Prognosis stratification of patients with breast invasive carcinoma based on cysteine metabolism-disulfidptosis affinity.

PURPOSE

The rise of female breast cancer has created a significant global public health issue that requires effective solutions. Disulfidptosis, a recently identified form of cell death characterized by an excessive accumulation of disulfides, has unique initiatory and regulatory mechanisms. The formation of disulfide bonds is a metabolic event typically associated with cysteines. This study aims to explore the potential of the affinity between cysteine metabolism and disulfidptosis in risk stratification for breast invasive carcinoma (BRCA).

METHODS

We used correlation analysis to decipher co-relation genes between cysteine metabolism and disulfidptosis (CMDCRGs). Both LASSO regression analysis and multivariate Cox regression analysis were employed to construct the prognostic signature. Additionally, we conducted investigations concerning subtype identification, functional enhancement, mutation landscape, immune infiltration, drug prioritization, and single-cell analysis.

RESULTS

We developed and validated a six-gene prognostic signature as an independent prognostic predictor for BRCA. The prognostic nomogram, based on risk score, demonstrated a favorable capability in predicting survival outcomes. We identified distinct gene mutations, functional enhancements, and immune infiltration patterns between the two risk groups. Four clusters of drugs were predicted as potentially effective for patients in the low-risk group. We identified seven cell clusters within the tumor microenvironment of breast cancer, and RPL27A was found to be widely expressed in this environment.

CONCLUSION

Multidimensional analyses confirmed the clinical utility of the cysteine metabolism-disulfidptosis affinity-based signature in risk stratification and guiding personalized treatment for patients with BRCA.