[Bioinformatic analysis of differentially expressed genes and Chinese medicine prediction for ulcerative colitis].
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The aim of this paper was to analyze the microarray data between ulcerative colitis(UC) patients and healthy people by bioinformatics technology, screen the differentially expressed genes of UC, and predict the potential Chinese medicines for UC. The GSE36807 gene expression profile was downloaded from the gene expression database(GEO) and the differentially expressed(both up-regulated and down-regulated) genes(DEGs) were analyzed by using R language software. The core genes in the DEGs were obtained by using String database, Cytoscape software and its plug-in analysis, and the gene ontology(GO) and Kyoto encyclopedia of genes and genomes(KEGG) were used to analyze the core genes. Moreover, the core genes and the medical ontology information retrieval platform(Coremine Medical) were mapped to each other to screen the traditional Chinese medicines and its active ingredients for treating UC. A total of 648 DEGs were screened, including 397 up-regulated genes and 251 down-regulated genes. Up-regulation of DEGs yielded 15 core genes including CXCL8, IL1 B, MMP9, CXCL1, CXCL10, CXCL9, CXCL2, CXCL5, TIMP1, CXCL11, STAT1,LCN2, IL1 RN, MMP1 and IDO1. Their biological processes and pathways were mainly enriched in interleukins, chemokine ligands and cytokines, chemokine-mediated signaling pathways, and were closely related to inflammatory responses, defense responses, cell chemotaxis, secretory granules, IL17 signaling pathways, Toll-like receptor signaling pathway, NOD-like receptor signaling pathway, and TNF signaling pathway. Potential Chinese medicines for the treatment of UC include Curcumae Longae Rhizoma, Coptidis Rhizoma, Scutellariae Radix, Dendrobii Caulis, Sanguisorbae Radix, Phellodendri Chinensis Cortex, Bletillae Rhizoma and Atractylodis Rhizoma. The analysis of DEGs and core genes could promote our understanding on pathogenesis of UC. This study provides potential gene targets and research ideas for the development of new drugs of Chinese medicine intervention for UC.Keywords:
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CXCL5
MMP1
CXCL9
Nasopharyngeal carcinoma (NPC) is a rare but highly aggressive tumor that is predominantly encountered in Southeast Asia and China in particular. Aside from radiotherapy, no effective therapy that specifically treats NPC is available, including targeted drugs. Finding more sensitive biomarkers is important for new drug discovery and for evaluating patient prognosis.mRNA expression datasets from the Gene Expression Omnibus database (GSE53819, GSE64634, and GSE40290) were selected. After all samples in each dataset were subjected to quality control using principal component analyses, the qualified samples were used for additional analyses. The genes that were significantly expressed in each dataset were intersected to identify the most significant of these. Gene functional enrichment analyses were performed on these genes, using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes analyses. The protein-protein interaction network of selected genes was analyzed using the Search Tool for the Retrieval of Interacting Genes database. Significantly, differentially expressed genes were further verified with two RNA-seq datasets (GSE68799 and GSE12452), as well as in clinical samples.In all, 34 (8 upregulated genes and 26 downregulated) genes were identified as significantly differentially expressed. The immune response and the regulation of cell proliferation were the most enriched biological GO terms. Using reverse transcription quantitative real-time PCR (RT-qPCR), the genes MMP1, AQP9, and TNFAIP6 were detected to be upregulated, and FAM3D, CR2, and LTF were downregulated in NPC tissue samples.This study provides information on the genes that may be involved in the development of NPC and suggests possible druggable targets and biomarkers for diagnosing and evaluating the prognosis of NPC.
