Analysis of differentially expressed genes related to cerebral ischaemia in young rats based on the Gene Expression Omnibus database

BACKGROUND The incidence rate of cerebral infarction in young people is increasing day by day,the age of onset tends to be younger,and its internal pathogenesis and mechanism are very complicated,which leads to greater difficulties in treatment.Therefore,it is essential to analyze the key pathway that affects the onset of cerebral infarction in young people from the perspective of genetics.AIM To compare the differentially expressed genes in the brain tissue of young and aged rats with middle cerebral artery occlusion and to analyse their effect on the key signalling pathway involved in the development of cerebral ischaemia in young rats.METHODS The Gene Expression Omnibus 2R online analysis tool was used to analyse the differentially expressed genes in the GSE166162 dataset regarding the development of cerebral ischaemia in young and aged groups of rats.DAVID 6.8 software was further used to filter the differentially expressed genes.These genes were subjected to Gene Ontology(GO)function analysis and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway enrichment analysis to determine the key gene pathway that affects the occurrence of cerebral ischaemia in young rats.RESULTS Thirty-five differentially expressed genes(such as Igf2,Col1a2,and Sfrp1)were obtained;73 GO enrichment analysis pathways are mainly involved in biological processes such as drug response,amino acid stimulation response,blood vessel development,various signalling pathways,and enzyme regulation.They are involved in molecular functions such as drug binding,protein binding,dopamine binding,metal ion binding,and dopamine neurotransmitter receptor activity.KEGG pathway enrichment analysis showed a significantly enriched pathway:The cyclic adenosine monophosphate(c-AMP)signalling pathway.CONCLUSION The c-AMP signalling pathway might be the key pathway in the intervention of cerebral infarction in young people.

Bioinformatics-based gene set enrichment and immune cell infiltration analysis of psoriasis

Objective:Based on bioinformatics,gene set enrichment analysis(GSEA)and immune infiltration analysis were carried out on the microarray data of psoriasis expression profile to further understand the pathogenesis of psoriasis.Methods:GSE6710 chip data were obtained from gene expression database(GEO),and gene ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)enrichment analysis were performed using GSEA software.22 kinds of immune cell gene expression matrices and R packages were downloaded from CIBERSOFT official website,and the immune cell infiltration matrix was obtained by R software and related graphs were drawn.Results:The pathways related to cell proliferation and innate immunity were highly expressed in psoriatic lesions,and some cancer-related pathways were highly expressed in psoriatic lesions.Immunized cell infiltration analysis showed that activated memory T cells,follicular helper T cells,M0 macrophages and activated dendritic cells were up-regulated in psoriatic skin lesion group,and inactive mast cells were down-regulated in psoriatic skin lesion group.Activated dendritic cells are positively correlated with follicular helper T cells,activated mast cells are positively correlated with M0 macrophages.Inactivated mast cells are negatively correlated with activated memory T cells,M1 macrophages are negatively correlated with regulatory T cells,M0 macrophages are negatively correlated with inactive mast cells.Conclusion:Cell proliferation and innate immunity are of great significance in the pathogenesis of psoriasis.Immune cell infiltration analysis is generally consistent with the current psoriasis pathogenesis model.Macrophages and mast cells also play a certain role in psoriasis.

Exploring the potential mechanisms of luteolin against ulcerative colitis and colorectal cancer via network pharmacology and molecular docking

