Identification of key immune related genes in diabetes retinopathy based on weighted gene co-expression network / 国际眼科杂志(Guoji Yanke Zazhi)

AIM: To explore the key genes related to immunity and immune cell infiltration levels in diabetes retinopathy(DR)using bioinformatics.METHODS: Differential expression genes(DEGs)were obtained by “limma” R from Gene Expression Omnibus(GEO)data from September to October 2022, Gene ontology(GO)and Kyoto encyclopedia of genes and genomes(KEGG)were analyzed, and the infiltration of immune cell types in each sample was calculated based on CIBERSORT algorithm. Weighted gene co-expression network analysis(WGCNA)was used to screen for DEGs in immune-related gene modules. The protein-protein interaction(PPI)network was established by STRING online database and Cytoscape, and the hub genes were screened by MCODE and cytoHubba plug-ins.RESULTS: The results showed that 1 426 up-regulated and 206 down-regulated differential genes were screened, where 7 immune cell types, including B cell naive, Plasma cells, CD4+T cells, T cells regulatory(Tregs), Macrophages M0, Macrophages M1 and Neutrophils were significantly overexpressed(P<0.05), while others were low expressed(P<0.05). After WGCNA, a total of 820 DEGs were found in the modules most related to immunity. After constructing the PPI network, 10 key genes were screened using plug-ins, and two key genes were further screened using the expression amount of each differential gene in PPI: DLGAP5 and AURKB.CONCLUSION: This study used bioinformatics to screen the infiltration of immune cells and key genes related to immunity in patients with DR. These findings may provide evidences for future research, diagnosis, and treatment of DR.

Mechanism of Calculus Bovis treating keratitis based on network pharmacology / 中华实验眼科杂志

Objective:To investigate the complex Calculus Bovis-target-keratitis network and to explore the molecular mechanism of Calculus Bovis treating keratitis through network pharmacology. Methods:Genes related to keratitis were searched in the online DisGeNET database and the protein-protein interaction (PPI) network of keratitis-associated proteins was constructed.The components isolated and identified in Calculus Bovis were collected through the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP, https: //tcmsp-e.com/tcmsp.php), Chemistry Database by Shanghai Institute of Organic Chemistry of CAS (http: //www.organchem.csdb.cn), and published literature.The canonical SMILES information of the collected components was exported, which were submitted to the SwissTargetPrediction platform to predict potential targets of the components.The active component-predicted target network of Calculus Bovis was constructed and merged with the PPI network of keratitis-associated proteins to build the active component-potential target network of Calculus Bovis and systemically investigate the potential targets and signal pathways of Calculus Bovis in treatment of keratitis.The component-target-pathway network was established to analyze the mechanism of Calculus Bovis treating keratitis. Results:Thirty-nine components isolated and identified in Calculus Bovis were searched and 65 target genes related to keratitis were screened.Of the 28 potential targets involved in Calculus Bovis treating keratitis, there were 7 direct targets, including tumor necrosis factor, caspase 1, Toll-like receptor 9, C-X-C motif chemokine ligand 8, interleukin-6, mitogen-activated protein kinase 8, neurotrophic receptor tyrosine kinase 1.The 28 potential targets were annotated to 12 entries for biological process, 18 for cellular components and 13 for molecular function.In the Kyoto encyclopedia of genes and genomes pathway enrichment analysis, 10 signal pathways were identified as enriched categories, which were mainly related to human cytomegalovirus infection, amoebiasis, antifolate resistance, PI3K-Akt signaling pathway, rheumatoid arthritis, apoptosis, cytokine-cytokine receptor interaction, malaria, non-alcoholic fatty liver disease, interleukin-17 signaling pathway. Conclusions:Calculus Bovis may play an adjuvant therapeutic effect on keratitis through anti-inflammatory, antibacterial, antiviral, immune regulation, inflammatory regulation and other functions.

