Integrative Bioinformatics Approaches to Map Potential Novel Genes and Pathways Involved in Ovarian Cancer

Kumar, Sanjit; Kumar, Dhirendra; Siva, Ramamoorthy; C, George Priya Doss; Zayed, Hatem

doi:10.3389/fbioe.2019.00391

Cited by 73 publications

(47 citation statements)

References 62 publications

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“…The Kyoto Encyclopedia of Genes and Genomes (KEGG) is a database resource for advanced biological functions and utilities. Both analyses and annotations are based on the DAVID database (https://DAVID.ncifcrf.gov/) (Kumar et al, 2019) and are used to explore and understand the biological significance of specific gene lists. In this study, GO and KEGG analysis of DEGs were based on standard error discovery rate (FDR) <0.05.…”

Section: Functional and Pathway Enrichment Analysesmentioning

confidence: 99%

Diagnostic model of combined ceRNA and DNA methylation related genes in esophageal carcinoma

Guan

Yao

Bao

et al. 2020

PeerJ

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Esophageal cancer is a common malignant tumor in the world, and the aim of this study was to screen key genes related to the development of esophageal cancer using a variety of bioinformatics analysis tools and analyze their biological functions. The data of esophageal squamous cell carcinoma from the Gene Expression Omnibus (GEO) were selected as the research object, processed and analyzed to screen differentially expressed microRNAs (miRNAs) and differential methylation genes. The competing endogenous RNAs (ceRNAs) interaction network of differentially expressed genes was constructed by bioinformatics tools DAVID, String, and Cytoscape. Biofunctional enrichment analysis was performed using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). The expression of the screened genes and the survival of the patients were verified. By analyzing GSE59973 and GSE114110, we found three down-regulated and nine up-regulated miRNAs. The gene expression matrix of GSE120356 was calculated by Pearson correlation coefficient, and the 11696 pairs of ceRNA relation were determined. In the ceRNA network, 643 lncRNAs and 147 mRNAs showed methylation difference. Functional enrichment analysis showed that these differentially expressed genes were mainly concentrated in the FoxO signaling pathway and were involved in the corresponding cascade of calcineurin. By analyzing the clinical data in The Cancer Genome Atlas (TCGA) database, it was found that four lncRNAs had an important impact on the survival and prognosis of esophageal carcinoma patients. QRT-PCR was also conducted to identify the expression of the key lncRNAs (RNF217-AS1, HCP5, ZFPM2-AS1 and HCG22) in ESCC samples. The selected key genes can provide theoretical guidance for further research on the molecular mechanism of esophageal carcinoma and the screening of molecular markers.

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Section: Functional and Pathway Enrichment Analysesmentioning

confidence: 99%

Diagnostic model of combined ceRNA and DNA methylation related genes in esophageal carcinoma

Guan

Yao

Bao

et al. 2020

PeerJ

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“…To establish the interrelationship between the core genes UQCR11 , UBE2N , ADD1 , TLN1 , IRAK3 , LY96 , and MAP3K1 , this interrelationship will help in understanding the coexpression and molecular pathways that pertain to FH. It is essential to comprehend the interactions between the core genes since these genes dysregulate the general molecular pathways in FH patients ( Kumar et al, 2019 ; Udhaya Kumar et al, 2020 ). The differentially expressed genes might be responsible for the clinical phenotypes of the patients and the progression of atherosclerosis.…”

Section: Discussionmentioning

confidence: 99%

Analysis of Differentially Expressed Genes and Molecular Pathways in Familial Hypercholesterolemia Involved in Atherosclerosis: A Systematic and Bioinformatics Approach

Kumar

Bithia

et al. 2020

Front. Genet.

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Background and Aims: Familial hypercholesterolemia (FH) is one of the major risk factor for the progression of atherosclerosis and coronary artery disease. This study focused on identifying the dysregulated molecular pathways and core genes that are differentially regulated in FH and to identify the possible genetic factors and potential underlying mechanisms that increase the risk to atherosclerosis in patients with FH. Methods: The Affymetrix microarray dataset (GSE13985) from the GEO database and the GEO2R statistical tool were used to identify the differentially expressed genes (DEGs) from the white blood cells (WBCs) of five heterozygous FH patients and five healthy controls. The interaction between the DEGs was identified by applying the STRING tool and visualized using Cytoscape software. MCODE was used to determine the gene cluster in the interactive networks. The identified DEGs were subjected to the DAVID v6.8 webserver and ClueGo/CluePedia for functional annotation, such as gene ontology (GO) and enriched molecular pathway analysis of DEGs. Results: We investigated the top 250 significant DEGs ( p -value < 0.05; fold two change ≥ 1 or ≤ −1). The GO analysis of DEGs with significant differences revealed that they are involved in critical biological processes and molecular pathways, such as myeloid cell differentiation, peptidyl-lysine modification, signaling pathway of MyD88-dependent Toll-like receptor, and cell-cell adhesion. The analysis of enriched KEGG pathways revealed the association of the DEGs in ubiquitin-mediated proteolysis and cardiac muscle contraction. The genes involved in the molecular pathways were shown to be differentially regulated by either activating or inhibiting the genes that are essential for the canonical signaling pathways. Our study identified seven core genes ( UQCR11, UBE2N, ADD1, TLN1, IRAK3, LY96 , and MAP3K1 ) that are strongly linked to FH and lead to a higher risk of atherosclerosis. Conclusion: We identified seven core genes that represent potential molecular biomarkers for the diagnosis of atherosclerosis and might serve as a platform for developing therapeutics against both FH and atherosclerosis. However, functional studies are further needed to validate their role in the pathogenesis of FH and atherosclerosis.

