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

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.

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Identification of potential crucial genes and pathways associated with vein graft restenosis based on gene expression analysis in experimental rabbits

PeerJ ◽

10.7717/peerj.4704 ◽

2018 ◽

Vol 6 ◽

pp. e4704 ◽

Cited By ~ 1

Author(s):

Qiang Liu ◽

Xiujie Yin ◽

Mingzhu Li ◽

Li Wan ◽

Liqiao Liu ◽

...

Keyword(s):

Gene Expression ◽

High Throughput ◽

Vein Graft ◽

Molecular Mechanisms ◽

Rapid Development ◽

Bypass Graft ◽

Functional Enrichment ◽

Ppi Network ◽

Hub Genes ◽

Network Analyses

Occlusive artery disease (CAD) is the leading cause of death worldwide. Bypass graft surgery remains the most prevalently performed treatment for occlusive arterial disease, and veins are the most frequently used conduits for surgical revascularization. However, the clinical efficacy of bypass graft surgery is highly affected by the long-term potency rates of vein grafts, and no optimal treatments are available for the prevention of vein graft restenosis (VGR) at present. Hence, there is an urgent need to improve our understanding of the molecular mechanisms involved in mediating VGR. The past decade has seen the rapid development of genomic technologies, such as genome sequencing and microarray technologies, which will provide novel insights into potential molecular mechanisms involved in the VGR program. Ironically, high throughput data associated with VGR are extremely scarce. The main goal of the current study was to explore potential crucial genes and pathways associated with VGR and to provide valid biological information for further investigation of VGR. A comprehensive bioinformatics analysis was performed using high throughput gene expression data. Differentially expressed genes (DEGs) were identified using the R and Bioconductor packages. After functional enrichment analysis of the DEGs, protein–protein interaction (PPI) network and sub-PPI network analyses were performed. Finally, nine potential hub genes and fourteen pathways were identified. These hub genes may interact with each other and regulate the VGR program by modulating the cell cycle pathway. Future studies focusing on revealing the specific cellular and molecular mechanisms of these key genes and pathways involved in regulating the VGR program may provide novel therapeutic targets for VGR inhibition.

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Up-regulation of FN1, Activation of Maturation Promoting Factor and Associated Signaling Pathway Facilitates Epithelial-Mesenchymal Transition, Inhibits Apoptosis and Elevates Proliferation Rate of Breast Cancer Cells: In Silico Analysis of Microarray Datasets

10.21203/rs.3.rs-71394/v1 ◽

2020 ◽

Author(s):

Yasir Hameed ◽

Samina Ejaz

Keyword(s):

Breast Cancer ◽

In Silico ◽

Molecular Mechanisms ◽

Methylation Status ◽

Functional Enrichment ◽

Ppi Network ◽

Hub Genes ◽

Online Tool ◽

Mesenchymal Transition ◽

Maturation Promoting Factor

Abstract Background: Knowing that the molecular mechanisms underlying breast cancer (BC) are not yet fully understood it was considered worth to launch investigation for the detection of key molecular pathways and associated genes and proteins. Methods: In total two microarray based datasets (GSE10810 and GSE29431), consisting of 89 breast cancer samples and 31 controls, were retrieved from the Gene Expression Omnibus (GEO) database and processed to identify the differentially expressed genes (DEGs). The pathway and functional enrichment analyses of DEGs were performed using DAVID online tool. Protein-Protein interaction (PPI) network was constructed using an online tool STRING and visualized through Cytoscape software to identify the significant module and hub genes via MCODE and Cytohubba applications. The identified hub genes were then further analyzed to document their response in Kaplan-Meier (KM) survival curve analysis, investigate differential expression profile and its correlation with promoter’s methylation status, and finally for the construction of the gene-drug interaction network. Results: In total 449 DEGs were detected including 151 up-regulated and 298 down-regulated genes. The identified DEGs were enriched in various cancer related biological functions and pathways. Based on PPI network analysis of the DEGs, six hub genes, CDK1, FN1, AURKA, CCNB2, BIRC5, and TOP2A, were correlated with worse overall survival (OS) of the breast cancer patients. Conclusion: The extensive in silico analysis has been helpful to evaluate evidence highlighting the prominent role played by the identified hub genes in stimulating breast tumor growth through the activation of Maturation Promoting Factor (MPF), PI3K/AKT signaling, and associated pathways that could be targeted for devising effective treatment strategies.

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

10.21203/rs.3.rs-42140/v1 ◽

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.

