MicroRNA-9-5p Is Involved in Lipopolysaccharide-Induced Acute Lung Injury Via the Regulation of Macrophage Polarization.

MicroRNA (miR)-9-5 p has been shown to affect lung cancer progression and lung fibrosis, but the efficacy of miR-9-5 p in acute lung injury (ALI) remained indefinite. The study was performed to probe the modulating mechanism of miR-9-5 p in ALI via regulating macrophage polarization. The ALI mouse model was established and blood samples of ALI patients were obtained. MiR-9-5 p levels in ALI mice and ALI patients were detected. Mouse pulmonary macrophages were extracted from bronchoalveolar lavage fluid and polarized into M1 and M2 macrophages. Intervention of miR-9-5 p expression was performed to observe the effects on M1 polarization and M2 polarization in lung macrophages, inflammatory factors in BALF, wet/dry weight ratio (W/D) in lung tissues, myeloperoxidase (MPO) activity in lung tissues, and lung tissue lesion condition. MiR-9-5 p levels were elevated in the lung tissues of ALI mice and ALI patients. MiR-9-5 p silencing could repress lung macrophages in ALI mice polarized toward the M1 phenotype and promoted the polarization toward the M2 phenotype, reduced the lung lesions, the lung water content, and the secretion levels of the pro-inflammatory factors TNF-α, IL-6, and IL-1ß in BALF, increased the secretion of the anti-inflammatory factor IL-10, as well as impeded the MPO activity in the lung tissues of ALI mice. MiR-9-5 p deletion ameliorates LPS-induced inflammatory infiltration in lung tissues via inhibiting the polarization of mouse lung macrophages to the M1 phenotype and promoting the polarization to the M2 phenotype.

Revealing potential diagnostic gene biomarkers of septic shock based on machine learning analysis.

BACKGROUND: Sepsis is an inflammatory response caused by infection with pathogenic microorganisms. The body shock caused by it is called septic shock. In view of this, we aimed to identify potential diagnostic gene biomarkers of the disease. MATERIAL AND METHODS: Firstly, mRNAs expression data sets of septic shock were retrieved and downloaded from the GEO (Gene Expression Omnibus) database for differential expression analysis. Functional enrichment analysis was then used to identify the biological function of DEmRNAs (differentially expressed mRNAs). Machine learning analysis was used to determine the diagnostic gene biomarkers for septic shock. Thirdly, RT-PCR (real-time polymerase chain reaction) verification was performed. Lastly, GSE65682 data set was utilized to further perform diagnostic and prognostic analysis of identified superlative diagnostic gene biomarkers. RESULTS: A total of 843 DEmRNAs, including 458 up-regulated and 385 down-regulated DEmRNAs were obtained in septic shock. 15 superlative diagnostic gene biomarkers (such as RAB13, KIF1B, CLEC5A, FCER1A, CACNA2D3, DUSP3, HMGN3, MGST1 and ARHGEF18) for septic shock were identified by machine learning analysis. RF (random forests), SVM (support vector machine) and DT (decision tree) models were used to construct classification models. The accuracy of the DT, SVM and RF models were very high. Interestingly, the RF model had the highest accuracy. It is worth mentioning that ARHGEF18 and FCER1A were related to survival. CACNA2D3 and DUSP3 participated in MAPK signaling pathway to regulate septic shock. CONCLUSION: Identified diagnostic gene biomarkers may be helpful in the diagnosis and therapy of patients with septic shock.

Revealing the potential role of hub metabolism-related genes and their correlation with immune cells in acute ischemic stroke.

OBJECTIVES: Acute ischemic stroke (AIS) is caused by cerebral ischemia due to thrombosis in the blood vessel. The purpose of this study is to identify key genes related to metabolism to aid in the mechanism research and management of AIS. MATERIALS AND METHODS: Gene expression data were downloaded from the Gene Expression Omnibus database. Weighted gene co-expression network analysis, Gene Ontology and kyoto encyclopedia of genes and genomes analysis were used to identify metabolism-related genes that may be involved in the regulation of AIS. A protein protein interaction network was mapped using Cytoscape based on the STRING database. Subsequently, hub metabolism-related genes were identified based on Cytoscape-CytoNCA and Cytoscape-MCODE plug-ins. Least absolute shrinkage and selection operator algorithm and differential expression analysis. In addition, drug prediction, molecular docking, ceRNA network construction, and correlation analysis with immune cell infiltration were performed to explore their potential molecular mechanisms of action in AIS. Finally, the expression of hub gene was verified by real-time PCR. RESULTS: Metabolism-related genes FBL, HEATR1, HSPA8, MTMR4, NDUFC1, NDUFS8 and SNU13 were identified. The AUC values of FBL, HEATR1, HSPA8, MTMR4, NDUFS8 and SNU13 were all greater than 0.8, suggesting that they had good diagnostic accuracy. Correlation analysis found that their expression levels were also related to the infiltration levels of multiple immune cells, such as Activated.CD8.T.cell and Activated.dendritic.cell. It was found that only HSPA8 was successfully matched to drugs with literature support, and these drugs were acetaminophen, bupivacaine, dexamethasone, gentamicin, tretinoin and cisplatin. Moreover, it was also identified that the ENSG000000218510-hsa-miR-330-3p-HEATR1 axis may be involved in regulating AIS. CONCLUSIONS: The identification of FBL, HEATR1, HSPA8, MTMR4, NDUFC1, NDUFS8 and SNU13 provides a new research direction for exploring the molecular mechanisms of AIS, which can help in clinical management and diagnosis.

Uncovering Candidate mRNAs, Signaling Pathways and Immune Cells in Atherosclerotic Plaque and Ischemic Stroke.

Background: The specific molecular mechanistic link between atherosclerotic plaques and ischemic stroke (IS) is not clear. The aim of this study is to explore the potential molecular relationship between atherosclerotic plaques and IS. Methods: All data were downloaded from the Gene Expression Omnibus (GEO) database. Key hub differentially expressed mRNAs (DEmRNAs) related to atherosclerotic plaques and IS were identified by differential expression analysis and least absolute shrinkage and selection operator (LASSO) analysis. Subsequently, a diagnostic model was established based on the expression of key hub DEmRNAs and logistic regression. In order to understand the molecular mechanism of key hub DEmRNAs, the transcription factor (TF) regulatory network and mRNA-miRNA-lncRNA regulatory network were also constructed. In addition, functional enrichment analysis and single-sample Gene Set Enrichment Analysis (ssGSEA) analysis were also performed. Results: Four key hub DEmRNAs (ADCY3, CLDN7, PPM1B and RRAS2) were identified by differential expression analysis and LASSO analysis. Moreover, the diagnostic model based on four key hub DEmRNAs has excellent diagnostic accuracy. We also found that Type 1 T helper cell may be associated with IS caused by atherosclerosis based on ssGSEA analysis. In the mRNA-miRNA-lncRNA regulatory network, we found that multiple signaling axes such as RRAS2-hsa-miR-3150b-3p-ILF3-AS1, PPM1B-hsa-miR-541-5p-LINC00294, CLDN7-hsa-miR-184-LINC00467 and ADCY3-hsa-miR-488-3p-URB1-AS1 may play an important role in the progression of IS. In addition, some signaling pathways, including chemokine signaling pathway, MAPK signaling pathway and cAMP signaling pathway, may be involved in regulating IS. Conclusion: The identified key molecules, signaling pathways and immune cells may help to provide a theoretical basis for exploring the relationship between atherosclerotic plaque and the progression of IS.