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BMC Ophthalmology
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A data-driven approach for the discovery of biomarkers associated with thyroid eye disease

Authors: Huihui Zou; Weiwei Xu; Ying Wang; Zhihong Wang;

A data-driven approach for the discovery of biomarkers associated with thyroid eye disease

Abstract

Abstract Background Thyroid eye disease (TED) is the most common autoimmune disease and usually occurs in patients with hyperthyroidism. In this disease, eye-related tissue, such as eye muscles, eyelids, tear glands, etc., become inflated, which causes the eyes and eyelids to become red, swollen, and uncomfortable. The pathophysiology of this disease is still poorly known. Aim This study aims to discover potential biomarkers and regulatory pathways of TED which will not only help to diagnose the disease and understand orbital involvement in thyroid dysfunction but also provide an insight for better therapeutics. Methods We applied a data-driven approach by combining gene biomarkers both from published literature and computationally predicted from microarray gene expression data. Further, the DAVID tool is used for Gene Ontology-based enrichment analysis. Results We obtained a total of 22 gene biomarkers, including 18 semi-automatically curated from the literature and 4 predicted using data-driven approaches, involved in the pathogenesis of TED that can be used as potential information for therapeutic targets. Further, we constructed a regulatory pathway of TED biomarkers comprises of 310 connected components, and 1134 interactions using four prominent interaction databases. Conclusion This constructed pathway can be further utilized for disease dynamics and simulation studies.

Related Organizations
Keywords

Eyelids, Biomarker, RE1-994, Graves Ophthalmopathy, Ophthalmology, Oculomotor Muscles, Autoimmune disease, Humans, Graves’ disease, Biomarkers, Thyroid eye disease, Research Article

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
2
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Average
Average
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