ArticleFrontiers in oncology2022
Reveal the Heterogeneity in the Tumor Microenvironment of Pancreatic Cancer and Analyze the Differences in Prognosis and Immunotherapy Responses of Distinct Immune Subtypes.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Intratumoral and peritumoral radiomics for preoperative prediction of the efficacy of HIFU ablation for uterine fibroids based on multiparametric MRI: a multicenter study.Frontiers in oncology · 2025Article
- Pancreatic cancer subtyping - the keystone of precision treatment.Frontiers in immunology · 2025Review
- Comprehensive multi-omics profiling identifies novel molecular subtypes of pancreatic ductal adenocarcinoma.Genes & diseases · 2024Review
- SPDEF drives pancreatic adenocarcinoma progression via transcriptional upregulation ofBiomolecules & biomedicine · 2024Article
- Review
- Prognostic value and immune infiltration of ARMC10 in pancreatic adenocarcinoma via integrated bioinformatics analyses.Heliyon · 2023Article
- Open-source curation of a pancreatic ductal adenocarcinoma gene expression analysis platform (pdacR) supports a two-subtype model.Communications biology · 2023Article
- Large-scale assessment of pros and cons of autopsy-derived or tumor-matched tissues as the norms for gene expression analysis in cancers.Computational and structural biotechnology journal · 2023Article
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6 authors.
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Abstract
purposeThe current clinical classification of pancreatic ductal adenocarcinoma (PDAC) cannot well predict the patient's possible response to the treatment plan, nor can it predict the patient's prognosis. We use the gene expression patterns of PDAC patients to reveal the heterogeneity of the tumor microenvironment of pancreatic cancer and analyze the differences in the prognosis and immunotherapy response of different immune subtypes.
methodsFirstly, use ICGC's PACA-AU PDAC expression profile data, combined with the ssGSEA algorithm, to analyze the immune enrichment of the patient's tumor microenvironment. Subsequently, the spectral clustering algorithm was used to extract different classifications, the PDAC cohort was divided into four subtypes, and the correlation between immune subtypes and clinical characteristics and survival prognosis was established. The patient's risk index is obtained through the prognostic prediction model, and the correlation between the risk index and immune cells is prompted.
resultsWe can divide the PDAC cohort into four subtypes: immune cell and stromal cell enrichment (Immune-enrich-Stroma), non-immune enrichment but stromal cell enrichment (Non-immune-Stroma), immune-enriched Collective but non-matrix enrichment (Immune-enrich-non-Stroma) and non-immune enrichment and non-stromal cell enrichment (Non-immune-non-Stroma). The five-year survival rate of immune-enrich-Stroma and non-immune-Stroma of PACA-CA is quite different. TCGA-PAAD's immune-enrich-Stroma and immune-enrich-non-Stroma groups have a large difference in productivity in one year. The results of the correlation analysis between the risk index and immune cells show that the patient's disease risk is significantly related to epithelial cells, megakaryocyte-erythroid progenitor (MEP), and Th2 cells.
conclusionThe tumor gene expression characteristics of pancreatic cancer patients are related to immune response, leading to morphologically recognizable PDAC subtypes with prognostic/predictive significance.
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