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Updated: May 13, 2025
Spontaneous Murine Model of Anaplastic Thyroid Cancer
Published on: February 3, 2023
Integrative bioinformatic analysis identifies differentially expressed gene targets as potential biomarkers for
Angel Sebastian Treviño-Juarez1, Jose Gerardo Gonzalez-Gonzalez2,3, Rene Rodriguez-Gutierrez2,3
1Endocrinology Division, Department of Internal Medicine, University Hospital "Dr. José E. González", Universidad Autónoma de Nuevo León, Monterrey, México. sebastiantrevj@gmail.com.
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.
Insights
Anaplastic thyroid carcinoma (ATC) is a lethal cancer. This study identified key genes like TPX2 and MAD2L1 involved in ATC progression, offering potential diagnostic and therapeutic targets.
Area of Science:
- Oncology
- Bioinformatics
- Molecular Biology
Background:
- Anaplastic thyroid carcinoma (ATC) is an aggressive malignancy with limited treatment options.
- Understanding the molecular mechanisms driving ATC progression is crucial for improving patient outcomes.
Purpose of the Study:
- To conduct an integrative bioinformatic analysis of gene expression profiles in ATC.
- To identify key genes and pathways involved in ATC pathogenesis.
Main Methods:
- Analysis of five microarray datasets from the GEO database.
- Identification of differentially expressed genes (DEGs) and construction of a protein-protein interaction (PPI) network.
- Functional enrichment analysis and identification of hub genes using bioinformatics tools.
Main Results:
- Identified 7532 DEGs, with distinct profiles for upregulated (cell division) and downregulated (thyroid hormone production) genes.
- Six hub genes (TPX2, MAD2L1, CDC20, CDKN3, CENPF, NEK2) were highlighted for their network centrality.
- Functional enrichment revealed pathways related to cell division and thyroid gland function.
Conclusions:
- The study elucidates critical genes and pathways implicated in ATC development.
- Findings provide a basis for developing novel diagnostic biomarkers and therapeutic strategies for ATC.
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