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Abstract
Background: Pancreatic cancer (PC) is one of the most lethal types of cancer with extremely poor diagnosis and prognosis, and the tumor microenvironment plays a pivotal role during PC progression. Poor prognosis is closely associated with the unsatisfactory results of currently available treatments, which are largely due to the unique pancreatic tumor microenvironment (TME). Methods: In this study, a total of 177 patients with PC from The Cancer Genome Atlas (TCGA) cohort and 65 patients with PC from the GSE62452 cohort in Gene Expression Omnibus (GEO) were included. Based on the proportions of 22 types of infiltrated immune cell subpopulations calculated by cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT), the TME was classified by K-means clustering and differentially expressed genes (DEGs) were determined. A combination of the elbow method and the gap statistic was used to explore the likely number of distinct clusters in the data. The ConsensusClusterPlus package was utilized to identify radiomics clusters, and the samples were divided into two subtypes. Result: Survival analysis showed that the patients with TMEscore-high phenotype had better prognosis. In addition, the TMEscore-high had better inhibitory effect on the immune checkpoint. A total of 10 miRNAs, 311 DEGs, and 68 methylation sites related to survival were obtained, which could be biomarkers to evaluate the prognosis of patients with pancreatic cancer. Conclusions: Therefore, a comprehensive description of TME characteristics of pancreatic cancer can help explain the response of pancreatic cancer to immunotherapy and provide a new strategy for cancer treatment.
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