biostudies-literatureUnknown16(1)Zhang XJ<h4>Background</h4>Pancreatic cancer (PAC) has a complex tumor immune microenvironment, and currently, there is a lack of accurate personalized treatment. Establishing a novel consensus machine learning driven signature (CMLS) that offers a unique predictive model and possible treatment targets for this condition was the goal of this study.<h4>Methods</h4>This study integrated multiple omics data of PAC patients, applied ten clustering techniques and ten machine learning approaches to construct molecular subtypes for PAC, and created a new CMLS.<h4>Results</h4>Using multi-omics clustering, we discovered two cancer subtypes (CSs) associated with prognosis, among which CS1 exhibited poor prognostic outcomes. Subsequently, 13 central genes were identified through screening, constituting CMLS Discover oncology96https://www.ebi.ac.uk/biostudies/studies/S-EPMC11775367biostudies-literatureImproving molecular subtypes and prognosis of pancreatic cancer through multi group analysis and machine learning.PMC11775367Guan KPZhang XJLin FFWen YQfalseImproving molecular subtypes and prognosis of pancreatic cancer through multi group analysis and machine learning.<h4>Background</h4>Pancreatic cancer (PAC) has a complex tumor immune microenvironment, and currently, there is a lack of accurate personalized treatment. Establishing a novel consensus machine learning driven signature (CMLS) that offers a unique predictive model and possible treatment targets for this condition was the goal of this study.<h4>Methods</h4>This study integrated multiple omics data of PAC patients, applied ten clustering techniques and ten machine learning approaches to construct molecular subtypes for PAC, and created a new CMLS.<h4>Results</h4>Using multi-omics clustering, we discovered two cancer subtypes (CSs) associated with prognosis, among which CS1 exhibited poor prognostic outcomes. Subsequently, 13 central genes were identified through screening, constituting CMLS 2025-01-01T00:00:00Z2025 Jan2025-04-22T10:26:27.213Z2025-04-05T23:34:13.024ZS-EPMC117753673987382010.1007/s12672-025-01841-8