Meta-Analysis of Gene Expression Datasets Reveals Key Differentially Expressed Genes and Enriched Pathways in Anaplastic Thyroid Cancer

Introduction Anaplastic thyroid cancer (ATC) is one of the most aggressive and deadly thyroid malignancies, known for its rapid progression and resistance to conventional treatments such as surgery, chemotherapy, and radiation. Due to the limited therapeutic options available for ATC, there is an urgent need to identify novel molecular targets and biomarkers to improve treatment outcomes. Recent advances in high-throughput technologies and publicly available gene expression datasets, such as those in the Gene Expression Omnibus (GEO), have enabled researchers to apply bioinformatics approaches like differential gene [removed]DEG) analysis. This study aimed to perform an in silico meta-analysis of multiple GEO datasets to identify DEGs associated with ATC, followed by functional enrichment analysis to explore their biological significance. Methods Three GEO datasets (GSE۶۵۱۴۴, GSE۷۶۰۳۹, GSE۸۵۴۵۷) containing gene expression data from ATC patients and healthy controls were selected for the meta-analysis. After downloading and preprocessing the datasets, including normalization and filtering, differential gene expression analysis was conducted using the limma package in R. Genes with a false discovery rate (FDR) less than ۰.۰۱ and an absolute log۲ fold change (FC) greater than ۱.۵ were considered significantly differentially expressed. The results from all datasets were combined, and overlapping genes were identified to ensure consistency and reliability. Functional enrichment analysis was performed using the Enrichr online tool, focusing on three Gene Ontology (GO) categories: Cellular Component (CC), Biological Process (BP), and Molecular Function (MF). This analysis helped identify the roles of DEGs in cellular structures, biological pathways, and molecular interactions. Key pathways involved in cancer progression, immune response, and cell cycle regulation were investigated to highlight their relevance to ATC. Results The meta-analysis identified several DEGs with significant fold changes across the three GEO datasets, emphasizing their potential role in ATC pathogenesis. Oncogenes and tumor suppressor genes were among the DEGs, displaying consistent patterns of differential expression, indicating their pivotal roles in ATC progression. GO enrichment analysis revealed that DEGs were significantly enriched in cellular components like the lysosome, cell-substrate junctions, intracellular membrane-bound organelles, and focal adhesions. Biological processes associated with DEGs included regulation of apoptosis, gene expression, and response to unfolded proteins. Molecular function analysis showed enrichment in RNA binding, cadherin binding, ubiquitin-like protein ligase binding, and GTPase binding. Pathway enrichment analysis further confirmed that DEGs were involved in important signaling pathways, including IL ۲۴ signaling, VEGFA-VEGFR۲ signaling, and nonalcoholic fatty liver disease. Conclusion This meta-analysis identified DEGs linked to ATC progression and highlighted their involvement in key biological pathways and processes. These findings may contribute to the development of new diagnostic biomarkers or therapeutic targets, though further experimental validation is required to confirm their clinical relevance.

کلیدواژه ها:

Anaplastic Thyroid Cancer ، Differential Gene [removed]DEG) ، In Silico Meta-Analysis ، Molecular Function ، Cancer Biomarkers