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Bioinformatics analysis of microarray data to identify the candidate biomarkers of lung adenocarcinoma

PeerJ ◽

10.7717/peerj.7313 ◽

2019 ◽

Vol 7 ◽

pp. e7313 ◽

Cited By ~ 6

Author(s):

Tingting Guo ◽

Hongtao Ma ◽

Yubai Zhou

Keyword(s):

Survival Analysis ◽

Lung Adenocarcinoma ◽

Molecular Mechanisms ◽

Prion Diseases ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Gene Expression Omnibus ◽

Functional Enrichment ◽

Ppi Network ◽

Hub Genes

Background Lung adenocarcinoma (LUAD) is the major subtype of lung cancer and the most lethal malignant disease worldwide. However, the molecular mechanisms underlying LUAD are not fully understood. Methods Four datasets (GSE118370, GSE85841, GSE43458 and GSE32863) were obtained from the gene expression omnibus (GEO). Identification of differentially expressed genes (DEGs) and functional enrichment analysis were performed using the limma and clusterProfiler packages, respectively. A protein–protein interaction (PPI) network was constructed via Search Tool for the Retrieval of Interacting Genes (STRING) database, and the module analysis was performed by Cytoscape. Then, overall survival analysis was performed using the Kaplan–Meier curve, and prognostic candidate biomarkers were further analyzed using the Oncomine database. Results Totally, 349 DEGs were identified, including 275 downregulated and 74 upregulated genes which were significantly enriched in the biological process of extracellular structure organization, leukocyte migration and response to peptide. The mainly enriched pathways were complement and coagulation cascades, malaria and prion diseases. By extracting key modules from the PPI network, 11 hub genes were screened out. Survival analysis showed that except VSIG4, other hub genes may be involved in the development of LUAD, in which MYH10, METTL7A, FCER1G and TMOD1 have not been reported previously to correlated with LUAD. Briefly, novel hub genes identified in this study will help to deepen our understanding of the molecular mechanisms of LUAD carcinogenesis and progression, and to discover candidate targets for early detection and treatment of LUAD.

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

10.21203/rs.3.rs-47567/v1 ◽

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.

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

PeerJ ◽

10.7717/peerj.7628 ◽

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.

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Bioinformatics analysis of differentially expressed genes and pathways in the development of cervical cancer

BMC Cancer ◽

10.1186/s12885-021-08412-4 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Baojie Wu ◽

Shuyi Xi

Keyword(s):

Gene Expression ◽

Cervical Cancer ◽

Differentially Expressed Genes ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Molecular Targets ◽

Enrichment Analysis ◽

Ppi Network ◽

Hub Genes ◽

Kegg Pathways

Abstract Background This study aimed to explore and identify key genes and signaling pathways that contribute to the progression of cervical cancer to improve prognosis. Methods Three gene expression profiles (GSE63514, GSE64217 and GSE138080) were screened and downloaded from the Gene Expression Omnibus database (GEO). Differentially expressed genes (DEGs) were screened using the GEO2R and Venn diagram tools. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. Gene set enrichment analysis (GSEA) was performed to analyze the three gene expression profiles. Moreover, a protein–protein interaction (PPI) network of the DEGs was constructed, and functional enrichment analysis was performed. On this basis, hub genes from critical PPI subnetworks were explored with Cytoscape software. The expression of these genes in tumors was verified, and survival analysis of potential prognostic genes from critical subnetworks was conducted. Functional annotation, multiple gene comparison and dimensionality reduction in candidate genes indicated the clinical significance of potential targets. Results A total of 476 DEGs were screened: 253 upregulated genes and 223 downregulated genes. DEGs were enriched in 22 biological processes, 16 cellular components and 9 molecular functions in precancerous lesions and cervical cancer. DEGs were mainly enriched in 10 KEGG pathways. Through intersection analysis and data mining, 3 key KEGG pathways and related core genes were revealed by GSEA. Moreover, a PPI network of 476 DEGs was constructed, hub genes from 12 critical subnetworks were explored, and a total of 14 potential molecular targets were obtained. Conclusions These findings promote the understanding of the molecular mechanism of and clinically related molecular targets for cervical cancer.

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Identification of Key Genes and Pathways Associated with Differences of Subchondral Bone in Osteoarthritis

10.21203/rs.3.rs-51799/v1 ◽

2020 ◽

Author(s):

Yiyuan Zhang ◽

Rongguo Yu ◽

Jiayu Zhang ◽

Eryou Feng ◽

Haiyang Wang ◽

...

Keyword(s):

Subchondral Bone ◽

Molecular Mechanisms ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Functional Enrichment ◽

Hub Genes ◽

Common Chronic Disease ◽

Drug Candidates ◽

Therapeutic Modalities ◽

Pathogenic Genes

Abstract BackgroundOsteoarthritis (OA) is a common chronic disease worldwide. Subchondral bone is an important pathological change in OA and responds more rapidly to adverse loading and events compared to cartilage. However, the pathogenic genes and pathways of subchondral bone are largely unclear.ObjectiveThis study aimed to identify signature differences in genes involved in knee lateral tibial (LT) and medial tibial (MT) plateaus of subchondral bone tissue while exploring their potential molecular mechanisms via bioinformatics analysis.MethodsFirst, the gene expression data of GSE51588 was downloaded from the GEO database. Differentially expressed genes (DEGs) between knee LT and MT were identified, and functional enrichment analyses were performed. Then, a protein-protein interactive network was constructed in order to acquire the hub genes, and modules analysis was conducted using STRING and Cytoscape for further analysis. The enriched hub genes were queried in DGIdb database to find suitable drug candidates in OA.ResultsA total of 202 DEGs (112 upregulated genes and 84 downregulated genes) were determined. In the PPI network, ten hub genes were identified. Five significant modules were identified using the MCODE plugin unit. Functional enrichment analysis revealed the most important signaling pathways. Six of the ten hub genes were targetable by a total of 35 drugs, suggesting their possible therapeutic use for OA .ConclusionsThe identified hub genes and functional enrichment pathways were implicated in the development and progression of subchondral bone in OA, thus improving our understanding of OA and offering molecular targets for future therapeutic modalities.

