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ObjectiveTo investigate the potential active ingredients and targets of Baihu Jia Renshentang（BHJRST） for the treatment of obesity combined with type 2 diabetes mellitus（T2DM） by network pharmacology and in vivo experiments. MethodUltra performance liquid chromatography-quadrupole/electrostatic field orbitrap high-resolution mass spectrometry（UPLC-Q-Exactive Orbitrap MS） was used to analyze and identify the material basis of BHJRST. Subsequently， potential targets for the action of the active ingredients were queried in databases such as ChEMBL， Therapeutic Target Database（TTD）， YaTCM， DisGeNET and Traditional Chinese Medicine on Immuno-Oncology（TCMIO）， and the shared targets were identified by taking the intersection of these targets with disease targets. The shared targets were imported into the STRING database to construct a protein-protein interaction（PPI） network， the hub genes were identified by cytoHubba plug-in， and molecular docking was used to validate the binding energy of the hub genes to the bioactive ingredients in BHJRST. Meanwhile， the shared targets were imported into the DAVID platform for gene ontology（GO） functional annotation and Kyoto Encyclopedia of Genes and Genomes（KEGG） pathway enrichment analysis. The predicted results were subsequently verified by animal experiments. Eighteen 8-week-old male skeletal muscle insulin-like growth factor-1 receptor dysfunction（MKR） mice were induced by a high-fat diet for 12 weeks in order to prepare a mouse model of obesity combined with T2DM. The mice were randomly divided into the model group， metformin group（0.2 g·kg-1） and BHJRST group（27 g·kg-1 in raw material）， and another 6 male FVB mice of the same age as the normal group. The mice in each group were were given the corresponding drugs by gavage， and the normal and model groups were given the same amount of distilled water by gavage， 1 time/d for 6 consecutive weeks. At the end of administration， the body mass， Lee's index， fasting blood glucose（FBG）， oral glucose tolerance test（OGTT） of mice in each group were examined， and the pathological morphology of the white adipose tissue of the epididymis was observed， and the expression of the mRNA of the hub genes in the white adipose tissue of the epididymis was detected by real-time fluorescence quantitative polymerase chain reaction（Real-time PCR）. ResultA total of 200 bioactive components of BHJRST were identified， of which 64 bioactive components were reverse-matched to 384 targets， and a total of 308 targets were associated with obesity combined with T2DM. Hub genes included mitogen-activated protein kinase 1（MAPK1）， signal transducer and activator of transcription 3（STAT3）， MAPK3， interleukin（IL）-2， Janus kinase 1（JAK1）， nuclear transcription factor-κB p65（RELA）， estrogen receptor 1（ESR1）， transcription factor AP-1（JUN）， MAPK14 and lymphocyte-specific protein tyrosine kinase（LCK）. GO functional annotation showed that it was mainly enriched in cytoplasm， cell membrane and nucleus， and was closely related to important biological processes such as peptide serine phosphorylation， protein phosphorylation and inflammation. In KEGG enrichment analysis， metabolic pathway， lipid and atherosclerosis， phosphatidylinositol 3-kinase（PI3K）/protein kinase B（Akt） and MAPK signal pathways were significantly enriched. The molecular docking results showed that the hub genes had a stable binding relationship with 10 bioactive components， including quercetin， isoliquiritigenin， and morin， in BHJRST. The results of animal experiments showed that BHJRST could significantly reduce body mass， Lee's index and FBG levels（P<0.01） in mice with obesity combined with T2DM， improve the pathological changes of white adipose tissue， and down-regulate the the mRNA expression of the hub genes in white adipose tissue of the epididymis（P<0.01）. ConclusionIn this study， 10 potentially active components such as quercetin， isoliquiritigenin， and morin in BHJRST are identified through network pharmacology and animal experiments， and it is possible to treat obesity combined with T2DM by regulating lipid and atherosclerosis， phosphatidylinositol PI3K/Akt and MAPK signal pathways， which provides important clues and theoretical basis for the study of its mechanism and clinical application.
