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Background <p>Anaplastic thyroid carcinoma (ATC) is among the most lethal thyroid malignancies, with poor clinical outcomes and limited treatment strategies. To gain insights into the molecular mechanisms involved in its progression, we performed an integrative bioinformatic analysis.</p> Methods <p>We analyzed five microarray datasets from the GEO database to compare gene expression profiles between ATC samples and normal thyroid tissues. Differentially expressed genes (DEGs) were identified using GEO2R, and overlapping genes across datasets were detected through Venn diagram analysis. Functional enrichment was performed using DAVID and Metascape. A protein–protein interaction (PPI) network was constructed with STRING, and significant gene modules were identified using the MCODE plugin in Cytoscape. Co-expression analysis was further explored with GeneMANIA.</p> Results <p>We identified 7532 DEGs, of which 3509 were upregulated and 4023 were downregulated. Upregulated genes were mainly involved in cell division and mitotic control, while downregulated genes were related to thyroid hormone production and gland development. Six hub genes stood out for their centrality in the network: TPX2, MAD2L1, CDC20, CDKN3, CENPF, and NEK2.</p> Conclusion <p>Our findings shed light on key genes and pathways that may contribute to ATC pathogenesis. These results provide a foundation for identifying potential diagnostic biomarkers and therapeutic targets for this aggressive cancer.</p>

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Integrative bioinformatic analysis identifies differentially expressed gene targets as potential biomarkers for anaplastic thyroid cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月12日

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  • Angel Sebastian Treviño-Juarez,
  • Jose Gerardo Gonzalez-Gonzalez,
  • Rene Rodriguez-Gutierrez,
  • Adriana Sanchez-Garcia,
  • Camilo Daniel Gonzalez-Velazquez

摘要

Background

Anaplastic thyroid carcinoma (ATC) is among the most lethal thyroid malignancies, with poor clinical outcomes and limited treatment strategies. To gain insights into the molecular mechanisms involved in its progression, we performed an integrative bioinformatic analysis.

Methods

We analyzed five microarray datasets from the GEO database to compare gene expression profiles between ATC samples and normal thyroid tissues. Differentially expressed genes (DEGs) were identified using GEO2R, and overlapping genes across datasets were detected through Venn diagram analysis. Functional enrichment was performed using DAVID and Metascape. A protein–protein interaction (PPI) network was constructed with STRING, and significant gene modules were identified using the MCODE plugin in Cytoscape. Co-expression analysis was further explored with GeneMANIA.

Results

We identified 7532 DEGs, of which 3509 were upregulated and 4023 were downregulated. Upregulated genes were mainly involved in cell division and mitotic control, while downregulated genes were related to thyroid hormone production and gland development. Six hub genes stood out for their centrality in the network: TPX2, MAD2L1, CDC20, CDKN3, CENPF, and NEK2.

Conclusion

Our findings shed light on key genes and pathways that may contribute to ATC pathogenesis. These results provide a foundation for identifying potential diagnostic biomarkers and therapeutic targets for this aggressive cancer.