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ObjectiveTo explore the hub genes of acute-on-chronic liver failure （ACLF） using bioinformatics methods， predict the potential traditional Chinese medicines （TCMs） against ACLF， and verify the treatment mechanism based on experiments. MethodPerl and R were used to analyze the GSE142255 dataset to obtain the differentially expressed genes （DEGs）， from which the hub genes in the protein-protein interaction of DEGs were identified by five algorithms of the CytoHubba plug-in. The receiver operating characteristic （ROC） curve and GSE168048 dataset were then used to verify the hub genes. Coremine Medical was employed to map the TCMs corresponding to the hub genes and then the natures， tastes， and meridian tropism of the TCMs were analyzed. The TCM systems pharmacology database and analysis platform （TCMSP） and DEGs were used to obtain the common targets shared by high-frequency TCMs and ACLF， and Cytoscape was used to establish the "hub gene-high-frequency TCM-active ingredient-common target" network. Furthermore， gene ontology （GO） annotation， Kyoto encyclopedia of genes and genomes （KEGG） enrichment analysis， and in vitro experiments were performed. ResultA total of 388 DEGs were obtained， in which the 7 hub genes encoded CD4 integrin subunit alpha M （ITGAM）， CD2， lymphocyte-specific protein tyrosine kinase （LCK） proto-oncogene， C-C motif chemokine ligand 5 （CCL5）， matrix metallopeptidase-9 （MMP-9）， and Fc epsilon receptor IG （FCER1G）. The TCM candidates for treating ACLF were mainly cold， bitter， and had tropism to the liver meridian， among which the high-frequency TCMs （Hedyotis Diffusae Herba， Ganoderma， and Astragali Radix） and the active ingredients （quercetin， kaempferol， and beta-sitosterol） had significant therapeutic potential. The enrichment analysis results showed that TCMs acted on multiple targets and pathways such as autophagy， oxidative stress， and inflammatory cytokines in addition to regulating hub genes. L02 cell experiments showed that the quercetin group had lower levels of tumor necrosis factor-α （TNF-α）， interleukin-6 （IL-6）， and malondialdehyde （MDA）， lower protein levels of ubiquitin-binding protein p62 and MMP-9， and higher levels of superoxide dismutase （SOD）， glutathione （GSH）， and microtubule-associated protein 1 light chain 3 Ⅱ/Ⅰ （LC3 Ⅱ/Ⅰ） than the D-galactosamine （D-GaLN） group （P<0.05， P<0.01）. In addition， the pretreatment with 3-methyladenine （3-MA） inhibited the activating effect of quercetin on the autophagy of L02 cells. ConclusionThe potential TCMs and active ingredients predicted based on the hub genes of ACLF have a great research value. Quercetin has the potential to treat ACLF by inhibiting the D-GaLN-induced oxidative stress and inflammatory response in L02 cells and regulating the expression of MMP-9， which may be associated with the activation of autophagy.
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BackgroundAlcohol use disorder （AUD） is a type of chronic relapsing brain disorder. Genetic factors play an important role in the pathogenesis of AUD. And screening for molecular markers of AUD is of great significance for further elucidating the pathogenesis of the disease， discovering novel therapeutic targets and preventing relapse. ObjectiveTo explore relevant hub genes and potential signal pathways associated with the development of AUD through bioinformatics analysis， and to provide a new direction for the prevention and treatment of AUD. MethodsThe GSE161986 dataset was acquired from the Gene Expression Omnibus （GEO） database. The limma package in R was utilized to identify differentially expressed genes （DEGs）. Gene set enrichment analysis （GSEA） was carried out using the Database for Annotation， Visualization and Integrated Discovery （DAVID）. A protein–protein interaction （PPI） network of DEGs was assessed using the STRING database and visualized by Cytoscape software. Finally， hub genes were validated in GSE44456 dataset. ResultsA total of 114 DEGs were identified. GSEA revealed that these genes were mainly involved in the regulation of signal transduction， protein binding， membrane trafficking and MAPK signaling pathway. PPI network and validation study indicated that GAD1， TIMP1 and CD44 were potential hub genes involved in AUD. ConclusionAberrant expression of GAD1 and TIMP1 as well as MAPK signaling pathway may play a key role in the pathogenesis of AUD， and may serve as potential molecular targets for the diagnosis and treatment of AUD. ［Funded by "Flying Project" of Shanghai Mental Health Center （number， 2022-FX-01）］
