Network analysis reveals essential proteins that regulate sodium-iodide symporter expression in anaplastic thyroid carcinoma

AbstractAnaplastic thyroid carcinoma (ATC) is the most rare and lethal form of thyroid cancer and requires effective treatment. Efforts have been made to restore sodium-iodide symporter (NIS) expression in ATC cells where it has been downregulated, yet without complete success. Systems biology approaches have been used to simplify complex biological networks. Here, we attempt to find more suitable targets in order to restore NIS expression in ATC cells. We have built a simplified protein interaction network including transcription factors and proteins involved in MAPK, TGFβ/SMAD, PI3K/AKT, and TSHR signaling pathways which regulate NIS expression, alongside proteins interacting with them. The network was analyzed, and proteins were ranked based on several centrality indices. Our results suggest that the protein interaction network of NIS expression regulation is modular, and distance-based and information-flow-based centrality indices may be better predictors of important proteins in such networks. We propose that the high-ranked proteins found in our analysis are expected to be more promising targets in attempts to restore NIS expression in ATC cells.

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Relating Diseases by Integrating Gene Associations and Information Flow through Protein Interaction Network

PLoS ONE ◽

10.1371/journal.pone.0110936 ◽

2014 ◽

Vol 9 (10) ◽

pp. e110936 ◽

Cited By ~ 13

Author(s):

Mehdi Bagheri Hamaneh ◽

Yi-Kuo Yu

Keyword(s):

Information Flow ◽

Protein Interaction ◽

Protein Interaction Network ◽

Interaction Network ◽

Gene Associations ◽

Flow Through

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An Effective Statistical Integrative Algorithm (Aeiapp) for Protein Prediction

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.k1253.0981119 ◽

2019 ◽

Vol 8 (11) ◽

pp. 132-137 ◽

Cited By ~ 1

Keyword(s):

Protein Interaction ◽

Biological Networks ◽

Protein Interaction Network ◽

Protein Identification ◽

Research Work ◽

Interaction Network ◽

Integrative Approach ◽

Target Proteins ◽

Protein Protein Interaction ◽

Protein Protein Interaction Network

The task of predicting target proteins for new drug discovery is typically difficult. Target proteins are biologically most important to control a keen functional process. The recent research of experimental and computational -based approaches has been widely used to predict target proteins using biological networks analysis techniques. Perhaps with available methods and statistical algorithm needs to be modified and should be clearer to tag the main target. Meanwhile identifying wrong protein leads to unwanted molecular interaction and pharmacological activity. In this research work, a novel method to identify essential target proteins using integrative graph coloring algorithm has been proposed. The proposed integrative approach helps to extract essential proteins in protein-protein interaction network (PPI) by analyzing neighborhood of the active target protein. Experimental results reviewed based on protein-protein interaction network for homosapiens showed that AEIAPP based approach shows an improvement in the essential protein identification by assuming the source protein as biologically proven protein. The AEIAPP statistical model has been compared with other state of art approaches on human PPI for various diseases to produce good accurate outcome in faster manner with little memory consumption.

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A Transfer Learning Approach and Selective Integration of Multiple Types of Assays for Biological Network Inference

Computational Knowledge Discovery for Bioinformatics Research ◽

10.4018/978-1-4666-1785-8.ch011 ◽

2013 ◽

pp. 188-202

Author(s):

Tsuyoshi Kato ◽

Kinya Okada ◽

Hisashi Kashima ◽

Masashi Sugiyama

Keyword(s):

Metabolic Network ◽

Transfer Learning ◽

Protein Interaction ◽

Biological Networks ◽

Protein Interaction Network ◽

Network Inference ◽

Interaction Network ◽

Statistical Test ◽

Learning Approach ◽

Selective Integration

The authors’ algorithm was favorably examined on two kinds of biological networks: a metabolic network and a protein interaction network. A statistical test confirmed that the weight that our algorithm assigned to each assay was meaningful.

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