Bioinformatic analysis identifies potential key genes in the pathogenesis of age-related macular degeneration

Purpose: Age?related macular degeneration (AMD) is the leading cause of irreversible blindness in older individuals. More studies focused on screening the genes, which may be correlated with the development of AMD. With advances in various technologies like multiple microarray datasets, researchers could identify differentially expressed genes (DEGs) more accurately. Exploring abnormal gene expression in disease status can help to understand pathophysiological changes in complex diseases. This study aims to identify the key genes and upstream regulators in AMD and reveal factors, especially genetic association, and the prognosis of the development of this disease. Methods: Data from expression profile GSE125564 and profile GSE29801 were obtained from the Gene Expression Omnibus (GEO) database. We analyzed DEGs using R software (version 3.6.3). Functional enrichment and PPI network analysis were performed using the R package and online database STRING (version 11.0). Results: We compared AMD with normal and found 68 up?regulated genes (URGs) and 25 down?regulated genes (DRGs). We also compared wet AMD with dry AMD and found 41 DRGs in dry AMD. Further work including PPI network analysis, GO classification, and KEGG analysis was done to find connections with AMD. The URGs were mainly enriched in the biological process such as DNA replication, nucleoplasm, extracellular exosome, and cadherin binding. Besides, DRGs were mainly enriched in these functions such as an integral component of membrane and formation of the blood?aqueous barrier (BAB). Conclusion: This study implied that core genes might involve in the process of AMD. Our findings may contribute to revealing the pathogenesis, developing new biomarkers, and raising strategies of treatment for AMD

Macrophage differential genes and their functions in silicosis based on single cell RNA sequencing and spatial transcriptome sequencing / 环境与职业医学

Background The rise of single cell RNA sequencing (scRNA-seq) and spatial transcriptome sequencing technologies has allowed for intensive study of lung diseases, but both have been poorly studied in silicosis. Objective To explore differentially expressed genes DEGs in silicosis macrophages by scRNA-seq combined with spatial transcriptome sequencing and analyze the potential diagnostic genes. Methods Male C57BL/6 mice (5-6 weeks old, 22-30 g) were randomly divided into 4 groups: normal saline (NS) group for 7 d, NS group for 56 d, SiO2 group for 7 d, and SiO2 group for 56 d, with 1 mouse in each group. A silicosis model was constructed by tracheal drip injection of SiO2 suspension (0.2 g·kg−1, 50 g·cm−2), and the control mice were given the same volume of NS. The right lung was removed for scRNA-seq and the left lung for spatial transcriptome sequencing on day 7 and day 56, respectively. Cell populations were captured using principal component analysis techniques and dimensionality reduction of uniform manifold approximation and projection. The Find Markers function in R language was applied to analyze the DEGs changes of macrophages in two groups of lung tissues, and the corresponding DEGs were subjected to Gene Ontology enrichment analysis and Kyoto Encyclopedia of Genes and Genomes signaling pathway analysis, while STRING and CytoHubba plug-ins of Cytoscape software were applied to protein-protein interaction network analysis to screen out key (Hub) genes. Spatial transcriptome sequencing was used to explore the original location of Hub genes on lung tissue sections and their mapping in lung macrophages. Finally, the correlation of Hub gene expression levels in lung tissues of silicosis patients and mouse silicosis models was verified, the diagnostic efficacy of Hub gene using subject operating characteristic curves (ROC). In vitro experiments by applying cell viability assay were conducted to verify the changes in viability of mouse macrophages (RAW264.7) under SiO2 stimulation. Results The scRNA-seq revealed a total of 20 clusters captured and defined. The results of scRNA-seq and spatial transcriptome sequencing showed an increased number of macrophages in the lung tissue of the SiO2 group compared to the NS group and clustered in the focal areas. Among the 97 macrophage DEGs screened out, 75 were up-regulated genes, and mainly enriched in chemotaxis and migration of neutrophils, chemokine receptor binding, tumor necrosis factor signaling pathway, cytokine-cytokine receptor interaction pathway, and interleukin-17 signaling pathway; and 22 were down-regulated genes, and mainly enriched in late endosomes, peroxisome proliferator-activated receptors signaling pathway, and alcoholic liver disease signaling pathway. A total of 2 core modules and 3 Hub genes were screened out, including Ccl2, Ccl7, and Ptgs2. The scRNA-seq showed that they were expressed at elevated levels in the SiO2 group compared to the NS group and clustered in additional macrophages, and the spatial transcriptome sequencing showed that they clustered in inflammatory areas with nodular lesions. The CCL7 and PTGS2 expressions were increased in the lung tissue of SiO2 patients compared with the healthy subjects, and the areas under the working curve of the subjects were 0.850 and 0.786, respectively. The viability of RAW264.7 cells was enhanced under SiO2 stimulation at 3 h, 6 h, and 12 h compared to those without the stimulation (P<0.05). Conclusion Bioinformatics screening have identified 3 Hub genes (Ccl2, Ccl7, and Ptgs2)and 2 potential diagnostic genes (CCL7 and PTGS2) in the lung tissue of silicosis mice, which may be potential molecular markers of early-stage silicosis with implications for the development and prognosis of silicosis.