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Objective:To analyze the data of ultra-high dose rate (FLASH) radiotherapy in GEO (Gene Expression Omnibus) database by bioinformatics method, in order to find the hub genes involved in flash radiotherapy induced acute T-lymphoblastic leukemia.Methods:The gene expression profiles of malignant tumors receiving FLASH radiotherapy were downloaded from GEO database. The R software was used to screen the differential expressed genes (DEGs) and analyze their biological functions and signal pathways. The protein-protein interaction (PPI) network of DEGs was analyzed by online tool of STRING, and Hub genes were screened by Cytoscape plug-in. The expressions of screened Hub genes in acute T lymphoblastic leukemia were identified with TCGA (The Cancer Genome Atlas) and GTEx (Genotype-Tissue Expression) database.Results:Based on the analysis of GSE100718 microarray dataset of GEO database, a total of 12 800 genes were found to be associated with radiosensitivity of acute T lymphoblastic leukemia, of which 61 significantly altered DEGs were selected for further analysis. It was found that these genes were involved in the biological processes of metabolism, stress response, and immune response through the pathways of oxidative phosphorylation, unfolded protein response, fatty acid metabolism, and so on. PPI analysis indicated that HSPA5 and SCD belonged to the Hub genes involved in the regulation of FLASH radiosensitivity, and they were significantly highly expressed in acute T lymphoblastic leukemia combined with TRD/LMO2-fusion gene.Conclusions:Through bioinformatics analysis, the Hub genes involved in regulating the sensitivity of FLASH radiotherapy and conventional radiotherapy can be effectively screened, and thus the gene expression profiles can be used to guide the stratification of cancer patients to achieve a precise radiotherapy.
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OBJECTIVE: To explore the related signaling pathways, biomarkers and prognostic genes of malignant pleural mesothelioma(MPM) based on the gene chip and second-generation sequencing datasets in public database by bioinformatics-related method. METHODS: MPM microarray expression datasets GSE51024 and GSE2549, with 82 and 49 MPM patients, respectively, were downloaded from the Gene Expression Omnibus database. The RNA sequencing data of 86 MPM patients were downloaded from the The Cancer Genome Atlas(TCGA). The weighted gene co-expression network analysis(WGCNA) and differentially expressed genes(DEGs) screening were used to screen and identify hub genes in the GSE51024 dataset by RStudio 4.0 software. The gene set enrichment analysis(GSEA) was used to explore relevant signaling pathways. Finally, a total of 135 MPM gene expression data from GSE2549 dataset and TCGA database were used to verify the hub genes. RESULTS: The green key gene module identified by the WGCNA was highly correlated with MPM, with a correlation coefficient of 0.83(P<0.01). A total of 3 245 DEGs were screened by DEGs analysis. Among them, 1 229 genes were up-regulated and 2 016 genes were down-regulated. GSEA results showed that the genes were significantly enriched in the areas of G2/M cell cycle checkpoint, epithelial-mesenchymal transition, E2 F target gene, and mitotic spindle pathways. Three hub genes were screened, including the proliferating cell nuclear antigen-associated factor(PCLAF), nucleolar and spindle-associated protein 1(NUSAP1) and topoisomerase Ⅱ-α(TOP2 A). Compared with para-cancerous tissues, normal pleural tissues or lung tissues, the relative expression of PCLAF, NUSAP1 and TOP2 A were increased in the MPM tissues(all P<0.05). Downregulation of these three genes was correlated with good prognosis, and upregulation of these three genes was correlated with poor prognosis in the patients. CONCLUSION: G2/M checkpoint, epithelial-mesenchymal transition, E2 F target gene and mitotic spindle pathway are the key signaling pathways in the occurrence and development of MPM. PCLAF, TOP2 A and NUSAP1 genes could be the biomarkers for the prognosis of MPM.
