scholarly journals Identification of hub genes and molecular mechanisms in infant acute lymphoblastic leukemia withMLLgene rearrangement

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7628
Author(s):  
Hao Zhang ◽  
Juan Cheng ◽  
Zijian Li ◽  
Yaming Xi
Keyword(s):  
Acute Lymphoblastic Leukemia ◽  
Cellular Response ◽  
Molecular Mechanisms ◽  
Lymphoblastic Leukemia ◽  
Gene Interactions ◽  
Ppi Network ◽  
Hub Genes ◽  
Kegg Pathways ◽  
Go Terms ◽  
Infant Acute Lymphoblastic Leukemia

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 ofMLL-R infant ALL, microarray expression data were downloaded from the Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) betweenMLL-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 ofMLL-R infant ALL including 77 (55.4%) fusions withAF4, 38 (27.3%) withENL, 14 (10.1%) withAF9, 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, withMYC,ALB,CD44,PTPRCandTNFidentified 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,PTPRCandTNFmay be potential bio-markers for the diagnosis and therapy ofMLL-R infant ALL.

scholarly journals Identification of Differentially Expressed Genes in COVID-19 and Integrated Bioinformatics Analysis of Signaling Pathways

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Linjie Fang ◽  
Tingyu Tang ◽  
Mengqi Hu
Keyword(s):  
Signaling Pathway ◽  
Differentially Expressed Genes ◽  
Cellular Response ◽  
Molecular Mechanisms ◽  
Interaction Network ◽  
Differentially Expressed ◽  
Type I ◽  
Hub Genes ◽  
Gene Set ◽  
Go Terms

Coronavirus disease 2019 (COVID-19) is acutely infectious pneumonia. Currently, the specific causes and treatment targets of COVID-19 are still unclear. Herein, comprehensive bioinformatics methods were employed to analyze the hub genes in COVID-19 and tried to reveal its potential mechanisms. First of all, 34 groups of COVID-19 lung tissues and 17 other diseases’ lung tissues were selected from the GSE151764 gene expression profile for research. According to the analysis of the DEGs (differentially expressed genes) in the samples using the limma software package, 84 upregulated DEGs and 46 downregulated DEGs were obtained. Later, by the Database for Annotation, Visualization, and Integrated Discovery (DAVID), they were enriched in the Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. It was found that the upregulated DEGs were enriched in the type I interferon signaling pathway, AGE-RAGE signaling pathway in diabetic complications, coronavirus disease, etc. Downregulated DEGs were in cellular response to cytokine stimulus, IL-17 signaling pathway, FoxO signaling pathway, etc. Then, based on GSEA, the enrichment of the gene set in the sample was analyzed in the GO terms, and the gene set was enriched in the positive regulation of myeloid leukocyte cytokine production involved in immune response, programmed necrotic cell death, translesion synthesis, necroptotic process, and condensed nuclear chromosome. Finally, with the help of STRING tools, the PPI (protein-protein interaction) network diagrams of DEGs were constructed. With degree ≥13 as the cutoff degree, 3 upregulated hub genes (ISG15, FN1, and HLA-G) and 4 downregulated hub genes (FOXP3, CXCR4, MMP9, and CD69) were screened out for high degree. All these findings will help us to understand the potential molecular mechanisms of COVID-19, which is also of great significance for its diagnosis and prevention.

scholarly journals Identification of Key Genes and Pathways for Childhood Obesity Using System Biology Approach Based on Comprehensive Gene Information

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. A49-A50
Author(s):  
Daisy Crispim ◽  
Felipe Mateus Pellenz ◽  
Tais Silveira Assmann
Keyword(s):  
Childhood Obesity ◽  
System Biology ◽  
Molecular Mechanisms ◽  
Functional Enrichment ◽  
Biological Processes ◽  
System Biology Approach ◽  
Ppi Network ◽  
Metabolic Processes ◽  
Hub Genes ◽  
Kegg Pathways

