ArticleOncology research2023
Prognostic-related genes for pancreatic cancer typing and immunotherapy response prediction based on single-cell sequencing data and bulk sequencing data.
Article in Oncology research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Epithelial membrane protein 1 in human cancer: a potential diagnostic biomarker and therapeutic target.Biomarkers in medicine · 2024Pooled it
- RAS pathway activity subtypes identified by machine learning define prognostic and immune microenvironment characteristics in lung adenocarcinoma.Discover oncology · 2026Article
- Clinical Application Progress of Artificial Intelligence in Pancreatic Cancer: From Diagnosis to Immunotherapy.Oncology research · 2026Review
- Role of a transmembrane protein, epithelial membrane protein 1, in the pathogenesis of pancreatic ductal adenocarcinoma.Oncogene · 2026Article
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Authors and funding
6 authors.
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Abstract
Background: Pancreatic cancer is associated with high mortality and is one of the most aggressive of malignancies, but studies have not fully evaluated its molecular subtypes, prognosis and response to immunotherapy of different subtypes. The purpose of this study was to explore the molecular subtypes and the key genes associated with the prognosis of pancreas cancer patients and study the clinical phenotype, prognosis and response to immunotherapy using single-cell seq data and bulk RNA seq data, and data retrieved from GEO and TCGA databases. Methods: Single-cell seq data and bioinformatics methods were used in this study. Pancreatic cancer data were retrieved from GEO and TCGA databases, the molecular subtypes of pancreatic cancer were determined using the six cGAS-STING related pathways, and the clinical phenotype, mutation, immunological characteristics and pathways related to pancreatic cancer were evaluated. Results: Pancreatic cancer was classified into 3 molecular subtypes, and survival analysis revealed that patients in Cluster3 (C3) had the worst prognosis, whereas Cluster1 (C1) had the best prognosis. The clinical phenotype and gene mutation were statistically different among the three molecular subtypes. Analysis of immunotherapy response revealed that most immune checkpoint genes were differentially expressed in the three subtypes. A lower risk of immune escape was observed in Cluster1 (C1), indicating higher sensitivity to immunotherapeutic drugs and subjects in this Cluster are more likely to benefit from immunotherapy. The pathways related to pancreatic cancer were differentially enriched among the three subtypes. Five genes, namely SFRP1, GIPR, EMP1, COL17A and CXCL11 were selected to construct a prognostic signature. Conclusions: Single-cell seq data were to classify pancreatic cancer into three molecular subtypes based on differences in clinical phenotype, mutation, immune characteristics and differentially enriched pathways. Five prognosis-related genes were identified for prediction of survival of pancreatic cancer patients and to evaluate the efficacy of immunotherapy in various subtypes.
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