Background:Patients diagnosed with ulcerative colitis(UC)are known to have an increased susceptibility to colorectal cancer(CRC).However,the shared underlying mechanisms between UC and CRC remain unclear.Given the therapeutic potential of luteolin in both UC and CRC,this study aims to elucidate the molecular targets and mechanisms through which luteolin exerts its effects against these diseases.Methods:The GeneCards database,DisGENet database,and Gene Expression Omnibus database were utilized to analyze the targets associated with UC and CRC.Subsequently,the Traditional Chinese Medicine Systems Pharmacology and SwissTargetPrediction databases were employed to identify luteolin-related targets.The identified luteolin-related targets were then mapped to official gene symbols using the UniProt database.The Cytoscape 3.9.0 software was utilized to construct a network of luteolin-associated targets.Venn diagram analysis was performed to identify common targets among UC,CRC,and luteolin.The common targets were further analyzed using the STRING database to construct a protein-protein interaction network.The“cytoHubba”plugin in Cytoscape 3.9.0 was employed to identify hub targets within the PPI network.Gene Ontology functional analysis and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis were conducted on the hub targets.Finally,molecular docking using AutoDock and PyMOL software was performed to assess the binding affinity between luteolin and the hub targets.Results:Luteolin was found to interact with a total of 149 pharmacological targets,while UC and CRC were associated with 1232 and 3278 targets,respectively.Forty-six common targets were identified among luteolin,UC,and CRC.Through the application of seven different algorithms,seven hub targets were identified,TP53,AKT1,TNF,SRC,EGFR,and MMP9.Bioinformatics enrichment analysis revealed 49 enriched pathways through Kyoto Encyclopedia of Genes and Genomes analysis,while Gene Ontology analysis yielded a total of 245 biological processes,4 cellular components,and 7 molecular functions.Molecular docking simulations demonstrated a good binding affinity between luteolin and the hub targets.Conclusion:This study identified multiple potential pharmacological targets and elucidated various biological pathways through which luteolin may exert its therapeutic effects in the treatment of UC and CRC.These findings provide a solid theoretical foundation for further experimental investigations in the treatment of UC and CRC.

Bioinformatics analysis of ferroptosis in spinal cord injury

Ferroptosis plays a key role in aggravating the progression of spinal cord injury(SCI),but the specific mechanism remains unknown.In this study,we constructed a rat model of T10 SCI using a modified Allen method.We identified 48,44,and 27 ferroptosis genes that were differentially expressed at 1,3,and 7 days after SCI induction.Compared with the sham group and other SCI subgroups,the subgroup at 1 day after SCI showed increased expression of the ferroptosis marker acyl-CoA synthetase long-chain family member 4 and the oxidative stress marker malondialdehyde in the injured spinal cord while glutathione in the injured spinal cord was lower.These findings with our bioinformatics results suggested that 1 day after SCI was the important period of ferroptosis progression.Bioinformatics analysis identified the following top ten hub ferroptosis genes in the subgroup at 1 day after SCI:STAT3,JUN,TLR4,ATF3,HMOX1,MAPK1,MAPK9,PTGS2,VEGFA,and RELA.Real-time polymerase chain reaction on rat spinal cord tissue confirmed that STAT3,JUN,TLR4,ATF3,HMOX1,PTGS2,and RELA mRNA levels were up-regulated and VEGFA,MAPK1 and MAPK9 mRNA levels were down-regulated.Ten potential compounds were predicted using the DSigDB database as potential drugs or molecules targeting ferroptosis to repair SCI.We also constructed a ferroptosis-related mRNA-miRNA-lncRNA network in SCI that included 66 lncRNAs,10 miRNAs,and 12 genes.Our results help further the understanding of the mechanism underlying ferroptosis in SCI.

Bioinformatics analysis of hepatic fibrosis based on hepatic stellate cells

Objective:To analyze differentially expressed genes in human hepatic stellate cells(HSCs)based on data from the GEO database and to identify important target genes for hepatic fibrosis(HF).Methods:In GEO database,microarray GSE11954 of the GEO database was used to obtain data on differential gene expression in human HSCs and was analyzed using GEO2R,using a P value of<0.01 and log2FC value of≥2 for the screening.The genes were input into the DAVID database for enrichment analysis of genes and pathways,followed by protein interaction analysis and module analysis.The results were compared with the results found through text mining.Results:Two hundred sixty two differentially expressed genes(DEGs)were identified.The results of gene bulk enrichment showed that the functional molecules encoded by the DEGS were mainly located in the cytoplasm,extracellular matrix and nucleosome,while the molecular functions were mainly related to"regulating actin binding","protein kinase binding"and"kinase activity".The biological processes they were found to be involved in"regulating cell division","immune response"and"collagen decomposition reaction".KEGG signaling pathway analysis found that they were mainly involved in"cell cycle signaling pathway","ECM receptor interaction signaling pathway","p53 signaling pathway"and"FOXO signaling pathway".Text mining results suggested that MMP1 and ETV6 are potential molecular targets for HF therapy.Conclusion:The results of bioinformatics analysis identified targets and signaling pathways involved in the pathogenesis of HF,but these require further experimental verification.