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The aim of this paper was to analyze the microarray data between ulcerative colitis(UC) patients and healthy people by bioinformatics technology, screen the differentially expressed genes of UC, and predict the potential Chinese medicines for UC. The GSE36807 gene expression profile was downloaded from the gene expression database(GEO) and the differentially expressed(both up-regulated and down-regulated) genes(DEGs) were analyzed by using R language software. The core genes in the DEGs were obtained by using String database, Cytoscape software and its plug-in analysis, and the gene ontology(GO) and Kyoto encyclopedia of genes and genomes(KEGG) were used to analyze the core genes. Moreover, the core genes and the medical ontology information retrieval platform(Coremine Medical) were mapped to each other to screen the traditional Chinese medicines and its active ingredients for treating UC. A total of 648 DEGs were screened, including 397 up-regulated genes and 251 down-regulated genes. Up-regulation of DEGs yielded 15 core genes including CXCL8, IL1 B, MMP9, CXCL1, CXCL10, CXCL9, CXCL2, CXCL5, TIMP1, CXCL11, STAT1,LCN2, IL1 RN, MMP1 and IDO1. Their biological processes and pathways were mainly enriched in interleukins, chemokine ligands and cytokines, chemokine-mediated signaling pathways, and were closely related to inflammatory responses, defense responses, cell chemotaxis, secretory granules, IL17 signaling pathways, Toll-like receptor signaling pathway, NOD-like receptor signaling pathway, and TNF signaling pathway. Potential Chinese medicines for the treatment of UC include Curcumae Longae Rhizoma, Coptidis Rhizoma, Scutellariae Radix, Dendrobii Caulis, Sanguisorbae Radix, Phellodendri Chinensis Cortex, Bletillae Rhizoma and Atractylodis Rhizoma. The analysis of DEGs and core genes could promote our understanding on pathogenesis of UC. This study provides potential gene targets and research ideas for the development of new drugs of Chinese medicine intervention for UC.
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Abstract Background: Pancreatic adenocarcinoma (PAAD) is a kind of highly malignant tumor and lacks early diagnosis method and effective treatment. Tumor microenvironment (TME) is of great importance for the occurrence and development of PAAD. Thus, a comprehensive overview of genes and tumor-infiltrating immune cells (TICs) related to TME dynamic changes conduce to develop novel therapeutic targets and prognostic indicators. Methods: We used MAlignant Tumors using Expression data (ESTIMATE) algorithm to analyze the transcriptome RNA-seq data of 182 PAAD cases on The Cancer Genome Atlas (TCGA) platform. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), protein–protein interaction (PPI) network, COX regression analysis and gene set enrichment analysis (GSEA) were carried out to get the hub genes related to the prognosis of PAAD patients. These core genes were validated in GEPIA. CXCL10 expression as a poor prognostic indicator was validated in GEO database. Finally, CIBERSORT algorithm was applied to understand the status of TICs. Results: A total of 715 up-regulated differential expression genes (DEGs) and 57 down-regulated DEGs were found simultaneously in stromal and immune groups. These DEGs were mainly enriched in immune recognition, activation and response processes. CD4, CXCL12, CXCL10, CCL5 and CXCL9 were the top five core genes. Then, the validation of these genes showed that CD4, CXCL10, CXCL5, CXCL9 were up-regulated in PAAD. Among the core genes, CXCL10 had a negative correlation with the survival time of PAAD patients. CD8+ T cells, CD4+ T cells memory activated, macrophages M1 had positive correlation of CXCL10 expression, whereas regulatory T cells (Tregs), macrophages M0 and B cells memory had negative correlation. Conclusion: We generated a series of genes related to TME with prognostic implications and TICs in PAAD, which have the potential to be novel immunotherapy targets and prognostic markers. The data showed that CXCL10 was favorable as a poor prognostic indicator in PAAD patients.
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Differential gene expression analysis using RNA-seq data is a popular approach for discovering specific regulation mechanisms under certain environmental settings. Both gene ontology (GO) and KEGG pathway enrichment analysis are major processes for investigating gene groups that participate in common biological responses or possess related functions. However, traditional approaches based on differentially expressed genes only detect a few significant GO terms and pathways, which are frequently insufficient to explain all-inclusive gene regulation mechanisms. Transcriptomes of survivin (birc5) gene knock-down experimental and wild-type control zebrafish embryos were sequenced and assembled, and a differential expression (DE) gene list was obtained for traditional functional enrichment analysis. In addition to including DE genes with significant fold-change levels, we considered additional associated genes near or overlapped with differentially expressed long noncoding RNAs (DE lncRNAs), which may directly or indirectly activate or inhibit target genes and play important roles in regulation networks. Both the original DE gene list and the additional DE lncRNA-associated genes were combined to perform a comprehensive overrepresentation analysis. In this study, a total of 638 DE genes and 616 DE lncRNA-associated genes (lncGenes) were leveraged simultaneously in searching for significant GO terms and KEGG pathways. Compared to the traditional approach of only using a differential expression gene list, the proposed method of employing DE lncRNA-associated genes identified several additional important GO terms and KEGG pathways. In GO enrichment analysis, 60% more GO terms were obtained, and several neuron development functional terms were retrieved as complete annotations. We also observed that additional important pathways such as the FoxO and MAPK signaling pathways were retrieved, which were shown in previous reports to play important roles in apoptosis and neuron development functions regulated by the survivin gene. We demonstrated that incorporating genes near or overlapped with DE lncRNAs into the DE gene list outperformed the traditional enrichment analysis method for effective biological functional interpretations. These hidden interactions between lncRNAs and target genes could facilitate more comprehensive analyses.
