Comparative analysis of protein interactome networks prioritizes candidate genes with cancer signatures

Li, Yongsheng; Sahni, Nidhi; Yi, S. Stephen

doi:10.18632/oncotarget.12879

Cited by 14 publications

(11 citation statements)

References 36 publications

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“…Protein interaction networks have been compared elsewhere. For example, comparing the power of five PPI networks to predict cancer genes [ 29 ], benchmarking 21 networks for their ability to predict disease genes [ 30 ], and investigating their impact on recovering novel PPIs from high-throughput data [ 31 ]. However, to our knowledge our study is the first to specifically address the context-specific nature of PPI entries in these databases.…”

Section: Discussionmentioning

confidence: 99%

Context-specific interactions in literature-curated protein interaction databases

et al. 2018

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BackgroundDatabases of literature-curated protein-protein interactions (PPIs) are often used to interpret high-throughput interactome mapping studies and estimate error rates. These databases combine interactions across thousands of published studies and experimental techniques. Because the tendency for two proteins to interact depends on the local conditions, this heterogeneity of conditions means that only a subset of database PPIs are interacting during any given experiment. A typical use of these databases as gold standards in interactome mapping projects, however, assumes that PPIs included in the database are indeed interacting under the experimental conditions of the study.ResultsUsing raw data from 20 co-fractionation experiments and six published interactomes, we demonstrate that this assumption is often false, with up to 55% of purported gold standard interactions showing no evidence of interaction, on average. We identify a subset of CORUM database complexes that do show consistent evidence of interaction in co-fractionation studies, and we use this subset as gold standards to dramatically improve interactome mapping as judged by the number of predicted interactions at a given error rate.ConclusionsWe recommend using this CORUM subset as the gold standard set in future co-fractionation studies. More generally, we recommend using the subset of literature-curated PPIs that are specific to the experimental context whenever possible.Electronic supplementary materialThe online version of this article (10.1186/s12864-018-5139-2) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

Context-specific interactions in literature-curated protein interaction databases

et al. 2018

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“…Network-based approaches for the identification of biomarkers for different diseases have been previously investigated ( 24 , 25 ). These approaches used the genes associated with the specific condition to construct the network and selected the hub molecules that may potentially be associated with the disease condition.…”

Section: Discussionmentioning

confidence: 99%

Integrative gene ontology and network analysis of coronary artery disease associated genes suggests potential role of ErbB pathway gene EGFR

et al. 2018

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Coronary artery disease (CAD) is a major cause of mortality in India, more importantly the young Indians. Combinatorial and integrative approaches to evaluate pathways and genes to gain an improved understanding and potential biomarkers for risk assessment are required. Therefore, 608 genes from the CADgene database version 2.0, classified into 12 functional classes representing the atherosclerotic disease process, were analyzed. Homology analysis of the unique list of gene ontologies (GO) from each functional class gave 8 GO terms represented in 11 and 10 functional classes. Using disease ontology analysis 80 genes belonging to 8 GO terms, using FunDO suggested that 29 of them were identified to be associated with CAD. Extended network analysis of these genes using STRING version 9.1 gave 328 nodes and 4,525 interactions of which the top 5% had a node degree of ≥75 associated with pathways including the ErbB signaling pathway with epidermal growth factor receptor (EGFR) gene as the central hub. Evaluation of EFGR protein levels in age and gender-matched 342 CAD patients vs. 342 control subjects demonstrated significant differences [controls=149.76±2.47 pg/ml and CAD patients stratified into stable angina (SA)=161.65±3.40 pg/ml and myocardial infarction (MI)=171.51±4.26 pg/ml]. Logistic regression analysis suggested that increased EGFR levels exhibit 3-fold higher risk of CAD [odds ratio (OR) 3.51, 95% confidence interval [CI] 1.96–6.28, P≤0.001], upon adjustment for hypertension, diabetes and smoking. A unit increase in EGFR levels increased the risk by 2-fold for SA (OR 2.58, 95% CI 1.25–5.33, P=0.01) and 3.8-fold for MI (OR 3.82, 95% CI 1.94–7.52, P≤0.001) following adjustment. Thus, the use of ontology mapping and network analysis in an integrative manner aids in the prioritization of biomarkers of complex disease.

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“…A systematic map of ~16,000 high-quality human binary protein-protein interaction network, which was identified by yeast two-hybrid (Y2H) assay, was used in our analysis (20,21). The homodimer interactions were removed and there are 15,957 interactions among 4,743 genes for further analysis.…”

Section: Protein-protein Interaction Network and Signaling Pathwaysmentioning

confidence: 99%

e-MutPath: Computational modelling reveals the functional landscape of genetic mutations rewiring interactome networks

McGrail

Burgman

et al. 2020

Preprint

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Understanding the functional impact of cancer somatic mutations represents a critical knowledge gap for implementing precision oncology. It has been increasingly appreciated that the ‘edgotype’ of a genomic mutation provides a fundamental link between genotype and phenotype. However, specific effects on biological signaling networks for the majority of mutations are largely unknown by experimental approaches. To resolve this challenge, we developed e-MutPath, a network-based computational method to identify candidate ‘edgetic’ mutations that perturb functional pathways. e-MutPath identifies informative paths that could be used to distinguish disease risk factors from neutral elements and to stratify disease subtypes with clinical relevance. The predicted targets are enriched in cancer vulnerability genes, known drug targets but depleted for proteins associated with side effects, demonstrating the power of network-based strategies to investigate the functional impact and perturbation profiles of genomic mutations. Together, e-MutPath represents a robust computational tool to systematically assign functions to genetic mutations, especially in the context of their specific pathway perturbation effect. The code for e-MutPath is available as a user-friendly R package at the GitHub website (https://github.com/lyshaerbin/eMutPath).

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Comparative analysis of protein interactome networks prioritizes candidate genes with cancer signatures

Cited by 14 publications

References 36 publications

Context-specific interactions in literature-curated protein interaction databases

Context-specific interactions in literature-curated protein interaction databases

Integrative gene ontology and network analysis of coronary artery disease associated genes suggests potential role of ErbB pathway gene EGFR

e-MutPath: Computational modelling reveals the functional landscape of genetic mutations rewiring interactome networks

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