Centralities Based Analysis of Complex Networks

Scardoni, Giovanni; Laudanna, Carlo

doi:10.5772/35846

Cited by 75 publications

(78 citation statements)

References 39 publications

(21 reference statements)

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“…To additionally assess the key genes of BC, a centricity analysis was performed based on the nodes degree in the networks (27). Centrality measures mainly contain degree centrality, closeness centrality and shortest path between centrality, in which degree, the equivalent of the number of nodes directly adjacent to a given node (indicating the degree the vertex), is the simplest topological index (27). In the present study, centrality analysis, which was particularly useful to identify key players in biological processes, was implemented to study the differential co-expression networks.…”

Section: Construction Of Differential Co-expression Network By Dcglmentioning

confidence: 99%

Identification of hub genes and pathways associated with bladder cancer based on co-expression network analysis

et al. 2017

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Abstract. The aim of the present study was to identify hub genes and signaling pathways associated with bladder cancer (BC) utilizing centrality analysis and pathway enrichment analysis. The differentially expressed genes (DEGs) were screened from the ArrayExpress database between normal subjects and BC patients. Co-expression networks of BC were constructed using differentially co-expressed genes and links, and hub genes were investigated by degree centrality analysis of co-expression networks in BC. The enriched signaling pathways were investigated by Kyoto Encyclopedia of Genes and Genomes database analysis based on the DEGs. The hub gene expression in BC tissues was validated using reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and western blotting. A total of 329 DEGs were screened, including 147 upregulated and 182 downregulated genes. The co-expression network constructed between BC and normal controls consisted of 182 nodes and 434 edges, and the two genes in each gene pair were differentially co-expressed genes. Centrality analysis of co-expression networks suggested that the top 5 hub genes with high degree included lectin, galactoside-binding, soluble, 4 (LGALS4), protein tyrosine phosphatase, receptor type N2 (PTPRN2), transmembrane protease, serine 11E (TMPRSS11E), tripartite motif containing 31 (TRIM31) and potassium voltage-gated channel subfamily D member 3 (KCND3). Pathway analysisrevealed that the 329 DEGs were significantly enriched in 5 terms (cell cycle, DNA replication, oocyte meiosis, p53 signaling pathway and peroxisome proliferator-activated receptor signaling pathway). According to RT-qPCR and western blot analysis, 4/5 hub genes were significantly expressed, including LGALS4, PTPRN2, TMPRSS11E, TRIM31; however, KCND3 was not significantly expressed. In the present study, 5 hub genes were successfully identified (LGALS4, PTPRN2, TMPRSS11E, TRIM31 and KCND3) and 5 biological pathways that may be underlying biomarkers for early diagnosis and treatment associated with bladder cancer were revealed.

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Section: Construction Of Differential Co-expression Network By Dcglmentioning

confidence: 99%

Identification of hub genes and pathways associated with bladder cancer based on co-expression network analysis

et al. 2017

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“…Researchers have revealed that topological centrality is effective for identifying essential nodes in well-characterized interaction networks (Prifti et al, 2010). In this paper, topological analyses consisting of degree (Haythornthwaite, 1996), betweenness (Abbasi et al, 2012), closeness (Scott, 2012), stress (Scardoni and Laudanna, 2012), and centrality were conducted for pathway networks.…”

Section: Pathway Network and Analysismentioning

confidence: 99%

Identifying significant pathways of hepatitis B virus-related hepatocellular carcinoma based on crosstalk and network pathways

Peng

2016

Genet. Mol. Res.

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ABSTRACT. This study aims to identify significant pathways in hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) based on the pathway network strategy. We proposed a pathway network where a protein-protein interaction (PPI) network was integrated with the crosstalk of pathways. Pathway data were first obtained from background PPI network, Reactome pathway database, and common genes between mRNA differentially expressed genes (DEGs), and miRNA target genes of HBV-related HCC. Pathway interactions were subsequently randomly extracted based on gene-gene interactions, and a weight value was assigned to each crosstalk using the Spearman correlation coefficient. Finally, pathways and crosstalk were visualized via Cytoscape to construct the final pathway network. A total of 9 common genes were identified between 396 mRNA DEGs and 400 miRNA target genes, and 17 pathways were identified based on background pathways and common genes. In addition, we constructed a pathway network that included 136 interactions and 17 pathways. The weight value of netrin-1 signaling and regulation of Frizzled proteins (FZD) by ubiquitination was the largest, at 0.228. In conclusion, we identified 17 significant pathways that might act as potential biomarkers of HBV-related HCC. This information may offer some insight into treatment and detection of HBV-related HCC.

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“…High stress values indicated that the protein was involved in connecting regulatory molecules (Scardoni and Laudanna, 2012). Another parameter of centrality was betweenness, which described the core of node in the network.…”

Section: Centralities Of Networkmentioning

confidence: 99%

Investigation of genes in chronic and acute morphine-treated mice using microarray datasets

Ding¹,

Zhang²,

Yu³

et al. 2015

Genet. Mol. Res.

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ABSTRACT. Morphine is a psychoactive medication that is used as a standard analgesic treatment to relieve pain in clinics. Many patients rely on chronic or acute treatment of morphine to treat pain. However, morphine is a narcotic and has a reverse effect when inappropriately used. Therefore, it is necessary to study chronic and acute morphine treatment to improve pain relief. In this study, differentially expressed genes of acute and chronic morphine-treated mice were identified using Array Express datasets. The genes that were associated with these two types of morphine treatment are discussed. A co-expression network was constructed, and the hub genes were identified. Gene ontology enrichment analysis and pathway analysis were performed using the Gene Ontology website and Kyoto Encyclopedia of Genes and Genomes, respectively. Our study revealed genes that are associated with acute and chronic morphine treatment. Therefore, this study is L. Ding et al. 10194©FUNPEC-RP www.funpecrp.com.br Genetics and Molecular Research 14 (3): 10193-10205 (2015) potentially useful for improving pain relief of patients by acute and chronic morphine treatment.

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Centralities Based Analysis of Complex Networks

Cited by 75 publications

References 39 publications

Identification of hub genes and pathways associated with bladder cancer based on co-expression network analysis

Identification of hub genes and pathways associated with bladder cancer based on co-expression network analysis

Identifying significant pathways of hepatitis B virus-related hepatocellular carcinoma based on crosstalk and network pathways

Investigation of genes in chronic and acute morphine-treated mice using microarray datasets

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