Background: Patients with pancreatic ductal adenocarcinoma (PDAC) face a highly unfavorable outcome and have a poor response to standard treatments. Immunotherapy, especially therapy based on natural killer (NK) cells, presents a promising avenue for the treatment of PDAC.

Aims: This research endeavor seeks to formulate a predictive tool specifically designed for PDAC based on NK cell-related long non-coding RNA (lncRNA), revealing new molecular subtypes of PDAC to promote personalized and precision treatment.

Methods: Utilizing the Tumor Immune Single-cell Hub 2 platform, we discovered genes associated with NK cells in PDAC. We employed the TCGA-PAAD dataset to ascertain the expression profiles of these NK cell-related genes and to screen for lncRNAs correlated with NK cells. Subsequently, we utilized Cox regression analysis for hazard ratios and LASSO regression analysis to identify three NK cell-related lncRNAs that were used to develop a prognostic assessment model. The forecasting accuracy of this model was appraised using the ROC curve and validated using a test set and the complete dataset.

Results: Successful construction of a prognostic model comprising three lncRNAs was achieved, demonstrating good predictive efficiency in the training set, validation dataset, and the entire dataset. NK cells display robust interactions with malignant cells, CD8 T cells, and fibroblasts in the PDAC tumor microenvironment and participate in the transport of various signaling molecules and following immune responses in PDAC. According to the expression patterns of NK cell-related lncRNA, we labeled PDAC patients as four molecular subtypes, exhibiting significant differences in immune cell infiltration, drug sensitivity, and other aspects.

Conclusion: This study Uncovered the activity of NK cells within PDAC, proposed an NK cell-related lncRNA model, and delineated new molecular subtypes, thereby providing targets for personalized therapy.

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11770056PMC
http://dx.doi.org/10.3389/fimmu.2024.1514259DOI Listing

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