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Objective@#To investigate the effects of decabromodiphenyl ether (BDE-209) on the size of subcutaneous transplanted tumors, related genes and signaling pathways of cervical cancer in mice.@*Methods@#Forty female C57BL/6 mice were subcutaneously inoculated with mouse cervical carcinoma U14 cells in the lateral axilla to establish a mouse subcutaneous transplanted tumor model. These mice were randomly divided into a high-dose group (500 mg/kg), a medium-dose group (100 mg/kg), a low-dose group (20 mg/kg) and a control group (corn oil), and were exposed to BDE-209 or corn oil by gavage. Subcutaneous transplanted tumor tissue was taken after 21 days of BDE-209 poisoning, and the differentially expressed genes in the subcutaneous transplanted tumors of cervical cancer among the four groups were analyzed by high-throughput transcriptome sequencing and were subjected to Gene Ontology (GO) annotations and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Protein-protein interactions (PPI) were analyzed using the STRING database, and the mRNA expression of hub genes was determined by real-time fluorescence polymerase chain reaction.@*Results@#Compared with the control group, low-dose group and medium-dose group, the mass of subcutaneous transplanted tumors in the high-dose group was decreased (all P<0.05). Transcriptome sequencing results showed that compared with the control group, 2 011 genes were up-regulated and 1 165 genes were down-regulated in the high-dose group; 960 genes were up-regulated and 357 genes were down-regulated in the medium-dose group; 537 genes were up-regulated and 262 genes were down-regulated in the low-dose group (all P<0.05). GO and KEGG analysis showed that the differentially expressed genes in the high-dose group were mainly involved in cell chemotaxis and NOD-like receptor signaling pathway; the differentially expressed genes in the medium-dose group were mainly involved in cell chemotaxis and cytokine-cytokine receptor interactions; and the differentially expressed genes in the medium-dose group were mainly involved in processing and presentation of antigens, and the signaling pathways of the complement and coagulation cascades. Compared with the control group, the mRNA expression of TLR2, MMP9, IL-6, Fos, and TNF was up-regulated in the high-dose group (all P<0.05).@*Conclusion@#High-dose BDE-209 may affect Toll-like receptors, NOD-like receptors, and other immune and inflammatory-related signaling pathways and cancer-related genes, leading to a decrease in the mass of subcutaneous transplanted cervical cancer tumors in mice.
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AIM:Using bioinformatics analysis methods to identify the hub genes involved in myocardial isch-emia-reperfusion injury(MIRI).METHODS:Firstly,the rat MIRI related dataset GSE122020,E-MEXP-2098,and E-GEOD-4105 were downloaded from the database.Secondly,differentially expressed genes(DEGs)were screened from each dataset using the linear models for microarray data(limma)package,and robust DEGs were filtered using the robust rank aggregation(RRA)method.In addition,the surrogate variable analysis(SVA)package was used to merge all datas-ets into one,and merged DEGs were screened using the limma package.The common DEGs were obtained by taking the intersection of the two channels of DEGs.Next,the protein-protein interaction(PPI)network of common DEGs was con-structed,and the hub genes were identified using the density-maximizing neighborhood component(DMNC)algorithm.The receiver operating characteristic curve(ROC)was plotted to evaluate the diagnostic performance of the hub gene.Then,the mRNA and protein expression levels of hub genes were detected in the rat MIRI model,and the literature re-view analysis was carried out on the involvement of hub genes in MIRI.Finally,the gene set enrichment analysis(GSEA)was performed on hub gene to further reveal the possible mechanism in mediating MIRI.RESULTS:A total of 143 robust DEGs and 48 merged DEGs were identified.After taking the intersection of the two,48 common DEGs were obtained.In the PPI network of common DEGs,5 hub genes were screened out,namely MYC proto-oncogene bHLH transcription fac-tor(MYC),prostaglandin-endoperoxide synthase 2(PTGS2),heme oxygenase 1(HMOX1),caspase-3(CASP3),and plasminogen activator urokinase receptor(PLAUR).The ROC results showed that the area under the curve values for all hub genes were greater than 0.8.MYC,PTGS2,CASP3,and PLAUR showed high mRNA and protein expression in rat MIRI,while there was no difference in mRNA and protein expression for HMOX1.The literature review revealed that among the 5 hub genes,only PLAUR has not been reported to be involved in MIRI.The GSEA results for PLAUR indicat-ed that its functional enrichment mainly focused on pathways such as NOD-like receptor signaling pathway,P53 signaling pathway,Toll-like receptor signaling pathway,apoptosis,and fatty acid metabolism.CONCLUSION:MYC,PTGS2,CASP3,HMOX1,and PLAUR are involved in the pathological process of MIRI.PLAUR is a potential hub gene that can mediate MIRI by regulating pathways such as NOD like receptor signaling,P53 signaling,Toll like receptor signaling,cell apoptosis,and fatty acid metabolism.The results can provide reference for further investigation into the molecular mechanisms and therapeutic targets of MIRI.
