ArticleTranslational oncology2023
Differential expression and bioinformatics analysis of exosome circRNAs in pancreatic ductal adenocarcinoma.
Article in Translational oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Predicting circRNA-RBP Binding Sites Using a Hybrid Deep Neural Network.Interdisciplinary sciences, computational life sciences · 2024Article
- CircRNA Regulation of T Cells in Cancer: Unraveling Potential Targets.International journal of molecular sciences · 2024Review
- Exosome and lipid metabolism-related genes in pancreatic adenocarcinoma: a prognosis analysis.Aging · 2023Article
- An integrated computational biology approach defines the crucial role of TRIP13 in pancreatic cancer.Computational and structural biotechnology journal · 2023Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPancreatic ductal adenocarcinoma (PDAC) is a fatal malignant tumor with an unfavorable prognosis. Increasing evidence indicated circRNAs were associated with the pathogenesis and progression of tumors, but data on the expression of serum exosomal circRNAs in PDAC are scarce. This study attempted to explore the prognostic value and function of serum exosomes in PDAC patients.
methodsMicroarray-based circRNA expression was determined in PDAC and paired with normal serum samples, and the intersection of differentially expressed circRNAs (DECs) in serum exosomal samples and GSE79634 tissue samples was conducted. A specific CircRNA database was applied to investigate DECs binding miRNAs. Target genes were predicted using the R package multiMiR. Cox regression analyses were applied for constructing a prognostic model. The immunological characteristics analysis was carried out through the TIMER, QUANTISEQ, XCELL, EPIC, and ssGSEA algorithms.
results15 DECs were finally identified, and a circRNA-miRNA-mRNA network was established. A prognostic risk model was developed to categorize patients according to the risk scores. Furthermore, the association between risk score and immune checkpoint genes including CD80, TNFSF9, CD276, CD274, LGALS9, and CD44 were significantly elevated in the high-risk group, while ICOSLG and ADORA2A were upregulated in the low-risk group.
conclusionsOur results may provide new clues for the prognosis and treatment of PDAC.
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