Comparative transcriptomic analysis of rat versus mouse cerebral cortex after traumatic brain injury

The heterogeneity of traumatic brain injury(TBI)-induced secondary injury has greatly hampered the development of effective treatments for TBI patients.Targeting common processes across species may be an innovative strategy to combat debilitating TBI.In the present study, a cross-species transcriptome comparison was performed for the first time to determine the fundamental processes of secondary brain injury in Sprague-Dawley rat and C57/BL6 mouse models of TBI, caused by acute controlled cortical impact.The RNA sequencing data from the mouse model of TBI were downloaded from the Gene Expression Omnibus(ID: GSE79441) at the National Center for Biotechnology Information.For the rat data, peri-injury cerebral cortex samples were collected for transcriptomic analysis 24 hours after TBI.Differentially expressed gene-based functional analysis revealed that common features between the two species were mainly involved in the regulation and activation of the innate immune response, including complement cascades as well as Toll-like and nucleotide oligomerization domain-like receptor pathways.These findings were further corroborated by gene set enrichment analysis.Moreover, transcription factor analysis revealed that the families of signal transducers and activators of transcription(STAT), basic leucine zipper(BZIP), Rel homology domain(RHD), and interferon regulatory factor(IRF) transcription factors play vital regulatory roles in the pathophysiological processes of TBI, and are also largely associated with inflammation.These findings suggest that targeting the common innate immune response might be a promising therapeutic approach for TBI.The animal experimental procedures were approved by the Beijing Neurosurgical Institute Animal Care and Use Committee(approval No.201802001) on June 6, 2018.

Genome-wide interrogation of transfer RNA-derived small RNAs in a mouse model of traumatic brain injury

Transfer RNA(t RNA)-derived small RNAs(ts RNAs) are a recently established family of regulatory small non-coding RNAs that modulate diverse biological processes. Growing evidence indicates that ts RNAs are involved in neurological disorders and play a role in the pathogenesis of neurodegenerative disease. However, whether ts RNAs are involved in traumatic brain injury-induced secondary injury remains poorly understood. In this study, a mouse controlled cortical impact model of traumatic brain injury was established, and integrated ts RNA and messenger RNA(m RNA) transcriptome sequencing were used. The results revealed that 103 ts RNAs were differentially expressed in the mouse model of traumatic brain injury at 72 hours, of which 56 ts RNAs were upregulated and 47 ts RNAs were downregulated. Based on micro RNA-like seed matching and Pearson correlation analysis, 57 differentially expressed ts RNA-m RNA interaction pairs were identified, including 29 ts RNAs and 26 m RNAs. Moreover, Gene Ontology annotation of target genes revealed that the significantly enriched terms were primarily associated with inflammation and synaptic function. Collectively, our findings suggest that ts RNAs may be associated with traumatic brain injury-induced secondary brain injury, and are thus a potential therapeutic target for traumatic brain injury. The study was approved by the Beijing Neurosurgical Institute Animal Care and Use Committee(approval No. 20190411) on April 11, 2019.

LncRNA SNHG15 predicts poor prognosis in uveal melanoma and its potential pathways

AIM:To evaluate the role of long noncoding RNA(lncR NA)SNHG15 and its potential pathways in uveal melanoma(UM).METHODS:The SNHG15 mRNA expression level and corresponding clinicopathological characteristics of 80 patients with UM were obtained from the Cancer Genome Atlas(TCGA)database and further analyzed.The SPSS 24.0 statistical software package was used for statistical analyses.To investigate the potential function of SNHG15 in UM,we conducted in-depth research on Gene Set Enrichment Analysis(GSEA).RESULTS:The univariate analysis revealed that the age,tumor diameter,pathological type,extrascleral extension,cancer status,and high expression of SNHG15 were statistical risk factors for death from all causes.The multivariate analysis suggested that the mR NA expression level of SNHG15 was an independent risk factor for death from all causes,as was age and pathological type.KaplanMeier survival analysis confirmed that UM patients with high SNHG15 expression might have a poor prognosis.In addition,SNHG15 was significantly differentially expressed in the different groups of tumor pathologic stage,metastasis and living status.Besides,the logistic regression analysis indicated that high SNHG15 expression group in UM was significantly associated with cancer status,pathologic stage,metastasis,and living status.Moreover,the GSEA indicated the potential pathways regulated by SNHG15 in UM.CONCLUSION:Our research suggests that SNHG15 may play a vital role as a potential marker in UM that predicts poor prognosis.Besides,GSEA indicates the underlying signaling pathways enriched differentially in SNHG15 high expression phenotype.

