Utilizing Bioinformatics Approaches to Conduct Comparative Analysis of the Thyroid Transcriptome in Thyroid Disorders

Abstract

Abstract Introduction: This study aims to identify common gene expression patterns and dysregulated pathways in various thyroid disorders by leveraging publicly available transcriptomic datasets. The integration of other omics data, when possible, will allow us to uncover potential molecular drivers and biomarkers associated with specific thyroid dysfunctions. However, there are still gaps in the analysis of the transcriptomes of the various thyroid disorders. Objective: To conduct a comparative analysis of the thyroid transcriptome in thyroid disorders using bioinformatics approaches. Methods: We retrieved publicly available gene expression datasets related to the thyroid from European Nucleotide Archive. Data preprocessing involved conducting quality control, trimming reads, and aligning them to a reference genome. Differential expression analysis was performed using bioinformatics packages, and functional enrichment analysis was conducted to gain insights into biological processes. Network analysis was conducted to explore interactions and regulatory relationships among differentially expressed genes (DEGs). Results: Our analysis included a total of 18 gene expression datasets, of which 15 were selected based on inclusion criteria and quality assessment. A large number of DEGs were identified (p < 0.01), and these genes were ranked according to their significance. Functional enrichment analysis revealed numerous biological processes associated with the DEGs, providing insights into the molecular mechanisms of thyroid disorders. Network analysis using Cytoscape software revealed potential interactions among DEGs and identified key hub genes and potential therapeutic targets. Conclusion: This study demonstrates an accessible methodology for conducting a comparative analysis of the thyroid transcriptome in different disorders without the need for thyroid tissue samples. The integration of bioinformatics approaches provides a comprehensive understanding of the molecular mechanisms underlying thyroid diseases.