The Impact of Pathway Database Choice on Statistical Enrichment Analysis and Predictive Modeling

Mubeen, Sarah; Hoyt, Charles Tapley; Gemünd, André; Hofmann‐Apitius, Martin; Fröhlich, Holger; Domingo‐Fernándéz, Daniel

doi:10.3389/fgene.2019.01203

Cited by 82 publications

(67 citation statements)

References 63 publications

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“…protein–protein interactions, complexes) ( 51 ). This information is integrated into PathwayForte ( 52 ) and the feedback from these analyses led to additional curation efforts on WikiPathways including renaming of pathways.…”

Section: Connections To Other Initiativesmentioning

confidence: 99%

WikiPathways: connecting communities

Martens

Ammar

Riutta

et al. 2020

Nucleic Acids Research

569

364

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WikiPathways (https://www.wikipathways.org) is a biological pathway database known for its collaborative nature and open science approaches. With the core idea of the scientific community developing and curating biological knowledge in pathway models, WikiPathways lowers all barriers for accessing and using its content. Increasingly more content creators, initiatives, projects and tools have started using WikiPathways. Central in this growth and increased use of WikiPathways are the various communities that focus on particular subsets of molecular pathways such as for rare diseases and lipid metabolism. Knowledge from published pathway figures helps prioritize pathway development, using optical character and named entity recognition. We show the growth of WikiPathways over the last three years, highlight the new communities and collaborations of pathway authors and curators, and describe various technologies to connect to external resources and initiatives. The road toward a sustainable, community-driven pathway database goes through integration with other resources such as Wikidata and allowing more use, curation and redistribution of WikiPathways content.

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Section: Connections To Other Initiativesmentioning

confidence: 99%

WikiPathways: connecting communities

Martens

Ammar

Riutta

et al. 2020

Nucleic Acids Research

569

364

View full text Add to dashboard Cite

show abstract

“…The gene set enrichment analysis highlighted cytokine, interleukin, and toll-like receptor signalling pathways that are involved in regulating various aspects of innate and adaptive immune responses 97 . Such results may be compromised when other pathway databases such as KEGG 98 and WikiPathways 99 are employed, as the relevant pathways and molecular interactions in the pathways are different from Reactome 100 , 101 . To circumvent this issue, one possibility could be to extract the overlapped networks between the different databases of pathways; however, this is not always possible due to the differences in annotation of genes and interactions.…”

Section: Discussionmentioning

confidence: 99%

Network- and systems-based re-engineering of dendritic cells with non-coding RNAs for cancer immunotherapy

et al. 2021

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Dendritic cells (DCs) are professional antigen-presenting cells that induce and regulate adaptive immunity by presenting antigens to T cells. Due to their coordinative role in adaptive immune responses, DCs have been used as cell-based therapeutic vaccination against cancer. The capacity of DCs to induce a therapeutic immune response can be enhanced by re-wiring of cellular signalling pathways with microRNAs (miRNAs). Methods: Since the activation and maturation of DCs is controlled by an interconnected signalling network, we deploy an approach that combines RNA sequencing data and systems biology methods to delineate miRNA-based strategies that enhance DC-elicited immune responses. Results: Through RNA sequencing of IKKβ-matured DCs that are currently being tested in a clinical trial on therapeutic anti-cancer vaccination, we identified 44 differentially expressed miRNAs. According to a network analysis, most of these miRNAs regulate targets that are linked to immune pathways, such as cytokine and interleukin signalling. We employed a network topology-oriented scoring model to rank the miRNAs, analysed their impact on immunogenic potency of DCs, and identified dozens of promising miRNA candidates, with miR-15a and miR-16 as the top ones. The results of our analysis are presented in a database that constitutes a tool to identify DC-relevant miRNA-gene interactions with therapeutic potential ( https://www.synmirapy.net/dc-optimization ). Conclusions: Our approach enables the systematic analysis and identification of functional miRNA-gene interactions that can be experimentally tested for improving DC immunogenic potency.

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“…Third, the definition of pathways in different sources may vary. The use of multiple pathway databases has been suggested because the choice of the database could impact the enrichment analysis and predictive model [55]. The need to name and annotate the pathway with a unique identification number has been called for in a comparison review of twenty-four human cell signaling pathway databases [56].…”

Section: Discussionmentioning

confidence: 99%

Network hub-node prioritization of gene regulation with intra-network association

et al. 2020

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Background: To identify and prioritize the influential hub genes in a gene-set or biological pathway, most analyses rely on calculation of marginal effects or tests of statistical significance. These procedures may be inappropriate since hub nodes are common connection points and therefore may interact with other nodes more often than non-hub nodes do. Such dependence among gene nodes can be conjectured based on the topology of the pathway network or the correlation between them. Results: Here we develop a pathway activity score incorporating the marginal (local) effects of gene nodes as well as intra-network affinity measures. This score summarizes the expression levels in a gene-set/pathway for each sample, with weights on local and network information, respectively. The score is next used to examine the impact of each node through a leave-one-out evaluation. To illustrate the procedure, two cancer studies, one involving RNA-Seq from breast cancer patients with high-grade ductal carcinoma in situ and one microarray expression data from ovarian cancer patients, are used to assess the performance of the procedure, and to compare with existing methods, both ones that do and do not take into consideration correlation and network information. The hub nodes identified by the proposed procedure in the two cancer studies are known influential genes; some have been included in standard treatments and some are currently considered in clinical trials for target therapy. The results from simulation studies show that when marginal effects are mild or weak, the proposed procedure can still identify causal nodes, whereas methods relying only on marginal effect size cannot. Conclusions: The NetworkHub procedure proposed in this research can effectively utilize the network information in combination with local effects derived from marker values, and provide a useful and complementary list of recommendations for prioritizing causal hubs.

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The Impact of Pathway Database Choice on Statistical Enrichment Analysis and Predictive Modeling

Cited by 82 publications

References 63 publications

WikiPathways: connecting communities

WikiPathways: connecting communities

Network- and systems-based re-engineering of dendritic cells with non-coding RNAs for cancer immunotherapy

Network hub-node prioritization of gene regulation with intra-network association

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