Deciphering the role of apoptosis signature on the immune dynamics and therapeutic prognosis in breast cancer: Implication for immunotherapy

Yunfang Yu; Xueyuan Jia; Sunyu Chen; Zijia Lai; Heran Deng; Yuqian Mo; Xinxin Xie; Zehua Wang; Ruichong Lin; Wenhao Ouyang; Herui Yao; Jiannan Wu

doi:10.3389/fgene.2024.1332935

Deciphering the role of apoptosis signature on the immune dynamics and therapeutic prognosis in breast cancer: Implication for immunotherapy

Front Genet. 2024 May 2:15:1332935. doi: 10.3389/fgene.2024.1332935. eCollection 2024.

Authors

Yunfang Yu^#^{1

2}, Xueyuan Jia^#¹, Sunyu Chen^#³, Zijia Lai^#³, Heran Deng², Yuqian Mo³, Xinxin Xie², Zehua Wang⁴, Ruichong Lin^{5

6}, Wenhao Ouyang², Herui Yao², Jiannan Wu²

Affiliations

¹ Faculty of Medicine, Macau University of Science and Technology, Taipa, Macao, China.
² Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Phase I Clinical Trial Cent, Breast Tumor Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.
³ School of Clinical Medicine, Guangdong Medical University, Zhanjiang, China.
⁴ Division of Science and Technology, Beijing Normal University-Hong Kong Baptist University United International College, Hong Kong Baptist University, Zhuhai, China.
⁵ School of Computer Engineering, Guangzhou Huali College, Guangzhou, China.
⁶ Faculty of Innovation Engineering, Macau University of Science and Technology, Taipa, Macao, China.

^# Contributed equally.

Abstract

Background: In breast cancer oncogenesis, the precise role of cell apoptosis holds untapped potential for prognostic and therapeutic insights. Thus, it is important to develop a model predicated for breast cancer patients' prognosis and immunotherapy response based on apoptosis-related signature. Methods: Our approach involved leveraging a training dataset from The Cancer Genome Atlas (TCGA) to construct an apoptosis-related gene prognostic model. The model's validity was then tested across several cohorts, including METABRIC, Sun Yat-sen Memorial Hospital Sun Yat-sen University (SYSMH), and IMvigor210, to ensure its applicability and robustness across different patient demographics and treatment scenarios. Furthermore, we utilized Quantitative Polymerase Chain Reaction (qPCR) analysis to explore the expression patterns of these model genes in breast cancer cell lines compared to immortalized mammary epithelial cell lines, aiming to confirm their differential expression and underline their significance in the context of breast cancer. Results: Through the development and validation of our prognostic model based on seven apoptosis-related genes, we have demonstrated its substantial predictive power for the survival outcomes of breast cancer patients. The model effectively stratified patients into high and low-risk categories, with high-risk patients showing significantly poorer overall survival in the training cohort and across all validation cohorts. Importantly, qPCR analysis confirmed that the genes constituting our model indeed exhibit differential expression in breast cancer cell lines when contrasted with immortalized mammary epithelial cell lines. Conclusion: Our study establishes a groundbreaking prognostic model using apoptosis-related genes to enhance the precision of breast cancer prognosis and treatment, particularly in predicting immunotherapy response.

Keywords: anti-PD-L1 therapy; apoptosis; breast cancer; oncogenic processes; prognostic model.

Grants and funding

The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article. This study was supported by grants 2023YFE0204000 from National Key R&D Program of China, grants 82273204 and 81972471 from the National Natural Science Foundation of China, grant 2023A1515012412 and 2023A1515011214 Guangdong Basic and Applied Basic Research Foundation, grant 2023A03J0722, 202206010078 and 202201020574 from the Guangzhou Science and Technology Project, grant 2018007 from the Sun Yat-Sen University Clinical Research 5010 Program, grant SYS-C-201801 from the Sun Yat-Sen Clinical Research Cultivating Program, grant A2020558 from the Guangdong Medical Science and Technology Program, grant 7670020025 from Tencent Charity Foundation, grant YXQH202209 from the Sun Yat-sen Pilot Scientific Research Fund, grant HL2021012 from the nursing research project of Sun Yat-Sen Memorial Hospital.