Analysis of sepsis-related genes through weighted gene co-expression network / 中华危重病急救医学

Objective:To identify the Key genes in the development of sepsis through weighted gene co-expression network analysis (WGCNA).Methods:The gene expression dataset GSE154918 was downloaded from the public database Gene Expression Omnibus (GEO) database, which containes data from 105 microarrays of 40 control cases, 12 cases of asymptomatic infection, 39 cases of sepsis, and 14 cases of follow-up sepsis. The R software was used to screen out differentially expressed genes (DEG) in sepsis, and the distributed access view integrated database (DAVID), search tool for retrieval of interacting neighbouring genes (STRING) and visualization software Cytoscape were used to perform gene function and pathway enrichment analysis, Protein-protein interaction (PPI) network analysis and key gene analysis to screen out the key genes in the development of sepsis.Results:Forty-six candidate genes were obtained by WGCNA and combined with DEG expression analysis, and these 46 genes were analyzed by gene ontology (GO) and Kyoto City Encyclopedia of Genes and Genomes (KEGG) pathway enrichment to obtain gene functions and involved signaling pathways. The PPI network was further constructed using the STRING database, and 5 key genes were selected by the PPI network visualization software Cytoscape, including the mast cell expressed membrane protein 1 gene (MCEMP1), the S100 calcium-binding protein A12 gene (S100A12), the adipokine resistance factor gene (RETN), the c-type lectin structural domain family 4 member gene (CLEC4D), and peroxisome proliferator-activated receptor gene (PPARG), and differential expression analysis of each of these 5 genes showed that the expression levels of the above 5 genes were significantly upregulated in sepsis patients compared with healthy controls.Conclusion:In this study, 5 key genes related to sepsis were screened by constructing WGCNA method, which may be potential candidate targets related to sepsis diagnosis and treatment.

Gene co-expression network analysis provides a novel insight into the dynamic response of wheat to powdery mildew stress

Powdery mildew (Blumeria graminis f. sp. Tritici, (Bgt)) is an important worldwide fungal foliar disease of wheat (Triticum aestivum) responsible for severe yield losses. The development of resistance genes and dissection of the resistance mechanism will therefore be beneficial in wheat breeding. The Bgt resistance gene PmAS846 was transferred to the hexaploid wheat lines N9134 from Triticum dicoccoides, and it is still one of the most effective resistance genes. Here, by RNA sequencing, we identified three co-expressed gene modules using pairwise comparisons and weighted gene co-expression network analysis during wheat–Bgt interactions compared with mock-infected plants. Hub genes of stress-specific modules were significantly enriched in spliceosomes, phagosomes, the mRNA surveillance pathway, protein processing in the endoplasmic reticulum, and endocytosis. Induced module genes located on chromosome 5BL were selected to construct a protein–protein interaction network. Several proteins were predicted as the key hub node, including Hsp70, DEAD/DEAH box RNA helicase PRH75, elongation factor EF-2, cell division cycle 5, ARF guanine-nucleotide exchange factor GNOM-like, and protein phosphatase 2C 70 protein, which interacted with several disease resistance proteins such as RLP37, RPP13 and RPS2 analogues. Gene ontology enrichment results showed that wheat could activate binding functional genes via an mRNA transcription mechanism in response to Bgt stress. Of these node genes, GNOM-like, PP2C isoform X1 and transmembrane 9 superfamily member 9 were mapped onto the genetic fragment of PmAS846 with a distance of 4.8 Mb. This work provides the foundations for understanding the resistance mechanism and cloning the resistance gene PmAS846

