Systems Biology Approaches to Investigate Genetic and Epigenetic Molecular Progression Mechanisms for Identifying Gene Expression Signatures in Papillary Thyroid Cancer.

Shan-Ju Yeh,Cheng-Wei Li,Bor-Sen Chen,Chien-Yu Lin

doi:10.3390/ijms20102536

Shan-Ju Yeh, Cheng-Wei Li + Show 2 more

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https://doi.org/10.3390/ijms20102536

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Abstract

Thyroid cancer is the most common endocrine cancer. Particularly, papillary thyroid cancer (PTC) accounts for the highest proportion of thyroid cancer. Up to now, there are few researches discussing the pathogenesis and progression mechanisms of PTC from the viewpoint of systems biology approaches. In this study, first we constructed the candidate genetic and epigenetic network (GEN) consisting of candidate protein–protein interaction network (PPIN) and candidate gene regulatory network (GRN) by big database mining. Secondly, system identification and system order detection methods were applied to prune candidate GEN via next-generation sequencing (NGS) and DNA methylation profiles to obtain the real GEN. After that, we extracted core GENs from real GENs by the principal network projection (PNP) method. To investigate the pathogenic and progression mechanisms in each stage of PTC, core GEN was denoted in respect of KEGG pathways. Finally, by comparing two successive core signaling pathways of PTC, we not only shed light on the causes of PTC progression, but also identified essential biomarkers with specific gene expression signature. Moreover, based on the identified gene expression signature, we suggested potential candidate drugs to prevent the progression of PTC with querying Connectivity Map (CMap).

Highlights

Thyroid cancer is the most common endocrine malignancy and usually originates in follicular or parafollicular C cells
We utilized reverse engineering and system order detection methods on candidate genetic and epigenetic network (GEN) with corresponding normal, early, and late stage of Papillary thyroid cancer (PTC) next-generation sequencing (NGS) data for obtaining real GENs shown in Figures S1–S3, respectively
After applying the principal network projection (PNP) method, we extracted core GENs from real GENs by selecting the top-ranked 2000 nodes with significant projection values that could reflect 85% of the real GENs in three stage of PTC, respectively

Summary

Introduction

Thyroid cancer is the most common endocrine malignancy and usually originates in follicular or parafollicular C cells. This is a disease in which cells grow abnormally and have the potential to spread to other parts of the body. In 2015, there were 3.2 million people living with thyroid cancer [1], resulting in 31,900 deaths [2]. It most commonly occurs on women whose ages are between 35 and 65 that are affected more often 3–4 times than men [3]. The most commonly used PTC therapy is thyroidectomy, followed by partial radioactive iodine residual ablation and thyroid hormone suppression therapy [5]

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