A novel disulfidptosis-associated expression pattern in breast cancer based on machine learning

Area of Science:

• Oncology
• Molecular Biology
• Genetics

Background:

• Breast cancer (BC) is a leading cause of cancer-related deaths globally.
• The role of disulfidptosis, a novel cell death pathway, in BC prognosis and treatment remains unexplored.

Purpose of the Study:

• To investigate the biological function and prognostic value of disulfidptosis in breast cancer.
• To develop a prognostic signature for BC based on disulfidptosis-related genes.

Main Methods:

• Evaluated gene mutations and CNVs in disulfidptosis genes.
• Performed differential expression, prognostic, and Cox analyses to identify BC-specific DRGs.
• Utilized unsupervised clustering and LASSO regression to develop a prognostic signature.
• Analyzed the signature's correlation with TME, immune infiltration, stemness, and drug sensitivity.

Main Results:

• Identified two distinct clusters based on three DRGs (DNUFS1, LRPPRC, SLC7A11), with Cluster A showing better survival and higher immune infiltration.
• Developed a four-gene prognostic signature (KIF21A, APOD, ALOX15B, ELOVL2) with good predictive ability.
• The signature demonstrated strong correlations with TME, immune cell infiltration, TMB, stemness, and drug sensitivity.

Conclusions:

• A prognostic signature based on disulfidptosis-DEGs effectively predicts BC patient prognosis.
• This signature is closely linked to the tumor microenvironment, suggesting potential therapeutic strategies.