ArticleInternational journal of molecular sciences2019
Systems Biology Approaches to Investigate Genetic and Epigenetic Molecular Progression Mechanisms for Identifying Gene Expression Signatures in Papillary Thyroid Cancer.
Article in International journal of molecular sciences, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 14 citations in OpenAlex.
- Splicing diversity enhances the molecular classification of pituitary neuroendocrine tumors.Nature communications · 2025Article
- Systems Drug Design for Muscle Invasive Bladder Cancer and Advanced Bladder Cancer by Genome-Wide Microarray Data and Deep Learning Method with Drug Design Specifications.International journal of molecular sciences · 2022Article
- Article
- Investigating the Role of Obesity in Prostate Cancer and Identifying Biomarkers for Drug Discovery: Systems Biology and Deep Learning Approaches.Molecules (Basel, Switzerland) · 2022Article
- Quantitative prediction model for affinity of drug-target interactions based on molecular vibrations and overall system of ligand-receptor.BMC bioinformatics · 2021Article
- Thymoquinone, as a Novel Therapeutic Candidate of Cancers.Pharmaceuticals (Basel, Switzerland) · 2021Review
- Genetic relationship between Hashimoto`s thyroiditis and papillary thyroid carcinoma with coexisting Hashimoto`s thyroiditis.PloS one · 2020Article
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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).
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