Identification of NETs and inflammation-related prognostic genes in breast cancer and PCR experimental validation.

BACKGROUND

Neutrophil extracellular trap-driven necrosis-related genes (NRGs) and inflammation-related genes (IRGs) are crucial in mitigating or inhibiting cancer progression in breast cancer patients. The interaction between NRGs and IRGs in breast cancer remains unclear. Thus, identifying prognostic genes associated with NETosis and inflammation in breast cancer may offer a novel approach to improving the outcome of breast cancer.

METHODS

Genes with statistically significant expression levels and correlations with IRGs and NRGs scores were selected as candidate genes, and the protein-protein interactions of the encoded proteins were explored. Subsequently, prognostic genes were further identified and build the risk model. Lastly, independent prognostic factors were determined through independent prognostic analysis, a prognostic model was established, and the immune microenvironment and drug sensitivity were analyzed.

RESULTS

The study identified ZMYND10, IL12B, CXCL13, TFF1, LTB, and SPIB as prognostic genes. Additionally, risk score and three clinical features, including age, were established as independent prognostic factors. A prognostic model with moderate predictive accuracy was developed. Further analysis revealed that six prognostic genes were considerably correlated with differential immune cells, among which CXCL13, IL12B, LTB, and SPIB were considerably positively correlated with most differential immune cells. Additionally, 18 drugs were considerably associated with the risk score, including six drugs such as Metformin and Thapsigargin, which could potentially be used to treat breast cancer.

CONCLUSION

This study constructed a risk model and a nomogram for breast cancer prognosis using bioinformatics methods, and analyzed prognostic genes of breast cancer, which will be helpful to the improvement of breast cancer's outcome and the development of clinical medicine.