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.

The DrugPattern tool for drug set enrichment analysis and its prediction for beneficial effects of oxLDL on type 2 diabetes

Enrichment analysis methods, e.g., gene set enrichment analysis, represent one class of important bio- informatical resources for mining patterns in biomedical datasets. However, tools for inferring patterns and rules of a list of drugs are limited. In this study, we developed a web-based tool, DrugPattern, for drug set enrichment analysis. We first collected and curated 7019 drug sets, including indications, adverse reactions, targets, pathways, etc. from public databases. For a list of interested drugs, DrugPat- tern then evaluates the significance of the enrichment of these drugs in each of the 7019 drug sets. To validate DrugPattern, we employed it for the prediction of the effects of oxidized low-density lipoprotein （oxLDL）, a factor expected to be deleterious. We predicted that oxLDL has beneficial effects on some diseases, most of which were supported by evidence in the literature. Because DrugPattern predicted the potential beneficial effects of oxLDL in type 2 diabetes （T2D）, animal experiments were then performed to further verify this prediction. As a result, the experimental evidences validated the DrugPattern prediction that oxLDL indeed has beneficial effects on T2D in the case of energy restriction. These data confirmed the prediction accuracy of our approach and revealed unexpected protective roles for oxLDL in various diseases. This study provides a tool to infer patterns and rules in biomedical datasets based on drug set enrichment analysis.

Bayesian functional enrichment analysis for the Reactome database

The first step in the analysis of high-throughput experiment results is often to identify genes orproteins with certain characteristics, such as genes being differentially expressed (DE). To gainmore insights into the underlying biology, functional enrichment analysis is then conductedto provide functional interpretation for the identified genes or proteins. The hypergeometricP value has been widely used to investigate whether genes from predefined functional terms,e.g., Reactome, are enriched in the DE genes. The hypergeometric P value has several limitations: (1) computed independently for each term, thus neglecting biological dependence;(2) subject to a size constraint that leads to the tendency of selecting less-specific terms. In this paper,a Bayesian approach is proposed to overcome these limitations by incorporating the interconnected dependence structure of biological functions in the Reactome database through a CARprior in a Bayesian hierarchical logistic model. The inference on functional enrichment is thenbased on posterior probabilities that are immune to the size constraint. This method can detectmoderate but consistent enrichment signals and identify sets of closely related and biologicallymeaningful functional terms rather than isolated terms. The performance of the Bayesian methodis demonstrated via a simulation study and a real data application.

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.

Analysis of Significant Genes and Pathways in Esophageal Cancer Based on Gene Expression Omnibus Database

Objective To screen antigen targets for immunotherapy by analyzing over-expressed genes,and to identify significant pathways and molecular mechanisms in esophageal cancer by using bioinformatic methods such as enrichment analysis,protein-protein interaction(PPI)network,and survival analysis based on the Gene Expression Omnibus(GEO)database.Methods By screening with highly expressed genes,we mainly analyzed proteins MUC13 and EPCAM with transmembrane domain and antigen epitope from TMHMM and IEDB websites.Significant genes and pathways associated with the pathogenesis of esophageal cancer were identified using enrichment analysis,PPI network,and survival analysis.Several software and platforms including Prism 8,R language,Cytoscape,DAVID,STRING,and GEPIA platform were used in the search and/or figure creation.Results Genes MUC13 and EPCAM were over-expressed with several antigen epitopes in esophageal squamous cell carcinoma(ESCC)tissue.Enrichment analysis revealed that the process of keratinization was focused and a series of genes were related with the development of esophageal cancer.Four genes including ALDH3A1,C2,SLC6A1,and ZBTB7C were screened with significant P value of survival curve.Conclusions Genes MUC13 and EPCAM may be promising antigen targets or biomarkers for esophageal cancer.Keratinization may greatly impact the pathogenesis of esophageal cancer.Genes ALDH3A1,C2,SLC6A1,and ZBTB7C may play important roles in the development of esophageal cancer.

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.