Identification of the molecular mechanisms associated with acute type A aortic dissection through bioinformatics methods

Aortic dissection is characterized by the redirection of blood flow, which flows through an intimal tear into the aortic media. The purpose of this study was to find potential acute type A aortic dissection (AAAD)-related genes and molecular mechanisms by bioinformatics. The gene expression profiles of GSE52093 were obtained from Gene Expression Omnibus (GEO) database, including 7 AAAD samples and 5 normal samples. The differentially expressed genes (DEGs) were detected between AAAD and normal samples. The functional annotation and pathway enrichment analysis were conducted through the Database for Annotation, Visualization and Integration Discovery (DAVID). A protein-protein interaction network was established by the Search Tool for the Retrieval of Interacting Genes (STRING) software. The microRNAs (miRNAs) of these differentially expressed genes were predicted using <microRNA.org> database. Moreover, DEGs were analyzed in the comparative toxicogenomics (CTD) database to screen out the potential therapeutic small molecules. As a result, there were 172 DEGs identified in patients with AAAD. These DEGs were significantly enriched in 6 pathways, including cell cycle, oocyte meiosis, DNA replication, extracellular matrix-receptor interaction, and mineral absorption pathway. Notably, CDC20, CDK1, CHEK1, KIF20A, MCM10, PBK, PTTG1, RACGAP, and TOP2A were crucial genes with a high degree in the protein-protein interaction network. Furthermore, potential miRNAs (miR-301, miR-302 family, and miR-130 family) were identified. In addition, small molecules like azathioprine and zoledronic acid were identified to be potential drugs for AAAD.

Identification of the targets of hematoporphyrin derivative in lung adenocarcinoma using integrated network analysis

BACKGROUND: Hematoporphyrin derivative (HPD) has a sensibilization effect in lung adenocarcinoma. This study was conducted to identify the target genes of HPD in lung adenocarcinoma. METHODS: RNA sequencing was performed using the lung adenocarcinoma cell line A549 after no treatment or treatment with X-ray or X-ray + HPD. The differentially expressed genes (DEGs) were screened using Mfuzz package by noise-robust soft clustering analysis. Enrichment analysis was carried out using "BioCloud" online tool. Protein-protein interaction (PPI) network and module analyses were performed using Cytoscape software. Using WebGestalt tool and integrated transcription factor platform (ITFP), microRNA target and transcription factor (TF) target pairs were separately predicted. An integrated regulatory network was visualized with Cytoscape software. RESULTS: A total of 815 DEGs in the gene set G1 (continuously dysregulated genes along with changes in processing conditions [untreated-treated with X-ray-X-ray + treated with HPD]) and 464 DEGs in the gene set G2 (significantly dysregulated between X-ray + HPD-treated group and untreated/X-ray-treated group) were screened. The significant module identified from the PPI network for gene set G1 showed that ribosomal protein L3 (RPL3) gene could interact with heat shock protein 90 kDa alpha, class A member 1 (HSP90AA1). TFs AAA domain containing 2 (ATAD2) and protein inhibitor of activated STAT 1 (PIAS1) were separately predicted for the genes in gene set G1 and G2, respectively. In the integrated network for gene set G2, ubiquitin-specific peptidase 25 (USP25) was targeted by miR-200b, miR-200c, and miR-429. CONCLUSION: RPL3, HSP90AA1, ATAD2, and PIAS1 as well as USP25, which is targeted by miR-200b, miR-200c, and miR-429, may be the potential targets of HPD in lung adenocarcinoma.

