Abstract
BACKGROUND: Pancreatic cancer is one of the most lethal malignancies, characterized by poor prognosis and low survival rates. Traditional prognostic factors for pancreatic cancer offer inadequate predictive accuracy, often failing to capture the complexity of the disease. The hypoxic tumor microenvironment has been recognized as a significant factor influencing cancer progression and resistance to treatment. This study aims to develop a prognostic model based on key hypoxia-related molecules to enhance prediction accuracy for patient outcomes and to guide more effective treatment strategies in pancreatic cancer. AIM: To develop and validate a prognostic model for predicting outcomes in patients with pancreatic cancer using key hypoxia-related molecules. METHODS: This pancreatic cancer prognostic model was developed based on the expression levels of the hypoxia-associated genes CAPN2, PLAU, and CCNA2. The results were validated in an independent dataset. This study also examined the correlations between the model risk score and various clinical features, components of the immune microenvironment, chemotherapeutic drug sensitivity, and metabolism-related pathways. Real-time quantitative PCR verification was conducted to confirm the differential expression of the target genes in hypoxic and normal pancreatic cancer cell lines. RESULTS: The prognostic model demonstrated significant predictive value, with the risk score showing a strong correlation with clinical features: It was significantly associated with tumor grade (G) ((b) P < 0.01), moderately associated with tumor stage (T) ((a) P < 0.05), and significantly correlated with residual tumor (R) status ((b) P < 0.01). There was also a significant negative correlation between the risk score and the half-maximal inhibitory concentration of some chemotherapeutic drugs. Furthermore, the risk score was linked to the enrichment of metabolism-related pathways in pancreatic cancer. CONCLUSION: The prognostic model based on hypoxia-related genes effectively predicts pancreatic cancer outcomes with improved accuracy over traditional factors and can guide treatment selection based on risk assessment.