Mechanism of pachymic acid in the treatment of gastric cancer based on network pharmacology and experimental verification

BACKGROUND Pachymic acid(PA)is derived from Poria cocos.PA has a variety of pharmacological and inhibitory effects on various tumors.However,the mechanism of action of PA in gastric cancer(GC)remains unclear.AIM To investigate the mechanism of PA in treating GC via the combination of network pharmacology and experimental verification.METHODS The GeneCards and OMIM databases were used to derive the GC targets,while the Pharm Mapper database provided the PA targets.Utilizing the STRING database,a protein-protein interaction network was constructed and core targets were screened.The analyses of Gene Ontology,Kyoto Encyclopedia of Genes and Genomes(KEGG),and gene set enrichment analysis were conducted,and molecular docking and clinical correlation analyses were performed on the core targets.Ultimately,the network pharmacology findings were validated through in vitro cell assays,encompassing assessments of cell viability,apoptosis,cell cycle,cloning,and western blot analysis.RESULTS According to network pharmacology analysis,the core targets were screened,and the PI3K/AKT signaling pathway is likely to be the mechanism by which PA effectively treats GC,according to KEGG enrichment analysis.The experimental findings showed that PA could control PI3K/AKT signaling to prevent GC cell proliferation,induce apoptosis,and pause the cell cycle.CONCLUSION Network pharmacology demonstrated that PA could treat GC by controlling a variety of signaling pathways and acting on a variety of targets.This has also been supported by in vitro cell studies,which serve as benchmarks for further research.

Exploring the potential mechanism of WuFuYin against hypertrophic scar using network pharmacology and molecular docking

BACKGROUND Hypertrophic scar(HTS)is dermal fibroproliferative disorder,which may cause physiological and psychological problems.Currently,the potential mechanism of WuFuYin(WFY)in the treatment of HTS remained to be elucidated.AIM To explore the potential mechanism of WFY in treating HTS.METHODS Active components and corresponding targets were retrieved from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform.HTSrelated genes were obtained from the GeneCards,DisGeNET,and National Center for Biotechnology Information.The function of targets was analyzed by performing Gene Ontology and Kyoto Encyclopaedia of Genes and Genome(KEGG)enrichment analysis.A protein+IBM-protein interaction(PPI)network was developed using STRING database and Cytoscape.To confirm the high affinity between compounds and targets,molecular docking was performed.RESULTS A total of 65 core genes,which were both related to compounds and HTS,were selected from multiple databases.PPI analysis showed that CKD2,ABCC1,MMP2,MMP9,glycogen synthase kinase 3 beta(GSK3B),PRARG,MMP3,and phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit gamma(PIK3CG)were the hub targets and MOL004941,MOL004935,MOL004866,MOL004993,and MOL004989 were the key compounds of WFY against HTS.The results of KEGG enrichment analysis demonstrated that the function of most genes were enriched in the PI3K-Akt pathway.Moreover,by performing molecular docking,we confirmed that GSK3B and 8-prenylated eriodictyol shared the highest affinity.CONCLUSION The current findings showed that the GSK3B and cyclin dependent kinase 2 were the potential targets and MOL004941,MOL004989,and MOL004993 were the main compounds of WFY in HTS treatment.

Des-Arg(9)bradykinin as a causal metabolite for autism spectrum disorder

BACKGROUND Early diagnosis and therapeutic interventions can greatly enhance the developmental trajectory of children with autism spectrum disorder(ASD).However,the etiology of ASD is not completely understood.The presence of confounding factors from environment and genetics has increased the difficulty of the identification of diagnostic biomarkers for ASD.AIM To estimate and interpret the causal relationship between ASD and metabolite profile,taking into consideration both genetic and environmental influences.METHODS A two-sample Mendelian randomization(MR)analysis was conducted using summarized data from large-scale genome-wide association studies(GWAS)including a metabolite GWAS dataset covering 453 metabolites from 7824 European and an ASD GWAS dataset comprising 18381 ASD cases and 27969 healthy controls.Metabolites in plasma were set as exposures with ASD as the main outcome.The causal relationships were estimated using the inverse variant weight(IVW)algorithm.We also performed leave-one-out sensitivity tests to validate the robustness of the results.Based on the drafted metabolites,enrichment analysis was conducted to interpret the association via constructing a protein-protein interaction network with multi-scale evidence from databases including Infinome,SwissTargetPrediction,STRING,and Metascape.RESULTS Des-Arg(9)-bradykinin was identified as a causal metabolite that increases the risk of ASD(β=0.262,SE=0.064,P_(IVW)=4.64×10^(-5)).The association was robust,with no significant heterogeneity among instrument variables(P_(MR Egger)=0.663,P_(IVW)=0.906)and no evidence of pleiotropy(P=0.949).Neuroinflammation and the response to stimulus were suggested as potential biological processes mediating the association between Des-Arg(9)bradykinin and ASD.CONCLUSION Through the application of MR,this study provides practical insights into the potential causal association between plasma metabolites and ASD.These findings offer perspectives for the discovery of diagnostic or predictive biomarkers to support clinical practice in treating ASD.

