scholarly journals Network-based analysis of differentially expressed genes in cerebrospinal fluid (CSF) and blood reveals new candidate genes for multiple sclerosis

PeerJ ◽  
2016 ◽  
Vol 4 ◽  
pp. e2775 ◽  
Author(s):  
Nahid Safari-Alighiarloo ◽  
Mostafa Rezaei-Tavirani ◽  
Mohammad Taghizadeh ◽  
Seyyed Mohammad Tabatabaei ◽  
Saeed Namaki
Keyword(s):  
Multiple Sclerosis ◽  
Cerebrospinal Fluid ◽  
Candidate Genes ◽  
Differentially Expressed Genes ◽  
Expression Profiles ◽  
Biological Pathways ◽  
Differentially Expressed ◽  
Potential Candidate ◽  
Functional Modules ◽  
Ppi Networks

BackgroundThe involvement of multiple genes and missing heritability, which are dominant in complex diseases such as multiple sclerosis (MS), entail using network biology to better elucidate their molecular basis and genetic factors. We therefore aimed to integrate interactome (protein–protein interaction (PPI)) and transcriptomes data to construct and analyze PPI networks for MS disease.MethodsGene expression profiles in paired cerebrospinal fluid (CSF) and peripheral blood mononuclear cells (PBMCs) samples from MS patients, sampled in relapse or remission and controls, were analyzed. Differentially expressed genes which determined only in CSF (MSvs.control) and PBMCs (relapsevs.remission) separately integrated with PPI data to construct the Query-Query PPI (QQPPI) networks. The networks were further analyzed to investigate more central genes, functional modules and complexes involved in MS progression.ResultsThe networks were analyzed and high centrality genes were identified. Exploration of functional modules and complexes showed that the majority of high centrality genes incorporated in biological pathways driving MS pathogenesis. Proteasome and spliceosome were also noticeable in enriched pathways in PBMCs (relapsevs.remission) which were identified by both modularity and clique analyses. Finally, STK4, RB1, CDKN1A, CDK1, RAC1, EZH2, SDCBP genes in CSF (MSvs.control) and CDC37, MAP3K3, MYC genes in PBMCs (relapsevs.remission) were identified as potential candidate genes for MS, which were the more central genes involved in biological pathways.DiscussionThis study showed that network-based analysis could explicate the complex interplay between biological processes underlying MS. Furthermore, an experimental validation of candidate genes can lead to identification of potential therapeutic targets.

scholarly journals Differentially expressed genes in preimplantation human embryos: potential candidate genes for blastocyst formation and implantation

2016 ◽  
Vol 33 (8) ◽  
pp. 1017-1025 ◽  
Author(s):  
Erika M. Munch ◽  
Amy E. Sparks ◽  
Jesus Gonzalez Bosquet ◽  
Lane K. Christenson ◽  
Eric J. Devor ◽  
...  
Keyword(s):  
Candidate Genes ◽  
Differentially Expressed Genes ◽  
Differentially Expressed ◽  
Human Embryos ◽  
Potential Candidate ◽  
Blastocyst Formation

scholarly journals Insight into Molecular Profile Changes after Skeletal Muscle Contusion Using Microarray and Bioinformatics Analyses

2020 ◽  
Author(s):  
Na Li ◽  
Ru-feng Bai ◽  
Chun Li ◽  
Li-hong Dang ◽  
Qiu-xiang Du ◽  
...  
Keyword(s):  
Gene Expression ◽  
Network Analysis ◽  
Differentially Expressed Genes ◽  
Molecular Mechanisms ◽  
Expression Profiles ◽  
Healing Process ◽  
Differentially Expressed ◽  
Molecular Profile ◽  
Wound Age ◽  
Functional Modules

