Identification of target gene of venous thromboembolism in patients with lymphoma via microarray analysis.
ABSTRACT: Patients with lymphoma are at high risk of developing venous thromboembolism (VTE). The purpose of the present study was to identify the target gene associated with VTE for patients with lymphoma. Microarray data was downloaded from the gene expression omnibus database (GSE17078), which comprised the control group, 27 normal blood outgrowth endothelial cell (BOEC) samples, and the case group, 3 BOEC samples of venous thrombosis with protein C deficiency. Differentially expressed genes (DEGs) were identified by the Limma package of R. Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway analyses were performed via the database for annotation, visualization and integrated discovery. Differentially coexpressed pairs were identified by the DCGL package of R. The subsequent protein-protein interaction (PPI) networks and gene coexpression networks were constructed by the Search Tool for the Retrieval of Interacting Genes/Proteins database, and were visualized by Cytoscape software. A total of 110 DEGs were obtained, including 73 upregulated and 37 downregulated genes. GO and KEGG pathway enrichment analyses identified 132 significant GO terms and 9 significant KEGG pathways. In total, 97 PPI pairs for PPI network and 309 differential coexpression pairs for the gene coexpression network were obtained. Additionally, the connective tissue growth factor (CTGF) gene was closely connected with other genes in the two networks. A total of 2 KEGG pathways were associated with VTE and CTGF may be the target gene of VTE in patients with lymphoma. The present study may identify the molecular mechanism of VTE, but additional clinical study is required to validate the results.
Project description:Purpose:The number of people with type 2 diabetes (T2D) is growing rapidly worldwide. Islet ?-cell dysfunction and failure are the main causes of T2D pathological processes. The aim of this study was to elucidate the underlying pathways and coexpression networks in T2D islets. Materials and methods:We analyzed the differentially expressed genes (DEGs) in the data set GSE41762, which contained 57 nondiabetic and 20 diabetic samples, and developed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Protein-protein interaction (PPI) network, the modules from the PPI network, and the gene annotation enrichment of modules were analyzed as well. Moreover, a weighted correlation network analysis (WGCNA) was applied to screen critical gene modules and coexpression networks and explore the biological significance. Results:We filtered 957 DEGs in T2D islets. Then GO and KEGG analyses identified that key pathways like inflammatory response, type B pancreatic cell differentiation, and calcium ion-dependent exocytosis were involved in human T2D. Three significant modules were filtered from the PPI network. Ribosome biogenesis, extrinsic apoptotic signaling pathway, and membrane depolarization during action potential were associated with the modules, respectively. Furthermore, coexpression network analysis by WGCNA identified 13 distinct gene modules of T2D islets and revealed four modules, which were strongly correlated with T2D and T2D biomarker hemoglobin A1c (HbA1c). Functional annotation showed that these modules mainly enriched KEGG pathways such as NF-kappa B signaling pathway, tumor necrosis factor signaling pathway, cyclic adenosine monophosphate signaling pathway, and peroxisome proliferators-activated receptor signaling pathway. Conclusion:The results provide potential gene pathways and underlying molecular mechanisms for the prevention, diagnosis, and treatment of T2D.
Project description:BACKGROUND:Gene alterations are crucial to the molecular pathogenesis of pancreatic cancer. The present study was designed to identify the potential candidate genes in the pancreatic carcinogenesis. METHODS:Gene Expression Omnibus database (GEO) datasets of pancreatic cancer tissue were retrieval and the differentially expressed genes (DEGs) from individual microarray data were merged. Gene Ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, protein-protein interaction (PPI) networks, and gene coexpression analysis were performed. RESULTS:Three GEO datasets, including 74 pancreatic cancer samples and 55 controls samples were selected. A total of 2325 DEGs were identified, including 1383 upregulated and 942 downregulated genes. The GO terms for molecular functions, biological processes, and cellular component were protein binding, small molecule metabolic process, and integral to membrane, respectively. The most significant pathway in KEGG analysis was metabolic pathways. PPI network analysis indicated that the significant hub genes including cytochrome P450, family 2, subfamily E, polypeptide 1 (CYP2E1), mitogen-activated protein kinase 3 (MAPK3), and phospholipase C, gamma 1 (PLCG1). Gene coexpression network analysis identified 4 major modules, and the potassium channel tetramerization domain containing 10 (KCTD10), kin of IRRE like (KIRREL), dipeptidyl-peptidase 10 (DPP10), and unc-80 homolog (UNC80) were the hub gene of each modules, respectively. CONCLUSION:Our integrative analysis provides a comprehensive view of gene expression patterns associated with the pancreatic carcinogenesis.