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Objective: The objective of this paper was to investigate hub genes of postmenopausal osteoporosis (PO) utilizing benchmarked dataset and gene regulatory network (GRN). Materials and Methods: To achieve this goal, the first step was to benchmark the dataset downloaded from the ArrayExpress database by adding local noise and global noise. Second, differentially expressed genes (DEGs) between PO and normal controls were identified using the Linear Models for Microarray Data package based on benchmarked dataset. Third, five kinds of GRN inference methods, which comprised Zscore, GeneNet, context likelihood of relatedness (CLR) algorithm, Partial Correlation coefficient with Information Theory (PCIT), and GEne Network Inference with Ensemble of trees (Genie3), were described and evaluated by receiver operating characteristic (ROC) and precision and recall (PR) curves. Finally, GRN constructed according to the method with best performance was implemented to conduct topological centrality (closeness) for the purpose of investigate hub genes of PO. Results:A total of 236 DEGs were obtained based on benchmarked dataset of 20,554 genes. By assessing Zscore, GeneNet, CLR, PCIT, and Genie3 on the basis of ROC and PR curves, Genie3 had a clear advantage than others and was applied to construct the GRN which was composed of 236 nodes and 27,730 edges. Closeness centrality analysis of GRN was carried out, and we identified 14 hub genes (such as TTN, ACTA1, and MYBPC1) for PO. Conclusion: In conclusion, we have identified 14 hub genes (such as TN, ACTA1, and MYBPC1) based on benchmarked dataset and GRN. These genes might be potential biomarkers and give insights for diagnose and treatment of PO
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Meta-analysis provides a systematic access to the previously studied microarray datasets that can recognize several common signatures of stresses. Three different datasets of abiotic stresses on rice were used for meta-analysis. These microarray datasets were normalized to regulate data for technical variation, as opposed to biological differences between the samples. A t-test was performed to recognize the differentially-expressed genes (DEGs) between stressed and normal samples. Gene ontology enrichment analysis revealed the functional distribution of DEGs in different stressed conditions. Further analysis was carried out using software RICE NET DB and divided into three different categories: biological process (homoiothermy and protein amino acid phosphorylation), cellular component (nucleus and membrane), and molecular function (zinc ion binding ad DNA binding). The study revealed that 5686 genes were constantly expressed differentially in Oryza sativa (2089 upregulated and 3597 downregulated). The lowest P value (P = 0.003756) among upregulated DEGs was observed for naringenin, 2-oxoglutrate 3-dioxygenase protein. The lowest P value (P = 0.002866816) among the downregulated DEGs was also recorded for retrotransposon protein. The network constructed from 48 genes revealed 10 hub genes that are connected with topological genes. These hub genes are stress responsive genes that may also be regarded as the marker genes for drought stress response. Our study reported a new set of hub genes (reference genes) that have potentially significant role in development of stress tolerant rice
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Myocardial ischemia/reperfusion (MI/R) injury is a complex phenomenon that causes severe damage to the myocardium. However, the potential molecular mechanisms of MI/R injury have not been fully clarified. We identified potential molecular mechanisms and therapeutic targets in MI/R injury through analysis of Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were found between MI/R injury and normal samples, and overlapping DEGs were found between GSE61592 and GSE67308. Gene Ontology (GO) and pathway analysis were performed for overlapping DEGs by Database for Annotation, Visualization and Integration Discovery (DAVID). Then, a network of protein-protein interaction (PPI) was constructed through the Search Tool for the Retrieval of Interacting Genes (STRING) database. Potential microRNAs (miRNAs) and therapeutic small molecules were screened out using microRNA.org database and the Comparative Toxicogenomics database (CTD), respectively. Finally, we identified 21 overlapping DEGs related to MI/R injury. These DEGs were significantly enriched in IL-17 signaling pathway, cytosolic DNA-sensing pathway, chemokine signaling, and cytokine-cytokine receptor interaction pathway. According to the degree in the PPI network, CCL2, LCN2, HP, CCL7, HMOX1, CCL4, and S100A8 were found to be hub genes. Furthermore, we identified potential miRNAs (miR-24-3p, miR-26b-5p, miR-2861, miR-217, miR-4251, and miR-124-3p) and therapeutic small molecules like ozone, troglitazone, rosiglitazone, and n-3 polyunsaturated fatty acids for MI/R injury. These results identified hub genes and potential small molecule drugs, which could contribute to the understanding of molecular mechanisms and treatment for MI/R injury.