Abstract Background: Muscle trauma frequently occurs in daily life. However, the molecular mechanisms of muscle healing, which partly depend on the extent of the damage, are not well understood. This study aimed to investigate gene expression profiles following mild and severe muscle contusion, and to provide more information about the molecular mechanisms underlying the repair process.Methods: A total of 33 rats were divided randomly into control (n = 3), mild contusion (n = 15), and severe contusion (n = 15) groups; the contusion groups were further divided into five subgroups (1, 3, 24, 48, and 168 h post-injury; n = 3 per subgroup). Then full genome microarray of RNA isolated from muscle tissue was performed to access the gene expression changes during healing process.Results: A total of 2,844 and 2,298 differentially expressed genes were identified in the mild and severe contusion groups, respectively. The analysis of the overlapping differentially expressed genes showed that there are common mechanisms of transcriptomic repair of mild and severe contusion within 48 h post-contusion. This was supported by the results of principal component analysis, hierarchical clustering, and weighted gene co‐expression network analysis of the 1,620 coexpressed genes in mildly and severely contused muscle. From these analyses, we discovered that the gene profiles in functional modules and temporal clusters were similar between the mild and severe contusion groups; moreover, the genes showed time-dependent patterns of expression, which allowed us to identify useful markers of wound age. We then performed an analysis of the functions of genes (including Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway annotation, and protein–protein interaction network analysis) in the functional modules and temporal clusters, and the hub genes in each module–cluster pair were identified. Interestingly, we found that genes downregulated within 24−48 h of the healing process were largely associated with metabolic processes, especially oxidative phosphorylation of reduced nicotinamide adenine dinucleotide phosphate, which has been rarely reported. Conclusions: These results improve our understanding of the molecular mechanisms underlying muscle repair, and provide a basis for further studies of wound age estimation.

A Comprehensive Data Analysis of Differentially Regulated Genes in Melanoma

2020 ◽  
Author(s):  
Yanjie Han ◽  
Xinxin Li ◽  
Jiliang Yan ◽  
Chunyan Ma ◽  
Xin Wang ◽  
...  
Keyword(s):  
Differentially Expressed Genes ◽  
Molecular Mechanisms ◽  
Expression Profiles ◽  
Growth Factor Receptor ◽  
Principal Component ◽  
Distant Metastases ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Hub Genes ◽  
Ppi Networks

Abstract Background: Melanoma is the most deadly tumor in skin tumors and is prone to distant metastases. The incidence of melanoma has increased rapidly in the past few decades, and current trends indicate that this growth is continuing. This study was aimed to explore the molecular mechanisms of melanoma pathogenesis and discover underlying pathways and genes associated with melanoma.Methods: We used high-throughput expression data to study differential expression profiles of related genes in melanoma. The differentially expressed genes (DEGs) of melanoma in GSE15605, GSE46517, GSE7553 and the Cancer Genome Atlas (TCGA) datasets were analyzed. Differentially expressed genes (DEGs) were identified by paired t-test. Then the DEGs were performed cluster and principal component analyses and protein–protein interaction (PPI) network construction. After that, we analyzed the differential genes through bioinformatics and got hub genes. Finally, the expression of hub genes was confirmed in the TCGA databases and collected patient tissue samples.Results: Total 144 up-regulated DEGs and 16 down-regulated DEGs were identified. A total of 17 gene ontology analysis (GO) terms and 11 pathways were closely related to melanoma. Pathway of pathways in cancer was enriched in 8 DEGs, such as junction plakoglobin (JUP) and epidermal growth factor receptor (EGFR). In the PPI networks, 9 hub genes were obtained, such as loricrin (LOR), filaggrin (FLG), keratin 5 (KRT5), corneodesmosin (CDSN), desmoglein 1 (DSG1), desmoglein 3 (DSG3), keratin 1 (KRT1), involucrin (IVL) and EGFR. The pathway of pathways in cancer and its enriched DEGs may play important roles in the process of melanoma. The hub genes of DEGs may become promising melanoma candidate genes. Five key genes FLG, DSG1, DSG3, IVL and EGFR were identified in the TCGA database and melanoma tissues.Conclusions: The results suggested that FLG, DSG1, DSG3, IVL and EGFR might play important roles and potentially be valuable in the prognosis and treatment of melanoma.

Trascriptome Analysis of Bone Marrow CD14+ Monocytes Revealed Differential Expression Profiles in Symptomatic Multiple Myeloma (MM) Compared to Smoldering MM and Monoclonal Gammopathy of Undetermined Significance

Blood ◽  
2012 ◽  
Vol 120 (21) ◽  
pp. 1811-1811
Author(s):  
Marco Peronaci ◽  
Paola Storti ◽  
Domenica Ronchetti ◽  
Luca Agnelli ◽  
Marina Bolzoni ◽  
...  
Keyword(s):  
Multiple Myeloma ◽  
Bone Marrow ◽  
Differentially Expressed Genes ◽  
Monoclonal Gammopathy ◽  
Conflicts Of Interest ◽  
Expression Profiles ◽  
Differentially Expressed ◽  
Potential Candidate ◽  
Molecular Alterations ◽  
Potential Candidate Gene

