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Protein-protein interaction network analysis of cirrhosis liver disease

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TLDR
The result indicates that regulation of lipid metabolism and cell survival are important biological processes involved in cirrhosis disease.
Abstract
Aim: Evaluation of biological characteristics of 13 identified proteins of patients with cirrhotic liver disease is the main aim of this research. Background: In clinical usage, liver biopsy remains the gold standard for diagnosis of hepatic fibrosis. Evaluation and confirmation of liver fibrosis stages and severity of chronic diseases require a precise and noninvasive biomarkers. Since the early detection of cirrhosis is a clinical problem, achieving a sensitive, specific and predictive novel method based on biomarkers is an important task. Methods: Essential analysis, such as gene ontology (GO) enrichment and protein-protein interactions (PPI) was undergone EXPASy, STRING Database and DAVID Bioinformatics Resources query. Results: Based on GO analysis, most of proteins are located in the endoplasmic reticulum lumen, intracellular organelle lumen, membrane-enclosed lumen, and extracellular region. The relevant molecular functions are actin binding, metal ion binding, cation binding and ion binding. Cell adhesion, biological adhesion, cellular amino acid derivative, metabolic process and homeostatic process are the related processes. Protein-protein interaction network analysis introduced five proteins (fibroblast growth factor receptor 4, tropomyosin 4, tropomyosin 2 (beta), lectin, Lectin galactoside-binding soluble 3 binding protein and apolipoprotein A-I) as hub and bottleneck proteins. Conclusion: Our result indicates that regulation of lipid metabolism and cell survival are important biological processes involved in cirrhosis disease. More investigation of above mentioned proteins will provide a better understanding of cirrhosis disease.

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References
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Journal ArticleDOI

Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources.

TL;DR: By following this protocol, investigators are able to gain an in-depth understanding of the biological themes in lists of genes that are enriched in genome-scale studies.
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Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists

TL;DR: The survey will help tool designers/developers and experienced end users understand the underlying algorithms and pertinent details of particular tool categories/tools, enabling them to make the best choices for their particular research interests.
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STRING v9.1: protein-protein interaction networks, with increased coverage and integration

TL;DR: The update to version 9.1 of STRING is described, introducing several improvements, including extending the automated mining of scientific texts for interaction information, to now also include full-text articles, and providing users with statistical information on any functional enrichment observed in their networks.
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Hepatic Stellate Cells: Protean, Multifunctional, and Enigmatic Cells of the Liver

TL;DR: The hepatic stellate cell has surprised and engaged physiologists, pathologists, and hepatologists for over 130 years, yet clear evidence of its role in hepatic injury and fibrosis only emerged following the refinement of methods for its isolation and characterization.
Journal ArticleDOI

The DAVID Gene Functional Classification Tool: a novel biological module-centric algorithm to functionally analyze large gene lists

TL;DR: The DAVID Gene Functional Classification Tool uses a novel agglomeration algorithm to condense a list of genes or associated biological terms into organized classes of related genes or biology, called biological modules, for efficient interpretation of gene lists in a network context.
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