Recognition of Key Genes in Human Anaplastic Thyroid Cancer via the Weighing Gene Coexpression Network

Yun Gong1, Fanghua Xu2, Lifei Deng3

  • 1Health Management Center, Jiangxi Provincial People's Hospital (the First Affiliated Hospital of Nanchang Medical College), Nanchang, Jiangxi 330006, China.

Abstract

Insights

This study identified 10 hub genes and key pathways involved in anaplastic thyroid carcinoma (ATC) development using bioinformatics. These findings offer potential biomarkers for ATC diagnosis and prediction.

Area of Science:

  • Genomics and Bioinformatics
  • Molecular Oncology
  • Biomarker Discovery

Background:

  • Anaplastic thyroid carcinoma (ATC) is an aggressive thyroid cancer with limited treatment options.
  • Understanding the molecular mechanisms underlying ATC development is crucial for improving patient outcomes.

Purpose of the Study:

  • To identify key genes and pathways associated with ATC development.
  • To discover potential diagnostic and predictive biomarkers for ATC.

Main Methods:

  • Differential gene expression analysis of microarray datasets (GSE33630, GSE65144).
  • Construction of protein-protein interaction (PPI) and weighted gene co-expression network (WGCNA).
  • Gene Ontology (GO) and KEGG pathway enrichment analyses using DAVID.
  • Validation using TCGA THCA and GSE53072 datasets.

Main Results:

  • Identified 1063 differentially expressed genes (DEGs) in ATC.
  • Upregulated DEGs enriched in extracellular matrix organization; downregulated DEGs in thyroid hormone metabolism.
  • Identified 10 hub genes (e.g., CXCL8, CDH1, AURKA) and 6 ATC-relevant modules linked to p53, Hippo, PI3K/Akt, and ECM-receptor signaling pathways.
  • Validated hub genes and pathways as potential biomarkers.

Conclusions:

  • Bioinformatics analysis revealed significant hub genes and pathways implicated in ATC.
  • The identified DEGs and hub genes hold promise as valuable diagnostic biomarkers for ATC patients.

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