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Lotia, Samad; Montojo, Jason; Dong, Yue; Bader, Gary D.; Pico, Alexander R.

doi:10.1093/bioinformatics/btt138

Cited by 153 publications

(130 citation statements)

References 3 publications

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“…For GO enrichment analysis, proteins were subjected to BiN-GO analysis (version 3.0.3) (Maere et al 2005) using Cytoscape software version 3.2.1 (Lotia et al 2013). Parameters were set to show overrepresentation of GO terms with P-values <0.05 (hygrometric statistical test with Benjamin and Hochberg false discovery rate [FDR] correction).…”

Section: Subcellular Compartment and Go Enrichment Analysesmentioning

confidence: 99%

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Identification of Nidogen 1 as a lung metastasis protein through secretome analysis

et al. 2017

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Secreted proteins play crucial roles in mediating tumor-stroma interactions during metastasis of cancer to different target organs. To comprehensively profile secreted proteins involved in lung metastasis, we applied quantitative mass spectrometry-based proteomics and identified 392 breast cancer-derived and 302 melanoma-derived proteins secreted from highly lung metastatic cells. The cancer-specific lung metastasis secretome signatures (LMSSs) displayed significant prognostic value in multiple cancer clinical data sets. Moreover, we observed a significant overlap of enriched pathways between the LMSSs of breast cancer and melanoma despite an overall small overlap of specific proteins, suggesting that common biological processes are executed by different proteins to enable the two cancer types to metastasize to the lung. Among the novel candidate lung metastasis proteins, Nidogen 1 (NID1) was confirmed to promote lung metastasis of breast cancer and melanoma, and its expression is correlated with poor clinical outcomes. In vitro functional analysis further revealed multiple prometastatic functions of NID1, including enhancing cancer cell migration and invasion, promoting adhesion to the endothelium and disrupting its integrity, and improving vascular tube formation capacity. As a secreted prometastatic protein, NID1 may be developed as a new biomarker for disease progression and therapeutic target in breast cancer and melanoma.

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Section: Subcellular Compartment and Go Enrichment Analysesmentioning

confidence: 99%

“…Protein interactions were mapped by displaying only high-confidence interactions with a score of 0.7. The networks were downloaded and then processed using Cytoscape (Lotia et al 2013). Clustering into protein families and functional categories was performed manually.…”

Section: Protein Interaction Network Analysismentioning

confidence: 99%

Identification of Nidogen 1 as a lung metastasis protein through secretome analysis

et al. 2017

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“…The p≤ 0.05 values were corrected using the Bonferroni method (Dmitrienko et al, 2013) to express important and dominant signaling pathways. Cytoscape Version 3.1.1 (Lotia et al, 2013) was used to plot and analyze the centralities, clustering and modularity of the network. In networks, nodes with degrees greater than or equal to the sum of mean and twice the standard deviation (S.D.…”

Section: Network Analysismentioning

confidence: 99%

“…STRING is able to calculate a score for each prediction method and count combined scores (Szklarczyk et al, 2011;von Mering al., 2007).Accordingly, different modules associated to PD in bovine were assigned and were found via Cytoscape Version 3.1.1 (Lotia et al, 2013). The network density, network diameter and clustering coefficient of the PIN for Bovinewere 0.065, 9 and 0.381, respectively.…”

Section: Pin Modules and Global Propertiesmentioning

confidence: 99%

Parkinson's disease and lactoferrin: Analysis of dependent protein networks

et al. 2016

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“…Comparable PathVisio code can be found in an online tutorial (). Arguably, Cytoscape is currently the most popular tool for pathway editing, featuring over 150 plugins in the Cytoscape App store [26]. An extensive review of standalone tools for pathway curation has been compiled by Sunderman and Hallett [27].…”

Section: Introductionmentioning

confidence: 99%

R-Based Software for the Integration of Pathway Data into Bioinformatic Algorithms

Bayerlová

Beißbarth

2014

Biology

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Putting new findings into the context of available literature knowledge is one approach to deal with the surge of high-throughput data results. Furthermore, prior knowledge can increase the performance and stability of bioinformatic algorithms, for example, methods for network reconstruction. In this review, we examine software packages for the statistical computing framework R, which enable the integration of pathway data for further bioinformatic analyses. Different approaches to integrate and visualize pathway data are identified and packages are stratified concerning their features according to a number of different aspects: data import strategies, the extent of available data, dependencies on external tools, integration with further analysis steps and visualization options are considered. A total of 12 packages integrating pathway data are reviewed in this manuscript. These are supplemented by five R-specific packages for visualization and six connector packages, which provide access to external tools.

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Cytoscape App Store

Cited by 153 publications

References 3 publications

Identification of Nidogen 1 as a lung metastasis protein through secretome analysis

Identification of Nidogen 1 as a lung metastasis protein through secretome analysis

Parkinson's disease and lactoferrin: Analysis of dependent protein networks

R-Based Software for the Integration of Pathway Data into Bioinformatic Algorithms

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