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Purpose: Based on the previous 3 well-defined subtypes of gastric adenocarcinoma (invasive, proliferative and metabolic), we aimed to find potential biomarkers and biological features of each subtype. Methods:The genome-wide co-expression network of each subtype of gastric cancer was firstly constructed.Then, the functional modules in each genome-wide co-expression network were divided.Next, the key genes were screened from each functional module.Finally, the enrichment analysis was performed on the key genes to mine the biological features of each subtype.Comparative analysis between each pair of subtypes was performed to find the common and unique features among different subtypes.Results: A total of 207 key genes were identified in invasive, 215 key genes in proliferative, and 204 key genes in metabolic subtypes.Most key genes in each subtype were unique and new findings compared with that of the existing related researches.The GO and KEGG enrichment analyses for the key genes of each subtype revealed important biological features of each subtype.Conclusions: For a subtype, most identified key genes and important biological features were unique, which means that the key genes can be used as the potential biomarker of a subtype, and each subtype of gastric cancer might have different occurrence and development mechanisms.Thus, different diagnosis and therapy methods should be applied to the invasive, proliferative and metabolic subtypes of gastric cancer.
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RNA-seq data of rectal adenocarcinoma (READ) were analyzed with bioinformatics tools to unveil potential biomarkers in the disease.RNA-seq data of READ were downloaded from The Cancer Genome Atlas (TCGA) database. Differential analysis was performed with package edgeR. False discovery rate (FDR) < 0.05 and |log2 (fold change)|>1 were set as cut-off values to screen out differentially expressed genes (DEGs). Gene coexpression network was constructed with package Ebcoexpress. Gene Ontology enrichment analysis was performed for the DEGs in the gene coexpression network with DAVID online tool. Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis was also performed for the genes with KOBASS 2.0.A total of 620 DEGs, 389 up-regulated genes, and 231 down-regulated genes, were identified from 163 READ samples and 9 normal controls. A gene coexpression network consisting of 71 DEGs and 253 edges were constructed. Genes were associated with ribosome and focal adhesion functions. Three modules were identified, in which genes were involved in muscle contraction, negative regulation of glial cell proliferation and extracellular matrix organization functions, respectively. Several critical hub genes were disclosed, such as RPS2, MMP1, MMP11 and FAM83H. Thirteen relevant small molecule drugs were identified, such as scriptaid and spaglumic acid. A total of 8 TFs and 5 miRNAs were acquired, such as MYC, NFY, STAT5A, miR-29, miR-200 and miR-19.Several critical genes and relevant drugs, TFs and miRNAs were revealed in READ. These findings could advance the understanding about the disease and benefit therapy development.