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ObjectiveTo investigate the effects of flavanomarein on the transcriptome of small intestinal organoids in insulin-resistant mice. MethodFirstly， small intestinal organoids of C57BL/6J and db/db mice were established. Ki-67 and E-cadherin expression was determined by immunofluorescence. Small intestinal organoids were divided into the following three groups： C57BL/6J mouse small intestinal organoids as the normal control group， db/db mouse small intestinal organoids as the model group （IR group）， and db/db mouse small intestinal organoids treated with flavanomarein as the administration group （FM group）. Western blot was used to detect the expression of glucagon-like peptide-1（GLP-1） protein on the small intestinal organoids of the three groups. Finally， transcriptome sequencing was performed on samples from the three groups. ResultOn the 6th day of small intestine organoids culture， a cyclic structure was formed around the lumen， and a small intestine organoids culture model was preliminarily established. Immunofluorescence detection showed that ki-67 and E-cadherin were expressed in small intestinal organoids. Western blot results showed that the expression of GLP-1 protein was increased by flavanomarein. In the results of differential expressed gene （DEG） screening， there were 1 862 DEGs in the IR group as compared with the normal control group， and 2 282 DEGs in the FM group as compared with the IR group. Through protein-protein interaction（PPI） network analysis of the DEGs of the two groups， 10 Hub genes， including Nr1i3， Cyp2c44， Ugt2b1， Gsta1， Gstm2， Ptgs1， Gstm4， Cyp2c38， Cyp4a32， and Gpx3， were obtained. These genes were highly expressed in the normal control group， and their expression was reduced in the IR group. After the intervention of flavanomarein， the expression of the above genes was reversed. ConclusionFlavanomarein may play its role in improving insulin resistance by reversing the expression levels of 10 Hub genes， including Nr1i3， Cyp2c44， Ugt2b1， Gsta1， Gstm2， Ptgs1， Gstm4， Cyp2c38， Cyp4a32， and Gpx3.
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Objective To investigate the expression of differential genes in testicular tissue of patients with obstructive and non-ob-structive azoospermia by bioinformatics,and provide new markers for the diagnosis of non-obstructive azoospermia(NOA).Methods The microarray data of azoospermia related genes(GSE45885 and GSE9210)were downloaded from the gene expression comprehensive database(GEO)and analyzed online by GEO2R,and the NOA related differentially expressed genes(DEGs)were obtained.The common DEGs were determined by using Wayne's intersection.GO annotation and KEGG enrichment analysis of DEGs were carried out by using R software.The DEGs-related protein interaction network(PPI)was constructed with STRING.Then,the most significant Hub gene of NOA was screened out by Cytoscape software and visualization was performed.The diagnostic value of Hub gene for NOA was estimated by the receiver operating characteristics(ROC)curve and verified in GSE145467 data set.Results A total of 83 DEGs were obtained,of which 78 were down-regulated and 5 were up-regulated.GO enrichment analysis showed that DEGs was involved in biological processes(BP),including the development and differentiation of sperm cells,development of germ cells,assembly of motor cilia,etc.Cell composition(CC)mainly included sperm flagella,motor cilia,acrosome vesicles,spermatogenic nuclei,etc.Molecular function(MF)mainly included structural components,protein binding,heat shock protein binding and so on,which endowed com-pressive strength for extracellular matrix.KEGG-related pathways were involved in longevity regulation pathways,cell cycle and apopto-sis,and meiosis of oocytes in multiple species.The five Hub genes closely related to NOA,including SPAG5,CCNB2,AURKC,NCAPH and PTTG1,were screened by PPI network.The ROC curve showed that all the five Hub genes were potential genetic markers of NOA.Conclusion SPAG5,CCNB2,AURKC,NCAPH and PTTG1 genes may play the key role in the development of NOA and may be used as the new biomarkers for NOA.