Gene signatures to therapeutics:Assessing the potential of ivermectin against t(4;14)multiple myeloma

BACKGROUND Multiple myeloma(MM)is a terminal differentiated B-cell tumor disease characterized by clonal proliferation of malignant plasma cells and excessive levels of monoclonal immunoglobulins in the bone marrow.The translocation,(t)(4;14),results in high-risk MM with limited treatment alternatives.Thus,there is an urgent need for identification and validation of potential treatments for this MM subtype.Microarray data and sequencing information from public databases could offer opportunities for the discovery of new diagnostic or therapeutic targets.AIM To elucidate the molecular basis and search for potential effective drugs of t(4;14)MM subtype by employing a comprehensive approach.METHODS The transcriptional signature of t(4;14)MM was sourced from the Gene Expression Omnibus.Two datasets,GSE16558 and GSE116294,which included 17 and 15 t(4;14)MM bone marrow samples,and five and four normal bone marrow samples,respectively.After the differentially expressed genes were identified,the Cytohubba tool was used to screen for hub genes.Then,the hub genes were analyzed using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis.Using the STRING database and Cytoscape,protein–protein interaction networks and core targets were identified.Potential small-molecule drugs were identified and validated using the Connectivity Map database and molecular docking analysis,respectively.RESULTS In this study,a total of 258 differentially expressed genes with enriched functions in cancer pathways,namely cytokine receptor interactions,nuclear factor(NF)-κB signaling pathway,lipid metabolism,atherosclerosis,and Hippo signaling pathway,were identified.Ten hub genes(cd45,vcam1,ccl3,cd56,app,cd48,btk,ccr2,cybb,and cxcl12)were identified.Nine drugs,including ivermectin,deforolimus,and isoliquiritigenin,were predicted by the Connectivity Map database to have potential therapeutic effects on t(4;14)MM.In molecular docking,ivermectin showed strong binding affinity to all 10 identified targets,especially cd45 and cybb.Ivermectin inhibited t(4;14)MM cell growth via the NF-κB pathway and induced MM cell apoptosis in vitro.Furthermore,ivermectin increased reactive oxygen species accumulation and altered the mitochondrial membrane potential in t(4;14)MM cells.CONCLUSION Collectively,the findings offer valuable molecular insights for biomarker validation and potential drug development in t(4;14)MM diagnosis and treatment,with ivermectin emerging as a potential therapeutic alternative.

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.

Comprehensive multi-omics analysis identified core molecular processes in esophageal cancer and revealed GNGT2 as a potential prognostic marker

BACKGROUND Esophageal cancer is one of the most poorly diagnosed and fatal cancers in the world.Although a series of studies on esophageal cancer have been reported,the molecular pathogenesis of the disease remains elusive.AIM To investigate comprehensively the molecular process of esophageal cancer.METHODS Differential expression analysis was performed to identify differentially expressed genes(DEGs)in different stages of esophageal cancer from The Cancer Genome Atlas data.Exacting gene interaction modules were generated,and hub genes in the module interaction network were found.Further,through survival analysis,methylation analysis,pivot analysis,and enrichment analysis,some important molecules and related functions/pathways were identified to elucidate potential mechanisms in esophageal cancer.RESULTS A total of 7457 DEGs and 14 gene interaction modules were identified.These module genes were significantly involved in the positive regulation of protein transport,gastric acid secretion,insulin-like growth factor receptor binding,and other biological processes as well as p53 signaling pathway,epidermal growth factor signaling pathway,and epidermal growth factor receptor signaling pathway.Transcription factors(including hypoxia inducible factor 1A)and noncoding RNAs(including colorectal differentially expressed and hsa-miR-330-3p)that significantly regulate dysfunction modules were identified.Survival analysis showed that G protein subunit gamma transducin 2(GNGT2)was closely related to survival of esophageal cancer.DEGs with strong methylation regulation ability were identified,including SST and SH3GL2.Furthermore,the expression of GNGT2 was evaluated by quantitative real time polymerase chain reaction,and the results showed that GNGT2 expression was significantly upregulated in esophageal cancer patient samples and cell lines.Moreover,cell counting kit-8 assay revealed that GNGT2 could promote the proliferation of esophageal cancer cell lines.CONCLUSION This study not only revealed the potential regulatory factors involved in the development of esophageal cancer but also deepens our understanding of its underlying mechanism.