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Identification of Novel Hub Genes Through Expression Profiles Analysis in Adrenocortical Carcinoma

10.21203/rs.3.rs-38768/v1 ◽

2020 ◽

Author(s):

Chenhe Yao ◽

Xiaoling Zhao ◽

Xuemeng Shang ◽

Binghan Jia ◽

Shuaijie Dou ◽

...

Keyword(s):

Adrenocortical Carcinoma ◽

Expression Profiling ◽

Molecular Mechanisms ◽

Cross Validation ◽

Expression Profiles ◽

Ppi Network ◽

Hub Genes ◽

Tumor Association ◽

Prediction Of Prognosis ◽

Geo Database

Abstract Background: Adrenocortical carcinoma (ACC) is a heterogeneous and rare malignant tumor associated with a poor prognosis. The molecular mechanisms of ACC remain elusive and more accurate biomarkers for the prediction of prognosis are needed.Methods: In this study, integrative profiling analyses were performed to identify novel hub genes in ACC to provide promising targets for future investigation. Three gene expression profiling datasets in the GEO database were used for the identification of overlapped differentially expressed genes (DEGs) following the criteria of adj.P.Value<0.05 and |log2 FC|>0.5 in ACC. Novel hub genes were screened out following a series of processes: the retrieval of DEGs with no known associations with ACC on Pubmed, then the cross-validation of expression values and significant associations with overall survival in the GEPIA2 and starBase databases, and finally the prediction of gene-tumor association in the GeneCards database.Results: Four novel hub genes were identified and two of them, TPX2 and RACGAP1, were positively correlated with the staging. Interestingly, co-expression analysis revealed that the association between TPX2 and RACGAP1 was the strongest and that the expression of HOXA5 was almost completely independent of that of RACGAP1 and TPX2. Furthermore, the PPI network consisting of four novel genes and seed genes in ACC revealed that HOXA5, TPX2, and RACGAP1 were all associated with TP53. Conclusions: This study identified four novel hub genes (TPX2, RACHAP1, HXOA5 and FMO2) that may play crucial roles in the tumorigenesis and the prediction of prognosis of ACC.

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Integrated Analysis of lncRNAs, mRNAs, and TFs to Identify Regulatory Networks Underlying MAP Infection in Cattle

Frontiers in Genetics ◽

10.3389/fgene.2021.668448 ◽

2021 ◽

Vol 12 ◽

Author(s):

Maryam Heidari ◽

Abbas Pakdel ◽

Mohammad Reza Bakhtiarizadeh ◽

Fariba Dehghanian

Keyword(s):

Regulatory Networks ◽

Molecular Mechanisms ◽

Enrichment Analysis ◽

Johne's Disease ◽

Johne’S Disease ◽

Functional Enrichment ◽

Integrated Analysis ◽

Complex Nature ◽

Hub Genes ◽

Cis And Trans

Johne’s disease is a chronic infection of ruminants that burdens dairy herds with a significant economic loss. The pathogenesis of the disease has not been revealed clearly due to its complex nature. In order to achieve deeper biological insights into molecular mechanisms involved in MAP infection resulting in Johne’s disease, a system biology approach was used. As far as is known, this is the first study that considers lncRNAs, TFs, and mRNAs, simultaneously, to construct an integrated gene regulatory network involved in MAP infection. Weighted gene coexpression network analysis (WGCNA) and functional enrichment analysis were conducted to explore coexpression modules from which nonpreserved modules had altered connectivity patterns. After identification of hub and hub-hub genes as well as TFs and lncRNAs in the nonpreserved modules, integrated networks of lncRNA-mRNA-TF were constructed, and cis and trans targets of lncRNAs were identified. Both cis and trans targets of lncRNAs were found in eight nonpreserved modules. Twenty-one of 47 nonpreserved modules showed significant biological processes related to the immune system and MAP infection. Some of the MAP infection’s related pathways in the most important nonpreserved modules comprise “positive regulation of cytokine-mediated signaling pathway,” “negative regulation of leukocyte migration,” “T-cell differentiation,” “neutrophil activation,” and “defense response.” Furthermore, several genes were identified in these modules, including SLC11A1, MAPK8IP1, HMGCR, IFNGR1, CMPK2, CORO1A, IRF1, LDLR, BOLA-DMB, and BOLA-DMA, which are potentially associated with MAP pathogenesis. This study not only enhanced our knowledge of molecular mechanisms behind MAP infection but also highlighted several promising hub and hub-hub genes involved in macrophage-pathogen interaction.

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