Our team previously defined six quantitative transcriptomic components, and a classification in five subtypes by association of these components. In this study, we compared the robustness of quantitative components and qualitative classifications from different transcriptomic profiling techniques, investigated their clinical relevance, and proposed a new prognostic model.

Experimental Design:

A total of 210 patients from a multicentric cohort and 149 patients from a monocentric cohort were included in this study. RNA microarray profiles were obtained from 165 patients of the multicentric cohort. RNA sequencing (RNA-seq) profiles were obtained from all the patients.


For the patients with both RNA microarray and RNA-seq profiles, the concordance in subtype assignment was partial with an 82.4% coherence rate. The correlation between the two technique projections of the six components ranged from 0.85 to 0.95, demonstrating an advantage of robustness. On the basis of the Akaike information criterion, the RNA components showed more prognostic value in univariate or multivariate models than the subtypes. Using the monocentric cohort for training, we developed a multivariate Cox regression model using all six components and clinicopathologic characteristics (node invasion and resection margins) on disease-free survival (DFS). This prognostic model was highly associated with DFS (P < 0.001). The evaluation of the model in the multicentric cohort showed significant association with DFS and overall survival (P < 0.001).


We described the advantage of the prognostic value and robustness of the whole-tumor transcriptomic components than subtypes. We created and validated a new DFS-based multivariate Cox regression prognostic model, including six pancreatic adenocarcinoma transcriptomic component levels and pathologic characteristics.

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