Identification of prognosis-related genes in hepatocellular carcinoma based on bioinformatical analysis / 中国肿瘤生物治疗杂志

@# Objective: To identify the differentially expressed genes (DEGs) between hepatocellular carcinoma (HCC) tissues and normal liver tissues by bioinformatic methods, and to explore the intrinsic mechanism of these candidate genes involving in the occurrence and development of HCC from transcriptome level as well as the clinical significance of their associations with the prognosis of HCC patients. Methods: Gene expression profiles of GSE45267, GSE64041, GSE84402 and TCGA were downloaded from GEO (Gene Expression Omnibus) and TCGA(The Cancer GenomeAtlas), respectively. R software and Bioconductor packages were used to identify the DEGs between HCC tissues and para-cancer tissues, and then Gene Ontology (GO) Enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, Protein-Protein Interaction (PPI) network analysis and survival analysis were performed. Results: Forty-six up-regulated genes and 154 down-regulated genes were screened out,and GO enrichment analysis showed that these DEGs were mainly related to cell division, proliferation, cycle regulation, oxidation-reduction process and certain metabolic pathways. KEGG pathway analysis revealed that DEGs were mainly involved in tryptophan metabolism, retinol metabolism and other metabolic pathways as well as p53 pathway. Over-expression of a panel of up-regulated genes (CCNA2, CDK1, DLGAP5, KIF20A, KPNA2 and MELK) was shown to be significantly negatively correlated with the prognosis of HCC patients in the TCGA dataset (all P<0.01). Conclusion: A set of up-regulated hub genes that are negatively correlated with prognosis will provide potential guiding value for the clinical research on the diagnosis and treatment of HCC.

Identification of Key Genes of Airway Epithelial Cells in Asthma Patients by Bioinformatics-based Analysis / 中国医科大学学报

Objective We aimed to identify key genes and pathways of airway epithelial cells involved in bronchial asthma by comparing genetic information in the databases for patients with bronchial asthma and normal people. Methods To find differentially expressed genes (DEGs), mRNA microarray dataset, GSE43696, of airway epithelial cells in asthma was analyzed by GE02R. Functional and pathway enrichment analyses were performed for DEGs using the DAVID database. The protein-protein interaction networks were established using STRING to identify key genes and important complexes. Results A total of 355 DEGs were identified; of which, 130 were up-regulated and 225, down-regulated. The genes identified were involved in cell movement, growth factor binding, and ion channel activity. Nine key genes were recognized, including BDNF, ERBB2 IL6, VEGFA, KIT, ADCY4, PRKAR2B, CCR6, and NMU. Conclusion All nine key genes identified play important roles in asthma and serve as potential targets for treatment of bronchial asthma.

Gender-related Gene Expression in Sarcopenia in Old People:A Bioinformatic Analysis / 中国康复理论与实践

Objective To explore the gender differences in the genes expression of sarcopenia with bioinformatics. Methods The gene expression profiles downloaded from Gene Expression Omnibus database were analysed with BRB-Array Tools and STRING,then the protein-protein interaction network was built with Cytoscape. Results There were 152 genes up-regulated and 67 genes down-regulated in sarcopenic men,and 90 up-regulated and 52 down-regulated in women,with the same 47 up-regulated and 21 down-regulated.The gene ontology(GO)terms were found to be more complex in the sarcopenic women.The function analysis showed the same module genes were enriched in regulation of fat cell differentiation,protein kinase inhibitor activity and protein kinase regula-tor activity.In the protein-protein interaction networks,dystrophin,vimentin and tropomyosin α-3 were the most important in men,and metallothionein 1H and dynein light chain in women. Conclusion The nosogenesis of sarcopenia is different between genders from differentially expressed genes,that may be important for the future study.

Dysregulation of NCAPG, KNL1, miR-148a-3p, miR-193b-3p, and miR-1179 may contribute to the progression of gastric cancer

