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 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.

Estimating the Empirical Null Distribution of Maxmean Statistics in Gene Set Analysis

Gene Set Analysis (GSA) is a framework for testing the association of a set of genes and the outcome, e.g. disease status or treatment group. The method replies on computing a maxmean statistic and estimating the null distribution of the maxmean statistics via a restandardization procedure. In practice, the pre-determined gene sets have stronger intra-correlation than genes across sets. This may result in biases in the estimated null distribution. We derive an asymptotic null distribution of the maxmean statistics based on sparsity assumption. We propose a flexible two group mixture model for the maxmean statistics. The mixture model allows us to estimate the null parameters empirically via maximum likelihood approach. Our empirical method is compared with the restandardization procedure of GSA in simulations. We show that our method is more accurate in null density estimation when the genes are strongly correlated within gene sets.

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.

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 C^(intermediate)macrophages after renal IRI.The gene expression profiles of CD11 b^(+)/Ly6 C^(intermediate)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 C^(intermediate)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.

Key Genes Involved in the Beneficial Mechanism of Hyperbaric Oxygen for Glioblastoma and Predictive Indicators of Hyperbaric Oxygen Prolonging Survival in Glioblastoma Patients

Objective The prognosis of glioblastoma is poor,and therapy-resistance is largely attributed to intratumor hypoxia.Hyperbaric oxygen(HBO)effectively alleviates hypoxia.However,the sole role of HBO in glioblastoma remains controversial.We previously reported that HBO can promote apoptosis,shorten protrusions,and delay growth of glioblastoma,but the molecular mechanism is unclear.We aimed to test candidate genes in HBO-exposed glioblastoma cells and to analyze their correlation with the survival of glioblastoma patients.Methods Glioblastoma cell lines exposed to repetitive HBO or normobaric air(NBA)were collected for RNA isolation and microarray data analysis.GO analysis,KEGG pathway analysis and survival analysis of the differentially expressed genes(DEGs)were performed.Results HBO not only inhibited hypoxia-inducing genes including CA9,FGF11,PPFIA4,TCAF2 and SLC2A12,but also regulated vascularization by downregulating the expression of COL1A1,COL8A1,COL12A1,RHOJ and FILIP1L,ultimately attenuated hypoxic microenvironment of glioblastoma.HBO attenuated inflammatory microenvironment by reducing the expression of NLRP2,CARD8,MYD88 and CD180.HBO prevented metastasis by downregulating the expression of NTM,CXCL12,CXCL13,CXCR4,CXCR5,CDC42,IGFBP3,IGFBP5,GPC6,MMP19,ADAMTS1,EFEMP1,PTBP3,NF1 and PDCD1.HBO upregulated the expression of BAK1,PPIF,DDIT3,TP53I11 and FAS,whereas downregulated the expression of MDM4 and SIVA1,thus promoting apoptosis.HBO upregulated the expression of CDC25A,MCM2,PCNA,RFC33,DSCC1 and CDC14A,whereas downregulated the expression of ASNS,CDK6,CDKN1B,PTBP3 and MAD2L1,thus inhibiting cell cycle progression.Among these DEGs,17 indicator-genes of HBO prolonging survival were detected.Conclusions HBO is beneficial for glioblastoma.Glioblastoma patients with these predictive indicators may prolong survival with HBO therapy.These potential therapeutic targets especially COL1A1,ADAMTS1 and PTBP3 deserve further validation.

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.

Identification of potential key molecules and signaling pathways for psoriasis based on weighted gene co-expression network analysis

