Identification and validation of a novel NK cells-related signature to predict prognosis and immune microenvironment in LUAD.

Background: The prevalence and fatality rates of lung cancer are experiencing a rapid escalation. Natural Killer (NK) cells have been established to have a crucial role in both tumor initiation and progression. Nevertheless, uncertainties persist regarding their precise implications in the prognosis of LUAD.

Methods: The data were obtained from reputable sources, such as the Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) database, and our internally generated sequencing data. Utilizing the TCGA data as a background, we selected intersecting genes, validated by cluster analysis, to establish a Cox model and validated it using the GEO datasets. Furthermore, we conducted extensive analyses to investigate the significance of potential biomarkers in relation to immune cell infiltration, single-cell data, differential gene expression, and drug sensitivity.

Results: 67 immune-related genes associated with NK cells (NK-IRGs) were identified in the TCGA datasets, whose research potential was demonstrated by cluster analysis. A prognostic signature was identified utilizing the univariate and multivariate Cox model, resulting in the identification of five genes, which was validated using GEO datasets. Additionally, the nomogram's calibration curve demonstrated exceptional concordance between the projected and actual survival rates. Subsequent investigations uncovered that this prognostic signature demonstrated its independence as a risk factor. Notably, in the low-risk group, NK cells exhibited elevated levels of immune checkpoint molecules, indicating heightened sensitivity to immune therapy. These findings highlight the potential of utilizing this signature as a valuable tool in the selection of patients who could benefit from targeted immune interventions.