Abstract
ABSTRACT
Pancreatic cancer (PC) presents a significant global health challenge because of its high mortality rate, highlighting the urgent requirement for effective early diagnostic and therapeutic strategies. This study examined the function of phenylalanine metabolism in PC and developed a high‐accuracy diagnostic model by integrating metabolomics, Mendelian randomization (MR), and machine learning (ML) algorithms. Initially, MR analysis was conducted on 55 plasma metabolites, revealing a significant causal link between phenylalanine and PC. Utilizing GeneCards and public transcriptomic databases, we determined eight differentially expressed genes (DEGs) in PC associated with phenylalanine. Based on these genes, we utilized 12 ML algorithms, totaling 113 combinations, to select the optimal diagnostic model. We applied Shapley Additive exPlanations (SHAP) for feature interpretation and constructed a prognostic nomogram with strong predictive performance by incorporating clinical variables. Furthermore, immune infiltration analysis demonstrated strong connections between these key genes and specific immune cell populations. Based on the SHAP value, we conducted single‐cell RNA sequencing (scRNA‐seq) data and simulated gene knockout analyses using
SLC6A14
as the key gene. Drug target prediction‐guided molecular docking and molecular dynamics simulations, focusing on the core gene
SLC6A14
, confirmed the high binding stability of candidate compounds. Finally, in vitro cell experiments quantitative real‐time PCR (RT‐qPCR) verified the expression trends of the key genes in PC cell lines. In conclusion, this study successfully developed an ML diagnostic model with high biological interpretability. This analysis aims to identify biomarkers related to phenylalanine metabolism and potential therapeutic drugs for PC, offering new strategies for personalized targeted therapy of PC.