Weighted Gene Co-expression Network Analysis of Gene Modules for the Prognosis of Esophageal Cancer

Esophageal cancer is a common malignant tumor, whose pathogenesis and prognosis factors are not fully understood. This study aimed to discover the gene clusters that have similar functions and can be used to predict the prognosis of esophageal cancer. The matched microarray and RNA sequencing data of 185 patients with esophageal cancer were downloaded from The Cancer Genome Atlas(TCGA), and gene co-expression networks were built without distinguishing between squamous carcinoma and adenocarcinoma. The result showed that 12 modules were associated with one or more survival data such as recurrence status, recurrence time, vital status or vital time. Furthermore, survival analysis showed that 5 out of the 12 modules were related to progression-free survival(PFS) or overall survival(OS). As the most important module, the midnight blue module with 82 genes was related to PFS, apart from the patient age, tumor grade, primary treatment success, and duration of smoking and tumor histological type. Gene ontology enrichment analysis revealed that 'glycoprotein binding' was the top enriched function of midnight blue module genes. Additionally, the blue module was the exclusive gene clusters related to OS. Platelet activating factor receptor(PTAFR) and feline Gardner-Rasheed(FGR) were the top hub genes in both modeling datasets and the STRING protein interaction database. In conclusion, our study provides novel insights into the prognosis-associated genes and screens out candidate biomarkers for esophageal cancer.

Bioinformatics Analysis of Genes and Pathways of CD11b^＋/Ly6C^intermediate Macrophages after Renal Ischemia-Reperfusion Injury

Renal ischemia-reperfusion injury（IRI） is a major cause of acute kidney injury（AKI）,which could induce the poor prognosis.The purpose of this study was to characterize the molecular mechanism of the functional changes of CD11 b＋/Ly6 Cintermediate macrophages after renal IRI.The gene expression profiles of CD11 b＋/Ly6 Cintermediate macrophages of the sham surgery mice,and the mice 4 h,24 h and 9 days after renal IRI were downloaded from the Gene Expression Omnibus database.Analysis of m RNA expression profiles was conducted to identify differentially expressed genes（DEGs）,biological processes and pathways by the series test of cluster.Protein-protein interaction network was constructed and analysed to discover the key genes.A total of 6738 DEGs were identified and assigned to 20 model profiles.DEGs in profile 13 were one of the predominant expression profiles,which are involved in immune cell chemotaxis and proliferation.Signet analysis showed that Atp5 a1,Atp5 o,Cox4 i,Cdc42,Rac2 and Nhp2 were the key genes involved in oxidation-reduction,apoptosis,migration,M1-M2 differentiation,and proliferation of macrophages.RPS18 may be an appreciate reference gene as it was stable in macrophages.The identified DEGs and their enriched pathways investigate factors that may participate in the functional changes of CD11 b＋/Ly6 Cintermediate macrophages after renal IRI.Moreover,the vital gene Nhp2 may involve the polarization of macrophages,which may be a new target to affect the process of AKI.

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.

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.