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Objective To investigate the causes of infertility and its pathological mechanism in female SD rats with spontaneous dwarfism(short stature rat,SSR).Methods Adult wildtype and SSR female SD rats were used in this study.A vaginal smear was used to observe changes in the motile cycle.Ovulation promotion was compared using the simultaneous estrus supernumerary ovulation method.Ovarian and uterine weight and body weight,and ovarian and uterine indices were measured.AMH,E2,FSH,LH,and FSH/LH levels in serum were measured.Transcriptome sequencing of ovarian tissues was performed to analyze gene expression differences.Results No abnormalities were observed in the estrous cycle of SSR female rats.The body weight of SSR female rats was significantly lower than that of wildtype rats,and their ovarian and uterine indices were significantly higher than that of wildtype rats.The mean number of ovulations was significantly higher in wildtype rats than in SSR female infertile rats(P<0.001).Serum AMH(P<0.01)and E2(P<0.05)levels were significantly higher in wildtype rats than in SSR female infertile rats,and serum levels of FSH,LH,and FSH/LH(P<0.05)were significantly lower in SSR infertile females than in SSR infertile rats,while PROG showed no significant difference.Transcriptome sequencing yielded 250 differentially expressed genes,including 190 upregulated and 60 downregulated genes.p53 signaling pathway and cytokine-cytokine receptor interaction.The MCC,MNC,EPC,and degree calculations of the CytoHubba plug-in were used to screen the top 10 significant nodes.The intersection was used to finally obtain nine hub genes,namely Cxcl1,Cxcl2,IL1a,IL1b,Cd80,Mmp13,Mmp8,Fgf3,and Ptgs2.Conclusions Infertility in SSR female rats may be related to a decreased ovarian reserve function and poor ovarian response.Cxcl1,Cxcl2,IL1a,IL1b,Cd80,Mmp13,Mmp8,Fgf3,and Ptgs2 were associated with infertility,laying a theoretical foundation to further explore infertility mechanisms.
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OBJECTIVES@#The high morbidity and mortality of colorectal cancer (CRC) have posed great threats to human health. Circular RNA (circRNA) and microRNA (miRNA), acting as competing endogenous RNAs (ceRNAs), have been found to play vital roles in carcinogenesis. This paper aims to construct a circRNA/miRNA/mRNA regulatory network so as to explore the molecular mechanism of CRC.@*METHODS@#The sequencing data of circRNA from CRC were obtained from Gene Expression Omnibus (GEO). The differential circRNA was screened and its structure was identified by Cancer-specific CircRNA Database (CSCD); the sequencing data of miRNA and messenger RNA (mRNAs) were downloaded from The Cancer Genome Atlas (TCGA) database and the differentially expressed genes were screened; the corresponding miRNA of differential circRNAs were predicted by CircInteractome database; DIANA, Miranda, PicTar, and TargetScan databases were used to predict the target genes of different miRNAs; the target genes from Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were enriched by R language; String database combined with Cytoscape 3.7.2 software was used to construct protein-protein interaction (PPI) network and hub genes were screened; the expressions of mRNAs in the Top10 hub genes were verified in CRC. The network diagrams of circRNAs/miRNAs/mRNAs and circRNAs/miRNAs/Top10 hub mRNAs were constructed by Cytoscape3.7.2. Real-time PCR was used to examine the expression levels of hsa_circRNA_0065173, hsa-mir-450b, hsa-mir-582, adenylate cyclase 5 (ADCY5), muscarinic acetylcholine receptor M2 (CHRM2), cannabinoid receptor 1 (CNR1), and lysophosphatidic acid receptor 1 (LPAR1) in the CRC tissues and the adjacent normal tissues.