TMEM158 May Serve as a Diagnostic Biomarker for Anaplastic Thyroid Carcinoma:An Integrated Bioinformatic Analysis

Anaplastic thyroid carcinoma(ATC)is a rare but extremely lethal malignancy.However,little is known about the pathogenesis of ATC.Given its high mortality,it is critical to improve our understanding of ATC pathogenesis and to find new diagnostic biomarkers.In the present study,two gene microarray profiles(GSE53072 and GSE65144),which included 17 ATC and 17 adjacent non-tumorous tissues,were obtained.Bioinformatic analyses were then performed.Immunohistochemistry(IHC)and receiver operating characteristic(ROC)curves were then used to detect transmembrane protein 158(TMEM158)expression and to assess diagnostic sensitivity.A total of 372 differentially expressed genes(DEGs)were identified.Through protein-protein interaction(PPI)analysis,we identified a significant module with 37 upregulated genes.Most of the genes in this module were related to cell-cycle processes.After co-expression analysis,132 hub genes were selected for further study.Nine genes were identified as both DEGs and genes of interest in the weighted gene co-expression network analysis(WGCNA).IHC and ROC curves confirmed that TMEM158 was overexpressed in ATC tissue as compared with other types of thyroid cancer and normal tissue samples.We identified 8 KEGG pathways that were associated with high expression of TMEM158,including aminoacyl-tRNA biosynthesis and DNA replication.Our results suggest that TMEM158 may be a potential oncogene and serve as a diagnostic indicator for ATC.

Association between chromosomal aberration of COX8C and tethered spinal cord syndrome:array-based comparative genomic hybridization analysis

Copy number variations have been found in patients with neural tube abnormalities.In this study,we performed genome-wide screening using high-resolution array-based comparative genomic hybridization in three children with tethered spinal cord syndrome and two healthy parents.Of eight copy number variations,four were non-polymorphic.These non-polymorphic copy number variations were associated with Angelman and Prader-Willi syndromes,and microcephaly.Gene function enrichment analysis revealed that COX8 C,a gene associated with metabolic disorders of the nervous system,was located in the copy number variation region of Patient 1.Our results indicate that array-based comparative genomic hybridization can be used to diagnose tethered spinal cord syndrome.Our results may help determine the pathogenesis of tethered spinal cord syndrome and prevent occurrence of this disease.

Using network pharmacology and molecular docking to explore the mechanism of action of Huajiao against colon cancer

Background:The mechanism of Huajiao(Zanthoxylum bungeanum Maxim.),as a commonly used herbal medicine,has been suggested as a potential agent for colon cancer.This study aims to use network pharmacology and molecular docking to identify the bioactive constituents of Huajiao and the underlying mechanisms of cancer prevention.Methods:Putative components of Huajiao and their relevant targets were identified from the Traditional Chinese Medicine Systematic Pharmacology and Swiss target prediction database.Subsequently,targets interacting with colon cancer were collected using the databases of GeneCards,OMIM and Drugbank.Kyoto Encyclopedia of Genes and Genomes pathway and Gene Ontology enrichment analyses were performed to explore the therapeutic signalling pathways related to Huajiao for carcinoma.P rotein-protein interaction and compound-target networks were constructed using Cytoscape 3.8.2.Finally,Discovery studio software was accustomed to identifying key genes and active components of Huajiao.Results:Seventeen potentially active compounds,197 interacting targets and 1,636 disease-related targets were collected,of which 111 cross-targets were obtained.A complete of twenty-two key targets were identified by PPI network analysis,including AKT1,TP53,TNF,JUN,IL6 and HSP90AA1.These key targets are significantly involved in biological processes and pathways,such as those involved in phosphatidylinositol 3-kinase signalling,promoting maturation,structural maintenance and proper regulation of specific target proteins,and regulating tumor cell growth arrest and apoptosis.KEGG enrichment showed that three signalling pathways were closely related to the cancer prevention,endocrine resistance and viral hepatitis pathways in carcinoma.AKT1,TP53,TNF,JUN,IL6 and HSP90AA1 were identified as the most vital genes and were validated by molecular docking simulations.Conclusion:The present study demonstrates that Huajiao produces preventive effects against colon cancer by modulating multiple components of multiple targets and pathways.Moreover,these data provide new insights into developing Huajiao compounds as new anti-colon cancer drugs.