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“…DNA microarrays and next generation sequencing (NGS) approaches are high-throughput technologies that have resulted in the emergence of new biomedical discoveries. Data from microarray and gene expression profiles have enabled a deeper understanding of the intrinsic molecular pathways of complex mechanisms of biological systems and their responses (Russo et al, 2003;Babu, 2004;Perez-Diez et al, 2013;Kumar et al, 2019). It is therefore highly relevant to examine the peripheral B-cell transcriptomes of SLE patients and healthy controls to determine genes that are differentially regulated and their target pathways.…”

Section: Discussionmentioning

confidence: 99%

“…In the network, the nodes were considered proteins, and the edges were their interactions. Using network topology features, the PPI network can be analyzed to distinguish the core proteins that are involved in the pathways (Barabási and Oltvai, 2004;Ideker and Sharan, 2008;Keskin et al, 2016;Kumar et al, 2019). The identified DEGs from the present study were analyzed with STRING to exploit the complex interactions between the DEGs via text mining, evidence from experiments, and repositories (Figure 2).…”

Section: Discussionmentioning

confidence: 99%

Dysregulation of Signaling Pathways Due to Differentially Expressed Genes From the B-Cell Transcriptomes of Systemic Lupus Erythematosus Patients – A Bioinformatics Approach

Kumar

Siva

et al. 2020

Front. Bioeng. Biotechnol.

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Systemic lupus erythematosus (SLE) is an autoimmune inflammatory disorder that is clinically complex and has increased production of autoantibodies. Via emerging technologies, researchers have identified genetic variants, expression profiling of genes, animal models, and epigenetic findings that have paved the way for a better understanding of the molecular and genetic mechanisms of SLE. Our current study aimed to illustrate the essential genes and molecular pathways that are potentially involved in the pathogenesis of SLE. This study incorporates the gene expression profiling data of the microarray dataset GSE30153 from the Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) between the B-cell transcriptomes of SLE patients and healthy controls were screened using the GEO2R web tool. The identified DEGs were subjected to STRING analysis and Cytoscape to explore the protein-protein interaction (PPI) networks between them. The MCODE (Molecular Complex Detection) plugin of Cytoscape was used to screen the cluster subnetworks that are highly interlinked between the DEGs. Subsequently, the clustered DEGs were subjected to functional annotation with ClueGO/CluePedia to identify the significant pathways that were enriched. For integrative analysis, we used GeneGo Metacore TM , a Cortellis Solution software, to exhibit the Gene Ontology (GO) and enriched pathways between the datasets. Our study identified 4 upregulated and 13 downregulated genes. Analysis of GO and functional enrichment using ClueGO revealed the pathways that were statistically significant, including pathways involving T-cell costimulation, lymphocyte costimulation, negative regulation of vascular permeability, and B-cell receptor signaling. The DEGs were mainly enriched in metabolic networks such as the phosphatidylinositol-3,4,5-triphosphate pathway and the carnitine pathway. Additionally, potentially enriched pathways, such as the signaling pathways induced by oxidative stress and reactive oxygen species (ROS), chemotaxis and lysophosphatidic acid signaling induced via G protein-coupled receptors (GPCRs), and the androgen

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Integrative Bioinformatics Approaches to Map Potential Novel Genes and Pathways Involved in Ovarian Cancer

Cited by 73 publications

References 62 publications

Diagnostic model of combined ceRNA and DNA methylation related genes in esophageal carcinoma

Diagnostic model of combined ceRNA and DNA methylation related genes in esophageal carcinoma

Analysis of Differentially Expressed Genes and Molecular Pathways in Familial Hypercholesterolemia Involved in Atherosclerosis: A Systematic and Bioinformatics Approach

Dysregulation of Signaling Pathways Due to Differentially Expressed Genes From the B-Cell Transcriptomes of Systemic Lupus Erythematosus Patients – A Bioinformatics Approach

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