Cytoscape stringApp 2.0: Analysis and Visualization of Heterogeneous Biological Networks

Doncheva, Nadezhda T.; Morris, John H.; Holze, Henrietta; Kirsch, Rebecca; Nastou, Katerina C.; Cuesta-Astroz, Yesid; Rattei, Thomas; Szklarczyk, Damian; Mering, Christian von; Jensen, Lars Juhl

doi:10.1021/acs.jproteome.2c00651

Cited by 70 publications

(35 citation statements)

References 47 publications

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“…Cytoscape provides an open source environment for the large-scale integration of molecular interaction network data. In addition, Cytoscape enables integration with stringApp [ 101 ] to facilitate the visualization of network data from the STRING database [ 102 ]. To provide more robustness to the analysis, the 50 nearest interactors to the identified SDRs were included.…”

Section: Methodsmentioning

confidence: 99%

Analysis of the Genetic Relationship between Atherosclerosis and Non-Alcoholic Fatty Liver Disease through Biological Interaction Networks

Andújar-Vera

Ferrer-Millán

García-Fontana

et al. 2023

IJMS

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Non-alcoholic fatty liver disease (NAFLD) seems to have some molecular links with atherosclerosis (ATH); however, the molecular pathways which connect both pathologies remain unexplored to date. The identification of common factors is of great interest to explore some therapeutic strategies to improve the outcomes for those affected patients. Differentially expressed genes (DEGs) for NAFLD and ATH were extracted from the GSE89632 and GSE100927 datasets, and common up- and downregulated DEGs were identified. Subsequently, a protein–protein interaction (PPI) network based on the common DEGs was performed. Functional modules were identified, and the hub genes were extracted. Then, a Gene Ontology (GO) and pathway analysis of common DEGs was performed. DEGs analysis in NAFLD and ATH showed 21 genes that were regulated similarly in both pathologies. The common DEGs with high centrality scores were ADAMTS1 and CEBPA which appeared to be down- and up-regulated in both disorders, respectively. For the analysis of functional modules, two modules were identified. The first one was oriented to post-translational protein modification, where ADAMTS1 and ADAMTS4 were identified, and the second one mainly related to the immune response, where CSF3 was identified. These factors could be key proteins with an important role in the NAFLD/ATH axis.

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Section: Methodsmentioning

confidence: 99%

Analysis of the Genetic Relationship between Atherosclerosis and Non-Alcoholic Fatty Liver Disease through Biological Interaction Networks

Andújar-Vera

Ferrer-Millán

García-Fontana

et al. 2023

IJMS

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“…Protein-protein interaction networks have been determined using the app stringApp (31) of Cytoscape (32). Widths of the edges are function of the “experiments” scores provided by STRING (33).…”

Section: Methodsmentioning

confidence: 99%

The RBPome of influenza A virus mRNA reveals a role for TDP-43 in viral replication

Dupont

Krischuns

Gianetto

et al. 2023

Preprint

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Recent technical advances have significantly improved our understanding of the RNA-binding protein (RBP) repertoire present within eukaryotic cells, with a particular focus on the RBPs that interact with cellular polyadenylated mRNAs. However, recent studies utilising the same technologies have begun to tease apart the RBP interactome of viral mRNAs, notably SARS-CoV-2, revealing both similarities and differences between the RBP profiles of viral and cellular mRNAs. Herein, we comprehensively identified the RBPs that associate with the NP mRNA of an influenza A virus. Moreover, we provide evidence that the viral polymerase is essential for the recruitment of RPBs to viral mRNAs through direct polymerase-RBP interactions during transcription. We show that loss of TDP-43, which associates with the viral mRNAs, results in lower levels of viral mRNAs within infected cells, and a decreased yield of infectious viral particles. Overall, our results uncover an important role for TDP-43 in the influenza A virus replication cycle via a direct interaction with viral mRNAs, and point to a role of the viral polymerase in orchestrating the assembly of viral mRNPs.

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“…We applied the String database to analyze the interaction and relationship between FRGs and FRLs. And the protein-protein interaction (PPI) network was visualized by using Cytoscape software (3.9.1) [23].…”

Section: Drawing Of Protein-protein Interaction Network Diagrammentioning

confidence: 99%

Prognostic Model Establishment and Immune Microenvironment Analysis of Lung Adenocarcinoma Based on Ferroptosis-Related Long Noncoding RNAs

Yuan

Jiang

Wang

et al. 2023

Preprint

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Background: Lung cancer is a common malignant tumor, which is divided into many subtypes. Lung adenocarcinoma (LUAD) is a most common subtype. More and more studies have confirmed that ferroptosis is involved in the occurrence and development of lung cancer. In this paper, we studied the prognostic ferroptosis-related long noncoding RNAs (FRLs) to build a LUAD-related prognosis model. Methods: We first downloaded the relevant data of 598 patients from the TCGA-LUAD dataset of The Cancer Genome Atlas, and then randomly divided them into training group and testing group in a 1:1 ratio. After that, we used Pearson correlation analysis and univariate Cox regression analysis to determine the FRLs related to prognosis. Then, according to the least absolute shrinkage and selection operator (LASSO) algorithm, the risk model was constructed using the optimized prognostic FRLs subset. We further used the receiver operating characteristic (ROC) curve and survival analysis to evaluate the performance of our model, meanwhile, Cox regression analysis was performed to analyze the risk score (RS). Finally, we also carried out gene set enrichment analysis (GSEA) , and differential analysis of immune-related genes and m6a-related genes. Results: In this study, we identified a total of 34 FRLs associated with the prognosis of lung adenocarcinoma, and established a prognostic model with 7 of them. Kaplan-Meier analysis showed that relevant characteristics of patients in high-risk group were correlated with poorer prognosis. The AUC value of our model was quite ideal, indicating that it could accurately predict the prognosis of LUAD patients. Further GSEA results showed that FRLs of individuals in high-risk groups were mainly enriched in cell cycle and related regulatory pathways, while those in low-risk groups were mainly enriched in immune-related pathways. We also employed immune function analysis and immune checkpoints expression analysis, and found that CCR, check-point, HLA, T cell co−inhibition, T cell co−stimulation and Type II IFN Reponse had significant differences between two groups, while most immune checkpoints had higher expression levels in low-risk groups. Conclusion: Our research has proved that FRls could indeed be used as a prognostic feature to build a prognostic model of lung adenocarcinoma. On the basis of this theory, it is of great significance and value to further study new treatment methods.

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Cytoscape stringApp 2.0: Analysis and Visualization of Heterogeneous Biological Networks

Cited by 70 publications

References 47 publications

Analysis of the Genetic Relationship between Atherosclerosis and Non-Alcoholic Fatty Liver Disease through Biological Interaction Networks

Analysis of the Genetic Relationship between Atherosclerosis and Non-Alcoholic Fatty Liver Disease through Biological Interaction Networks

The RBPome of influenza A virus mRNA reveals a role for TDP-43 in viral replication

Prognostic Model Establishment and Immune Microenvironment Analysis of Lung Adenocarcinoma Based on Ferroptosis-Related Long Noncoding RNAs

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