Objective:

To explore vinorelbine-related prognostic genes and their mechanisms for breast cancer treatment.

Approach:

• Data Collection: BC-related data were collected from public databases, including TCGA and GEO.
• Gene Screening: Differentially expressed genes (DEGs) were identified, and candidate genes were selected by intersecting with vinorelbine-related genes (Vino-RGs).
• Prognostic Analysis: Cox regression and machine learning algorithms were used to determine prognostic genes, followed by the construction of a random survival forest (RSF) model and a nomogram.
• Enrichment and Immune Analysis: Enrichment analysis and immune microenvironment analysis were conducted, alongside single-cell analysis.
• Experimental Validation: RT-qPCR was performed to validate the expression of prognostic genes.

Key Findings:

• TUBA1C, BRCA1, TGFB1, TUBA1B, XRCC1, PTGS2, IL7, and TUBB2B were identified as prognostic genes for BC.
• The RSF model and nomogram demonstrated good predictive accuracy for BC prognosis.
• Immune microenvironment analysis indicated correlations between risk scores and immune cells, with poor prognosis linked to lower tumor microenvironment scores.
• Macrophages were identified as crucial cells in BC development, showing active communication with other cells.

Interpretation:

The study identifies specific prognostic genes associated with vinorelbine treatment in breast cancer.

Limitations:

• The study relies on data from public databases, which may have inherent biases.
• Further validation in larger, independent cohorts is necessary.

Conclusion:

The identified prognostic genes may assist in understanding the prognosis of patients undergoing vinorelbine treatment.