Identification and evaluation of hub mRNAs and long non-coding RNAs in neutrophils during sepsis

Identification of a novel four-gene diagnostic signature for patients with sepsis by integrating weighted gene co-expression network analysis and support vector machine algorithm

Sepsis is a life-threatening condition in which the immune response is directed towards the host tissues, causing organ failure. Since sepsis does not present with specific symptoms, its diagnosis is often delayed. The lack of diagnostic accuracy results in a non-specific diagnosis, and to date, a standard diagnostic test to detect sepsis in patients remains lacking. Therefore, it is vital to identify sepsis-related diagnostic genes. This study aimed to conduct an integrated analysis to assess the immune scores of samples from patients diagnosed with sepsis and normal samples, followed by weighted gene co-expression network analysis (WGCNA) to identify immune infiltration-related genes and potential transcriptome markers in sepsis. Furthermore, gene regulatory networks were established to screen diagnostic markers for sepsis based on the protein-protein interaction networks involving these immune infiltration-related genes. Moreover, we integrated WGCNA with the support vector machine (SVM) algorithm to build a diagnostic model for sepsis. Results showed that the immune score was significantly lower in the samples from patients with sepsis than in normal samples. A total of 328 and 333 genes were positively and negatively correlated with the immune score, respectively. Using the MCODE plugin in Cytoscape, we identified four modules, and through functional annotation, we found that these modules were related to the immune response. Gene Ontology functional enrichment analysis showed that the identified genes were associated with functions such as neutrophil degranulation, neutrophil activation in the immune response, neutrophil activation, and neutrophil-mediated immunity. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis showed the enrichment of pathways such as primary immunodeficiency, Th1- and Th2-cell differentiation, T-cell receptor signaling pathway, and natural killer cell-mediated cytotoxicity. Finally, we identified a four-gene signature, containing the hub genes LCK, CCL5, ITGAM, and MMP9, and established a model that could be used to diagnose patients with sepsis.

LncRNA TUG1 exhibits pro-fibrosis activity in hypertrophic scar through TAK1/YAP/TAZ pathway via miR-27b-3p

Hypertrophic Scar (HS) is a complicated fibrotic disease. In addition, its pathogenesis is still to be further explored. Long non-coding RNAs (lncRNAs) have been proved to be participated in multiple diseases, including HS. However, the role of lncRNA TUG1 in HS remains unclear. The expression level of RNA and protein in cells were detected by q-PCR and western blot, respectively. MTT assay was performed to test the cell proliferation. Cell migration was detected by transwell assay. Cell apoptosis was measured by flow cytometry. Dual luciferase report assay and RNA pull down were used to verify the relationship between TUG1, miR-27b-3p and TAK1.TUG1 and TAK1 were upregulated in HS, while miR-27b-3p was downregulated. Knockdown of TUG1 significantly suppressed the proliferation and migration and induced the apoptosis of HS fibroblasts (HSF). In addition, silencing of TUG1 notably inhibited the extracellular matrix (ECM) biosynthesis in HSF. Overexpression of miR-27b-3p has the same effect on HS as that of TUG1 knockdown. Meanwhile, TUG1 could sponge miR-27b-3p, and TAK1 was the direct target of miR-27b-3p. Furthermore, knockdown of TUG1 significantly suppressed the fibrosis in HS via miR-27b-3p/TAK1/YAP/TAZ axis mediation. LncRNA TUG1 promotes the fibrosis in HS via sponging miR-27b-3p and then activates TAK1/YAP/TAZ pathway, which may serve as a potential target for treatment of HS.

Gene correlation network analysis to identify regulatory factors in sepsis

BACKGROUND AND OBJECTIVES

Sepsis is a leading cause of mortality and morbidity in the intensive care unit. Regulatory mechanisms underlying the disease progression and prognosis are largely unknown. The study aimed to identify master regulators of mortality-related modules, providing potential therapeutic target for further translational experiments.

METHODS

The dataset GSE65682 from the Gene Expression Omnibus (GEO) database was utilized for bioinformatic analysis. Consensus weighted gene co-expression netwoek analysis (WGCNA) was performed to identify modules of sepsis. The module most significantly associated with mortality were further analyzed for the identification of master regulators of transcription factors and miRNA.

RESULTS

A total number of 682 subjects with various causes of sepsis were included for consensus WGCNA analysis, which identified 27 modules. The network was well preserved among different causes of sepsis. Two modules designated as black and light yellow module were found to be associated with mortality outcome. Key regulators of the black and light yellow modules were the transcription factor CEBPB (normalized enrichment score = 5.53) and ETV6 (NES = 6), respectively. The top 5 miRNA regulated the most number of genes were hsa-miR-335-5p (n = 59), hsa-miR-26b-5p (n = 57), hsa-miR-16-5p (n = 44), hsa-miR-17-5p (n = 42), and hsa-miR-124-3p (n = 38). Clustering analysis in 2-dimension space derived from manifold learning identified two subclasses of sepsis, which showed significant association with survival in Cox proportional hazard model (p = 0.018).

CONCLUSIONS

The present study showed that the black and light-yellow modules were significantly associated with mortality outcome. Master regulators of the module included transcription factor CEBPB and ETV6. miRNA-target interactions identified significantly enriched miRNA.