@*RESULTS@#A total of 14 differential circRNAs were identified, and 8 were found in CSCD; 34 miRNAs targeted by circRNAs were obtained. The PPI network was constructed, and the Top10 hub genes were identified, which were CHRM2, melanin concentrating hormone receptor 2 (MCHR2), G-protein gamma 3 subunit (GNG3), neuropeptide Y receptor Y1 (NPY1R), CNR1, LPAR1, ADCY5, adenylate cyclase 2 (ADCY2), gamma 7 (GNG7) and chemokine 12 (CXCL12), respectively. The expressions of Top 10 hub genes were also verified, and the results showed that the Top 10 hub genes were down-regulated in CRC; the constructed network diagram showed that hsa_circRNA_0065173 may regulate ADCY5, CHRM2, and Hsa-mir-450b by modulating hsa-mir-450b and hsa-mir-582. CNR1 and LPAR1 genes might serve as potentially relevant targets for the treatment of CRC. Real-time PCR results showed that the expression levels of hsa_circRNA_0065173, ADCY5, CHRM2, CNR1 and LPAR1 in the CRC tissues were significantly reduced compared with the adjacent normal tissues (all P<0.05); the expression levels of hsa-mir-450b and hsa-miR-582 were significantly increased (both P<0.05).@*CONCLUSIONS@#In this study, a potential circRNAs/miRNAs/mRNAs network is successfully constructed, which provides a new insight for CRC development mechanism through ceRNA mediated by circRNAs.
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Humanos , Neoplasias Colorrectales/genética , Biología Computacional/métodos , Redes Reguladoras de Genes , MicroARNs/genética , ARN Circular/genética , ARN Mensajero/genéticaRESUMEN
This study was designed to obtain recombinant human thioredoxin (rhTXN) by gene cloning and prokaryotic expression, and evaluated its therapeutic effect in the mouse ulcerative colitis (UC) model induced by dextran sulfate sodium (DSS). The human thioredoxin gene TXN was cloned from the cDNA of Jurkat cells. The recombinant expression plasmid pCold TF-rhTXN was constructed by restriction enzyme digestion. After expression in E. coli BL21 (DE3), recombinant human thioredoxin was purified by a nickel column. Intact rhTXN recombinant protein was obtained after removal of the fusion partner-tag by enzyme digestion and the activity of disulfide reductase was detected by the insulin reduction method. The animal experiments in this study were performed in accordance with the ethical guidelines of the Laboratory Animal Welfare Ethical Review Committee of Nanjing University. Experiment ulcerative colitis was induced by providing mice with sterilized drinking water which contained 3% DSS. rhTXN was injected intraperitoneally. The therapeutic effect was studied by weight change, colon length and HE (hematoxylin and eosin) stained sections. In vivo imaging was used to study the targeting of rhTXN to DSS mice. The GSE107499 data set of GEO database was used to screen the hub genes at the lesional sites of UC and study the correlation with TXN. The experimental results showed that rhTXN was successfully expressed and purified with disulfide reductase activity. rhTXN (100 μg·kg-1) had a significant therapeutic effect on maintaining the weight change of mice (P = 0.000 5) and reducing intestinal injury (P < 0.000 1), and had a colon targeting effect on DSS mice. In GSE107499 data set, TXN in inflammatory sites of UC patients was significantly down regulated (P < 0.01) and negatively correlated with hub gene CD40 (P < 0.01) and positively correlated with hub gene fibronectin 1 (FN1) (P < 0.01). In this study, biologically active rhTXN was successfully prepared and proved to have a promising therapeutic effect on the DSS mouse model, and TXN gene was significantly correlated with the UC hub genes CD40 and FN1.
