Transcriptome signatures reveal candidate key genes in the whole blood of patients with lumbar disc prolapse.
ABSTRACT: The present study aimed to investigate differentially expressed genes (DEGs) in whole blood (WB) obtained from patients with lumbar disc prolapse (LDP) and healthy volunteers. A total of 8 patients with LDP and 8 healthy volunteers were recruited. An Agilent SurePrint G3 human gene expression microarray 8×60 K was used to perform the microarray analyses. R was employed to identify DEGs, which were then subjected to bioinformatics analysis, including a Gene Ontology (GO) analysis, Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway analysis and protein-protein interaction (PPI) network analysis. DEGs in the degenerative annulus fibrosis (AF) and nucleus pulposus (NP) compared with non-degenerative tissues were also identified based on microarray data and the intersections of the three were assessed. Furthermore, reverse transcription-quantitative (RT-q)PCR was performed to confirm the aberrant expression levels of selected DEGs in the WB of all subjects. A total of 161 DEGs between LDP patients and the healthy controls were identified (128 upregulated and 33 downregulated). These DEGs were enriched in 293 biological process, 36 cellular component and 21 molecular function GO terms, as well as in 24 KEGG pathways. The PPI network contained 4 submodules, and Toll-like receptor 4 had the highest degree centrality. A total of 22 DEGs were common to the three groups of DEGs. The RT-qPCR assay confirmed that the expression levels of cytochrome P450 family 27 subfamily A member 1, superoxide dismutase 2, protein disulfide isomerase family A member 4, FKBP prolyl isomerase 11 and ectonucleotide pyrophosphatase/phosphodiesterase 4 were significantly different between the patient group and the volunteer group. In conclusion, several genes were identified as potential biomarkers in WB that should be further explored in future studies to determine their potential application in the clinical treatment and diagnosis of LDP, and the present bioinformatics analysis revealed several GO terms, KEGG pathways and submodules of the PPI network that may be involved in LDP, although the exact mechanisms remain elusive.
Project description:PURPOSE:The study aimed to explore the anticancer effects of a novel proteasome inhibitor, ixazomib, on colorectal cancer (CRC) using a combined method of microarray and bioinformatics analysis. MATERIALS AND METHODS:Cell proliferation was tested by Cell Counting Kit-8 (CCK-8) assay for SW620 cells treated with different concentrations of ixazomib and different treatment times. The microarray analysis was conducted for six samples, including three samples of SW620 cells untreated with ixazomib and three samples of SW620 cells treated with ixazomib. The differentially expressed genes (DEGs) between untreated and treated samples were identified by the Linear Models for Microarray data (LIMMA) package in R language. The Gene Ontology (GO) functional and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed for the DEGs using the Database for Annotation, Visualization and Integrated Discovery (DAVID) and KEGG Orthology-Based Annotation System (KOBAS) online tool. The protein-protein interaction (PPI) network was constructed using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database, and module analysis was performed for the PPI network. RESULTS:Ixazomib could inhibit the proliferation of SW620 cells in a dose-dependent and time-dependent manner. A total of 743 DEGs, including 203 upregulated DEGs such as HSPA6 and 540 downregulated DEGs such as APCDD1, were identified. Eighty-three GO terms were enriched for DEGs, which were mainly related to protein folding, apoptotic process, transcription factor activity, and proteasome. Thirty-seven KEGG pathways were perturbed, including pathway of apoptosis and cell cycle. Forty-six hub genes, such as TP53, JUN, and ITGA2, were screened out, and three modules with important functions were mined from the PPI network. CONCLUSION:The novel proteasome inhibitor ixazomib significantly inhibited the proliferation of human CRC SW620 cells. It exerted anticancer effects through targeting the expression of DEGs, such as HSPA6, APCDD1, TP53, and JUN, and affecting the signaling pathways including apoptosis and cell cycle pathway, which demonstrated the promising potential of ixazomib for CRC therapy.
Project description:The current study aimed to elucidate the molecular mechanisms and identify the potential key genes and pathways for metastatic uveal melanoma (UM) using bioinformatics analysis.Gene expression microarray data from GSE39717 included 39 primary UM tissue samples and 2 metastatic UM tissue samples. Differentially expressed genes (DEGs) were generated using Gene Expression Omnibus 2R. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using the online Database for Annotation, Visualization and Integrated Discovery (DAVID) tool. The web-based STRING tool was adopted to construct a protein--protein interaction (PPI) network. The MCODE tool in Cytoscape was used to generate significant modules of the PPI network.A total of 213 DEGs were identified. GO and KEGG analyses revealed that the upregulated genes were mainly enriched in extracellular matrix organization and blood coagulation cascades, while the downregulated DEGs were mainly related to protein binding, negative regulation of ERK cascade, nucleus and chromatin modification, and lung and renal cell carcinoma. The most significant module was extracted from the PPI network. GO and KEGG enrichment analyses of the module revealed that the genes were mainly enriched in the extracellular region and space organization, blood coagulation process, and PI3K-Akt signaling pathway. Hub genes, including FN1, APOB, F2, SERPINC1, SERPINA1, APOA1, FGG, PROC, ITIH2, VCAN, TFPI, CXCL8, CDH2, and HP, were identified from DEGs. Survival analysis and hierarchical clustering results revealed that most of the hub genes were associated with prognosis and clinical progression.Results of this bioinformatics analysis may provide predictive biomarkers and potential candidate therapeutic targets for individuals with metastatic UM.
