ArticlePeerJ2026
Molecular classification and prognosis study of pancreatic ductal adenocarcinoma through multi-omics integrated clustering analysis.
Article in PeerJ, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
8 authors.
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Abstract
Background: Pancreatic ductal adenocarcinoma (PDAC) is a lethal malignancy characterized by significant heterogeneity. We conducted a multi-omics integrated clustering analysis to categorize PDAC molecular subtypes. Methods: Multi-omics data from The Cancer Genome Atlas-Pancreatic Adenocarcinoma (TCGA-PAAD) were integrated using ten clustering algorithms. Comparisons across PDAC subtypes were performed regarding prognosis, gene mutations, pathways, tumor microenvironment (TME), and chemotherapy sensitivity. A prognostic model was constructed utilizing Cox and Lasso regression based on subtype-related genes. Results: Samples from the TCGA-PAAD cohort were classified into two subtypes. The CS1 subtype was identified as a high-risk, immunosilent subtype, while the CS2 subtype was characterized as a low-risk, immunoactive subtype. Compared to CS2 subtype, CS1 subtype exhibited shorter survival, higher frequency of genetic mutations, more aggressive tumor-promoting nature, lower TME immune score, and increased sensitivity to chemotherapy. The prognostic model related to PDAC subtypes displayed robust predictive efficiency; IL20RB gene emerged having superior predictive capability. Conclusions: We successfully identified two distinct PDAC subtypes. The developed prognostic model exhibited strong predictive efficacy; and the upregulation of IL20RB was identified as a promising therapeutic target for PDAC.
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