Search

Background MiRNAs can regulate gene expression directly or indirectly, and long noncoding RNAs as competing endogenous RNA (ceRNAs) can bind to miRNAs competitively and affect mRNA expression. The ceRNA network is still unclear in breast cancer. In this study, a ceRNA network was constructed, and new treatment and prognosis targets and biomarkers for breast cancer were explored. Methods A total of 1 096 cancer tissues and 112 adjacent normal tissues to cancer from the TCGA database were used to screen out significant differentially expressed mRNAs (DEMs), lncRNAs (DELs), and miRNAs (DEMis) to construct a ceRNA network. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were used to predict potential functions. Survival analysis was performed to predict which functions were significant for prognosis. Results From the analysis, 2 139 DEMs, 1 059 DELs, and 84 DEMis were obtained. Targeting predictions for DEMis‐DELs and DEMis‐DEMs can yield 26 DEMs, 90 DELs, and 18 DEMis. We performed GO enrichment analysis, and the results showed that the upregulated DEMs were involved in nucleosomes, extracellular regions, and nucleosome assembly, while the downregulated DEMs were mainly involved in Z disk, muscle contraction, and structural constituents of muscle. KEGG pathway analysis was performed on all DEMs, and the pathways were enriched in retinol metabolism, steroid hormone biosynthesis, and tyrosine metabolism. Through survival analysis of the ceRNA network, we identified four DEMs, two DELs, and two DEMis that were significant for poor prognosis. Conclusions This study suggested that constructing a ceRNA network and performing survival analysis on the network could screen out new significant treatment and prognosis targets and biomarkers.

Purpose: To develop a comprehensive differential expression gene profile as well as a prediction model based on the expression analysis of pediatric sepsis specimens.Methods: In this study, compared with control specimens, a total of 708 differentially expressed genes in pediatric sepsis (case–control at a ratio of 1:3) were identified, including 507 up-regulated and 201 down-regulated ones. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of differentially expressed genes indicated the close interaction between neutrophil activation, neutrophil degranulation, hematopoietic cell lineage, Staphylococcus aureus infection, and periodontitis. Meanwhile, the results also suggested a significant difference for 16 kinds of immune cell compositions between two sample sets. The two potential selected biomarkers (MMP and MPO) had been validated in septic children patients by the ELISA method.Conclusion: This study identified two potential hub gene biomarkers and established a differentially expressed genes-based prediction model for pediatric sepsis, which provided a valuable reference for future clinical research.

The methanolic and water extracts of Carapa procera leaves and stem barks were screened for their phytochemical content using a multi-technique approach. The extracts were also assessed for their in vitro antioxidant capacity along with their anti-diabetic (α-amylase, α-glucosidase), anti-tyrosinase, anti-elastase and anti-cholinesterase (AChE, BChE) activities. Furthermore, antibacterial and antifungal effects were determined against several bacterial and fungal strains. Data Integration Analysis for Biomarker discovery using Latent components (DIABLO) integrative analysis was conducted on collected data to determine the influence of extraction solvents and plant parts on phytochemical content, antioxidant properties and enzyme inhibitory properties of C. procera samples. Additionally, the major identified compounds were screened as modulators of multiple pathways involved in human diseases via Gene Ontology (GO) enrichment analysis. Results showed that methanolic stem bark extract exhibited the most potent ABTS scavenging, Cu2+ and Fe3+ reducing power, total antioxidant capacity and Fe2+ chelating power and displayed the highest total flavanol content. Methanolic extracts of leaves and stem barks were abounded with phenolics and had the greatest anti-AChE, anti-BChE, anti-tyrosinase and anti-elastase activities. A significant antifungal activity was observed, with the lowest minimum inhibitory concentration and minimum fungicidal values of 0.07 and 0.15 mg/mL, respectively. DIABLO integrative analysis suggested that the phytochemical content and biological activities varied significantly within the plant parts and were influenced by types of solvent used. GO enrichment analysis on the main bioactive compounds showed modulation of multiple pathways associated with cancer. Obtained results demonstrated that stem bark and leaves of C. procera can be considered as promising sources of bioactive molecules with high pharmacological values.