Subject(s)
Myocardial Reperfusion Injury , MicroRNAs , Computational Biology , Gene Expression Profiling , Gene Regulatory Networks , Protein Interaction Maps , Gene OntologyABSTRACT
Objective::Bioinformatic analysis was used to compare the gene expression profile between asthma patients and healthy people, and the gene characteristics of asthma were preliminarily identified and the potential mechanism and drugs were revealed. Method::The GSE74986 gene expression profile was downloaded from the gene expression omnibus (GEO) and the differentially expressed genes (DEGs) were analyzed by GEO2R. Then the gene heat map of DEGs was made by Morpheus, and their gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) analysis were performed by DAVID 6.8. Moreover, the protein-protein interaction (PPI) network and hub genes were constructed by String 10.5. Finally, the significant modules were analyzed by MCODE in Cytoscape 3.6.1, small molecule drugs related to asthma were screened through Coremine Medical. Result::A total of 510 DEGs were screened, including 29 up-regulated genes and 481 down-regulated genes. DEGs were mainly involved in these biological processes and pathways, including chromatin silencing, transcriptional regulation of RNA polymerase Ⅱ promoter, protein transport, messenger RNA (mRNA) processing, RNA splicing, ubiquitin-mediated proteolysis, protein processing in the endoplasmic reticulum, RNA transport, and myeloid differentiation factor (MyD)-dependent Toll-like receptor signaling pathway, platelet activation, nucleotide binding oligomerization domain (NOD)-like receptor signaling pathway and so on. A total of 9 hub genes were obtained, including T-complex protein 1 subunit theta (CCT8), T-complex protein 1 subunit alpha (TCP1), 26S protease regulatory subunit S10B (PSMC6), heat shock protein 90 alpha (HSP90A)A1, cell cycle protein C (CCNC), HSP90AB1, 26S proteasome non-ATPase regulatory subunit 6 (PSMD6), ubiquitin-specific protease 14 (USP14) and eukaryotic translation initiation factor 4E (EIF4E). Two important modules were obtained. The genes in two modules mainly involved these biological process, such as splice, ubiquitin-mediated proteolysis, protein modification, RNA modification and so on. Some potential molecular drugs for the treatment of asthma, such as anisomycin and genistein, have been developed. Conclusion::DEGs and hub genes can contribute to understanding the molecular mechanism of asthma and providing potential therapeutic targets and drugs for the diagnosis and treatment of asthma.
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Abstract Introduction : Aging is the main risk factor for the development of chronic diseases such as cancer, diabetes, Parkinson's disease, and Alzheimer's disease. The central nervous system is particularly susceptible to progressive functional deterioration associated with age, among the brain regions the prefrontal cortex (PFC) has one of the highest involvements. Transcriptomics studies of this brain region have identified the decrease in synaptic function and activation of neuroglia cells as fundamental characteristics of the aging process. The aim of this study was to identify hub genes in the transcriptomic deregulation in the PFC aging to advance in the knowledge of this process. Materials and methods : A gene co-expression analysis was carried out for 45 people 60 to 80 years old compared with 38 people 20 to 40 years old. The networks were visualized and analyzed using Cytoscape; citoHubba was used to determine which genes had the best topological characteristics in the co-expression networks. Results : Five genes with high topological characteristics were identified. Four of them -HPCA, CACNG3, CA10, PLPPR4- were repressed and one was over-expressed -CRYAB-. Conclusion: The four repressed genes are expressed preferentially in neurons and regulate the synaptic function and the neuronal plasticity, while the overexpressed gene is typical of glial cells and is expressed as a response to neuronal damage, facilitating myelination and neuronal regeneration.