Project description: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:BACKGROUND:Dedifferentiated liposarcoma (DDLPS) is one of the most deadly types of soft tissue sarcoma. To date, there have been few studies dedicated to elucidating the molecular mechanisms behind the disease; therefore, the molecular mechanisms behind this malignancy remain largely unknown. MATERIALS AND METHODS:Microarray profiles of 46 DDLPS samples and nine normal fat controls were extracted from Gene Expression Omnibus (GEO). Quality control for these microarray profiles was performed before analysis. Hierarchical clustering and principal component analysis were used to distinguish the general differences in gene expression between DDLPS samples and the normal fat controls. Differentially expressed genes (DEGs) were identified using the Limma package in R. Next, the enriched Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were obtained using the online tool DAVID (http://david.abcc.ncifcrf.gov/). A protein-protein interaction (PPI) network was constructed using the STRING database and Cytoscape software. Furthermore, the hub genes within the PPI network were identified. RESULTS:All 55 microarray profiles were confirmed to be of high quality. The gene expression pattern of DDLPS samples was significantly different from that of normal fat controls. In total, 700 DEGs were identified, and 83 enriched GO terms and three KEGG pathways were obtained. Specifically, within the DEGs of DDLPS samples, several pathways were identified as being significantly enriched, including the PPAR signaling pathway, cell cycle pathway, and pyruvate metabolism pathway. Furthermore, the dysregulated PPI network of DDLPS was constructed, and 14 hub genes were identified. Characteristic of DDLPS, the genes CDK4 and MDM2 were universally found to be up-regulated and amplified in gene copy number. CONCLUSION:This study used bioinformatics to comprehensively mine DDLPS microarray data in order to obtain a deeper understanding of the molecular mechanism of DDLPS.
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.
Project description:Infant acute lymphoblastic leukemia (ALL) with the mixed lineage leukemia (MLL) gene rearrangement (MLL-R) is considered a distinct leukemia from childhood or non-MLL-R infant ALL. To detect key genes and elucidate the molecular mechanisms of MLL-R infant ALL, microarray expression data were downloaded from the Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) between MLL-R and non-MLL-R infant ALL were identified. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were carried out. Then, we constructed a protein-protein interaction (PPI) network and identified the hub genes. Finally, drug-gene interactions were mined. A total of 139 cases of MLL-R infant ALL including 77 (55.4%) fusions with AF4, 38 (27.3%) with ENL, 14 (10.1%) with AF9, and 10 (7.2%) other gene fusions were characterized. A total of 236 up-regulated and 84 down-regulated DEGs were identified. The up-regulated DEGs were mainly involved in homophilic cell adhesion, negative regulation of apoptotic process and cellular response to drug GO terms, while down-regulated DEGs were mainly enriched in extracellular matrix organization, protein kinase C signaling and neuron projection extension GO terms. The up-regulated DEGs were enriched in seven KEGG pathways, mainly involving transcriptional regulation and signaling pathways, and down-regulated DEGs were involved in three main KEGG pathways including Alzheimer's disease, TGF-beta signaling pathway, and hematopoietic cell lineage. The PPI network included 297 nodes and 410 edges, with MYC, ALB, CD44, PTPRC and TNF identified as hub genes. Twenty-three drug-gene interactions including four up-regulated hub genes and 24 drugs were constructed by Drug Gene Interaction database (DGIdb). In conclusion, MYC, ALB, CD44, PTPRC and TNF may be potential bio-markers for the diagnosis and therapy of MLL-R infant ALL.
Project description:BACKGROUND: To uncover the genes involved in the development of osteosarcoma (OS), we performed a meta-analysis of OS microarray data to identify differentially expressed genes (DEGs) and biological functions associated with gene expression changes between OS and normal control (NC) tissues. METHODS: We used publicly available GEO datasets of OS to perform a meta-analysis. We performed Gene Ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and Protein-Protein interaction (PPI) networks analysis. RESULTS: Eight GEO datasets, including 240 samples of OS and 35 samples of controls, were available for the meta-analysis. We identified 979 DEGs across the studies between OS and NC tissues (472 up-regulated and 507 down-regulated). We found GO terms for molecular functions significantly enriched in protein binding (GO: 0005515, P = 3.83E-60) and calcium ion binding (GO: 0005509, P?=?3.79E-13), while for biological processes, the enriched GO terms were cell adhesion (GO:0007155, P?=?2.26E-19) and negative regulation of apoptotic process (GO: 0043066, P?=?3.24E-15), and for cellular component, the enriched GO terms were cytoplasm (GO: 0005737, P?=?9.18E-63) and extracellular region (GO: 0005576, P?=?2.28E-47). The most significant pathway in our KEGG analysis was Focal adhesion (P?=?5.70E-15). Furthermore, ECM-receptor interaction (P?=?1.27E-13) and Cell cycle (P?=?4.53E-11) are found to be highly enriched. PPI network analysis indicated that the significant hub proteins containing PTBP2 (Degree?=?33), RGS4 (Degree?=?15) and FXYD6 (Degree?=?13). CONCLUSIONS: Our meta-analysis detected DEGs and biological functions associated with gene expression changes between OS and NC tissues, guiding further identification and treatment for OS.
