ABSTRACT
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.
ABSTRACT
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.
ABSTRACT
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.
Subject(s)
Humans , Colorectal Neoplasms/genetics , Computational Biology/methods , Gene Regulatory Networks , MicroRNAs/genetics , RNA, Circular/genetics , RNA, Messenger/geneticsABSTRACT
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.
ABSTRACT
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.
ABSTRACT
@#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．
ABSTRACT
@# 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.
ABSTRACT
@#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.
ABSTRACT
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.
ABSTRACT
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.