Simple SummaryPancreatic cancer is one of the deadliest forms of cancer, with low survival rates and limited treatment options. Anoikis resistance, or the ability of cancer cells to survive and grow in the absence of attachment to the extracellular matrix, is thought to play a critical role in pancreatic cancer progression and treatment resistance. In this study, a multi-omics approach was used to identify a set of signature genes associated with anoikis in pancreatic cancer. The approach involved integrating data from large-scale cancer genomics databases such as TCGA and GEO, as well as single cell sequencing databases and in vitro assays. The identified signature genes have the potential to serve as prognostic biomarkers, allowing clinicians to predict patient outcomes and tailor treatment strategies accordingly. Additionally, these genes may also predict the chemo-sensitivity of pancreatic cancer cells, helping to guide the selection of chemotherapy regimens. The use of a comprehensive multi-omics approach provides a complete understanding of the molecular mechanisms underlying anoikis resistance in pancreatic cancer. This study highlights the importance of integrating multiple data sources and experimental approaches in cancer research to facilitate the discovery of new biomarkers and therapeutic targets. Such approaches have the potential to improve patient outcomes and lead to more effective cancer treatments.AbstractAnoikis, a form of apoptosis that occurs due to detachment of cells from the extracellular matrix, has been linked to the development of cancer in several studies. However, its role in pancreatic cancer remains incompletely understood. In this study, we utilized univariate Cox regression and LASSO regression analyses to establish a prognostic model for pancreatic adenocarcinoma based on anoikis-related genes in the TCGA database. Additionally, we performed univariate and multifactorial Cox analyses of protein expression results for TCGA pancreatic adenocarcinoma. We further explored the difference in immune infiltration between the high-risk and low-risk groups and verified the expression of the screened genes using quantitative real-time PCR (qRT-PCR). Our findings indicate that numerous anoikis-related genes are linked to pancreatic adenocarcinoma prognosis. We identified seven prognostic genes (MET, DYNLL2, CDK1, TNFSF10, PIP5K1C, MSLN, GKN1) and validated that their related proteins, such as EGFR and MMP2, have a significant impact on the prognosis of pancreatic adenocarcinoma. Based on clustering analyses of the seven prognostic genes, patients could be classified into three distinct categories, for which somatic mutations varied significantly across the groups. High-risk and low-risk groups also exhibited significant differences in immune infiltration. All genes were found to be highly expressed in pancreatic cancer cell lines (ASPC-1, CFPAC-1) as compared to a normal pancreatic cell line (HPDE). Based on the seven anoikis-related genes, we formulated a robust prognostic model with high predictive accuracy. We also identified the significant impact of KRAS, P53, and CDKN2A mutations on the prognosis of this fatal disease. Therefore, our study highlights the crucial role of anoikis in the development of the pancreatic adenocarcinoma tumor microenvironment.