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Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán

2018, Number 4

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Rev Invest Clin 2018; 70 (4)

Prediction of Molecular Signature, Potential Biomarkers, and Molecular Pathways Associated with Membranous Nephropathy Based on Protein-Protein Interactions

Taherkhani A, Kalantari S, Nafar M

Language: English
References: 43
Page: 184-191
PDF size: 320.55 Kb.


ABSTRACT

Background: Membranous nephropathy (MN) is one of the causes of nephrotic syndrome in adults that lead to end-stage renal disease with an unknown molecular signature. The current diagnosis is based on renal biopsy, which is an invasive method and has several complications and challenges. Thus, identification of the novel biomarker candidates, as well as impaired pathways, will be helpful for non-invasive molecular-based diagnosis. Objectives: We aimed to study the molecular signature of MN and facilitate the systematic discovery of diagnostic candidate biomarkers, molecular pathway, and potential therapeutic targets using bioinformatics predictions. Methods: The protein-protein interaction (PPI) network of an integrated list of downloaded microarray data, differential proteins from a published proteomic study, and a list of retrieved scientific literature mining was constructed and analyzed in terms of functional modules, enriched biological pathways, hub genes, master regulator, and target genes. Results: These network analyses revealed several functional modules and hub genes including Vitamin D3 receptor, retinoic acid receptor RXR-alpha, interleukin 8, and SH3GL2. TEAD4 and FOXA1 were identified as the regulatory master molecules. LRP1 and ITGA3 were identified as the important target genes. Extracellular matrix organization, cell surface receptor signaling pathway, and defense and inflammatory response were found to be impaired in MN using functional analyses. A specific subnetwork for MN was suggested using PPI approach. Discussion: Omics data integration and systems biology analysis on the level of interaction networks provide a powerful approach for identification of pathway-specific biomarkers for MN.


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