Seven-gene signature on tumor microenvironment for predicting the prognosis of patients with pancreatic cancer

Background: The aim of the present study was to construct a novel gene signature on the tumor microenvironment (TME) to predict the prognosis of patients with pancreatic ductal adenocarcinoma (PDAC). Methods: We downloaded gene expression profiles and clinical information of PDAC from The Cancer Genome Atlas (TCGA) datasets, as well as Gene Expression Omnibus (GEO) datasets (GSE78229, GSE62452, and GSE28735). Differentially expressed genes were generated by comparing high versus low score groups of immune/stromal subgroups based on the Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data algorithm. Subsequently, a prognostic risk score model was constructed and validated through univariate and multivariate Cox regression analyses. Finally, functional enrichment analysis and protein-protein interactions were performed to predict the functional implication of the prognostic model. Results: We picked out 1,797 upregulated genes in immune groups and stromal groups. Through further analysis, we constructed a 7-gene signature on the TME. The risk score from the model effectively differentiated patients into high-risk and low-risk groups with different overall survival and was validated by GEO datasets. A functional analysis suggested that 7 selected genes and their co-expressed genes were mainly enriched in immune response, extracellular structure organization, and cell adhesion molecule binding. Conclusions: Our results showed that the 7-gene model on the TME can be used to assess the prognosis of patients with PDAC.