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Identification and Validation of a Novel Glycolysis-Related Gene Signature for Predicting the Prognosis and Therapeutic Response in Triple-Negative Breast Cancer

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Abstract

Introduction

A high malignancy rate and poor prognosis are common problems with triple-negative breast cancer (TNBC). There is increasing evidence that glycolysis plays vital roles in tumorigenesis, tumor invasion, immune evasion, chemoresistance, and metastasis. However, a comprehensive analysis of the diagnostic and prognostic significance of glycolysis in TNBC is lacking.

Methods

Transcriptomic and clinical data of TNBC patients were obtained from The Cancer Genome Atlas (TCGA) and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) databases, respectively. Glycolysis-related genes (GRGs) were collected from the Molecular Signatures Database (MSigDB). Differential comparative analysis was performed to obtain the differentially expressed (DE)-GRGs associated with TNBC. Based on the DE-GRGs, a glycolysis-related risk signature was established using Least Absolute Shrinkage and Selector Operation (LASSO) and multivariable Cox regression analyses. The prognostic value, tumor microenvironment, mutation status, and chemotherapy response of different risk groups were analyzed. An independent cohort from the METABRIC database was used for external validation. Furthermore, the expression patterns of five genes derived from the prognostic model were validated by quantitative real-time polymerase chain reaction (RT-qPCR).

Results

The glycolysis-related prognostic signature included five genes (IFNG, ACSS2, IRS2, GFUS, and GAL3ST1) and predicted the prognosis of TNBC patients independent of clinical factors (p < 0.05). Patients were divided into high- and low-risk groups based on the median risk score. Compared to low-risk TNBC patients, high-risk patients had significantly decreased overall survival (HR = 2.718, p < 0.001). Receiver operating characteristic and calibration curves demonstrated that the model had high performance in terms of predicting survival and risk stratification. The results remained consistent after external verification. Additionally, the tumor immune microenvironment significantly differed between the risk groups. Low-risk TNBC patients had a better immunotherapy response than high-risk patients. High-risk TNBC patients with a poor prognosis may benefit from targeted therapy.

Conclusions

This study developed a novel glycolysis and prognosis-related (GRP) signature based on GRGs to predict the prognosis of TNBC patients, and may aid clinical decision-making for these patients.

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Acknowledgements

Funding

This project was financially supported by the National Science Foundation of China (grant number: 81871295, 82001740). Ji-min Cao funded the journal’s Rapid Service fees.

Editorial Assistance

We gratefully acknowledge contributions from the GEO, TCGA, and METABRIC databases. The English in this document has been checked by at least two professional editors of Textcheck (http://www.textcheck.com/) both native speakers of English.

Author Contributions

All authors were involved in drafting the article or revising it critically for important content. Jian Zheng and Ji-Min Cao developed the methodology and acquired the related data. Conception and design of the study: Jian Zheng and Ji-Min Cao. Acquisition of data: Yi-Fan Zhang, Guo-Hui Han, Meng-Ying Fan, Ming-Hui Du, Guo-Chen Zhang and Yue Feng. Analysis and interpretation of data: Run-Qi Chen, Jun Qiao, Jian Zheng, Bo Zhang, Yi-Fan Zhang and Sheng-Xiao Zhang. Jian Zheng collected specimens and carried out in vitro assays. Drafting the article: Jian Zheng, Yi-Fan Zhang and Sheng-Xiao Zhang. Revising the article critically: Ji-Min Cao, Sheng-Xiao Zhang, and Guo-Hui Han. All authors contributed to the article and approved the submitted version.

Disclosures

All named authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Compliance with Ethics Guidelines

The study was approved by the Ethics Review Board at Shanxi Cancer Hospital. All patients provided written informed consent for the experimental analysis of their excised tissues.

Data Availability

The datasets analyzed for this study can be found in the [TCGA database] [https://cancergenome.nih.gov], [METABRIC database] [http://molonc.bccrc.ca/aparicio-lab/research/metabric/] and [GTEx database] [https://gtexportal.org/]. All data generated or analyzed during this study are included as supplementary information files.

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Authors and Affiliations

  1. Key Laboratory of Cellular Physiology at Shanxi Medical University, Ministry of Education, Taiyuan, 030000, China

    Jian Zheng, Yi-Fan Zhang, Jun Qiao, Sheng-Xiao Zhang & Ji-Min Cao

  2. Department of Physiology, Shanxi Medical University, Taiyuan, China

    Jian Zheng, Yi-Fan Zhang, Jun Qiao, Sheng-Xiao Zhang & Ji-Min Cao

  3. Department of Breast Surgery, Shanxi Cancer Hospital, Taiyuan, China

    Jian Zheng, Guo-Hui Han, Guo-Chen Zhang & Bo Zhang

  4. The First Clinical Medical College, Shanxi Medical University, Taiyuan, China

    Yi-Fan Zhang

  5. The Anesthesiology College, Shanxi Medical University, Taiyuan, China

    Meng-Ying Fan

  6. The Second Clinical Medical College, Shanxi Medical University, Taiyuan, China

    Ming-Hui Du

  7. Department of Rheumatology, The Second Hospital of Shanxi Medical University, Taiyuan, China

    Jun Qiao & Sheng-Xiao Zhang

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  1. Jian Zheng
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Zheng, J., Zhang, YF., Han, GH. et al. Identification and Validation of a Novel Glycolysis-Related Gene Signature for Predicting the Prognosis and Therapeutic Response in Triple-Negative Breast Cancer. Adv Ther 40, 310–330 (2023). https://doi.org/10.1007/s12325-022-02330-y

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