Dataset Information

New Drug Targets to Prevent Death Due to Stroke: A Review Based on Results of Protein-Protein Interaction Network, Enrichment, and Annotation Analyses.

ABSTRACT: This study used established biomarkers of death from ischemic stroke (IS) versus stroke survival to perform network, enrichment, and annotation analyses. Protein-protein interaction (PPI) network analysis revealed that the backbone of the highly connective network of IS death consisted of IL6, ALB, TNF, SERPINE1, VWF, VCAM1, TGFB1, and SELE. Cluster analysis revealed immune and hemostasis subnetworks, which were strongly interconnected through the major switches ALB and VWF. Enrichment analysis revealed that the PPI immune subnetwork of death due to IS was highly associated with TLR2/4, TNF, JAK-STAT, NOD, IL10, IL13, IL4, and TGF-β1/SMAD pathways. The top biological and molecular functions and pathways enriched in the hemostasis network of death due to IS were platelet degranulation and activation, the intrinsic pathway of fibrin clot formation, the urokinase-type plasminogen activator pathway, post-translational protein phosphorylation, integrin cell-surface interactions, and the proteoglycan-integrin extracellular matrix complex (ECM). Regulation Explorer analysis of transcriptional factors shows: (a) that NFKB1, RELA and SP1 were the major regulating actors of the PPI network; and (b) hsa-mir-26-5p and hsa-16-5p were the major regulating microRNA actors. In conclusion, prevention of death due to IS should consider that current IS treatments may be improved by targeting VWF, the proteoglycan-integrin-ECM complex, TGF-β1/SMAD, NF-κB/RELA and SP1.

SUBMITTER: Maes M

PROVIDER: S-EPMC8619767 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature

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New Drug Targets to Prevent Death Due to Stroke: A Review Based on Results of Protein-Protein Interaction Network, Enrichment, and Annotation Analyses.

Maes Michael M Nikiforov Nikita G NG Plaimas Kitiporn K Suratanee Apichat A Alfieri Daniela Frizon DF Vissoci Reiche Edna Maria EM

International journal of molecular sciences 20211109 22

This study used established biomarkers of death from ischemic stroke (IS) versus stroke survival to perform network, enrichment, and annotation analyses. Protein-protein interaction (PPI) network analysis revealed that the backbone of the highly connective network of IS death consisted of IL6, ALB, TNF, SERPINE1, VWF, VCAM1, TGFB1, and SELE. Cluster analysis revealed immune and hemostasis subnetworks, which were strongly interconnected thro ...[more]

PMID: 34829993

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Project description:BackgroundQuantitative proteomics studies are often used to detect proteins that are differentially expressed across different experimental conditions. Functional enrichment analyses are then typically used to detect annotations, such as biological processes that are significantly enriched among such differentially expressed proteins to provide insights into the molecular impacts of the studied conditions. While common, this analytical pipeline often heavily relies on arbitrary thresholds of significance. However, a functional annotation may be dysregulated in a given experimental condition, while none, or very few of its proteins may be individually considered to be significantly differentially expressed. Such an annotation would therefore be missed by standard approaches.ResultsHerein, we propose a novel graph theory-based method, PIGNON, for the detection of differentially expressed functional annotations in different conditions. PIGNON does not assess the statistical significance of the differential expression of individual proteins, but rather maps protein differential expression levels onto a protein-protein interaction network and measures the clustering of proteins from a given functional annotation within the network. This process allows the detection of functional annotations for which the proteins are differentially expressed and grouped in the network. A Monte-Carlo sampling approach is used to assess the clustering significance of proteins in an expression-weighted network. When applied to a quantitative proteomics analysis of different molecular subtypes of breast cancer, PIGNON detects Gene Ontology terms that are both significantly clustered in a protein-protein interaction network and differentially expressed across different breast cancer subtypes. PIGNON identified functional annotations that are dysregulated and clustered within the network between the HER2+, triple negative and hormone receptor positive subtypes. We show that PIGNON's results are complementary to those of state-of-the-art functional enrichment analyses and that it highlights functional annotations missed by standard approaches. Furthermore, PIGNON detects functional annotations that have been previously associated with specific breast cancer subtypes.ConclusionPIGNON provides an alternative to functional enrichment analyses and a more comprehensive characterization of quantitative datasets. Hence, it contributes to yielding a better understanding of dysregulated functions and processes in biological samples under different experimental conditions.

Dataset Information

New Drug Targets to Prevent Death Due to Stroke: A Review Based on Results of Protein-Protein Interaction Network, Enrichment, and Annotation Analyses.

Publications

New Drug Targets to Prevent Death Due to Stroke: A Review Based on Results of Protein-Protein Interaction Network, Enrichment, and Annotation Analyses.

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