BACKGROUND: Emerging evidence indicate that miRNAs play an important role on gastric cancer (GC) progression via regulating several downstream targets, but it is still partially uncovered. This study aimed to explore the molecular mechanisms of GC by comprehensive analysis of mRNAs and miRNA expression profiles. METHODS: The mRNA and miRNA expression profiles of GSE79973 and GSE67354 downloaded from Gene Expression Omnibus were used to analyze the differentially expressed genes (DEGs) and DE-miRNAs among GC tissues and normal tissues. Then, targets genes of DE-miRNAs were predicted and the DE-miRNA-DEG regulatory network was constructed. Next, function enrichment analysis of the overlapped genes between the predicted DE-miRNAs targets and DEGs was performed and a protein-protein interactions network of overlapped genes was constructed. Finally, RT-PCR analysis was performed to detect the expression levels of several key DEGs and DE-miRNAs. RESULTS: A set of 703 upregulated and 600 downregulated DEGs, as well as 8 upregulated DE-miRNAs and 27 downregulated DE-miRNAs were identified in GC tissue. hsa-miR-193b-3p and hsa-miR-148a-3p, which targeted most DEGs, were highlighted in the DE-miRNA-DEG regulatory network, as well as hsa-miR-1179, which targeted KNL1, was newly predicted to be associated with GC. In addition, NCAPG, which is targeted by miR-193b-3p, and KNL1, which is targeted by hsa-miR-1179, had higher degrees in the PPI network. RT-qPCR results showed that hsa-miR-148a-3p, hsa-miR-193b-3p, and hsa-miR-1179 were downregulated, and NCAPG and KNL1 were upregulated in GC tissues; this is consistent with our bioinformatics-predicted results. CONCLUSIONS: The downregulation of miR-193b-3p might contribute to GC cell proliferation by mediating the upregulation of NCAPG; as additionally, the downregulation of miR-193b-3p might contribute to the mitotic nuclear division of GC cells by mediating the upregulation of KNL1.

Bioinformatics analysis of microarray data to reveal the pathogenesis of diffuse intrinsic pontine glioma

BACKGROUND: Diffuse intrinsic pontine glioma (DIPG) is the main cause of pediatric brain tumor death. This study was designed to identify key genes associated with DIPG. METHODS: The gene expression profile GSE50021, which consisted of 35 pediatric DIPG samples and 10 normal brain samples, was downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified by limma package. Functional and pathway enrichment analyses were performed by the DAVID tool. Protein-protein interaction (PPI) network, and transcription factor (TF)-microRNA (miRNA)-target gene network were constructed using Cytoscape. Moreover, the expression levels of several genes were validated in human glioma cell line U251 and normal glia HEB cells through real-time polymerase chain reaction (PCR). RESULTS: A total of 378 DEGs were screened (74 up-regulated and 304 down-regulated genes). In the PPI network, GRM1, HTR2A, GRM7 and GRM2 had higher degrees. Besides, GRM1 and HTR2A were significantly enriched in the neuroactive ligand-receptor interaction pathway, and calcium signaling pathway. In addition, TFAP2C was a significant down-regulated functional gene and hsa-miR-26b-5p had a higher degree in the TF-miRNA-target gene network. PCR analysis revealed that GRM7 and HTR2A were significantly downregulated while TFAP2C was upregulated in U251 cells compared with that in HEB cells (p < 0.001). GRM2 was not detected in cells. CONCLUSIONS: GRM1 and HTR2A might function in DIPG through the neuroactive ligand-receptor interaction pathway and the calcium signaling pathway. Furthermore, the TFAP2C and hsa-miR-26b-5p might play important roles in the development and progression mechanisms of DIPG.

Design of new traditional Chinese medicine herbal formulae for treatment of type 2 diabetes mellitus based on network pharmacology / 中国天然药物

In the present study, 28 Chinese medicinal herbs belonging to traditional Chinese medicine (TCM) for the treatment of type 2 diabetes were selected to explore the application of network pharmacology in developing new Chinese herbal medicine formulae for the treatment of type 2 diabetes mellitus (T2DM). These herbs have the highest appearance rate in the literature, and their compounds are listed. The human protein-protein interaction network and the T2DM disease protein interaction network were constructed. Then, the related algorithm for network topology was used to perform interventions on the interaction network of disease proteins and normal human proteins to test different Chinese herbal medicine compound combinations, according to the information on the interaction of compounds-targets in two databases, namely TarNet and the Medicinal Plants Database. Results of the intervention scores indicate that the method proposed in this study can provide new effective combinations of Chinese herbal medicines for T2DM. Network pharmacology can effectively promote the modernization and development of TCM.