BACKGROUND Psoriasis is a chronic inflammatory skin disease,the pathogenesis of which is more complicated and often requires long-term treatment.In particular,moderate to severe psoriasis usually requires systemic treatment.Psoriasis is also associated with many diseases,such as cardiometabolic diseases,malignant tumors,infections,and mood disorders.Psoriasis can appear at any age,and lead to a substantial burden for individuals and society.At present,psoriasis is still a treatable,but incurable,disease.Previous studies have found that micro RNAs(mi RNAs)play an important regulatory role in the progression of various diseases.Currently,mi RNAs studies in psoriasis and dermatology are relatively new.Therefore,the identification of key mi RNAs in psoriasis is helpful to elucidate the molecular mechanism of psoriasis.AIM To identify key molecular markers and signaling pathways to provide potential basis for the treatment and management of psoriasis.METHODS The mi RNA and m RNA data were obtained from the Gene Expression Omnibus database.Then,differentially expressed m RNAs(DEm RNAs)and differentially expressed mi RNAs(DEmi RNAs)were screened out by limma R package.Subsequently,DEm RNAs were analyzed for Gene Ontology and Kyoto Encyclopedia of Genes and Genomics functional enrichment.The“WGCNA”R package was used to analyze the co-expression network of all mi RNAs.In addition,we constructed mi RNA-m RNA regulatory networks based on identified hub mi RNAs.Finally,in vitro validation was performed.All experimental procedures were approved by the ethics committee of Chinese PLA General Hospital(S2021-012-01).RESULTS A total of 639 DEm RNAs and 84 DEmi RNAs were identified.DEm RNAs screening criteria were adjusted P(adj.P)value<0.01 and|log Fold Change|(|log FC|)>1.DEmi RNAs screening criteria were adj.P value<0.01 and|logFC|>1.5.KEGG functional analysis demonstrated that DEm RNAs were significantly enriched in immune-related biological functions,for example,tolllike receptor signaling pathway,cytokine-cytokine receptor interaction,and chemokine signaling pathway.In weighted gene co-expression network analysis,turquoise module was the hub module.Moreover,10 hub mi RNAs were identified.Among these 10 hub mi RNAs,only 8 hub mi RNAs predicted the corresponding target m RNAs.97 negatively regulated mi RNA-m RNA pairs were involved in the mi RNA-m RNA regulatory network,for example,hsa-mi R-21-5 pclaudin 8(CLDN8),hsa-mi R-30 a-3 p-interleukin-1 B(IL-1 B),and hsa-mi R-181 a-5 p/hsa-mi R-30 c-2-3 p-C-X-C motif chemokine ligand 9(CXCL9).Real-time polymerase chain reaction results showed that IL-1 B and CXCL9 were up-regulated and CLDN8 was down-regulated in psoriasis with statistically significant differences.CONCLUSION The identification of potential key molecular markers and signaling pathways provides potential research directions for further understanding the molecular mechanisms of psoriasis.This may also provide new research ideas for the prevention and treatment of psoriasis in the future.

Threshold Selection Study on Fisher Discriminant Analysis Used in Exon Prediction for Unbalanced Data Sets

In gene prediction, the Fisher discriminant analysis (FDA) is used to separate protein coding region (exon) from non-coding regions (intron). Usually, the positive data set and the negative data set are of the same size if the number of the data is big enough. But for some situations the data are not sufficient or not equal, the threshold used in FDA may have important influence on prediction results. This paper presents a study on the selection of the threshold. The eigen value of each exon/intron sequence is computed using the Z-curve method with 69 variables. The experiments results suggest that the size and the standard deviation of the data sets and the threshold are the three key elements to be taken into consideration to improve the prediction results.

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.

Three-microRNA signature identified by bioinformatics analysis predicts prognosis of gastric cancer patients

