Network-based approach to identify prognosis-related genes in tamoxifen-treated patients with estrogen receptor-positive breast cancer.

Yanyan Wang,Yujie Zhang,Xiaonan Gong

doi:10.1042/bsr20203020

Yanyan Wang, Yujie Zhang + Show 1 more

Open Access

https://doi.org/10.1042/bsr20203020

Copy DOI

Abstract

Tamoxifen is an estrogen receptor (ER) antagonist that is most commonly used for the treatment of ER-positive breast cancer. However, tamoxifen resistance remains a major cause of cancer recurrence and progression. Here, we aimed to identify hub genes implicated in the progression and prognosis of ER-positive breast cancer following tamoxifen treatment. Microarray data (GSE9893) for 155 tamoxifen-treated primary ER-positive breast cancer samples were obtained from the Gene Expression Omnibus database. In total, 1706 differentially expressed genes (DEGs), including 859 up-regulated and 847 down-regulated genes, were identified between relapse and relapse-free samples. Weighted correlation network analysis clustered genes into 13 modules, among which the tan and blue modules were the most significantly related to prognosis. From these two modules, we further identified and validated two prognosis-related hub genes (G-rich RNA sequence binding factor 1 (GRSF1) and microtubule-associated protein τ (MAPT)) via survival analysis based on several publicly available datasets. High expression of GRSF1 predicted poor prognosis, whereas MAPT indicated favorable outcomes in ER-positive breast cancer. Using breast cancer cell lines and tissue samples, we confirmed that GRSF1 was significantly up-regulated and MAPT was down-regulated in the tamoxifen-resistant group compared with the tamoxifen-sensitive group. The prognostic value of GRSF1 and MAPT was also verified in 48 tamoxifen-treated ER-positive breast cancer patients in our hospital. Gene set enrichment analysis (GSEA) suggested that GRSF1 was potentially involved in RNA degradation and cell cycle pathways, while MAPT was strongly linked to immune-related signaling pathways. Taken together, our findings established novel prognostic biomarkers to predict tamoxifen sensitivity, which may facilitate individualized management of breast cancer.

Highlights

Breast cancer is a heterogeneous cancer, displaying a variety of molecular features, prognostic patterns, and therapeutic responses [1]
After applying thresholds of |log2 FC| > 1 and P
According to Gene Ontology (GO) enrichment analysis, up-regulated genes were significantly enriched in various biological processes (BPs) including ‘protein targeting to the estrogen receptor (ER)’

Summary

Introduction

Breast cancer is a heterogeneous cancer, displaying a variety of molecular features, prognostic patterns, and therapeutic responses [1]. Up to two-thirds of all cases express estrogen receptor (ER), and can be treated using hormone-based therapy. The administration of tamoxifen greatly minimizes the risk of recurrence of ER-positive breast cancer, for premenopausal women [3]. Approximately 40% of ER-positive patients are less sensitive to tamoxifen treatment, and will eventually relapse. The exact mechanisms of tamoxifen insensitivity in breast cancer remain largely unknown, and tamoxifen-resistant cancer is difficult to treat, due to lack of therapeutic targets. Since tamoxifen therapy fails for a large number of patients, there is an urgent need to elucidate the molecular mechanisms of tamoxifen resistance, to identify novel potential genes for monitoring treatment efficacy and predicting prognosis

Objectives

Methods

Results

Discussion

Conclusion