Jun-Gang Wang and Yun Zhang*
Published September 14, 2024
Genet. Mol. Res. 23 (3): gmr2338
DOI http://dx.doi.org/10.4238/gmr2338

About the Authors
Jun-Gang Wang and Yun Zhang*

Corresponding author: 
Yun Zhang
E-mail: 33304124@qq.com

ABSTRACT

Purpose. To explore the association between disulfidptosis and breast cancer. Methods. The METABRIC cohort containing clinical data and gene expression data was downloaded. Based on 9 previously reported disulfidptosis-related genes, we performed correlation analysis, Cox regression analysis, and least absolute shrinkage and selection operator (LASSO) regression analysis to screen genes and then constructed a prognostic model, the effectiveness of which was validated in external data sets including METABRIC validation data and GSE20685. A risk score was calculated then and samples were divided into high- and lowrisk group based on the median value. Enrichment analysis, immune microenvironmental analysis, and drug sensitivity analysis were also carried out. Results. A total of 7 genes was finally identified after LASSO regression analysis. We constructed a nomogram predict model. The area under curve (AUC) values for survival rates at most time points were larger than 0.650, no matter in the METABRIC training data, METABRIC validation data, or in the GSE20685 data. Dividing into two groups based on the median risk score can effectively distinguish the prognosis of patients (P < 0.001 in the internal group, P = 0.064 in the METABRIC training data, and P < 0.001 in the GSE20685 data). High-risk group was associated with a significantly lower infiltration level of cytotoxic lymphocytes compared with low-risk group (P = 0.026), while the infiltration levels of stromal cells were the opposite. Conclusions. The study provided valuable insights into the association between breast cancer and disulfidptosis. The findings needs
to be validated in further studies.

Key words: Disulfidptosis; Prognosis; LASSO; Breast cancer