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.

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.

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.

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.

Hub genes and their key effects on prognosis of Burkitt lymphoma

BACKGROUND Burkitt lymphoma(BL)is an exceptionally aggressive malignant neoplasm that arises from either the germinal center or post-germinal center B cells.Patients with BL often present with rapid tumor growth and require high-intensity multidrug therapy combined with adequate intrathecal chemotherapy prophylaxis,however,a standard treatment program for BL has not yet been established.It is important to identify biomarkers for predicting the prognosis of BLs and discriminating patients who might benefit from the therapy.Microarray data and sequencing information from public databases could offer opportunities for the discovery of new diagnostic or therapeutic targets.AIM To identify hub genes and perform gene ontology(GO)and survival analysis in BL.METHODS Gene expression profiles and clinical traits of BL patients were collected from the Gene Expression Omnibus database.Weighted gene co-expression network analysis(WGCNA)was applied to construct gene co-expression modules,and the cytoHubba tool was used to find the hub genes.Then,the hub genes were analyzed using GO and Kyoto Encyclopedia of Genes and Genomes analysis.Additionally,a Protein-Protein Interaction network and a Genetic Interaction network were constructed.Prognostic candidate genes were identified through overall survival analysis.Finally,a nomogram was established to assess the predictive value of hub genes,and drug-gene interactions were also constructed.RESULTS In this study,we obtained 8 modules through WGCNA analysis,and there was a significant correlation between the yellow module and age.Then we identified 10 hub genes(SRC,TLR4,CD40,STAT3,SELL,CXCL10,IL2RA,IL10RA,CCR7 and FCGR2B)by cytoHubba tool.Within these hubs,two genes were found to be associated with OS(CXCL10,P=0.029 and IL2RA,P=0.0066)by survival analysis.Additionally,we combined these two hub genes and age to build a nomogram.Moreover,the drugs related to IL2RA and CXCL10 might have a potential therapeutic role in relapsed and refractory BL.CONCLUSION From WGCNA and survival analysis,we identified CXCL10 and IL2RA that might be prognostic markers for BL.

Key genes of deficiency of liver and kidney type chronic pain through bioinformatics analyses

Objective To screen the key genes of chronic pain and provide a reference for the treatment of chronic pain.Methods We performed comprehensive bioinformatics analysis by screening chronic primary pain-related datasets to obtain differentially expressed genes(DEGs)and then imported DEGs into the DAVID database for Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)enrichment analysis.Gene Set Enrichment Analysis(GESA)analysis was done by GSEA_4.1.0 software.At the same time,we imported the intersecting genes into the STRING database and processed them by Cytoscape_3.8.1 software to obtain the protein-protein interaction(PPI)network and the central gene.Results As a result,a total of 54 DEGs were screened,including 47 up-regulated genes,1 down-regulated gene,and 6 genes that were expressed differently in different datasets.23 GO terms and 8 KEGG pathways were enriched by DAVID.PPI network analysis found that SPI1,STAT3,TNFRSF1B,PTGS2,and CXCL1 genes interacted more strongly with other genes,and were predicted to be key genes in chronic primary pain.Conclusion Our results suggested that 5 DEGs,STAT3,SPI1,TNFRSF1B,PTGS2,and CXCL1,have the potential to be used as prognostic and predictive markers for the clinical management of patients with this disease.

Bioinformatics prediction of potential mechanisms and biomarkers underlying dilated cardiomyopathy

BACKGROUND Heart failure is a health burden responsible for high morbidity and mortality worldwide, and dilated cardiomyopathy(DCM) is one of the most common causes of heart failure. DCM is a disease of the heart muscle and is characterized by enlargement and dilation of at least one ventricle alongside impaired contractility with left ventricular ejection fraction < 40%. It is also associated with abnormalities in cytoskeletal proteins, mitochondrial ATP transporter, microvasculature, and fibrosis. However, the pathogenesis and potential biomarkers of DCM remain to be investigated.AIM To investigate the candidate genes and pathways involved in DCM patients.METHODS Two expression datasets(GSE3585 and GSE5406) were downloaded from the Gene Expression Omnibus database. The differentially expressed genes(DEGs) between the DCM patients and healthy individuals were identified using the R package “linear models for microarray data.” The pathways with common DEGs were analyzed via Gene Ontology(GO), Kyoto Encyclopedia of Genes and Genomes(KEGG), and gene set enrichment analyses. Moreover, a protein-protein interaction network(PPI) was constructed to identify the hub genes and modules. The MicroRNA Database was applied to predict the microRNAs(miRNAs) targeting the hub genes. Additionally, immune cell infiltration in DCM was analyzed using CIBERSORT.RESULTS In total, 97 DEGs(47 upregulated and 50 downregulated) were identified. GO analysis showed that the DEGs were mainly enriched in “response to growth factor,” “extracellular matrix,” and “extracellular matrix structural constituent.” KEGG pathway analysis indicated that the DEGs were mainly enriched in “protein digestion and absorption” and “interleukin 17(IL-17) signaling pathway.” The PPI network suggested that collagen type Ⅲ alpha 1 chain(COL3A1) and COL1A2 contribute to the pathogenesis of DCM. Additionally, visualization of the interactions between miRNAs and the hub genes revealed that hsa-miR-5682 and hsa-miR-4500 interacted with both COL3A1 and COL1A2, and thus these miRNAs might play roles in DCM. Immune cell infiltration analysis revealed that DCM patients had more infiltrated plasma cells and fewer infiltrated B memory cells, T follicular helper cells, and resting dendritic cells.CONCLUSION COL1A2 and COL3A1 and their targeting miRNAs, hsa-miR-5682 and hsa-miR-4500, may play critical roles in the pathogenesis of DCM, which are closely related to the IL-17 signaling pathway and acute inflammatory response. These results may provide useful clues for the diagnosis and treatment of DCM.

Bioinformatic identification of key genes and pathways that may be involved in the pathogenesis of HBV-associated acute liver failure

In order to explore the molecular mechanisms behind the pathogenesis of acute liver failure(ALF)associated with hepatitis B virus(HBV)infection,the present study aimed to identify potential key genes and pathways involved using samples from patients with HBV-associated ALF.The GSE38941 array dataset was downloaded from the Gene Expression Omnibus database,and differentially expressed genes(DEGs)between 10 liver samples from 10 healthy donors and 17 liver specimens from 4 patients with HBV-associated ALF were analyzed using the Linear Models for Microarray Data package.Gene Ontology and KEGG pathway enrichment analyses of the DEGs were performed,followed by functional annotation of the genes and construction of a proteineprotein interaction(PPI)network.Subnetwork modules were subsequently identified and analyzed.In total,3142 DEGs were identified,of which 1755 were upregulated and 1387 were downregulated.The extracellular exosome,immune response,and inflammatory response pathways may potentially be used as biomarkers of ALF pathogenesis.In total,17 genes(including CCR5,CXCR4,ALB,C3,VGEFA,and IGF1)were identified as hub genes in the PPI network and may therefore be potential marker genes for HBV-associated ALF.