In this study, we conducted a bioinformatics analysis of the thyroid transcriptome in different disorders to gain insights into the molecular mechanisms underlying thyroid dysfunction. We identified a significant number of DEGs in the thyroid transcriptome across various disorders. This provides evidence of dysregulated gene expression patterns in thyroid disorders, suggesting potential therapeutic targets and molecular pathways for further investigation.
The bioinformatics analysis of the thyroid transcriptome in different disorders yielded several interesting findings.7 By retrieving publicly available gene expression datasets, we were able to access a substantial amount of data for our analysis.
To contextualize our findings, we compared our results with those reported in the existing literature. Comparing our results with previous literature, several studies have also reported the identification of DEGs in thyroid disorders.8 For instance, He H et al. identified a similar number of DEGs in a transcriptome analysis of thyroid cancer patients, highlighting the consistency with our findings.9 This overlap suggests a common molecular basis underlying thyroid disorders across different studies. However, our study provides a more comprehensive perspective by including a larger number of datasets, enhancing the reliability of our results. Moreover, our study utilized advanced data preprocessing techniques to ensure data quality. We achieved a high percentage of sequence reads passing quality control, demonstrating the robustness of our dataset. This aligns with the findings of Shih ML et al,10 who reported a similar high-quality dataset in their thyroid transcriptome analysis. Such methodological consistency in generating high-quality data is vital for accurate down-stream analysis.11 This is in line with our functional enrichment analysis, which revealed a significant number of biological processes associated with the DEGs identified in our study.
To gain insights into the functional implications of the identified DEGs, we performed functional enrichment analysis using three gene ontology and pathway enrichment tools.12–14 This analysis revealed several biological processes associated with the DEGs. These findings shed light on the molecular mechanisms underlying thyroid disorders. This extensive repertoire of affected processes underscores the complexity of thyroid disorders and provides a broader understanding of their underlying molecular mechanisms.15 These results are in line with previous literature reports that have highlighted the multifaceted nature of thyroid disorders.
The data retrieval stage of our analysis involved the selection of European Nucleotide Archive available gene expression datasets.16 This is a crucial step in ensuring the quality and relevance of the data used in our study. We conducted a quality assessment to ensure the reliability of the selected datasets. Similar approaches have been used by other researchers in the field, emphasizing the importance of rigorous data selection for accurate comparative analysis.17
Differential expression analysis is a fundamental component of transcriptome analysis, as it allows for the identification of genes that are dysregulated in different disorders.18 Our analysis identified a significant number of DEGs, indicating the presence of altered gene expression patterns in thyroid disorders. This aligns with a study, who also reported a large number of DEGs in a study investigating gene expression patterns in thyroid nodules.19 The identification of DEGs provides important insights into the molecular mechanisms underlying thyroid disorders and offers potential targets for future therapeutic interventions.20
Network analysis is a powerful approach that allows for the exploration of potential interactions and regulatory relationships among DEGs.21 Our analysis using Cytoscape software22 revealed several potential interactions and regulatory relationships among the identified DEGs, providing insights into the complex regulatory networks involved in thyroid disorders. This is in line with studies conducted who also employed network analysis to identify key hub genes in thyroid cancer and autoimmune thyroid diseases, respectively.23,24 The integration of protein-protein interaction networks offers a holistic perspective on the molecular interactions involved in thyroid dysfunction and enables the identification of potential therapeutic targets, and aided in the identification of key hub genes, which play crucial roles in modulating the activity of multiple genes within the network.25