Resumen Introducción : el envejecimiento es el principal factor de riesgo para el desarrollo de enfermedades crónicas como el cáncer, la diabetes, el Parkinson y el Alzheimer. El sistema nervioso central es particularmente susceptible al deterioro funcional progresivo asociado con la edad, entre las regiones cerebrales con mayor compromiso se encuentra la corteza prefrontal (CPF). Estudios de transcriptómica de esta región han identificado como características fundamentales del proceso de envejecimiento la disminución de la función sináptica y la activación de las células de la neuroglia. No es claro cuáles son las causas iniciales, ni los mecanismos moleculares subyacentes a estas alteraciones. El objetivo de este estudio fue identificar genes clave en la desregulación transcriptómica en el envejecimiento de la CPF para avanzar en el conocimiento de este proceso. Materiales y métodos : se hizo un análisis de coexpresión de genes de los transcriptomas de 45 personas entre 60 y 80 años con el de 38 personas entre 20 y 40 años. Las redes fueron visualizadas y analizadas usando Cytoscape, se usó citoHubba para determinar qué genes tenían las mejores características topológicas en las redes de coexpresión. Resultados : se identificaron cinco genes con características topológicas altas. Cuatro de ellos -HPCA, CACNG3, CA10, PLPPR4- reprimidos y uno sobreexpresado -CRYAB-. Conclusión : los cuatro genes reprimidos se expresan preferencialmente en neuronas y regulan la función sináptica y la plasticidad neuronal, mientras el gen sobreexpresado es típico de células de la glía y se expresa como respuesta a daño neuronal facilitando la mielinización y la regeneración neuronal.
Resumo Introdução : o envelhecimento é o principal fator de risco pra o desenvolvimento de doenças crónicas como o câncer, a diabetes, o Parkinson e o Alzheimer. O sistema nervoso central é particularmente susceptível ao deterioro funcional progressivo associado à idade, uma das regiões do cérebro com maior compromisso é o pré-frontal (CPF). Estudos de transcritoma desta região têm identificado como características fundamentais do processo de envelhecimento a diminuição da função sináptica e ativação das células da neuroglia. Não é claro quais são as causas iniciais, nem os mecanismos moleculares subjacentes a estas alterações. O objetivo deste estudo foi identificar genes chave na desregulação transcritoma no envelhecimento da CPF para avançar no conhecimento deste processo. Materiais e métodos : se fez uma análise de co-expressão de genes dos transcritomas de 45 pessoas entre 60 e 80 anos com o de 38 pessoas entre 20 e 40 anos. As redes foram visualizadas e analisadas usando Cytoscape, usou-se citoHubba para determinar que genes tinham as melhores características topológicas nas redes de co-expressão. Resultados : identificaram-se cinco genes com características topológicas altas. Quatro deles -HPCA, CACNG3, CA10, PLPPR4- reprimidos e um superexpresso -CRYAB-. Conclusão : os quatro genes reprimidos se expressam preferencialmente em neurônios e regulam a função sináptica e plasticidade neuronal, enquanto o gene superexpresso é típico de células da glia e se expressa como resposta ao dano neuronal facilitado a mielinização e a regeneração neuronal.
Subject(s)
Humans , Aging , Prefrontal Cortex , TranscriptomeABSTRACT
@#Objective: To screen the Hub genes associated with the occurrence and development of esophageal squamous cell carcinoma (ESCC) and to analyze their biological functions by using various bioinformatics analysis tools. Methods: ESCC chip profile GSE100942 from GEO database was used as study subject; GEO2R tool was used to analyze the data and to screen the differentially expressed genes (DEGs), and the bioinformatics tools (DAVID, String, Cytoscape) were further used to construct protein-protein interaction (PPI) network and identify the key Hub genes. GO and KEGG were used for the biological function enrichment analysis. In the meanwhile, MiRDB was applied to identify the miRNAs that might regulate Hub genes and to construct Hub gene–miRNA network. Importantly, the expression of DEGs and the patient survival were verified by the GEPIA analysis tool. Results: By analyzing GSE100942 database, a total of 1229 DEGs with difference of 2 times and 223 DEGs with difference of 4 times were screened out. In addition, 20 Hub genes, which were all up-regulated in ESCC tissues, were also identified. The functional enrichment analysis showed that these DEGs were mainly enriched in cancer related pathways and involved in cell division and mitotic nuclear division. Among those 20 Hub genes, DLGAP5, BUB1B, TPX2, TTK, CDC20, CCNB2, AURKA and DEPDC1 were identified as 8 key Hub genes that related with ESCC, and involved in many important biological processes, such as cell proliferation, cell cycle and signal pathway. Five Hub genes, CEP55, ECT2, NEK2, DEPDC1 and NUSAP1, were identified to be highly regulated by the miRNA regulatory network. Conclusion: Microarray combined with bioinformatics can effectively analyze the DEGs associated with the occurrence and development of ESCC. The identification of the 20 Hub genes and the 8 key Hub genes can provide theoretical guidance for further research on the molecular mechanism and molecular marker screening of ESCC. ·