Identification and verification of feature biomarkers associated with immune cells in neonatal sepsis

Abstract

Background: Neonatal sepsis (NS), a life-threatening condition, is characterized by organ dysfunction and is the most common cause of neonatal death. However, the pathogenesis of NS is unclear and the clinical inflammatory markers currently used are not ideal for diagnosis of NS. Thus, exploring the link between immune responses in NS pathogenesis, elucidating the molecular mechanisms involved, and identifying potential therapeutic targets is of great significance in clinical practice. Herein, our study aimed to explore immune-related genes in NS and identify potential diagnostic biomarkers. Datasets for patients with NS and healthy controls were downloaded from the GEO database; GSE69686 and GSE25504 were used as the analysis and validation datasets, respectively. Differentially expressed genes (DEGs) were identified and Gene Set Enrichment Analysis (GSEA) was performed to determine their biological functions. Composition of immune cells was determined and immune-related genes (IRGs) between the two clusters were identified and their metabolic pathways were determined. Key genes with correlation coefficient > 0.5 and p < 0.05 were selected as screening biomarkers. Logistic regression models were constructed based on the selected biomarkers, and the diagnostic models were validated.

Results: Fifty-two DEGs were identified, and GSEA indicated involvement in acute inflammatory response, bacterial detection, and regulation of macrophage activation. Most infiltrating immune cells, including activated CD8 + T cells, were significantly different in patients with NS compared to the healthy controls. Fifty-four IRGs were identified, and GSEA indicated involvement in immune response and macrophage activation and regulation of T cell activation. Diagnostic models of DEGs containing five genes (PROS1, TDRD9, RETN, LOC728401, and METTL7B) and IRG with one gene (NSUN7) constructed using LASSO algorithm were validated using the GPL6947 and GPL13667 subset datasets, respectively. The IRG model outperformed the DEG model. Additionally, statistical analysis suggested that risk scores may be related to gestational age and birth weight, regardless of sex.

Conclusions: We identified six IRGs as potential diagnostic biomarkers for NS and developed diagnostic models for NS. Our findings provide a new perspective for future research on NS pathogenesis.

Keywords: Biomarkers; Diagnosis model; Immune infiltration; Logistic regression; Neonatal sepsis.

Fig. 1

Gene expression characteristics in neonatal sepsis (NS) samples. a Dimension reduction algorithm was used to evaluate the differences between patients with NS and normal samples. b The differentially expressed genes (DEGs) in total RNA expression profiles between NS and normal samples were visualized by Vioplot. c Heatmaps presented the expression of all DEGs. d Gene Set Enrichment Analysis (GSEA) analysis was performed to evaluate the differences of the biological states between NS and normal samples

Fig. 2

Characteristics of the immune cell microenvironment in NS. a Differences in immune cell compositions between NS and normal samples. b The differences of immune cell compositions between NS and normal samples were visualized by heatmap; grouped by age. c The correlation of the immune cells was visualized by corrplot. d Dimension reduction algorithm was conducted to evaluate the differences in immune cell compositions between NS and normal samples. t-SNE, t-distributed stochastic neighbour embedding

Fig. 3

Gene expression characteristics of the two immune-related clusters. a Dimension reduction algorithm was used to evaluate the differences between cluster 1 and cluster 2. b Immune-related DEGs (IRGs) of total RNA expression profile between cluster 1 and cluster 2 were visualized by Vioplot. c Heatmaps presented the expression of all IRGs. d GSEA was performed to evaluate the differences of the biological states between cluster 1 and cluster 2

Fig. 4

Construction of NS diagnostic models based on DEGs and IRGs. a Top 20 DEGs sorted by mean decrease accuracy based on random forest method. b Top 15 IRGs sorted by mean decrease accuracy based on random forest method. c Receiver operating characteristic (ROC) curves were calculated to evaluate the diagnostic efficiency of the DEG and IRG gene signatures with the training dataset. d AUC values of both models obtained by 1000 repeated tests based on bootstrap methods were shown in the density plot to validate the conclusions. AUC, area under the curve; CI, confidence interval; DEG, differentially expressed gene; IRG, immune-related gene

Fig. 5

Evaluation of NS diagnostic models based on DEGs and IRGs. a ROC curves were calculated to evaluate the diagnostic efficiency of the DEG and IRG gene signatures with the validation dataset 1. b AUC values of both models obtained by 1000 repeated tests based on bootstrap methods were shown in the density plot to validate the conclusions. c ROC curves were calculated to evaluate the diagnostic efficiency of the DEG and IRG signatures with the validation dataset 2. d AUC values of both models obtained by 1000 repeated tests based on bootstrap methods were shown in the density plot to validate the conclusions. AUC area under the curve, CI confidence interval, DEG differentially expressed gene, IRG immune-related gene