Two Gene Set Variation Index as Biomarker of Bacterial and Fungal Sepsis

Single-Cell RNA Sequencing and Transcriptome Analysis Revealed the Immune Microenvironment and Gene Markers of Acute Respiratory Distress Syndrome

Background

Acute respiratory distress syndrome (ARDS) is caused by severe pulmonary inflammation and the leading cause of death in the intensive care unit.

Methods

We used single-cell RNA sequencing to compare peripheral blood mononuclear cells from sepsis-induced ARDS (SEP-ARDS) and pneumonic ARDS (PNE-ARDS) patient. Then, we used the GSE152978 and GSE152979 datasets to identify molecular dysregulation mechanisms at the transcriptional level in ARDS.

Results

Markedly increased CD14 cells were the predominant immune cell type observed in SEP-ARDS and PNE-ARDS patients. Cytotoxic cells and natural killer (NK) T cells were exclusively identified in patients with PNE-ARDS. An enrichment analysis of differentially expressed genes (DEGs) suggested that Th1 cell differentiation and Th2 cell differentiation were enriched in cytotoxic cells, and that the IL-17 signaling pathway, NOD receptor signaling pathway, and complement and coagulation cascades were enriched in CD14 cells. Furthermore, according to GSE152978 and GSE152979, 1939 DEGs were identified in patients with ARDS and controls; they were mainly enriched in the Kyoto Encyclopedia of Genes and Genomes pathways. RBP7 had the highest area under the curve values among the 12 hub genes and was mainly expressed in CD14 cells. Additionally, hub genes were negatively correlated with NK cells and positively correlated with neutrophils, cytotoxic cells, B cells, and macrophages.

Conclusion

A severe imbalance in the proportion of immune cells and immune dysfunction were observed in SEP-ARDS and PNE-ARDS patients. RBP7 may be immunologically associated with CD14 cells and serve as a potential marker of ARDS.

Identification of an Alveolar Macrophage-Related Core Gene Set in Acute Respiratory Distress Syndrome

Purpose

Acute respiratory distress syndrome (ARDS) is a rapidly progressive diffuse lung injury that is characterized by high mortality and acute onset. The pathological mechanisms of ARDS are still unclear. But alveolar macrophages have been shown to play an important role in inflammatory responses during ARDS. We aimed to find the biomarkers for ARDS for early diagnosis, to give ARDS patients timely treatment.

Methods

Gene expression profiles were downloaded from Gene Expression Omnibus (GEO) and screened for differentially expressed genes (DEGs). The common upregulated genes in all the datasets were defined as circulating ARDS alveolar macrophage-related genes (cARDSAMGs). We performed a functional enrichment analysis to explore potential biological functions of cARDSAMGs, and we built protein–protein interaction networks. Gene set variation analysis (GSVA) was used to calculate the core gene set variation analysis (CGSVA) score for individual samples. Receiver operating characteristic (ROC) curve analysis was applied on the CGSVA score to evaluate its ability for diagnosis of ARDS.

Results

A total of 60 genes were upregulated in all ARDS datasets and were therefore denominated as cARDSAMGs. The cARDSAMGs were significantly involved in multiple inflammation-, immunity- and phagocytosis-related biological processes and pathways. In the protein–protein interaction network associated with host responses to ADRS, eight genes were identified as a core gene set: PTCRA, JAG1, C1QB, ADAM17, C1QA, MMP9, VSIG4 and TNFAIP3. ROC curve analysis showed that the CGSVA score may be considered as a biomarker for ARDS: it was significantly higher in patients with ARDS than those in healthy in both alveolar lavage fluid and whole blood.

Conclusion

The ARDS alveolar macrophage-related CGSVA score may be useful as a biomarker for ARDS.

Identification of Potential Early Diagnostic Biomarkers of Sepsis

Objective

The goal of this article was to identify potential biomarkers for early diagnosis of sepsis in order to improve their survival.

Methods

We analyzed differential gene expression between adult sepsis patients and controls in the GSE54514 dataset. Coexpression analysis was used to cluster coexpression modules, and enrichment analysis was performed on module genes. We also analyzed differential gene expression between neonatal sepsis patients and controls in the GSE25504 dataset, and we identified the subset of differentially expressed genes (DEGs) common to neonates and adults. All samples in the GSE54514 dataset were randomly divided into training and validation sets, and diagnostic signatures were constructed using least absolute shrink and selection operator (LASSO) regression. The key gene signature was screened for diagnostic value based on area under the receiver operating characteristic curve (AUC). STEM software identified dysregulated genes associated with sepsis-associated mortality. The ssGSEA method was used to quantify differences in immune cell infiltration between sepsis and control samples.

Results

A total of 6316 DEGs in GSE54514 were obtained spanning 10 modules. Module genes were mainly enriched in immune and metabolic responses. Screening 51 genes from among common genes based on AUC > 0.7 led to a LASSO model for the training set. We obtained a 25-gene signature, which we validated in the validation set and in the GSE25504 dataset. Among the signature genes, SLC2A6, C1ORF55, DUSP5 and RHOB were recognized as key genes (AUC > 0.75) in both the GSE54514 and GSE25504 datasets. SLC2A6 was identified by STEM as associated with sepsis-associated mortality and showed the strongest positive correlation with infiltration levels of Th1 cells.

Conclusion

In summary, our four key genes may have important implications for the early diagnosis of sepsis patients. In particular, SLC2A6 may be a critical biomarker for predicting survival in sepsis.