ArticlePeerJ2015
Nodes with high centrality in protein interaction networks are responsible for driving signaling pathways in diabetic nephropathy.
Article in PeerJ, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed, 45 citations in OpenAlex.
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- Defining the characteristics of interferon-alpha-stimulated human genes: insight from expression data and machine learning.GigaScience · 2022Article
- Integrative Functional Genomic Analysis in Multiplex Autism Families from Kazakhstan.Disease markers · 2022Article
- Integrative analysis of lung molecular signatures reveals key drivers of idiopathic pulmonary fibrosis.BMC pulmonary medicine · 2021Article
- Systems biology and machine learning approaches identify drug targets in diabetic nephropathy.Scientific reports · 2021Article
- Transcriptomic analysis of equine chorioallantois reveals immune networks and molecular mechanisms involved in nocardioform placentitis.Veterinary research · 2021Article
- Transcriptional and Histochemical Signatures of Bone Marrow Mononuclear Cell-Mediated Resolution of Synovitis.Frontiers in immunology · 2021Article
- Article
- Identification of Key Genes in Gastric Cancer by Bioinformatics Analysis.BioMed research international · 2020Article
- A systematic integrative approach reveals novel microRNAs in diabetic nephropathy.Journal of research in medical sciences : the official journal of Isfahan University of Medical Sciences · 2020Article
- Equine arteritis virus long-term persistence is orchestrated by CD8+ T lymphocyte transcription factors, inhibitory receptors, and the CXCL16/CXCR6 axis.PLoS pathogens · 2019Article
- The analysis of a time-course transcriptome profile by systems biology approaches reveals key molecular processes in acute kidney injury.Journal of research in medical sciences : the official journal of Isfahan University of Medical Sciences · 2019Article
- Analysis of Predicted Host-Parasite Interactomes Reveals Commonalities and Specificities Related to Parasitic Lifestyle and Tissues Tropism.Frontiers in immunology · 2019Article
- Network analysis of membranous glomerulonephritis based on metabolomics data.Molecular medicine reports · 2018Article
- The impact of cytokine responses in the intra- and extracellular signaling network of a traumatic injury.Cytokine · 2018Article
- Proteomic and bioinformatic discovery of biomarkers for diabetic nephropathy.EXCLI journal · 2018Review
- Central Nodes in Protein Interaction Networks Drive Critical Functions in Transforming Growth Factor Beta-1 Stimulated Kidney Cells.Cell journal · 2017Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In spite of huge efforts, chronic diseases remain an unresolved problem in medicine. Systems biology could assist to develop more efficient therapies through providing quantitative holistic sights to these complex disorders. In this study, we have re-analyzed a microarray dataset to identify critical signaling pathways related to diabetic nephropathy. GSE1009 dataset was downloaded from Gene Expression Omnibus database and the gene expression profile of glomeruli from diabetic nephropathy patients and those from healthy individuals were compared. The protein-protein interaction network for differentially expressed genes was constructed and enriched. In addition, topology of the network was analyzed to identify the genes with high centrality parameters and then pathway enrichment analysis was performed. We found 49 genes to be variably expressed between the two groups. The network of these genes had few interactions so it was enriched and a network with 137 nodes was constructed. Based on different parameters, 34 nodes were considered to have high centrality in this network. Pathway enrichment analysis with these central genes identified 62 inter-connected signaling pathways related to diabetic nephropathy. Interestingly, the central nodes were more informative for pathway enrichment analysis compared to all network nodes and also 49 differentially expressed genes. In conclusion, we here show that central nodes in protein interaction networks tend to be present in pathways that co-occur in a biological state. Also, this study suggests a computational method for inferring underlying mechanisms of complex disorders from raw high-throughput data.
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