Abstract Introduction: Childhood obesity is one of the most important public health issues of the 21st century. Epidemiological studies have suggested that obesity during childhood increases the risk of developing comorbidities, such as type 2 diabetes, later in life. Childhood obesity is a complex disease whose molecular mechanisms are not completely elucidated. In this context, a system biology approach could contribute to the scientific knowledge regarding genetic factors related to childhood obesity onset. Aim: To identify molecular mechanisms involved in childhood obesity by implementing a system biology approach. Methods: Experimentally validated and computationally predicted genes related to Pediatric Obesity (C2362324) were downloaded from the DisGeNET v7.0 database. The protein-protein interaction (PPI) network was constructed using the STRING v11.0 database and analyzed using NetworkAnalyst v3.0 and Cytoscape v3.8.1. The relevance of each node for the network structure and functionality was assessed using the degree method to define hub genes. Functional and pathway enrichment analyses were performed based on Gene Ontology (GO) terms and KEGG Pathways. Results: The search on the DisGeNET database retrieved 191 childhood obesity-related genes. The PPI network of these genes showed 19 hub genes (STAT3, SIRT1, BCL2, IRS1, PPARG, SOCS3, TGFB1, HDAC4, DNMT1, ADCY3, PPARA, NEDD4L, ACACB, NR0B2, VEGFA, APOA1, GHR, CALR, and MKKS). These hub genes were involved in biological processes of lipid storage / kinase activity, regulation of fatty-acid metabolic processes, regulation of pri-miRNA transcription by RNA polymerase II, and negative regulation of small molecules and carbohydrate metabolic processes. In terms of molecular functions, repressing of transcription factors biding was found enriched. Regarding KEGG Pathways, the hub genes are involved with adipocytokine signaling, insulin resistance, longevity regulation, and cytokine signaling pathways. Conclusion: Our approach identified 19 hub genes, which are highly connected and probably have a key role in childhood obesity. Moreover, functional enrichment analyses demonstrated they are enriched in several biological processes and pathways related to the underlying molecular mechanisms of obesity. These findings provide a more comprehensive information regarding genetic and molecular factors behind childhood obesity pathogenesis. Further experimental investigation of our findings may shed light on the pathophysiology of this disease and contribute to the identification of new therapeutic targets.

scholarly journals Identification Of Both Shared And Specific Potential Molecular Mechanisms Of Arvc And Dcm Based On A Genome-Wide Microarray Dataset

2020 ◽  
Author(s):  
Tang Zhang ◽  
Yao-Zong Guan ◽  
Hao Liu
Keyword(s):  
Molecular Mechanisms ◽  
Arrhythmogenic Right Ventricular Cardiomyopathy ◽  
Venn Diagram ◽  
Ppi Network ◽  
Hub Genes ◽  
Go Annotation ◽  
Kegg Pathways ◽  
Ppi Networks ◽  
A Genome ◽  
Specific Potential

Abstract Background: The study aimed to detect the shared differentially expressed genes (DEGs) and specific DEGs of arrhythmogenic right ventricular cardiomyopathy (ARVC) and dilated cardiomyopathy (DCM) as well as their pathways.Methods: The GSE29819 dataset was examined for the DEGs of ARVC vs. non-failing transplant donor hearts (NF), DCM vs. NF, and ARVC vs. DCM based on 6 patients with ARVC, 7 patients with DCM, and 6 non-failing transplant donor hearts that were never actually transplanted. The shared DEGs and specific DEGs were screened out using a Venn diagram. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, Gene Ontology (GO) annotation, and protein-protein interaction (PPI) of the DEGs were determined using online analytical tools. Then, the modules and hub genes were identified using Cytoscape software.Results: A total of 684 shared DEGs of ARVC vs. NF and DCM vs. NF, 1371 specific DEGs of ARVC vs. NF, and 1075 specific DEGs of DCM vs. NF were identified. The shared DEGs were enriched in 63 biological processes (BP), 11 molecular functions (MF), 10 cellular components (CC), and 25 KEGG pathways. The DEGs of ARVC vs. DCM were enriched in 71 BPs, 19 MFs, 14 CCs, and 26 KEGG pathways. A PPI network with 187 nodes, 700 edges, and 2 modules, and another PPI network with 575 nodes, 2834 edges, and 7 modules were constructed based on the shared and specific DEGs, respectively. The top ten hub genes CCR3, CCR5, CXCL2, CXCL10, CXCR4, FPR1, APLNR, PENK, BDKRB2, GRM8, and RPS8, RPS3A, RPS12, RPS14, RPS21, RPL14, RPL18A, RPL21, RPL31 were identified for the shared and specific PPI networks, respectively.Conclusions: Our findings may help further the understanding of both shared and specific potential molecular mechanisms of ARVC and DCM.

scholarly journals Identification of Both Shared and Specific Potential Molecular Mechanisms of ARVC and DCM Based on A Genome-Wide Microarray Dataset