Project description:BACKGROUND:Alcoholic hepatitis (AH) is an acute manifestation of alcoholic liver disease with high mortality rates. OBJECTIVES:Our aim was to study the molecular mechanisms of AH. MATERIALS AND METHODS:The differentially expressed genes (DEGs) in liver between AH and control cases were identified by analyzing the GSE28619 microarray data using t-test. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and Gene Ontology (GO) enrichment analyses were performed using DAVID online tool. The protein-protein interaction (PPI) network was constructed using Search Tool for the Retrieval of Interacting Genes (STRING) and the subnetwork was identified by BioNet. Both PPI network and subnetwork were visualized using the Cytoscape software. RESULTS:Total 908 DEGs (551 up- and 357 down-regulated DEGs) were obtained. The up-regulated DEGs were significantly enriched in 15 pathways and 112 GO biological processes. The down-regulated DEGs were significantly enriched in 22 pathways and 84 GO biological processes. The PPI network with 608 nodes and 2878 interactions was constructed and the subnetwork with 53 nodes and 131 interactions was also identified. The hub DEGs (TSPO, PPIB, NME1 and NME2) were extracted in this subnetwork. CONCLUSIONS:TSPO might contribute to the liver damage and AH progression induced by mitochondrial dysfunction through oxidative stress of liver. TSPO interacted with PPIB might play important roles in liver damage in AH. The interaction between NME1 and NME2 might contribute to the transformation from AH to hepatocellular carcinoma.
Project description:The aim of the present study was to screen the differentially expressed genes (DEGs) associated with familial combined hyperlipidemia (FCHL) and examine the changing patterns. The transcription profile of GSE18965 was obtained from the NCBI Gene Expression Omnibus database, including 12 FCHL samples and 12 control specimens. The DEGs were identified using a linear models for microarray data package in the R programming language. Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was also performed. Protein?protein interaction (PPI) networks of the DEGs were constructed using the EnrichNet online tool. In addition, cluster analysis of the genes in networks was performed using ClusterONE. A total of 879 DEGs were screened, including 394 upregulated and 485 downregulated genes. Enrichment analysis identified four important KEGG pathways associated with FCHL: One carbon pool by folate, ??linolenic acid metabolism, asthma and the glycosphingolipid biosynthesis?globo series. GO annotation identified 12 enriched biological processes, including one associated with hematopoiesis and four associated with bone cell differentiation. This identification was in accordance with clinical data and experiments into hyperlipidemia and bone lesions. Based on PPI networks, these DEGs had a close association with immune responses, hormone responses and cytokine?cytokine receptors. In conclusion, these DEGs may be used as specific therapeutic molecular targets in the treatment of FCHL. The present findings may provide the basis for understanding the pathogenesis of FCHL in future studies. However, further experiments are required to confirm these results.
Project description:We aimed to identify key genes associated with rheumatoid arthritis (RA).The microarray datasets of GSE1919, GSE12021, and GSE21959 (35 RA samples and 32 normal controls) were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) in RA samples were identified using the t test in limma package. Functional enrichment analysis was performed using clusterProfiler package. A protein-protein interaction (PPI) network of selected DEGs was constructed based on the Human Protein Reference Database. Active modules were explored using the jActiveModules plug-in in the Cytoscape Network Modeling package.In total, 537 DEGs in RA samples were identified, including 241 upregulated and 296 downregulated genes. A total of 24,451 PPI pairs were collected, and 5 active modules were screened. Furthermore, 19 submodules were acquired from the 5 active modules. Discs large homolog 1 (DLG1) and related DEGs such as guanylate cyclase 1, soluble, alpha 2 (GUCY1A2), N-methyl d-aspartate receptor 2A subunit (GRIN2A), and potassium voltage-gated channel member 1 (KCNA1) were identified in 8 submodules. Plasminogen (PLG) and related DEGs such as chemokine (C-X-C motif) ligand 2 (CXCL2), laminin, alpha 3 (LAMA3), complement component 7 (C7), and coagulation factor X (F10) were identified in 4 submodules.Our results indicate that DLG1, GUCY1A2, GRIN2A, KCNA1, PLG, CXCL2, LAMA3, C7, and F10 may play key roles in the progression of RA and may serve as putative therapeutic targets for treating RA.