ArticlePrecision clinical medicine2022
A novel immunogenomic signature to predict prognosis and reveal immune infiltration characteristics in pancreatic ductal adenocarcinoma.
Article in Precision clinical medicine, 2022. 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.
- Quiescent tumor cells shape the immunosuppressive microenvironment in pancreatic cancer.Nature communications · 2026Article
- Unveiling the impact of ERAP1 and ERAP2 on migration, angiogenesis and ER stress response.Frontiers in cell and developmental biology · 2025Review
- Keratin 17 modulates the immune topography of pancreatic cancer.Journal of translational medicine · 2024Article
- Perspectives for immunotherapy of EBV-associated GLELC: A relatively "hot" tumor microenvironment.Cancer medicine · 2023Article
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Authors and funding
6 authors.
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Abstract
Background: The immune response in the tumor microenvironment (TME) plays a crucial role in cancer progression and recurrence. We aimed to develop an immune-related gene (IRG) signature to improve prognostic predictive power and reveal the immune infiltration characteristics of pancreatic ductal adenocarcinoma (PDAC). Methods: The Cancer Genome Atlas (TCGA) PDAC was used to construct a prognostic model as a training cohort. The International Cancer Genome Consortium (ICGC) and the Gene Expression Omnibus (GEO) databases were set as validation datasets. Prognostic genes were screened by using univariate Cox regression. Then, a novel optimal prognostic model was developed by using least absolute shrinkage and selection operator (LASSO) Cox regression. Cell type identification by estimating the relative subsets of RNA transcripts (CIBERSORT) and estimation of stromal and immune cells in malignant tumors using expression data (ESTIMATE) algorithms were used to characterize tumor immune infiltrating patterns. The tumor immune dysfunction and exclusion (TIDE) algorithm was used to predict immunotherapy responsiveness. Results: A prognostic signature based on five IRGs ( Conclusions: We constructed an optimal prognostic model which had independent prognostic value and was comprehensively validated in external PDAC databases. Additionally, this five-genes signature could predict immune infiltration characteristics. Moreover, the signature helped stratify PDAC patients who might be more responsive to immunotherapy.
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