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Abstract Background : Oral tongue squamous cell carcinoma (OTSCC) is the most common malignant tumor of the oral cavity. The aim of this study was to use text mining and data analysis to discover some existing drugs that target genes and to explore potential therapeutic drugs for OTSCC. Methods : We used the text mining tool pubmed2ensembl to extract genes associated with OTSCC, and two datasets (GSE30784, GSE23558) from Gene Expression Omnibus (GEO) were used for the data analysis. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed for the intersection of the three gene sets. Protein-protein interaction (PPI) network was constructed by STRING, gene module analysis was performed using the Molecular Complex Detection (MCODE), a plug-in in Cytoscape. Lastly, a database of drug-gene interactions was used to identify significant genes to explore potential drugs for the treatment of OTSCC. Results : We produced 403 unique genes associated with oral tongue squamous cell carcinoma through text mining. GSE23558 and GSE 30784 obtained 1637 and 1159 differentially expressed genes (DEGs) through data analysis, respectively. A total of 28 genes were obtained from the intersection gene sets, including 20 up-regulated genes and 8 down-regulated genes. We screened the most significant modules by using MCODE, among which 8 genes were associated with oral tongue squamous cell carcinoma as core genes. Eventually, nine drugs were found to target eight genes. Conclusions : In this study, PLAU, SERPINE1, MMP1, MMP3, MMP10, CXCL10, CXCL12, and SPP1 were potentially key genes involved in the treatment of OTSCC. Furthermore, 12 drugs were identified as potential therapeutic agents for oral tongue squamous cell carcinoma treatment and management
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Systemic juvenile idiopathic arthritis (sJIA) is a severe autoinflammatory disorder with a still not clearly defined molecular mechanism. To better understand the disease, we used scattered datasets from public domains and performed a weighted gene coexpression network analysis (WGCNA) to identify key modules and hub genes underlying sJIA pathogenesis. Two gene expression datasets, GSE7753 and GSE13501, were used to construct the WGCNA. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were applied to the genes and hub genes in the sJIA modules. Cytoscape was used to screen and visualize the hub genes. We further compared the hub genes with the genome-wide association study (GWAS) genes and used a consensus WGCNA to verify that our conclusions were conservative and reproducible across multiple independent datasets. A total of 5,414 genes were obtained for WGCNA, from which highly correlated genes were divided into 17 modules. The red module demonstrated the highest correlation with the sJIA module (r = 0.8, p = 3e-29), whereas the green-yellow module was found to be closely related to the non-sJIA module (r = 0.62, p = 1e-14). Functional enrichment analysis demonstrated that the red module was mostly enriched in the activation of immune responses, infection, nucleosomes, and erythrocytes, and the green-yellow module was mostly enriched in immune responses and inflammation. Additionally, the hub genes in the red module were highly enriched in erythrocyte differentiation, including ALAS2, AHSP, TRIM10, TRIM58, and KLF1. The hub genes from the green-yellow module were mainly associated with immune responses, as exemplified by the genes KLRB1, KLRF1, CD160, and KIRs. We identified sJIA-related modules and several hub genes that might be associated with the development of sJIA. Particularly, the modules may help understand the mechanisms of sJIA, and the hub genes may become biomarkers and therapeutic targets of sJIA in the future.
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Observing what phenotype the overexpression or knockdown of gene can cause is the basic method of investigating gene functions. Many advanced biotechnologies, such as RNAi, were developed to study the gene phenotype. But there are still many limitations. Besides the time and cost, the knockdown of some gene may be lethal which makes the observation of other phenotypes impossible. Due to ethical and technological reasons, the knockdown of genes in complex species, such as mammal, is extremely difficult. Thus, we proposed a new sequence-based computational method called k NNA-based method for gene phenotypes prediction. Different to the traditional sequence-based computational method, our method regards the multiphenotype as a whole network which can rank the possible phenotypes associated with the query protein and shows a more comprehensive view of the protein's biological effects. According to the prediction result of yeast, we also find some more related features, including GO and KEGG information, which are making more contributions in identifying protein phenotypes. This method can be applied in gene phenotype prediction in other species.
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Papillary thyroid carcinoma (PTC) is the most common type of thyroid cancer. Identifying characteristic genes of PTC are of great importance to reveal its potential genetic mechanisms. In this paper, we proposed a framework, as well as a measure named Normalized Centrality Measure (NCM), to identify characteristic genes of PTC. The framework consisted of four steps. First, both up-regulated genes and down-regulated genes, collectively called differentially expressed genes (DEGs), were screened and integrated together from four datasets, that is, GSE3467, GSE3678, GSE33630, and GSE58545; second, an interaction network of DEGs was constructed, where each node represented a gene and each edge represented an interaction between linking nodes; third, both traditional measures and the NCM measure were used to analyze the topological properties of each node in the network. Compared with traditional measures, more genes related to PTC were identified by the NCM measure; fourth, by mining the high-density subgraphs of this network and performing Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, several meaningful results were captured, most of which were demonstrated to be associated with PTC. The experimental results proved that this network framework and the NCM measure are useful for identifying more characteristic genes of PTC.
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