Abstract Abstract 1811 Symptomatic multiple myeloma (MM), smoldering MM (SMM) and monoclonal gammopathy of uncertain significance (MGUS) are well known different pathological and clinical entities of plasma cell (PC) disorders. Nevertheless molecular studies performed on clonal CD138+ PC do not clear distinguished these disorders that share common alterations. Studies focusing on the presence of potential molecular alterations in the microenvironment cells are ongoing. Because monocytes are the cells primarily involved in osteoclastogenesis, angiogenesis and immune system disfuction, that are the hallmark of symptomatic MM compared to SMM and MGUS, in this study we have analyzed the transcriptional profile of the bone marrow (BM) CD14+ cells in these settings of patients. BM CD14+ monocytes were purified from a total cohort of 36 patients with PC disorders including 21 patients with symptomatic MM, 8 patients with SMM and 7 patients with MGUS. CD14+ cells were isolated from the CD138 negative fraction of BM samples of patients by immunomagnetic method with anti-CD14 monoclonal antibody conjugated with microbeads. The presence of potential haemopoietic and CD138+ contaminating cells was excluded by FACS analysis. Only samples with CD14 purity greater than 95% were analyzed by microarrays by GeneChip® HG-U133Plus 2.0 arrays (Affymetrix®) (13 MM, 8 SMM and 7 MGUS). Data obtained were then validated on selected genes by Real-Time quantitative PCR. A multiclass analysis identified 14 differentially expressed genes, which characterized MGUS vs SMM vs symptomatic MM. A supervised analysis between symptomatic MM vs. SMM and MGUS samples identified 101 genes differentially expressed in CD14+ (58 genes up-regulated in MM vs SMM and MGUS and 43 genes donwregulated). Interestingly, among the differentially expressed genes we found that cytokines and cytokine receptors (IL21, IL21R, IL15, IL15R), chemokines (CXCL10, CXCL11) and interferon-inducible proteins (IFI27, IFI44) were up-regulated in CD14+ of MM patients as compared to SMM and MGUS. A supervised analysis between MM and MGUS identified 6 differentially expressed genes in CD14+ whereas 37 genes distinguished MM and SMM patients. Notably the SLAMF7 (CS1) gene recently indentified as a therapeutic target in CD138+ MM cells was up-regulated in CD14+ monocytes of MM patients as compared either to MGUS alone or to MGUS plus SMM could be a potential candidate gene. Overall our preliminary results indicate that a different transcriptional fingerprint may be identified in BM CD14+ cells of patients with symptomatic MM as compared to those with indolent PC disorders such as SMM and MGUS with a greater number of differentially expressed genes between symptomatic MM and SMM patient rather than between MM and MGUS. Disclosures: No relevant conflicts of interest to declare.

scholarly journals Comprehensive analysis of DNA methylation and gene expression profiles in cholangiocarcinoma

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Cheng Zhang ◽  
Bingye Zhang ◽  
Di Meng ◽  
Chunlin Ge
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Differentially Expressed Genes ◽  
Expression Profiles ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Hub Genes ◽  
Ppi Networks ◽  
Differentially Methylated Genes ◽  
Tumorigenesis And Progression

Abstract Background The incidence of cholangiocarcinoma (CCA) has risen in recent years, and it has become a significant health burden worldwide. However, the mechanisms underlying tumorigenesis and progression of this disease remain largely unknown. An increasing number of studies have demonstrated crucial biological functions of epigenetic modifications, especially DNA methylation, in CCA. The present study aimed to identify and analyze methylation-regulated differentially expressed genes (MeDEGs) involved in CCA tumorigenesis and progression by bioinformatics analysis. Methods The gene expression profiling dataset (GSE119336) and gene methylation profiling dataset (GSE38860) were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) and differentially methylated genes (DMGs) were identified using the limma packages of R and GEO2R, respectively. The MeDEGs were obtained by overlapping the DEGs and DMGs. Functional enrichment analyses of these genes were then carried out. Protein–protein interaction (PPI) networks were constructed using STRING and visualized in Cytoscape to determine hub genes. Finally, the results were verified based on The Cancer Genome Atlas (TCGA) database. Results We identified 98 hypermethylated, downregulated genes and 93 hypomethylated, upregulated genes after overlapping the DEGs and DMGs. These genes were mainly enriched in the biological processes of the cell cycle, nuclear division, xenobiotic metabolism, drug catabolism, and negative regulation of proteolysis. The top nine hub genes of the PPI network were F2, AHSG, RRM2, AURKB, CCNA2, TOP2A, BIRC5, PLK1, and ASPM. Moreover, the expression and methylation status of the hub genes were significantly altered in TCGA. Conclusions Our study identified novel methylation-regulated differentially expressed genes (MeDEGs) and explored their related pathways and functions in CCA, which may provide novel insights into a further understanding of methylation-mediated regulatory mechanisms in CCA.

