Integrative Analysis of Bioinformatics Databases to Identify Thyroid Cancer Drug Target Candidates

Vollständiger Abstract

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Background: Thyroid cancer is an endocrine malignancy whose incidence continues to rise globally. Although thyroid cancer generally has a favourable prognosis, aggressive subtypes such as anaplastic thyroid cancer remain a therapeutic challenge. Drug repurposing strategies offer the opportunity to accelerate the development of therapies by utilising drugs already available and with an established safety profile. Objective: This study aims to identify candidate thyroid cancer risk genes by analyzing data extracted from GWAS and PheWAS databases, and to evaluate their potential therapeutic relevance through drug repositioning Methods: Significant gene variants were obtained from GWAS and PheWAS databases and functionally annotated using HaploReg, ClinVar, SIFT, PolyPhen, CADD, cis-eQTL, and PubMed literature review. Candidate genes with a score of ≥2 from seven functional annotations were designated as risk genes and further analyzed with the STRING database. Drug candidate searches were conducted through DrugBank and ClinicalTrials.gov. Results: Ten biological risk genes were found to potentially play a role in thyroid cancer pathogenesis, with DIRC3 scoring the highest. However, no drugs were identified that directly interact with these risk genes. These findings highlight the therapeutic gap between thyroid cancer risk genes and currently available drugs. Conclusion: This study identified ten biological risk genes involved in thyroid cancer pathogenesis, with DIRC3 as the top candidate. Although no approved drugs were found to directly target these genes, the findings highlight their potential as biomarkers and novel therapeutic targets. Integrative genomics and drug repurposing approaches remain promising strategies for advancing precision therapy in thyroid cancer. Keywords: Bioinformatics; drug repurposing; GWAS; PheWAS; thyroid cancer