 differential pathways. Y-axis represents the sum weights of pathways. X-axis represents the number of pathways. (a) All Sources, (b) Blood Source, (c) Lymphocyte Source, and (d) Monocyte Source.

Figure 3

Expression levels of the top 3 significant signaling pathways. (a) hsa05220: Chronic myeloid leukemia from All Sources, (b) hsa05120: Epithelial cell signaling in Helicobacter pylori infection from Blood Source, and (c) hsa05222: Small-cell lung cancer from Lymphocyte Source. Y-axis represents expression levels of pathways. X-axis represents control and several time points after admission to the paediatric intensive care unit. The graphs were made with GraphPad Prism 7.0.

Figure 4

Venn diagram of hub genes based on the coexpression networks. Venn diagram showing the number of hub genes obtained from All Sources (Blue), Blood Source (Red), and Lymphocyte Source (Green).

Figure 5

Box-whisker plot of expression levels of genes from GSE11755. (a) GAPDH (internal reference) from All Sources; (b) PIK3CA from All Sources; (c) TGFBR2 from All Sources. (d) GAPDH from Blood Source; (e) PIK3CA from Blood Source; (f) TGFBR2 from Blood Source. (g) GAPDH from Lymphocyte Source; (h) PIK3CA from Lymphocyte Source; (i) TGFBR2 from Lymphocyte Source. Levels of (j) IL-6, (k) IL-10, (l) TNF-α, (m) IL-18, (n) IL-7, and (o) IFNA1 from All Sources. Y-axis represents the expression levels of genes. X-axis represents control and several time points after admission to the paediatric intensive care unit. The box represents the express range and the central line was the median of the data. All graphs were made with GraphPad Prism 7.0.