Background The aim of this research was to investigate the retinal transcriptome changes in long‐term streptozotocin (STZ)‐induced rats' retinas using RNA sequencing (RNA‐seq), to explore the molecular mechanisms of diabetic retinopathy (DR), and to identify novel targets for the treatment of DR by comparing the gene expression profile we obtained. Methods In this study, 6 healthy male SD rats were randomly divided into wild‐type (WT) group and streptozotocin (STZ)‐induced group, 3 rats each group. After 6 months, 3 normal retina samples and 3 DM retina samples (2 retinas from the same rat were considered as 1 sample) were tested and differentially expressed genes (DEGs) were measured by RNA‐seq technology. Then, we did Gene Ontology (GO) enrichment analysis and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway analysis and validated the results of RNA‐seq through qRT–PCR. Results A total of 118 DEGs were identified, of which 72 were up‐regulated and 46 were down‐regulated. The enriched GO terms showed that 3 most significant enrichment terms were binding (molecular function), cell part (cellular component), and biological regulation (biological process). The results of the KEGG pathway analysis revealed a significant enrichment in cell adhesion molecules, PI3K‐Akt signaling pathway, and allograft rejection, etc. Conclusion Our research has identified specific DEGs and also speculated their potential functions, which will provide novel targets to explore the molecular mechanisms of DR., This study examined retina from WT and diabetic SD male rats to investigate the changes in a variety of retinal transcripts as a result of diabetes using RNA‐seq. We identified a total of 118 DEGs, of which 72 were up‐regulated and 46 were down‐regulated. We also found 66 GO terms and 41 KEGG pathways which were significantly enriched by GO and KEGG analysis.

Network pharmacology (NP) is a useful, emerging means of understanding the complex pharmacological mechanisms of traditional herbal medicines. Sochehwan (SCH) is a candidate herbal prescription for drug repurposing as it has been suggested to have beneficial effects on metabolic syndrome. In this study, NP was adopted to complement the shortcomings of literature-based drug repurposing strategies in traditional herbal medicine. We conducted in vitro studies to confirm the effects of SCH on potential pharmacological targets identified by NP analysis. Herbal compounds and molecular targets of SCH were explored and screened from a traditional Chinese medicine systems pharmacology database and analysis platform (TCMSP) and an oriental medicine advanced searching integrated system (OASIS). Forty-seven key targets selected from a protein-protein interaction (PPI) network were analyzed with gene ontology (GO) term enrichment and KEGG pathway enrichment analysis to identify relevant categories. The tumor necrosis factor (TNF) and mitogen-activated protein kinase (MAPK) signaling pathways were presented as significant signaling pathways with lowest p-values by NP analysis, which were downregulated by SCH treatment. The signal transducer and activator of transcription 3 (STAT3) was identified as a core key target by NP analysis, and its phosphorylation ratio was confirmed to be significantly suppressed by SCH. In conclusion, the NP-based approach used for target prediction and experimental data obtained from Raw 264.7 cells strongly suggested that SCH can attenuate inflammatory status by modulating the phosphorylation status of STAT3.

The identification of functional gene modules that are derived from integration of information from different types of networks is a powerful strategy for interpreting the etiology of complex diseases such as rheumatoid arthritis (RA). Genetic variants are known to increase the risk of developing RA. Here, a novel method, the construction of a genetic network, was used to mine functional gene modules linked with RA. A polymorphism interaction analysis (PIA) algorithm was used to obtain cooperating single nucleotide polymorphisms (SNPs) that contribute to RA disease. The acquired SNP pairs were used to construct a SNP-SNP network. Sub-networks defined by hub SNPs were then extracted and turned into gene modules by mapping SNPs to genes using dbSNP database. We performed Gene Ontology (GO) analysis on each gene module, and some GO terms enriched in the gene modules can be used to investigate clustered gene function for better understanding RA pathogenesis. This method was applied to the Genetic Analysis Workshop 15 (GAW 15) RA dataset. The results show that genes involved in functional gene modules, such as CD160 (rs744877) and RUNX1 (rs2051179), are especially relevant to RA, which is supported by previous reports. Furthermore, the 43 SNPs involved in the identified gene modules were found to be the best classifiers when used as variables for sample classification.