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Molecular Network-Based Drug Prediction in Thyroid Cancer.

Xingyu Xu1, Haixia Long2, Baohang Xi3

  • 1College of Life Sciences, Zhejiang Sci-Tech University, Hangzhou 310018, China. xingyuxu821@163.com.

International Journal of Molecular Sciences
|January 16, 2019
PubMed
Summary
This summary is machine-generated.

This study developed a novel drug selection method for thyroid cancer by analyzing gene expression and co-expression in patient tissues. The approach identified potential drugs to reverse cancer-induced changes, offering new therapeutic avenues for thyroid cancer.

Keywords:
co-expression networkdifferential co-expressiondifferential genedrug redirectiongene perturbationthyroid cancer

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Area of Science:

  • Oncology
  • Genomics
  • Pharmacology

Background:

  • Thyroid cancer lacks effective treatments, necessitating improved drug selection strategies.
  • Existing methods like the Connectivity Map (CMAP) have limitations, including reliance on cell lines and ignoring gene regulation.
  • A comprehensive analysis of thyroid cancer perturbations at gene expression, co-expression, and module levels is needed.

Purpose of the Study:

  • To develop and apply a novel drug selection pipeline for thyroid cancer.
  • To identify drugs capable of reversing gene expression and co-expression changes in thyroid cancer tissues.
  • To explore the relationship between thyroid hormone secretion and thyroid cancer proliferation.

Main Methods:

  • Comprehensive analysis of thyroid cancer perturbations using The Cancer Genome Atlas (TCGA) data (56 normal, 500 cancer samples).
  • Assessment of gene expression, gene co-expression, and gene module variations.
  • Development of a drug selection pipeline based on tissue-derived drug signatures and Fisher's exact test.

Main Results:

  • Identified 812 up-regulated and 213 down-regulated genes, enriched in extracellular matrix and synaptic functions.
  • Discovered 33,778 differentiated co-expressed gene pairs forming a module linked to impaired immunity.
  • Predicted drugs including baclofen, nevirapine, and glucocorticoids that could reverse cancer-induced changes.

Conclusions:

  • The developed pipeline effectively identifies potential drugs for thyroid cancer by analyzing tissue-level perturbations.
  • Gene co-expression analysis reveals immune dysfunction associated with thyroid cancer.
  • Thyroid hormone secretion regulation may be a key factor in inhibiting thyroid cancer cell proliferation.