AIM To identify multiple micro RNAs(mi RNAs) for predicting the prognosis of gastric cancer(GC) patients by bioinformatics analysis.METHODS The original microarray dataset GSE93415,which included 20 GC and 20 tumor adjacent normal gastric mucosal tissues,was downloaded from the Gene Expression Omnibus database and used for screening differentially expressed mi RNAs(DEMs).The cutoff criteria were P < 0.05 and fold change > 2.0.In addition,we acquired the mi RNA expression profiles and clinical information of 361 GC patients from The Cancer Genome Atlas database to assess the prognostic role of the DEMs.The target genes of mi RNAs were predicted using Target Scan,mi RDB,mi RWalk,and DIANA,and then the common target genes were selected for functional enrichment analysis.RESULTS A total of 110 DEMs including 19 up-regulated and 91 down-regulated mi RNAs were identified between 20 pairs of GC and tumor adjacent normal tissues,and the Kaplan-Meier survival analysis found that a threemi RNA signature(mi R-145-3 p,mi R-125 b-5 p,and mi R-99 a-5 p) had an obvious correlation with the survival of GC patients.Furthermore,univariate and multivariate Cox regression analyses indicated that the three-mi RNA signature could be a significant prognostic marker in GC patients.The common target genes of the three mi RNAs are added up to 108 and used for Gene Functional Enrichment analysis.Biological Process and Molecular Function analyses showed that the target genes are involved in cell recognition,gene silencing and nucleic acid binding,transcription factor activity,and transmembrane receptor activity.Cellular Component analysis revealed that the genes are portion of nucleus,chromatin silencing complex,and TORC1/2 complex.Biological Pathway analysis indicated that the genes participate in several cancer-related pathways,such as the focal adhesion,PI3 K,and m TOR signaling pathways.CONCLUSION This study justified that a three-mi RNA signature could play a role in predicting the survival of GC patients.

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.

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.

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.

BioinformaticsAnalysis of the Muscle-invasive Bladder Cancer Subtypes

Objective In order to improve the accuracy in distinguishing subtypes of bladder cancer and to explore its potential therapeutic targets,we identify differences between two kinds of bladder cancer subtypes(basal-like and luminal)in molecular mechanism and molecular characteristics based on the bioinformatics analysis.Methods In this study,the RMA(robust multichip averaging)was applied to normalize the mRNA profile which included 22 samples from basal-like subtype and 132 from luminal subtype,and the differential expression analysis of genes with top 1000 highest standard deviation was performed.Then,the Gene Ontology and KEGG pathway enrichment analysis of differentially expressed genes was performed.In addition,the proteinprotein interactions networks analysis for the top 100 most significant differentially expressed genes was performed.Results A total of 742 differentially expressed genes distinguishing basal-like and luminal subtypes were found,of which 405 were upregulated and 337 genes were down-regulated in basal-like subtype.GO enrichment analysis showed that differentially expressed genes were significantly enriched in the extracellular matrix,chemotaxis and inflammatory response.KEGG pathway enrichment analysis showed that the differentially expressed genes were significantly enriched in the pathway of extracellular matrix receptor interaction.The hub proteins we founded in protein-protein interaction networks were LNX1,MSN and PPARG.Conclusion In this study,the mainly difference of molecular mechanism between basal-like and luminal subtypes are alteration in extracellular matrix region,cell chemotaxis and inflammatory response.Genes such as LNX1,MSN and PPARG were forecast to play important roles in the classification of bladder carcinoma subtypes.

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.

Sum-Based Meta-Analytical Enrichment of Gene Expression Data to Identify Pathway Signatures of Cancers

A new method for analysis of microarray gene expression experiments referred to as Sum-based Meta-analytical Enrichment?(SME) is proposed in this manuscript. SME is a combined enrichment and meta-analytical approach to infer?on the association of gene sets with particular phenotypes. SME allows enrichment to be performed across datasets,?which to our knowledge was not earlier possible. As a proof of concept study, this technique is applied to datasets from?Oncomine, a publicly available cancer microarray database. The genes that are significantly up-/down-regulated?(p-value ≤ 10-4) in various cancer types in Oncomine were listed. These genes were assigned to biological processes?using GO annotations. The SME algorithm was applied to identify a list of GO processes most deregulated in 4 major?cancer types. For validation we examined whether the processes predicted by SME were already documented in literature.SME method identified several known processes for the 4 cancer types and identified several novel processes?which are biologically plausible. Nearly all the pathways identified by SME as common to the 4 cancers were found to?contribute to processes which are widely regarded as cancer hallmarks. SME provides an intuitive yet objective ‘process-centric’ interpretation of the ‘gene-centric’ output of individual microarray comparison studies. The methods described?here should be applicable in the next-generation sequencing based gene expression analysis as well.