Bioinformatics analysis of potential hub genes associated with biological characteristics and survival in patients with gastric cancer

Objective Gastric cancer(GC)is a serious threat to human health.In this study,we aimed to explore the differentially expressed genes(DEGs)and identify potential targets for the treatment of GC.Methods The gene expression profile of GSE79973 which compared tissue samples from gastric cancer patients and healthy individuals,downloaded from the GEO database,was submitted to the GCBI online analysis platform to screen for DEGs.Gene ontology(GO)analysis,pathway analysis,and construction of networks,including gene signal and gene co-expression networks,were performed to identify the core DEGs.Survival analysis was performed to determine the relationship between these genes and patient survival time.Results Nine hundred eighty-three genes were identified as DEGs(P<0.001;FC>2).GO analysis showed that DEGs were primarily involved in processes such as angiogenesis,cell metabolism,cell adhesion,redox processes,and cell migration.The metabolism of xenobiotics by cytochrome P450,ECM-receptor interaction,drug metabolism by cytochrome P450,metabolic pathways,and the PI3K-Akt signaling pathway were significantly enriched in pathway analysis.Genes such as UGT2B15,Hepatocyte growth factor(HGF),Nidogen-2(NID2),Follistatin-like protein 1(FSTL1),and Inhibin beta A chain(INHBA)were closely linked to other genes in the network.Survival analyses indicated that HGF,NID2,FSTL1,and INHBA expression levels were inversely correlated with survival time in patients with gastric cancer.Conclusion HGF,NID2,FSTL1,and INHBA may be potential key genes associated with the biological characteristics and survival in patients with gastric cancer.

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.

Quantitative Analysis of Heavy Metals in Water Based on LIBS with an Automatic Device for Sample Preparation

Abstract Heavy metals in water can be deposited on graphite flakes, which can be used as an enrichment method for laser-induced breakdown spectroscopy （LIBS） and is studied in this paper. The graphite samples were prepared with an automatic device, which was composed of a loading and unloading module, a quantitatively adding solution module, a rapid heating and drying module and a precise rotating module. The experimental results showed that the sample preparation methods had no significant effect on sample distribution and the LIBS signal accumulated in 20 pulses was stable and repeatable. With an increasing amount of the sample solution on the graphite flake, the peak intensity at Cu I 324.75 nm accorded with the exponential function with a correlation coefficient of 0.9963 and the background intensity remained unchanged. The limit of detection （LOD） was calculated through linear fitting of the peak intensity versus the concentration. The LOD decreased rapidly with an increasing amount of sample solution until the amount exceeded 20 mL and the correlation coefficient of exponential function fitting was 0.991. The LOD of Pb, Ni, Cd, Cr and Zn after evaporating different amounts of sample solution on the graphite flakes was measured and the variation tendency of their LOD with sample solution amounts was similar to the tendency for Cu. The experimental data and conclusions could provide a reference for automatic sample preparation and heavy metal in situ detection.

Identification of potential diagnostic and prognostic biomarkers for breast cancer based on gene expression omnibus

BACKGROUND Breast cancer is regarded as a highly malignant neoplasm in the female population,posing a significant risk to women’s overall well-being.The prevalence of breast cancer has been observed to rise in China,accompanied by an earlier age of onset when compared to Western countries.Breast cancer continues to be a prominent contributor to cancer-related mortality and morbidity among women,primarily due to its limited responsiveness to conventional treatment modalities.The diagnostic process is challenging due to the presence of non-specific clinical manifestations and the suboptimal precision of conventional diagnostic tests.There is a prevailing uncertainty regarding the most effective screening method and target populations,as well as the specificities and execution of screening programs.AIM To identify diagnostic and prognostic biomarkers for breast cancer.METHODS Overlapping differentially expressed genes were screened based on Gene Expression Omnibus(GSE36765,GSE10810,and GSE20086)and The Cancer Genome Atlas datasets.A protein-protein interaction network was applied to excavate the hub genes among these differentially expressed genes.Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses,as well as gene set enrichment analyses,were conducted to examine the functions of these genes and their potential mechanisms in the development of breast cancer.For clarification of the diagnostic and prognostic roles of these genes,Kaplan–Mei-er and Cox proportional hazards analyses were conducted.RESULTS This study demonstrated that calreticulin,heat shock protein family B member 1,insulin-like growth Factor 1,interleukin-1 receptor 1,Krüppel-like factor 4,suppressor of cytokine signaling 3,and triosephosphate isomerase 1 are potential diagnostic biomarkers of breast cancer as well as potential treatment targets with clinical implications.CONCLUSION The screening of biomarkers is of guiding significance for the diagnosis and prognosis of the diseases.