Long noncoding RNAs (lncRNAs) have important biological functions as competing endogenous RNAs (ceRNAs) in tumors, yet the functions and regulatory mechanisms of lncRNA‐related ceRNAs in gastric cancer have not been fully elucidated. In this study, we constructed a lncRNA‐miRNA‐mRNA ceRNA network and identified potential lncRNA biomarkers in gastric cancer. Basing on the RNA profiles downloaded from The Cancer Genome Atlas (TCGA) platform, the gastric cancer‐specific differentially expressed lncRNAs, miRNAs, and mRNAs were screened for constructing a ceRNA network using bioinformatic tools. The enrichment analysis of the biological processes in Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes pathways was performed on the ceRNA‐related DEmRNAs. According to the modularization of protein‐protein interaction (PPI) network, we extracted a ceRNA subnetwork and analyzed the correlation between the expression of the lncRNAs involved and specific clinical features of patients. Next, the expression of highly up‐regulated in liver cancer (HULC) and RP11‐314B1.2 showed significant changes in several pathological processes involved in gastric cancer, and nine lncRNAs were found to be correlated with the overall survival of patients with gastric cancer. Through the univariate and multivariate Cox regression analyses, two lncRNAs (LINC00106 and RP11‐999E24.3) were identified and utilized to establish a risk score model for assessing the prognosis of patients. The analysis results were also partially verified using quantitative real‐time PCR. The findings from this study indicate that HULC, RP11‐314B1.2, LINC00106, and RP11‐999E24.3 could be considered as potential therapeutic targets or prognostic biomarkers in gastric cancer, and provide a new perspective for cancer pathogenesis research.

Atherosclerosis is one of the most common vascular diseases, and inflammation participates in all stages of its progression. Laminar shear stress protects arteries from atherosclerosis and reduces endothelial inflammation. Long noncoding RNAs have emerged as critical regulators in many diseases, including atherosclerosis. However, the expression and functions of long noncoding RNAs subjected to laminar shear stress in endothelial cells remain unclear. This study aimed to reveal the mechanism by which shear stress–regulated long noncoding RNAs contribute to anti-inflammation. In this study, we identified a novel long noncoding RNA AF131217.1, which was upregulated after laminar shear stress treatment in human umbilical vein endothelial cells. Knockdown of AF131217.1 inhibited flow-mediated reduction of monocyte adhesion VCAM-1 (vascular cell adhesion molecule-1) and ICAM-1 (intercellular adhesion molecule-1) expression and inhibited flow-mediated enhancement of flow-responsive expression of KLF (Kruppel-like factor) 2 and eNOS (endothelial NO synthase). Furthermore, TNF-α (tumor necrosis factor-α) was used to induce an inflammatory response in human umbilical vein endothelial cells. Knockdown of AF131217.1 promoted ICAM-1 and VCAM-1 expression, as well as changes in monocyte adhesion and KLF2 and eNOS expression induced by TNF-α. Mechanistic investigations indicated that AF131217.1 acted as a competing endogenous RNA for miR-128-3p, leading to regulation of its target gene KLF4. In conclusion, our study demonstrates for the first time that laminar shear stress regulates the expression of AF131217.1 in human umbilical vein endothelial cells, and the AF131217.1/miR-128-3p/KLF4 axis plays a vital role in atherosclerosis development.

Allergic rhinitis (AR) is a common heterogeneous chronic disease with a high prevalence and a complex pathogenesis influenced by numerous factors, involving a combination of genetic and environmental factors. To gain insight into the pathogenesis of AR and to identity diagnostic biomarkers, we combined systems biology approach to analyze microbiome and serum composition. We collected inferior turbinate swabs and serum samples to study the microbiome and serum metabolome of 28 patients with allergic rhinitis and 15 healthy individuals. We sequenced the V3 and V4 regions of the 16S rDNA gene from the upper respiratory samples. Metabolomics was used to examine serum samples. Finally, we combined differential microbiota and differential metabolites to find potential biomarkers. We found no significant differences in diversity between the disease and control groups, but changes in the structure of the microbiota. Compared to the HC group, the AR group showed a significantly higher abundance of 1 phylum (Actinobacteria) and 7 genera (Klebsiella, Prevotella and Staphylococcus, etc.) and a significantly lower abundance of 1 genus (Pelomonas). Serum metabolomics revealed 26 different metabolites (Prostaglandin D2, 20-Hydroxy-leukotriene B4 and Linoleic acid, etc.) and 16 disrupted metabolic pathways (Linoleic acid metabolism, Arachidonic acid metabolism and Tryptophan metabolism, etc.). The combined respiratory microbiome and serum metabolomics datasets showed a degree of correlation reflecting the influence of the microbiome on metabolic activity. Our results show that microbiome and metabolomics analyses provide important candidate biomarkers, and in particular, differential genera in the microbiome have also been validated by random forest prediction models. Differential microbes and differential metabolites have the potential to be used as biomarkers for the diagnosis of allergic rhinitis.

Introduction:The current COVID-19 pandemic caused by a novel coronavirus SARS-CoV -2 is a quickly developing global health crisis, yet the mechanisms of pathogenesis in COVID-19 are not fully understood. Methods:The RNA sequencing data of SARS-CoV-2-infected cells was obtained from the Gene Expression Omnibus (GEO). The differentially expressed mRNAs (DEmRNAs), long non-coding RNAs (DElncRNAs), and microRNAs (DEmiRNAs) were identified by edgeR, and the SARS-CoV-2-associated competing endogenous RNA (ceRNA) network was constructed based on the prediction of bioinformatic databases. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted with the SARS-CoV-2-related DEmRNAs, and the protein-protein interaction network was also built basing on STRING database. The ROC analysis was performed for assessing the diagnostic efficiency of hub genes. Results:The results indicated that SARS-CoV-2-related DEmRNAs were associated with the interferon signaling pathway and other antiviral processes, such as IFNL3, IFNL1 and CH25H. Our analysis suggested that lncRNA NEAT1 might regulate the host immune response through two miRNAs, hsa-miR-374-5p and hsa-miR-155-5p, which control the expression of SOCS1, IL6, IL1B, CSF1R, CD274, TLR6, and TNF. Additionally, IFI6, HRASLS2, IGFBP4 and PTN may be potential targets based on an analysis comparing the transcriptional responses of SARS-CoV-2 infection with that of other respiratory viruses. Discussion: The unique ceRNA network identified potential non-coding RNAs and their possible targets as well as a new perspective to understand the molecular mechanisms of the host immune response to SARS-CoV-2. This study may also aid in the development of innovative diagnostic and therapeutic strategies.

Brain organoids can reproduce the regional three-dimensional (3D) tissue structure of human brains, following the in vivo developmental trajectory at the cellular level; therefore, they are considered to present one of the best brain simulation model systems. By briefly summarizing the latest research concerning brain organoid construction methods, the basic principles, and challenges, this review intends to identify the potential role of the physiological electric field (EF) in the construction of brain organoids because of its important regulatory function in neurogenesis. EFs could initiate neural tissue formation, inducing the neuronal differentiation of NSCs, both of which capabilities make it an important element of the in vitro construction of brain organoids. More importantly, by adjusting the stimulation protocol and special/temporal distributions of EFs, neural organoids might be created following a predesigned 3D framework, particularly a specific neural network, because this promotes the orderly growth of neural processes, coordinate neuronal migration and maturation, and stimulate synapse and myelin sheath formation. Thus, the application of EF for constructing brain organoids in a3D matrix could be a promising future direction in neural tissue engineering.