Identification By Genetic Algorithm Optimized Back Propagation Artificial Neural Network and Validation of A Four-Gene Signature for Diagnosis and Prognosis of Pancreatic Cancer

Abstract

Background: Although some improvements in the management of pancreatic cancer (PC) have been made, no major breakthroughs in terms of biomarker discovery or effective treatment have emerged. Here, we applied artificial intelligence (AI)-based methods to develop a model to diagnose PC and predict survival outcome.Methods: Multiple bioinformatics methods, including RankProd, were performed to identify differentially expressed genes (DEGs) in PC. A Back Propagation (BP) model was constructed, followed by Genetic Algorithm (GA) filtering and verification of its prognosis capacity in the TCGA cohort. Furthermore, we validated the protein expression of the selected DEGs in 92 clinical PC tissues using immunohistochemistry.Results: Four candidate genes (LCN2, SLC6A14, SPOCK1, and VCAN) were selected to establish a four-gene signature for PC. The gene signature was validated in the TCGA PC cohort, and found to show satisfactory discrimination and prognostic power. Areas under the curve (AUC) values of overall survival were both greater than 0.60 in the TCGA training cohort, test cohort, and the entire cohort. Kaplan-Meier analyses showed that high-risk group had a significantly shorter overall survival and disease-free survival than the low-risk group. Further, the elevated expression of SLC6A14 and SPOCK1 in PC tissues was validated in the TCGA+GETx datasets and 92 clinical PC tissues, and was significantly associated with poor survival in PC.Conclusions: Using RankProd and GA-ANN, we developed and validated a diagnostic and prognostic gene signature that yielded excellent predictive capacity for PC patients’ survival.