Molecular classification and prognosis study of pancreatic ductal adenocarcinoma through multi-omics integrated clustering analysis

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.