基于单细胞测序鉴定 NK 细胞标记基因以建立乳腺癌预后特征

Tumor-infiltrating natural killer (NK) cells are pivotal in modulating tumor progression, either by promoting or inhibiting neoplastic development. Nevertheless, the implications of NK cells in breast carcinoma remain inadequately understood. This investigation aimed to delineate the impact of NK cells on both the prognosis and the immune infiltration landscape in breast cancer.

NK cell marker genes were identified using single-cell sequencing data from breast cancer available in the Gene Expression Omnibus (GEO) database. A prognostic model was constructed based on data from The Cancer Genome Atlas (TCGA) and subsequently validated with the GEO dataset. Disparities in immune cell infiltration between low-risk and high-risk cohorts, as stratified by the prognostic model, were examined. Additionally, genes differentially expressed between these cohorts were subjected to enrichment analysis.

A total of 29 NK cell marker genes were identified through single-cell sequencing, and a prognostic model was subsequently developed using machine learning techniques based on the TCGA data. This model demonstrated robust predictive performance when applied to both TCGA and GEO datasets. Notably, a significant difference in immune infiltration was observed between the low-risk and high-risk groups. The findings were further validated through enrichment analysis.

In summary, we constructed a prognostic signature characterized by strong predictive performance, which has elucidated the critical role of NK cells in the pathogenesis of breast cancer. Furthermore, this model offers a predictive index and identifies a novel therapeutic target for the advancement of immunotherapeutic strategies in the clinical management of breast cancer patients.