Prognostic analysis of patients with breast cancer based on tumor mutational burden and DNA damage repair genes

Xu Teng; Tianshu Yang; Baowen Yuan; Yunkai Yang; Jiaxiang Liu; Xin Wang; Yong Wang; Tianyu Ma; Xin Yin; Hefen Yu; Shuang Wang; Wei Huang

doi:10.3389/fonc.2023.1177133

Prognostic analysis of patients with breast cancer based on tumor mutational burden and DNA damage repair genes

Front Oncol. 2023 Jun 7:13:1177133. doi: 10.3389/fonc.2023.1177133. eCollection 2023.

Authors

Xu Teng¹, Tianshu Yang¹, Baowen Yuan², Yunkai Yang², Jiaxiang Liu³, Xin Wang³, Yong Wang⁴, Tianyu Ma², Xin Yin¹, Hefen Yu¹, Shuang Wang⁵, Wei Huang¹

Affiliations

¹ Beijing Key Laboratory of Cancer Invasion and Metastasis Research, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Capital Medical University, Beijing, China.
² Key Laboratory of Cancer and Microbiome, State Key Laboratory of Molecular Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
³ Department of Breast Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
⁴ Department of Ultrasound, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
⁵ Department of Cardio Surgery Center, Shandong Second Provincial General Hospital, Jinan, China.

Abstract

Background: Breast cancer has a high tumor-specific death rate and poor prognosis. In this study, we aimed to provide a basis for the prognostic risk in patients with breast cancer using significant gene sets selected by analyzing tumor mutational burden (TMB) and DNA damage repair (DDR).

Methods: Breast cancer genomic and transcriptomic data were obtained from The Cancer Genome Atlas (TCGA). Breast cancer samples were dichotomized into high- and low-TMB groups according to TMB values. Differentially expressed DDR genes between high- and low-TMB groups were incorporated into univariate and multivariate cox regression model to build prognosis model. Performance of the prognosis model was validated in an independently new GEO dataset and evaluated by time-dependent ROC curves.

Results: Between high- and low-TMB groups, there were 6,424 differentially expressed genes, including 67 DDR genes. Ten genes associated with prognosis were selected by univariate cox regression analysis, among which seven genes constituted a panel to predict breast cancer prognosis. The seven-gene prognostic model, as well as the gene copy numbers are closely associated with tumor-infiltrating immune cells.

Conclusion: We established a seven-gene prognostic model comprising MDC1, PARP3, PSMB1, PSMB9, PSMD2, PSMD7, and PSMD14 genes, which provides a basis for further exploration of a population-based prediction of prognosis and immunotherapy response in patients with breast cancer.

Keywords: Cox-LASSO regression analysis; TMB; breast cancer; cox regression analysis; prognostic model; tumor-infiltrating immune cells.

Grants and funding

This work was supported by the National Natural Science Foundation of China (41931291, 42125707, 82273403, 81974268), the Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences (2019PT310027, 2021-RC310-006, 2021-RC310-018), the Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences (2021-I2M-1-018), the State Key Laboratory of Molecular Oncology (SKLMO-2021-21, SKLMO-KF2021-21).