A Bioinformatics-Based Analysis of an Anoikis-Related Gene Signature Predicts the Prognosis of Patients with pancreatic adenocarcinoma

Background: Anoikis acts as an important defense for the organism by preventing shedding tumor cells from re-adhering to incorrect locations and preventing their growth. In this study, anoikis-related genes (ARGs) were used to construct a prognostic model for PAAD patients. Methods: TCGA database was used to acquire RNA sequencing data and clinical information for PAAD samples. The Cox regression analysis, LASSO regression were performed to construct the prognostic ARGs signature. In addition, GSEA, GO, KEGG were performed to investigate the potential molecular mechanism. Moreover, we analyzed the relationship between our identified signature and immune cell infiltration, tumor microenvironment, mutation landscape, immunotherapy response, drug sensitivity analysis. Results: Two ARGs were slected, including MET and SLCO1B3. Finally, in vitro experiments we performed qRT-PCR, western blot, scratch test, colony-formation analysis to validate the expression and function of MET gene. Conclusion: Combined with clinicopathological characteristics, the risk model was validated as a new independent prognostic factor for PAAD.