scholarly journals Screening and Identification of Muscle-Specific Candidate Genes via Mouse Microarray Data Analysis

2021 ◽  
Vol 8 ◽  
Author(s):  
Sayed Haidar Abbas Raza ◽  
Chengcheng Liang ◽  
Wang Guohua ◽  
Sameer D. Pant ◽  
Zuhair M. Mohammedsaleh ◽  
...  
Keyword(s):  
Candidate Genes ◽  
Differentially Expressed Genes ◽  
Muscle Tissue ◽  
Microarray Data ◽  
Muscle Development ◽  
The Other ◽  
Differentially Expressed ◽  
Regulatory Mechanisms ◽  
Potential Candidate ◽  
Differential Genes

Muscle tissue is involved with every stage of life activities and has roles in biological processes. For example, the blood circulation system needs the heart muscle to transport blood to all parts, and the movement cannot be separated from the participation of skeletal muscle. However, the process of muscle development and the regulatory mechanisms of muscle development are not clear at present. In this study, we used bioinformatics techniques to identify differentially expressed genes specifically expressed in multiple muscle tissues of mice as potential candidate genes for studying the regulatory mechanisms of muscle development. Mouse tissue microarray data from 18 tissue samples was selected from the GEO database for analysis. Muscle tissue as the treatment group, and the other 17 tissues as the control group. Genes expressed in the muscle tissue were different to those in the other 17 tissues and identified 272 differential genes with highly specific expression in muscle tissue, including 260 up-regulated genes and 12 down regulated genes. is the genes were associated with the myofibril, contractile fibers, and sarcomere, cytoskeletal protein binding, and actin binding. KEGG pathway analysis showed that the differentially expressed genes in muscle tissue were mainly concentrated in pathways for AMPK signaling, cGMP PKG signaling calcium signaling, glycolysis, and, arginine and proline metabolism. A PPI protein interaction network was constructed for the selected differential genes, and the MCODE module used for modular analysis. Five modules with Score > 3.0 are selected. Then the Cytoscape software was used to analyze the tissue specificity of differential genes, and the genes with high degree scores collected, and some common genes selected for quantitative PCR verification. The conclusion is that we have screened the differentially expressed gene set specific to mouse muscle to provide potential candidate genes for the study of the important mechanisms of muscle development.

scholarly journals Screening and identification of muscle-specific candidate genes via mouse microarray data analysis

2021 ◽  
Author(s):  
Sayed Haidar Abbas Raza ◽  
Chengcheng Liang ◽  
Wang Guohua ◽  
Linsen Zan
Keyword(s):  
Candidate Genes ◽  
Differentially Expressed Genes ◽  
Muscle Tissue ◽  
Microarray Data ◽  
Muscle Development ◽  
The Other ◽  
Differentially Expressed ◽  
Regulatory Mechanisms ◽  
Potential Candidate ◽  
Differential Genes

Muscle tissue is involved with every stage of life activities and has roles in biological processes. For example, the blood circulation system needs the heart muscle to transport blood to all parts, and the movement cannot be separated from the participation of skeletal muscle. However, the process of muscle development and the regulatory mechanisms of muscle development are not clear at present. In this study, we used bioinformatics techniques to identify differentially expressed genes specifically expressed in multiple muscle tissues of mice as potential candidate genes for studying the regulatory mechanisms of muscle development. Mouse tissue microarray data from 17 tissue samples was selected from the GEO database for analysis. Muscle tissue as the treatment group, and the other 16 tissues as the control group. Genes expressed in the muscle tissue were different to those in the other 16 tissues and identified 272 differential genes with highly specific expression in muscle tissue, including 260 up-regulated genes and 12 down regulated genes. is the genes were associated with the myofibril, contractile fibers, and sarcomere, cytoskeletal protein binding, and actin binding. KEGG pathway analysis showed that the differentially expressed genes in muscle tissue were mainly concentrated in pathways for AMPK signaling, cGMP PKG signaling calcium signaling, glycolysis, and, arginine and proline metabolism. A PPI protein interaction network was constructed for the selected differential genes, and the MCODE module used for modular analysis. Five modules with Score > 3.0 are selected. Then the Cytoscape software was used to analyze the tissue specificity of differential genes, and the genes with high degree scores collected, and some common genes selected for quantitative PCR verification. The conclusion is that we have screened the differentially expressed gene set specific to mouse muscle to provide potential candidate genes for the study of the important mechanisms of muscle development.

Sign in / Sign up

Export Citation Format

Share Document