Gene module analysis of nasopharyngeal carcinoma based on protein-protein interaction network / 局解手术学杂志

Objective Based on protein-protein interaction network,gene modules were identified to provide new targets for molecular therapy of nasopharyngeal carcinoma.Methods GEO dataset (GSE12452)and SAMsoftware were employed to screen the differentially ex-pressed gene in nasopharyngeal carcinoma.Protein-protein interaction network was established by using String database.Based on the net-work,the gene modules were identified by using bioinformatics gene module analysis method.GO analysis was used to analyze the function of gene modules.Results In this study,2 634 differentially expressed genes were identified in nasopharyngeal carcinoma.There were 4 729 protein-protein interaction pairs among the differentially expressed genes according to the String database.We established the protein-protein interaction network based on these pairs.Seven gene modules were identified by bioinformatics methods.GO analysis results showed that the function of the gene modules including regulation of cell cycle,glycosylation,cell adhesion,oxidation reduction and so on.Conclusion There are 7 gene modules in protein-protein interaction network in nasopharyngeal carcinoma.These modules may play important roles in the progression and development of nasopharyngeal carcinoma.Our finding can provide a new sight for molecular diagnose and therapy of nasopharyngeal carcinoma.

Identification of novel gene and pathway targets for human epilepsy treatment

BACKGROUND: The aim of this study was to explore epilepsy-related mechanism so as to figure out the possible targets for epilepsy treatment. METHODS: The gene expression profile dataset GES32534 was downloaded from Gene Expression Omnibus database. We identified the differentially expressed genes (DEGs) by Affy package. Then the DEGs were used to perform gene ontology (GO) and pathway enrichment analyses. Furthermore, a protein-protein interaction (PPI) network was constructed with the DEGs followed by co-expression modules construction and analysis. RESULTS: Total 420 DEGs were screened out, including 214 up-regulated and 206 down-regulated genes. Functional enrichment analysis revealed that down-regulated genes were mainly involved in the process of immunity regulation and biological repairing process while up-regulated genes were closely related to transporter activity. PPI network analysis showed the top ten genes with high degrees were all down-regulated, among which FN1 had the highest degree. The up-regulated and down-regulated DEGs in the PPI network generated two obvious sub-co-expression modules, respectively. In up-co-expression module, SCN3B (sodium channel, voltage gated, type III beta subunit) was enriched in GO:0006814 ~ sodium ion transport. In down-co-expression module, C1QB (complement C1s), CIS (complement component 1, S subcomponent) and CFI (complement factor I) were enriched in GO:0006955 ~ immune response. CONCLUSION: The immune response and complement system play a major role in the pathogenesis of epilepsy. Additionally, C1QB, C1S, CFI, SCN3B and FN1 may be potential therapeutic targets for epilepsy.

Radioresistance related genes screened by protein-protein interaction network analysis in nasopharyngeal carcinoma / 中华放射医学与防护杂志

Objective To discover radioresistance associated molecular biomarkers and its mechanism in nasopharyngeal carcinoma by protein-protein interaction network analysis.Methods Whole genome expression microarray was applied to screen out differentially expressed genes in two cell lines CNE- 2R and CNE-2 with different radiosensitivity.Four differentially expressed genes were randomly selected for further verification by the semi-quantitative RT-PCR analysis with self-designed primers. The common differentially expressed genes from two experiments were analyzed with the SNOW online database in order to find out the central node related to the biomarkers of nasopharyngeal carcinoma radioresistance. The expression of STAT1 in CNE-2R and CNE-2 cells was measured by Western blot.Results Compared with CNE-2 cells,374 genes in CNE-2R cells were differentially expressed while 197 genes showed significant differences.Four randomly selected differentially expressed genes were verified by RT-PCR and had same change trend in consistent with the results of chip assay. Analysis with the SNOW database demonstrated that those 197 genes could form a complicated interaction network where STAT1 and JUN might be two key nodes.Indeed,the STAT1-α expression in CNE-2R was higher than that in CNE-2 (t =4.96,P ＜ 0.05).Conclusions The key nodes of STAT1 and JUN may be the molecular biomarkers leading to radioresistance in nasopharyngeal carcinoma,and STAT1-α might have close relationship with radioresistance.