In Heliyon
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 Limma Package, 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. Finally, intro studies were performed to assess the function of SLC6A14 and SPOCK1 on pancreatic ductal adenocarcinoma (PDAC) cells proliferation and apoptosis.
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. In PDAC cell line, SLC6A14 or SPOCK1 knockdown inhibited cells proliferation, migration and promoted cells apoptosis.
Conclusions : Using Limma Package and GA-ANN, we developed and validated a diagnostic and prognostic gene signature that yielded excellent predictive capacity for PC patients' survival. In vitro studies were further conducted to verify the functions of SLC6A14 and SPOCK1 in PC progression.
Li Zhenchong, Ma Zuyi, Zhou Qi, Wang Shujie, Yan Qian, Zhuang Hongkai, Zhou Zixuan, Liu Chunsheng, Wu Zhongshi, Zhao Jinglin, Huang Shanzhou, Zhang Chuanzhao, Hou Baohua
2022-Nov
Biomarker, Diagnosis, Machine learning, Pancreatic cancer, Prognosis
