Identifying the relationship between exosome genes and breast cancer risk using bioinformatics and machine learning...

Area of Science:

• Oncology
• Nanotechnology
• Genomics

Background:

• Breast cancer (BC) is a leading global cancer in women with high mortality.
• Exosomes, crucial for cell communication, are implicated in various diseases, but their role in BC is unclear.
• Understanding exosome involvement in BC is vital for improved treatment and prevention strategies.

Purpose of the Study:

• To elucidate the relationship between exosome-related genes and breast cancer (BC) development and prognosis.
• To identify potential diagnostic and therapeutic targets for BC based on exosome gene expression.
• To explore drug-targeting strategies for identified BC-associated exosome genes.

Main Methods:

• Differential gene expression analysis of BC and normal tissues from the NCBI GEO database.
• Identification of differentially expressed exosome-related genes using machine learning algorithms (LASSO, SVM-RFE, RF).
• Construction of gene interaction networks and a nomogram for BC risk and prognosis prediction, including molecular docking analyses.

Main Results:

• Identified 595 differentially expressed genes and 37 exosome-related genes.
• Pinpointed five differentially expressed exosome-related genes linked to BC risk, involved in biological regulation and immune cell infiltration.
• Discovered drugs targeting four of these five key genes.

Conclusions:

• Exosome-related genes are significantly associated with breast cancer (BC) occurrence and prognosis.
• These identified exosome genes represent promising therapeutic targets for BC.
• Potential drug therapies targeting these genes offer new treatment possibilities for BC.