2020 ◽  
Author(s):  
Tang Zhang ◽  
Yao-Zong Guan ◽  
Hao Liu
Keyword(s):  
Molecular Mechanisms ◽  
Arrhythmogenic Right Ventricular Cardiomyopathy ◽  
Venn Diagram ◽  
Ppi Network ◽  
Hub Genes ◽  
Go Annotation ◽  
Kegg Pathways ◽  
Ppi Networks ◽  
A Genome ◽  
Specific Potential

Abstract Background：The study aimed to detect the shared differentially expressed genes (DEGs) and specific DEGs of arrhythmogenic right ventricular cardiomyopathy (ARVC) and dilated cardiomyopathy (DCM) as well as their pathways. Methods: The GSE29819 dataset was examined for the DEGs of ARVC vs. non-failing transplant donor hearts (NF), DCM vs. NF, and ARVC vs. DCM based on 8 patients with ARVC, 7 patients with DCM, and 4 non-failing transplant donor hearts that were never actually transplanted. The shared DEGs and specific DEGs were screened out using a Venn diagram. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, Gene Ontology (GO) annotation, and protein-protein interaction (PPI) of the DEGs were determined using online analytical tools. Then, the modules and hub genes were identified using Cytoscape software. Results: A total of 684 shared DEGs of ARVC vs. NF and DCM vs. NF, 1371 specific DEGs of ARVC vs. NF, and 1075 specific DEGs of DCM vs. NF were identified. The shared DEGs were enriched in 63 biological processes (BP), 11 molecular functions (MF), 10 cellular components (CC), and 25 KEGG pathways. The DEGs of ARVC vs. DCM were enriched in 71 BPs, 19 MFs, 14 CCs, and 26 KEGG pathways. A PPI network with 187 nodes, 700 edges, and 2 modules, and another PPI network with 575 nodes, 2834 edges, and 7 modules were constructed based on the shared and specific DEGs, respectively. The top ten hub genes CCR3, CCR5, CXCL2, CXCL10, CXCR4, FPR1, APLNR, PENK, BDKRB2, GRM8, and RPS8, PRS3A, PRS12, RPS14, RPS21, RPL14, RPL18A, RPL21, RPL31 were identified for the shared and specific PPI networks, respectively. Conclusions: Our findings may help further the understanding of both shared and specific potential molecular mechanisms of ARVC and DCM.

scholarly journals Bioinformatics Analysis based on Multiple Databases Identifies Hub Genes Associated with Hepatocellular Carcinoma

2019 ◽  
Vol 20 (5) ◽  
pp. 349-361
Author(s):  
Lu Zeng ◽  
Xiude Fan ◽  
Xiaoyun Wang ◽  
Huan Deng ◽  
Kun Zhang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Cell Cycle ◽  
Chemical Carcinogenesis ◽  
Mitotic Cell ◽  
R Package ◽  
Cycle Phase ◽  
Ppi Network ◽  
Hub Genes ◽  
Kegg Pathways ◽  
Go Terms

Background: Hepatocellular carcinoma (HCC) is the most common liver cancer and the mechanisms of hepatocarcinogenesis remain elusive. Objective: This study aims to mine hub genes associated with HCC using multiple databases. Methods: Data sets GSE45267, GSE60502, GSE74656 were downloaded from GEO database. Differentially expressed genes (DEGs) between HCC and control in each set were identified by limma software. The GO term and KEGG pathway enrichment of the DEGs aggregated in the datasets (aggregated DEGs) were analyzed using DAVID and KOBAS 3.0 databases. Protein-protein interaction (PPI) network of the aggregated DEGs was constructed using STRING database. GSEA software was used to verify the biological process. Association between hub genes and HCC prognosis was analyzed using patients’ information from TCGA database by survminer R package. Results: From GSE45267, GSE60502 and GSE74656, 7583, 2349, and 553 DEGs were identified respectively. A total of 221 aggregated DEGs, which were mainly enriched in 109 GO terms and 29 KEGG pathways, were identified. Cell cycle phase, mitotic cell cycle, cell division, nuclear division and mitosis were the most significant GO terms. Metabolic pathways, cell cycle, chemical carcinogenesis, retinol metabolism and fatty acid degradation were the main KEGG pathways. Nine hub genes (TOP2A, NDC80, CDK1, CCNB1, KIF11, BUB1, CCNB2, CCNA2 and TTK) were selected by PPI network and all of them were associated with prognosis of HCC patients. Conclusion: TOP2A, NDC80, CDK1, CCNB1, KIF11, BUB1, CCNB2, CCNA2 and TTK were hub genes in HCC, which may be potential biomarkers of HCC and targets of HCC therapy.

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