Purpose: 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. Results: 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). Conclusions: 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.

中文翻译：

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胰腺腺癌全肿瘤转录组亚型和成分的预后相关性

目的：我们的团队之前定义了六种定量转录组学成分，并通过这些成分的关联将其分类为五种亚型。在这项研究中，我们比较了来自不同转录组分析技术的定量成分和定性分类的稳健性，研究了它们的临床相关性，并提出了一种新的预后模型。实验设计：本研究共纳入来自多中心队列的 210 名患者和来自单中心队列的 149 名患者。从多中心队列的 165 名患者中获得 RNA 微阵列图谱。从所有患者获得RNA测序（RNA-seq）谱。结果：对于同时具有 RNA 微阵列和 RNA-seq 谱的患者，亚型分配的一致性是部分的，一致性率为 82.4%。六个组件的两种技术预测之间的相关性在 0.85 到 0.95 之间，显示出稳健性的优势。根据 Akaike 信息标准，RNA 成分在单变量或多变量模型中显示出比亚型更大的预后价值。使用单中心队列进行训练，我们开发了一个多变量 Cox 回归模型，该模型使用所有六个组成部分和临床病理学特征（淋巴结侵袭和切除边缘）对无病生存 (DFS) 的影响。这种预后模型与 DFS 高度相关（P < 0.001）。多中心队列中模型的评估显示与 DFS 和总生存期显着相关（P < 0.001）。结论：我们描述了与亚型相比，全肿瘤转录组成分的预后价值和稳健性的优势。我们创建并验证了一个新的基于 DFS 的多变量 Cox 回归预后模型，包括六个胰腺癌转录组学成分水平和病理特征。