Project description:Purpose:Mantle cell lymphoma (MCL) is a rare and aggressive subtype of non-Hodgkin lymphoma that is incurable with standard therapies. The use of gene expression analysis has been of interest, recently, to detect biomarkers for cancer. There is a great need for systemic coexpression network analysis of MCL and this study aims to establish a gene coexpression network to forecast key genes related to the pathogenesis and prognosis of MCL. Methods:The microarray dataset GSE93291 was downloaded from the Gene Expression Omnibus database. We systematically identified coexpression modules using the weighted gene coexpression network analysis method (WGCNA). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analysis were performed on the modules deemed important. The protein-protein interaction networks were constructed and visualized using Cytoscape software on the basis of the STRING website; the hub genes in the top weighted network were identified. Survival data were analyzed using the Kaplan-Meier method and were compared using the log-rank test. Results:Seven coexpression modules consisting of different genes were applied to 5,000 genes in the 121 human MCL samples using WGCNA software. GO and KEGG enrichment analysis identified the blue module as one of the most important modules; the most critical pathways identified were the ribosome, oxidative phosphorylation and proteasome pathways. The hub genes in the top weighted network were regarded as real hub genes (IL2RB, CD3D, RPL26L1, POLR2K, KIF11, CDC20, CCNB1, CCNA2, PUF60, SNRNP70, AKT1 and PRPF40A). Survival analysis revealed that seven genes (KIF11, CDC20, CCNB1, CCNA2, PRPF40A, CD3D and PUF60) were associated with overall survival time (p < 0.05). Conclusions:The blue module may play a vital role in the pathogenesis of MCL. Five real hub genes (KIF11, CDC20, CCNB1, CCNA2 and PUF60) were identified as potential prognostic biomarkers as well as therapeutic targets with clinical utility for MCL.
Project description:Background:This study is aimed at investigating natriuretic peptide B (NPPB) coexpression genes and their pathways involved in heart failure (HF) among patients both with and without type 2 diabetes mellitus (T2DM). Methods:The microarray dataset GSE26887, containing 19 postischemic HF patients' peripheral blood samples (7 with T2DM and 12 without T2DM), was examined to detect the genes coexpressed with NPPB using the corr.test function in the R packet. Furthermore, using online analytical tools, we determined the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, Gene Ontology (GO) annotation, and protein-protein interaction (PPI) network of the coexpression genes. The modules and hub genes of the PPI network were then identified using the Cytoscape software. Results:In patients with T2DM, a total of 41 biological processes (BP), 20 cellular components (CC), 13 molecular functions (MF), and 41 pathways were identified. Furthermore, a total of 61 BPs, 16 CCs, 13 MFs, and 22 pathways in patients without T2DM were identified. In both groups of patients, 17 BPs, 10 CCs, 6 MFs, and 13 pathways were enriched. We also identified 173 intersectional coexpression genes (63 positively, 106 negatively, and 4 differently coexpressed in patients with and without T2DM, respectively) in both types of patients, which were enriched in 16 BPs, 8 CCs, 3 MFs, and 8 KEGG pathways. Moreover, the PPI network (containing 237 edges and 170 nodes) with the top module significantly enriched in 4 BPs (tricarboxylic acid metabolic process, citrate metabolic process, tricarboxylic acid cycle, and aerobic respiration) and 3 pathways (citrate cycle, malaria parasite metabolic pathway, and AGE-RAGE signaling pathway in diabetic complications) was constructed. DECR1, BGN, TIMP1, VCAN, and CTCF are the top hub genes. Conclusions:Our findings may elucidate the functions and roles of the NPPB gene in patients with postischemic HF and facilitate HF management.