CGPS:A machine learning-based approach integrating multiple gene set analysis tools for better prioritization of biologically relevant pathways

Gene set enrichment（GSE） analyses play an important role in the interpretation of large-scale transcriptome datasets. Multiple GSE tools can be integrated into a single method as obtaining optimal results is challenging due to the plethora of GSE tools and their discrepant performances. Several existing ensemble methods lead to different scores in sorting pathways as integrated results; furthermore, it is difficult for users to choose a single ensemble score to obtain optimal final results. Here, we develop an ensemble method using a machine learning approach called Combined Gene set analysis incorporating Prioritization and Sensitivity（CGPS） that integrates the results provided by nine prominent GSE tools into a single ensemble score（R score） to sort pathways as integrated results. Moreover, to the best of our knowledge, CGPS is the first GSE ensemble method built based on a priori knowledge of pathways and phenotypes. Compared with 10 widely used individual methods and five types of ensemble scores from two ensemble methods, we demonstrate that sorting pathways based on the R score can better prioritize relevant pathways, as established by an evaluation of 120 simulated datasets and 45 real datasets.Additionally, CGPS is applied to expression data involving the drug panobinostat, which is an anticancer treatment against multiple myeloma. The results identify cell processes associated with cancer, such as the p53 signaling pathway（hsa04115）; by contrast, according to two ensemble methods（EnrichmentBrowser and EGSEA）, this pathway has a rank higher than 20, which may cause users to miss the pathway in their analyses. We show that this method, which is based on a priori knowledge, can capture valuable biological information from numerous types of gene set collections, such as KEGG pathways, GO terms, Reactome, and BioCarta. CGPS is publicly available as a standalone source code at ftp：//ftp.cbi.pku.edu.cn/pub/CGPS_download/cgps-1.0.0.tar.gz.

Prediction of the anti-inflammatory mechanisms of curcumin by module-based protein interaction network analysis

Curcumin, the medically active component from Curcuma Tonga (Turmeric), is widely used to treat inflammatory diseases. Protein interaction network (PIN) analysis was used to predict its mechanisms of molecular action. Targets of curcumin were obtained based on ChEMBL and STITCH databases. Protein protein interactions (PPIs) were extracted from the String database. The PIN of curcumin was constructed by Cytoscape and the function modules identified by gene ontology (GO) enrichment analysis based on molecular complex detection (MCODE). A PIN of curcumin with 482 nodes and 1688 interactions was constructed, which has scale-free, small world and modular properties. Based on analysis of these function modules, the mechanism of curcumin is proposed. Two modules were found to be intimately associated with inflammation. With function modules analysis, the anti-inflammatory effects of curcumin were related to SMAD, ERG and mediation by the TLR family. TLR9 may be a potential target of curcumin to treat inflammation. (C) 2015 Chinese Pharmaceutical Association and Institute of Materia Medica, Chinese Academy of Medical Sciences. Production and hosting by Elsevier B.V.

ASpediaFI:Functional Interaction Analysis of Alternative Splicing Events

Alternative splicing(AS)regulates biological processes governing phenotypes and diseases.Differential AS(DAS)gene test methods have been developed to investigate important exonic expression from high-throughput datasets.However,the DAS events extracted using statistical tests are insufficient to delineate relevant biological processes.In this study,we developed a novel application,Alternative Splicing Encyclopedia:Functional Interaction(ASpediaFI),to systemically identify DAS events and co-regulated genes and pathways.ASpediaFI establishes a heterogeneous interaction network of genes and their feature nodes(i.e.,AS events and pathways)connected by coexpression or pathway gene set knowledge.Next,ASpediaFI explores the interaction network using the random walk with restart algorithm and interrogates the proximity from a query gene set.Finally,ASpediaFI extracts significant AS events,genes,and pathways.To evaluate the performance of our method,we simulated RNA sequencing(RNA-seq)datasets to consider various conditions of sequencing depth and sample size.The performance was compared with that of other methods.Additionally,we analyzed three public datasets of cancer patients or cell lines to evaluate how well ASpediaFI detects biologically relevant candidates.ASpediaFI exhibits strong performance in both simulated and public datasets.Our integrative approach reveals that DAS events that recognize a global co-expression network and relevant pathways determine the functional importance of spliced genes in the subnetwork.ASpediaFI is publicly available at https://bioconductor.org/packages/ASpediaFI.