Machine learning-based prognostic modelling of NK cells in PAAD for immunotherapy guidance

Pancreatic cancer's high incidence and mortality rates are underscored by ineffective treatments, particularly immunotherapy's poor performance. This could stem from an unclear immune microenvironment, where NK cells may play a unique role. Analyzing the NK cell-differentially expressed genes (NKDEGs) from the PAAD_GSE162708 single-cell dataset and utilizing the TCGA-PAAD and ICGC-PACA-AU datasets, we identified 11 NKDEGs linked to pancreatic adenocarcinoma (PAAD) prognosis and developed a prognostic model. This model's risk scores significantly outperformed traditional grading and TNM staging systems, validated through clinical and pathological analyses. Functional enrichment analysis pointed to the Neuroactive ligand-receptor interaction and MAPK signaling pathways, suggesting NK cells' distinctive role in PAAD. High-risk groups showed decreased overall NK cells but increased activated NK cells, which may mediate adverse inflammatory responses. NK cells exhibit synergistic interactions with plasma cells and macrophages and negative regulation by monocytes and naive B cells. Our model accurately predicts immunotherapy responses, indicating potential for targeted drugs to enhance treatment. Additionally, we introduced an NKDEGs-based immunotyping approach for personalized medicine and clinical decision-making in PAAD. This study emphasizes NK cells' potential in PAAD treatment, offering precise patient stratification and therapeutic targets for immunotherapy.