Development of a basement membrane gene signature and identification of the potential candidate therapeutic targets for pancreatic cancer.

Background: Pancreatic cancer is a deadly cancer with a poor prognosis. In light of mounting evidence that basement membrane genes (BMGs) play a role in the development of cancer, we sought to examine the prognostic importance and role of BMGs in pancreatic ductal adenocarcinoma (PDAC) patients. Methods: BMGs were obtained from previous top research studies. The clinical and messenger ribonucleic acid expression data were retrieved from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) data sets, respectively. Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses were used for the PDAC risk modeling and gene identification. The Kaplan-Meier method was used to compare outcomes between the low- and high-risk groups. Finally, we analyzed small-molecule drugs that could be used to target BMGs for treatment using the Enrichr data set and validated the function of the tubulointerstitial nephritis antigen ( Results: We successfully constructed and validated a 7 BMG-based model to predict PDAC patient outcomes. Additionally, we discovered that 7 BMG-based model was an independent predictive factor for PDAC. According to our functional analysis, the majority of the signaling pathways enriched in BMGs were those connected to malignancy. Immune cell infiltration and immunological checkpoints were also linked to the BMG-based model. Further, we identified 5 small-molecule drugs that may be useful in treating PDAC patients. We also found that Conclusions: Our study extended understandings of how BMGs work in PDAC. We identified a credible predictive biomarker for PDAC patients' survival.