Identification of key genes involved in tamoxifen-resistant breast cancer using bioinformatics analysis.

Abstract

BACKGROUND:

The purpose of the present study was to investigate the molecular mechanisms of tamoxifen resistance in breast cancer and to identify potential targets for antitamoxifen resistance.

METHODS:

Differentially expressed genes (DEGs) in tamoxifen-resistant and tamoxifen-sensitive breast cancer cells were assessed using the GSE67916 dataset acquired from the Gene Expression Omnibus database. Gene ontology (GO) and pathway enrichment analyses were applied to investigate the functions and pathways of the DEGs. Subsequently, the protein-protein interaction (PPI) network was constructed using the Search Tool for the Retrieval of Interacting Genes (STRING), and subnetworks were further analyzed by Molecular Complex Detection (MCODE). The PPI network and subnetworks were visualized using Cytoscape software.

RESULTS:

In total, 438 DEGs were identified, of which 300 were upregulated and 138 were downregulated. The DEGs were significantly enriched in the protein binding, cellular response to estradiol stimulus, and immune response GO terms while the most significant pathways included the mitogen-activated protein kinase (MAPK) signaling pathway in cancer. The PPI network of DEGs was constructed with 288 nodes and 629 edges, and 2 subnetworks were screened out from the entire network.

CONCLUSIONS:

A number of significant hub DEGs were identified based on their degree of connectivity in the PPI network, , included MAPK1 (node degree 36), ESR1 (node degree 27), SMARCA4 (node degree 27), RANBP2 (node degree 25), and PRKCA (node degree 21). These critical hub genes were found to be related to tamoxifen resistance in breast cancer. The results of this study further the understanding of tamoxifen resistance at the molecular level and identify potential therapeutic targets for tamoxifen-resistant breast cancer.

• Authors: Xiaopeng Wang, Shixia Wang
• Journal: Translational cancer research (Transl Cancer Res) Vol. 10 Issue 12 Pg. 5246-5257 (Dec 2021) ISSN: 2219-6803 [Electronic] China
• PMID: 35116374 (Publication Type: Journal Article)
• Copyright: 2021 Translational Cancer Research. All rights reserved.