Project description:The use of high-throughput techniques to generate large volumes of protein-protein interaction (PPI) data has increased the need for methods that systematically and automatically suggest functional relationships among proteins. In a yeast PPI network, previous work has shown that the local connection topology, particularly for two proteins sharing an unusually large number of neighbors, can predict functional association. In this study we improved the prediction scheme by developing a new algorithm and applied it on a human PPI network to make a genome-wide functional inference. We used the new algorithm to measure and reduce the influence of hub proteins on detecting function-associated protein pairs. We used the annotations of the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) as benchmarks to compare and evaluate the function relevance. The application of our algorithms to human PPI data yielded 4,233 significant functional associations among 1,754 proteins. Further functional comparisons between them allowed us to assign 466 KEGG pathway annotations to 274 proteins and 123 GO annotations to 114 proteins with estimated false discovery rates of <21% for KEGG and <30% for GO. We clustered 1,729 proteins by their functional associations and made functional inferences from detailed analysis on one subcluster highly enriched in the TGF-beta signaling pathway (P<10(-50)). Analysis of another four subclusters also suggested potential new players in six signaling pathways worthy of further experimental investigations. Our study gives clear insight into the common neighbor-based prediction scheme and provides a reliable method for large-scale functional annotation in this post-genomic era.
Project description:In the current study, we aimed to identify potential biomarkers for salt sensitivity of blood pressure (SSBP), which may provide a novel insight into the pathogenic mechanisms of salt-sensitive hypertension. Firstly, we conducted weighted gene coexpression network analysis (WGCNA) and selected a gene module and 60 hub genes significantly correlated to SSBP. Then, GO function and KEGG signaling pathway enrichment analysis and protein-protein interaction (PPI) network analysis were performed. Furthermore, we identified a five-gene signature with high connectivity degree in the PPI network and high AUC of ROC curves, which may have high diagnosis value for SSBP. Moreover, through combining two gene screening methods, we identified 23 differentially expressed circRNAs and selected the top 5% circRNAs (1 circRNA) with the highest connectivity degree in the coexpression network as hub circRNA highly associated with SSBP. Finally, we carried out RT-qPCR to validate the expression of five hub genes, and our results showed that the expression of HECTD1 (P = 0.017), SRSF5 (P = 0.003), SRSF1 (P = 0.006), HERC2 (P = 0.004), and TNPO1 (P = 0.002) was significantly upregulated in the renal tissue in salt-sensitive rats compared to salt-resistant rats, indicating that these five hub genes can serve as potential biomarkers for SSBP.
Project description:Our study intended to identify potential long non-coding RNAs (lncRNAs) and genes, and to elucidate the underlying mechanisms of intervertebral disc degeneration (IDD).The microarray of GSE56081 was downloaded from the Gene Expression Omnibus database, including 5 human control nucleus pulposus tissues and 5 degenerative nucleus pulposus tissues, which was on the basis of GPL15314 platform. Identification of differentially expressed lncRNAs and mRNAs were performed between the 2 groups. Then, gene ontology (GO) and pathway enrichment analyses were performed to analyze the biological functions and pathways for the differentially expressed mRNAs. Simultaneously, lncRNA-mRNA weighted coexpression network was constructed using the WGCNA package, followed by GO and KEGG pathway enrichment analyses for the genes in the modules. Finally, the protein-protein interaction (PPI) network was visualized.A total of 135 significantly up- and 170 down-regulated lncRNAs and 2133 significantly up- and 1098 down-regulated mRNAs were identified. Additionally, UBA52 (ubiquitin A-52 residue ribosomal protein fusion product 1), with the highest connectivity degree in PPI network, was remarkably enriched in the pathway of metabolism of proteins. Eight lncRNAs - LINC00917, CTD-2246P4.1, CTC-523E23.5, RP4-639J15.1, RP11-363G2.4, AC005082.12, MIR132, and RP11-38F22.1 - were observed in the modules of lncRNA-mRNA weighted coexpression network. Moreover, SPHK1 in the green-yellow module was significantly enriched in positive regulation of cell migration.LncRNAs LINC00917, CTD-2246P4.1, CTC-523E23.5, RP4-639J15.1, RP11-363G2.4, AC005082.12, MIR132, and RP11-38F22.1 were differentially expressed and might play important roles in the development of IDD. Key genes, such as UBA52 and SPHK1, may be pivotal biomarkers for IDD.
