An EMT-Related Gene Signature to Predict the Prognosis of Triple-Negative Breast Cancer

Bo Zhang; Rong Zhao; Qi Wang; Ya-Jing Zhang; Liu Yang; Zhou-Jun Yuan; Jun Yang; Qian-Jun Wang; Liang Yao

doi:10.1007/s12325-023-02577-z

An EMT-Related Gene Signature to Predict the Prognosis of Triple-Negative Breast Cancer

Adv Ther. 2023 Oct;40(10):4339-4357. doi: 10.1007/s12325-023-02577-z. Epub 2023 Jul 18.

Authors

Bo Zhang^#¹, Rong Zhao^#², Qi Wang³, Ya-Jing Zhang⁴, Liu Yang⁴, Zhou-Jun Yuan⁴, Jun Yang¹, Qian-Jun Wang¹, Liang Yao⁵

Affiliations

¹ Department of Breast Oncology, Shanxi Provincial Cancer Hospital, Taiyuan, China.
² Department of Rheumatology, The Second Hospital of Shanxi Medical University, Taiyuan, Shanxi, China.
³ School of Basic Medical Sciences, Shanxi Medical University, Taiyuan, China.
⁴ Key Laboratory of Cellular Physiology at Shanxi Medical University, Ministry of Education, Shanxi Medical University, Taiyuan, China.
⁵ Department of Breast Oncology, Shanxi Provincial Cancer Hospital, Taiyuan, China. zhaorong25@sxmu.edu.cn.

^# Contributed equally.

Abstract

Introduction: Epithelial-mesenchymal transition (EMT) is an important biological process in tumor invasion and metastasis, and thus a potential indicator of the progression and drug resistance of breast cancer. This study comprehensively analyzed EMT-related genes in triple-negative breast cancer (TNBC) to develop an EMT-related prognostic gene signature.

Methods: With the application of The Cancer Genome Atlas (TCGA) database, Molecular Taxonomy of Breast Cancer International Consortium (METABRIC), and the Genotype-Tissue Expression (GTEx) database, we identified EMT-related signature genes (EMGs) by Cox univariate regression and LASSO regression analysis. Risk scores were calculated and used to divide patients with TNBC into high-risk group and low-risk groups by the median value. Kaplan-Meier (K-M) and receiver operating characteristic (ROC) curve analyses were applied for model validation. Independent prognostic predictors were used to develop nomograms. Then, we assessed the risk model in terms of the immune microenvironment, genetic alteration and DNA methylation effects on prognosis, the probability of response to immunotherapy and chemotherapy, and small molecule drugs predicted by The Connectivity Map (Cmap) database.

Results: Thirteen EMT-related genes with independent prognostic value were identified and used to stratify the patients with TNBC into high- and low-risk groups. The survival analysis revealed that patients in the high-risk group had significantly poorer overall survival than patients in the low-risk group. Populations of immune cells, including CD4 memory resting T cells, CD4 memory activated T cells, and activated dendritic cells, significantly differed between the high- and low-risk groups. Moreover, some therapeutic drugs to which the high-risk group might show sensitivity were identified.

Conclusions: Our research identified the significant impact of EMGs on prognosis in TNBC, providing new strategies for personalizing TNBC treatment and improving clinical outcomes.

Keywords: Chemotherapy; EMT; Immunity; Mutation; Prognosis; Treatment; Triple-negative breast cancer.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Epithelial-Mesenchymal Transition / genetics
Humans
Nomograms
Prognosis
Risk Factors
Triple Negative Breast Neoplasms* / genetics
Tumor Microenvironment