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Background Hexavalent chromium [Cr(VI)] can induce malignant transformation of lung epithelial cells, but its molecular mechanism is still unclear. Objective This study aims to explore the key genes of Cr(VI)-induced malignant transformation of lung epithelial cells and the mechanism of the transformation by bioinformatics analysis. Methods High-throughput gene expression profile data related to Cr(VI)-induced toxic effect was downloaded from the Gene Expression Omnibus(GEO) database, and the co-expressed genes were obtained by the intersection of differentially expressed genes in each dataset. DAVID 6.8 was used to analyze the function enrichment of gene ontology(GO) and Kyoto Encyclopedia of Genes and Genomes(KEGG) pathways of the selected differential expression genes. STRING, and Cytoscape 3.8.2 were applied to construct and visualize the protein-protein interaction network. The expressions of Hub genes in lung tumor were obtained by GEPIA2. Results A total of 234 differentially expressed genes were screened out from the GSE24025 and GSE36684 datasets, among which 99 genes were up-regulated while 135 genes were down-regulated. The results of GO and KEGG analyse were mainly concentrated in cell adhesion, negative regulation of cell proliferation, and transcription disorders. A rotein-protein interaction network was generated by STRING database and Cytoscape software. Four functional modules with high scores and 6 Hub genes were finally retrieved. The expression trend of FBLN1 in lung cancer subtypes was consistent with the results of transcriptome screening. Conclusion Cr(VI) exposure causes the differential expression of multiple genes in lung epithelial cells, involving cell morphology, movement, survival fate, phenotype function and signal pathway related to cancer development. FBLN1 may be the critical gene related to malignant cytopathy.
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【Objective】 To make bioinformatics analysis of inflammatory cardiomyopathy so as to screen out hub genes related to etiology and therapeutic targets. 【Methods】 Differential expression analysis of inflammatory cardiomyopathy gene chip data from Gene Expression Omnibus （GEO） Database was carried out via GEO2R tool. Protein-protein interaction（PPI）network and hub genes identification were realized by String database and CytoHubba. GO and KEGG enrichment analysis for functional annotation and pathway analysis of hub genes were conducted by R language. Web-based enrichment analysis platform Enrichr and Drug Signatures database were applied to screen out candidate drugs targeting hub genes for inflammatory cardiomyopathy. 【Results】 The 149 DEGs were statistically significant， among which 44 were upregulated and 105 were downregulated. To identify hub genes， PPI network consisting of 37 nodes and 116 edges was constructed， and 16 hub genes were NDUFB7， POLR2L， NDUFS7， UQCR11， NDUFA13， NDUFA2， PHPT1， NDUFB10， UBA52， ATP5D， NDUFA3， COX6B1， POLR2J， COX4I2， AURKAIP1 and MRPL41. Hub genes were enriched to 113 different GO terms， and the most significant terms were mitochondrial ATP synthesis coupled electron transport， respiratory electron transport chain， oxidative phosphorylation， respiratory chain， mitochondrial inner membrane， NADH dehydrogenase activity and oxidoreductase activity. DEGs were enriched to 13 different signal pathways， including oxidative phosphorylation， non-alcoholic fatty liver disease， diabetic cardiomyopathy， and cardiac muscle contraction. We screened out candidate drugs targeting hub genes， namely， metformin hydrochloride， clindamycin， and hydralazine. 【Conclusion】 Hub genes screened out by decoding the expression profiles are convolved in the etiology and mechanism of inflammatory cardiomyopathy， which might serve as latent therapeutic targets and benefit patients with inflammatory cardiomyopathy.
