Combination use of protein–protein interaction network topological features improves the predictive scores of deleterious non-synonymous single-nucleotide polymorphisms

Wu, Yiming; Jing, Runyu; Jiang, Lin; Jiang, Yanping; Kuang, Qifan; Ye, Ling; Yang, Lijun; Li, Yizhou; Li, Menglong

doi:10.1007/s00726-014-1760-9

Cited by 18 publications

(15 citation statements)

References 46 publications

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“…Although the data of large-scale protein interactions is accumulating with the development of high throughput testing technology, a certain number of significant interactions have not been tested, such as key genes in certain pathways (9). This type of difficulty might be resolved to some extent by utilizing sub-networks or modules of the complex network (10). Ning et al (11) identified pathway-related modules in high-grade OS based on topological centralities analyses of co-expression networks and sub-networks, and made contributions in understanding the molecular pathogenesis of high-grade OS and identifying potential biomarkers for effective therapies.…”

Section: Introductionmentioning

confidence: 99%

Investigating ego modules and pathways in osteosarcoma by integrating the EgoNet algorithm and pathway analysis

Chen

Zhang

et al. 2017

Braz J Med Biol Res

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Osteosarcoma (OS) is the most common primary bone malignancy, but current therapies are far from effective for all patients. A better understanding of the pathological mechanism of OS may help to achieve new treatments for this tumor. Hence, the objective of this study was to investigate ego modules and pathways in OS utilizing EgoNet algorithm and pathway-related analysis, and reveal pathological mechanisms underlying OS. The EgoNet algorithm comprises four steps: constructing background protein-protein interaction (PPI) network (PPIN) based on gene expression data and PPI data; extracting differential expression network (DEN) from the background PPIN; identifying ego genes according to topological features of genes in reweighted DEN; and collecting ego modules using module search by ego gene expansion. Consequently, we obtained 5 ego modules (Modules 2, 3, 4, 5, and 6) in total. After applying the permutation test, all presented statistical significance between OS and normal controls. Finally, pathway enrichment analysis combined with Reactome pathway database was performed to investigate pathways, and Fisher's exact test was conducted to capture ego pathways for OS. The ego pathway for Module 2 was CLEC7A/inflammasome pathway, while for Module 3 a tetrasaccharide linker sequence was required for glycosaminoglycan (GAG) synthesis, and for Module 6 was the Rho GTPase cycle. Interestingly, genes in Modules 4 and 5 were enriched in the same pathway, the 2-LTR circle formation. In conclusion, the ego modules and pathways might be potential biomarkers for OS therapeutic index, and give great insight of the molecular mechanism underlying this tumor.

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Section: Introductionmentioning

confidence: 99%

Investigating ego modules and pathways in osteosarcoma by integrating the EgoNet algorithm and pathway analysis

Chen

Zhang

et al. 2017

Braz J Med Biol Res

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“…Although the availability of data on large-scale protein interactions is increasing with the development of high throughput testing technology, certain important interactions are rarely studied, including significant pathways (26). This may be resolved to some extent by utilizing modules of the complex network (27). However, from a systems biology outlook, diseases are caused by perturbations of the gene network and such perturbations dynamically alter the disease process (25).…”

Section: Discussionmentioning

confidence: 99%

Revealing radiotherapy- and chemoradiation-induced pathway dynamics in glioblastoma by analyzing multiple differential networks

Zhou

Chen

et al. 2017

Molecular Medicine Reports

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The progression of glioblastoma (GBM) is driven by dynamic alterations in the activity and connectivity of gene pathways. Revealing these dynamic events is necessary in order to understand the pathological mechanisms of, and develop effective treatments for, GBM. The present study aimed to investigate dynamic alterations in pathway activity and connectivity across radiotherapy and chemoradiation conditions in GBM, and to give system-level insights into molecular mechanisms for GBM therapy. A total of two differential co-expression networks (DCNs) were constructed using Pearson correlation coefficient analysis and one sided t-tests, based on gene expression profiles and protein-protein interaction networks, one for each condition. Subsequently, shared differential modules across DCNs were detected via significance analysis for candidate modules, which were obtained according to seed selection, module search by seed expansion and refinement of searched modules. As condition-specific differential modules mediate differential biological processes, the module connectivity dynamic score (MCDS) was implemented to explore dynamic alterations among them. Based on DCNs with 287 nodes and 1,052 edges, a total of 28 seed genes and seven candidate modules were identified. Following significance analysis, five shared differential modules were identified in total. Dynamic alterations among these differential modules were identified using the MCDS, and one module with significant dynamic alterations was identified, termed the dynamic module. The present study revealed the dynamic alterations of shared differential modules, identified one dynamic module between the radiotherapy and chemoradiation conditions, and demonstrated that pathway dynamics may applied to the study of the pathogenesis and therapy of GBM.