Project description:BACKGROUND As all we know, gastric cancer (GC) is a highly aggressive disease. Recently, circular RNA (circRNA) was found to play a vital role in regulation of GC. Some circRNAs could regulate messenger RNA (mRNA) expression by functioning as a microRNA (miRNA) sponge. Nevertheless, the circRNA-miRNA-mRNA regulatory network involved GC rarely has been explored and researched. MATERIAL AND METHODS All the differentially expressed circRNAs, miRNAs, and mRNA were derived from Gene Expression Omnibus (GEO) microarray data (GSE78092, GSE89143, GSE93415, and GSE54129). GC level 3 miRNA-sequencing data and clinical information were downloaded from The Cancer Genome Atlas (TCGA) database. Furthermore, a circRNA-miRNA-mRNA regulatory network was constructed by Cytoscape (version 3.6.1). Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway revealed the functions and signaling pathways associated with these target genes. Hub genes of protein-protein interaction (PPI) network were identified by STRING database and cytoHubba. RESULTS The regulatory network consists of 3 circRNAs, 22 miRNAs, and 128 mRNAs. Only 3 miRNAs of the network were consistent with the expression of TCGA and were associated with some clinical features. The results of the functional analysis of 128 mRNAs showed that GO analysis and KEGG pathways of inclusion criteria were 49 and 24, respectively. PPI network and Cytoscape showed that the top 10 hub genes were MYC, CTGF, TGFBR2, TGFBR1, SERPINE1, KRAS, ZEB1, THBS1, CDK6, and TNS1; 4 of which were verified by GEPIA based on TCGA. Highly expressed SERPINE1 had a poor OS (over survival) and DFS (disease-free survival), and TGFBR1 expression increased along with the increase of clinical stages. CONCLUSIONS This study looked at a circRNA-miRNA-mRNA regulatory network associated with GC and explored the potential functions of mRNA in the network, then identified a new molecular marker for prediction, prognosis, and therapeutic targets for clinical patients.
Project description:Protein-protein interaction (PPI) plays an extremely remarkable role in the growth, reproduction, and metabolism of all lives. A thorough investigation of PPI can uncover the mechanism of how proteins express their functions. In this study, we used gene ontology (GO) terms and biological pathways to study an extended version of PPI (protein-protein functional associations) and subsequently identify some essential GO terms and pathways that can indicate the difference between two proteins with and without functional associations. The protein-protein functional associations validated by experiments were retrieved from STRING, a well-known database on collected associations between proteins from multiple sources, and they were termed as positive samples. The negative samples were constructed by randomly pairing two proteins. Each sample was represented by several features based on GO and KEGG pathway information of two proteins. Then, the mutual information was adopted to evaluate the importance of all features and some important ones could be accessed, from which a number of essential GO terms or KEGG pathways were identified. The final analysis of some important GO terms and one KEGG pathway can partly uncover the difference between proteins with and without functional associations.
Project description:Gastric adenocarcinoma (GAC), also known as stomach adenocarcinoma (STAD), is one of the most lethal malignancies in the world. It is vital to classify and detect the hub genes and key pathways participated in the initiation and progression of GAC. In this study, we collected and sequenced 15 pairs of GAC tumor tissues and the adjacent normal tissues. Differentially expressed genes (DEGs) were analyzed and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) analysis were used to annotate the unique biological significance and important pathways of enriched DEGs. Moreover, we constructed the protein-protein interaction (PPI) network by Cytoscape and conducted KEGG enrichment analysis of the prime module. We further applied the TCGA database to start the survival analysis of these hub genes by Kaplan-Meier estimates. Finally, we obtained total 233 DEGs consisted of 64 up-regulated genes and 169 down-regulated genes. GO enrichment analysis found that DEGs most significantly enriched in single organism process, extracellular region, and extracellular region part. KEGG pathway enrichment analysis suggested that DEGs most significantly enriched in Protein digestion and absorption, Gastric acid secretion, and ECM-receptor interaction. Furthermore, the PPI network showed that the top 10 hub genes in GAC were IL8, COL1A1, MMP9, SST, COL1A2, TIMP1, FN1, SPARC, ALDH1A1, and SERPINE1 respectively. The prime gene interaction module in PPI network was enriched in protein digestion and absorption, ECM receptor interaction, the PI3K-Akt signaling pathway, and pathway in cancer. Survival analysis based on the TCGA database found that the expression of the FN1, SERPINE1, and SPARC significantly predicted poor prognosis of GAC. Collectively, we identified several hub genes and key pathways associated with GAC initiation and progression by analyzing the microarray data on DEGs, which provided a detailed molecular mechanism underlying GAC occurrence and progression.