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@#Objective Focusing on four types acute myeloid leukemia ( AML) fusion oncogenes，so as to explore the network difference with time series expression data and further identify important genes in networks． Methods Gene network difference analysis was conducted while focusing on the global attributes of the union network． The CompNet neighborhood similarity index ( CNSI) was adopted to assess network similarity．“fast-greedy”algorithm was used to detect communities based on the union network，and further identify hub genes． Ｒesults The CNSI value between NUP98-HOXA9-3 d and NUP98-HOXA9-8 d was 0． 73，while AML1-ETO-6 h and PML-ＲAＲA-6 h was 0．25． We identified ten AML associated genes and sev- en of them ( TNF，VEGFA，EP300，EGF，CD44，PTGS2，SMAD3) were reported in the literature． Conclu- sions The network difference analysis revealed the pattern and heterogeneity of AML gene expression change across different time points，and further provided target genes for efficient treatment of AML with different types of fusion oncogenes．
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Objective: To identify hub genes and key pathways in breast cancer by bioinformatics analysis that integrated gene expression data with clinical survival analysis. Methods: Three gene expression profilings downloaded from Gene Expression Omnibus (GEO) were used to identify differentially expressed genes (DEGs) in breast cancer. Kaplan-Meier plotter was used to identify the DEGs that were significantly associated with overall survival in breast cancer. Gene Ontology (GO) function analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed. Next, hub genes were identified from the protein-protein interaction (PPI) network. Oncomine and the Human Protein Atlas (HPA) database were used to validate the expression of the hub genes. The expressions of hub genes in MDA-MB-231 cells and MCF-10A cells were detected by quantitative real-time PCR (qPCR). Results: Among the DEGs, 262 genes were significantly correlated with overall survival of breast cancer patients. The results of GO functional analysis and KEGG pathway analysis showed that these genes were associated with nuclear division, cell division and chromosome segregation, and were mainly enriched on the pathways such as cell cycle, FoxO signaling pathway and oocyte meiosis. PPI network construction identified ten hub genes. They were all highly expressed in breast cancer, which were validated by the databases. The results of qPCR showed that 8 out of 10 hub genes were highly expressed in breast cancer cells. Conclusion: The hub genes and key pathways involved in the development of breast cancer are identified by survival-based bioinformatics analysis, which are mainly associated with cell cycle regulation and cell division.
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@#Objective: To identify the specific Hub genes in young hepatocellular carcinoma (HCC) patients, and to explore their biological and clinical significance by using bioinformatic methods. Methods: The data information of HCC and normal tissues of young (≤40 years old at diagnosis) and old (＞40 years old at diagnosis) HCC patients were obtained from GEO chip data set GSE45267. The differentially expressed genes (DEGs) in HCC tissues as comparing to normal tissues in the two groups were screened by using GEO2R and Venn chart software. The Protein-Protein Interaction (PPI) network of the specific DEGs in young group was constructed by bioinformatics tools STRING and Cytoscape to screen the Hub genes and significant modules. The Hub genes were verified by GEPIA database, and the overall survival time was analyzed by Kaplan-Meier. Finally, Gene Ontology (GO) Enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were used to analyze the DEGs specific to young group and the common DEGs of the two groups by DAVID. Results: Finally, 117 up-regulated and 179 down-regulated DEGs specific to the young group were screened out, and PPI network screened 10 most connected genes as Hub genes, among which 7 Hub genes were concentrated in the first module. Six up-regulated Hub genes, including TYMS, CDC6, BUB1, TPX2, OIP5 and KIF23, were indicated to associate with the poor prognosis in young HCC patients by GEPIA and Kaplan-Meier analysis. GO function and KEGG pathway analyses showed that the DEGs specific to young HCC patients were mainly involved in biological processes such as ATP binding, and were mainly enriched in S phase of cell cycle; while the common DEGs of two groups were mainly involved in biological processes such as cyclooxygenase P450 and cell division, and were mainly enriched in the G2/M phase of the cell cycle. Conclusion: In this study, 6 up-regulated DEGs specific to young group that suggested poor prognosis were identified, which may be the potential therapeutic and prognostic targets for young patients with HCC.