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“…The cross validation of datasets, including via network-based methods, reduces false findings and increases the sensitivity of the identification of significant DEGs (26). Furthermore, pathway networks provide insight into the potential underlying molecular mechanisms of disease (27). Therefore, in the present study, differential pathway network analysis was used to identify hub signaling pathways in CSVD.…”

Section: Discussionmentioning

confidence: 99%

Identification of key signaling pathways in cerebral small vessel disease using differential pathway network analysis

Zheng¹,

Zheng²,

Xu³

2017

Exp Ther Med

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Abstract. Cerebral small vessel disease (CSVD) primarily affects the perforating cerebral arterioles and capillaries, and results in injury to subcortical grey and white matter. Despite advances in determining the genetic basis of CSVD, the molecular mechanisms underlying the development and progression of CSVD remain unclear. The present study aimed to identify significant signaling pathways associated with CSVD based on differential pathway network analysis. Combining CSVD microarray data with human protein-protein interaction data and data from the Reactome pathway database, pathway interactions were constructed using the Spearman's correlation coefficient strategy. Pathway interactions with weight values of >0.95 were selected to construct the differential pathway network, which contained 715 differential pathway interactions covering 312 nodes and was visualized using Cytoscape software. A total of 15 hub pathways with a top 5% degree distribution in the differential pathway network were identified. The top 5 hub pathways were associated with the synthesis and metabolism of fatty acids. The results of the present study indicate that the synthesis and metabolism of fatty acids is associated with the occurrence and development of CSVD, and may thus provide insights to improve the early diagnosis and treatment of CSVD. IntroductionCerebral small vessel disease (CSVD) predominantly affects the perforating cerebral arterioles and capillaries, and results in injury to subcortical grey and white matter (1). CSVD is associated with focal motor deficits, stroke and cognitive decline, which typically progresses to dementia (2). Genetic studies of CSVD indicate that the development and progression of the disease can be attributed to the accumulation of genomic changes (3). Gene expression profiling has been widely used to research the pathogenesis of diseases, including CSVD. However, despite advances in our knowledge of the genetic basis of CSVD, the underlying molecular mechanisms of the development and progression of CSVD remain unclear.Pathway analysis is a useful tool for gaining insight into the biological functions of genes and proteins (4). Given the complex nature of biological systems, signaling pathways are typically required in order for systems to function in a coordinated fashion to produce the appropriate physiological responses to internal and external stimuli (5). However, previous studies have focused on identifying altered signaling pathways between normal and diseased groups, and common genes between different signaling pathways. For example, a previous study identified differential interactions between two signaling pathways across diseased and normal samples (6). Network-based methods have been used to analyze this interaction data and gain insights into the underlying molecular mechanisms by which biological systems operate (7). Sun et al (8) introduced a network-based approach, differential expression network analysis, which reflects phenotypic differences at a network level. Similarly, in the ...

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Combination use of protein–protein interaction network topological features improves the predictive scores of deleterious non-synonymous single-nucleotide polymorphisms

Cited by 18 publications

References 46 publications

Investigating ego modules and pathways in osteosarcoma by integrating the EgoNet algorithm and pathway analysis

Investigating ego modules and pathways in osteosarcoma by integrating the EgoNet algorithm and pathway analysis

Revealing radiotherapy- and chemoradiation-induced pathway dynamics in glioblastoma by analyzing multiple differential networks

Identification of key signaling pathways in cerebral small vessel disease using differential pathway network analysis

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