This study investigates the molecular mechanisms by which extracellular vesicles (EVs) derived from adipose-derived mesenchymal stem cells (ADSCs) promote M2 polarization of macrophages and thus reduce lung injury caused by sepsis. High-throughput sequencing was used to identify differentially expressed genes related to long non-coding RNA (lncRNA) in ADSC-derived EVs (ADSC-EVs) in sepsis lung tissue. Weighted gene co-expression network analysis (WGCNA) was employed to predict the downstream target genes of the lncRNA DLEU2.
View Article and Find Full Text PDFExtracellular vesicles (EVs) derived from human adipose mesenchymal stem cells (hADSCs) may exert a therapeutic benefit in alleviating sepsis-induced organ dysfunction by delivering cargos that include RNAs and proteins to target cells. The current study aims to explore the protective effect of miR-150-5p delivered by hADSC-EVs on sepsis-induced acute lung injury (ALI). We noted low expression of miR-150-5p in plasma and bronchoalveolar lavage fluid samples from patients with sepsis-induced ALI.
View Article and Find Full Text PDFInvestigation on a competitive endogenous RNA (ceRNA) network attracted lots of attention due its function in cancer regulation. Here, we probed into the possible molecular mechanism of circSSPO/microRNA-6820-5p (miR-6820-5p)/kallikrein-related peptidase 8 (KLK8)/PKD1 network in the esophageal squamous cell carcinoma (ESCC). Following whole-transcriptome sequencing and differential analysis in collected ESCC tissue samples, circRNA-miRNA-mRNA regulatory network affecting ESCC was investigated.
View Article and Find Full Text PDFInt Immunopharmacol
September 2023
Monocyte-derived exosomes (Exos) have been implicated in inflammation-related autoimmune/inflammatory diseases via transferring bioactive cargoes to recipient cells. The purpose of this study was to investigate the possible effect of monocyte-derived Exos on the initiation and the development of acute lung injury (ALI) by delivering long non-coding RNA XIST. Key factors and regulatory mechanisms in ALI were predicted by bioinformatics methods.
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