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@# Objective: To investigate the differentially expressed genes (DEGs) associated with the occurrence and development of breast cancer and to screen the molecular markers for breast cancer by bioinformatic analysis. Methods: Three breast cancer microarray datasets were downloaded from Gene Expression Omnibus (GEO) database. GEO2R was used to identify DEGs. The differentially co-expressed genes in the three datasets were screened by Venn diagram. GO function enrichment analysis and KEGG signal pathway analysis were performed using DAVID. The protein-protein interaction (PPI) network of DEGs was constructed using STRING. The most important modules in the PPI network were analyzed using Molecular Complex Detection (MCODE), and the genes with degree≥10 were identified as Hub genes. Hierarchical clustering analysis of hub genes was conducted using UCSC Cancer Genomics Brower. The survival curve and the co-expression network of hub genes were constructed using cBioPortal. Results: A total of 65 DEGs were screened from the three data sets. Eight hub genes, CTNNB1, CDKN1A, CXCR4, RUNX3, CASP8, TNFRSF10B, CFLAR and NRG1, were finally obtained, which exerted important roles in cell adhesion, proliferation and apoptosis regulation etc. Clustering analysis showed that the differential expression levels of CTNNB1, CFLAR, NRG1 and CXCR4 were associated with the occurrence of breast cancer. The overall survival analysis indicated that the patients with elevated CDKN1Aexpression had significantly shorter overall survival time (P<0.01). Conclusion: The hub genes identified in the present study can be used as molecular markers for breast cancer, providing candidate targets for diagnosis, treatment and prognostic prediction of breast cancer.
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Biclustering is an increasingly used data mining technique for searching groups of co-expressed genes across the subset ofexperimental conditions from the gene-expression data. The group of co-expressed genes is present in the form of variouspatterns called a bicluster. A bicluster provides significant insights related to the functionality of genes and plays animportant role in various clinical applications such as drug discovery, biomarker discovery, gene network analysis, geneidentification, disease diagnosis, pathway analysis etc. This paper presents a novel unsupervised approach ‘COmprehensiveSearch for Column-Coherent Evolution Biclusters (COSCEB)’ for a comprehensive search of biologically significantcolumn-coherent evolution biclusters. The concept of column subspace extraction from each gene pair and LongestCommon Contiguous Subsequence (LCCS) is employed to identify significant biclusters. The experiments have beenperformed on both synthetic as well as real datasets. The performance of COSCEB is evaluated with the help of key issues.The issues are comprehensive search, Deep OPSM bicluster, bicluster types, bicluster accuracy, bicluster size, noise,overlapping, output nature, computational complexity and biologically significant biclusters. The performance of COSCEBis compared with six all-time famous biclustering algorithms SAMBA, OPSM, xMotif, Bimax, Deep OPSM- and UniBic.The result shows that the proposed approach performs effectively on most of the issues and extracts all possible biologicallysignificant column-coherent evolution biclusters which are far more than other biclustering algorithms. Along with theproposed approach, we have also presented the case study which shows the application of significant biclusters for hub geneidentification.
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Objective To identify potential molecule targets of type 2 diabetes using weighted gene co-expression network analysis. Methods Microarray data of type 2 diabetes (GSE38642) were downloaded from Gene Expression Omnibus of NCBI, including 9 type 2 diabetic patients, 9 pre-diabetic patients ( 6%≤HbA1C<6.5%), and 31 normal controls (HbA1C<6%). Using weighted gene co-expression network analysis (WGCNA) package in R, the weighted gene co-expression network was built and significant modules related to clinical traits were identified. Then, functional and pathway enrichment analysis were conducted for genes in the most significant modules using GeneAnswers package in R. Upstream transcription factor enrichment analysis were conducted using TRANSFAC database. The hub genes and upstream transcription factors were selected as potential molecule targets of type 2 diabetes. Results 34 modules were identified in the co-expression network. Green module was positive correlated with HbA1C(R＝0.47, P＝1×10－4). The enriched functions were cell adhesion, extracellular matrix disassembly, etc. The enriched KEGG pathways were Pancreatic secretion, Focal adhesion, etc. ITGA6, ZAK, and YBX3 are hub genes of Green module. Brown module was negative correlated with HbA1C(R＝0.46, P＝1×10－4). The enriched functions were synapse, transmembrane transporter activity, etc. The enriched KEGG pathways were Insulin secretion, Dopaminergic synapse, etc. The upstream transcription factors PAX6, REST, and PDX1 of Brown module might play important roles. 30 hub genes, including SLC4A10, ELAVL4, and SYT14, were identified in Brown module. The relationships between these genes and type 2 diabetes were confirmed by previously published studies. Conclusion Important genes related to type 2 diabetes can be filtered out from transcriptome profiles using gene co-expression analysis. Our finding might provide a novel insight into the underlying molecular mechanism of type 2 diabetes.