AI 中文总结
研究脓毒症死亡率预测中跨队列转移下转录组特征的共形可靠性，提出评估框架，比较基因、通路和混合分子表征，发现通路水平表征在多方面表现良好，表明分子表征对预测等多方面有重要影响。
AI 中文摘要
血液转录组分析能够通过在分子水平捕获宿主免疫反应来进行预后建模。然而，许多转录组模型采用的队列内评估策略无法充分反映在独立医院中的应用情况。外部应用场景会引入队列转移，这可能会大幅降低预测性能和不确定性估计的可靠性。我们提出了一个框架，用于在现实的跨队列应用中评估转录组脓毒症死亡率预测，系统地比较基因水平、通路水平和混合分子表征。将四个包含936名患者和248例死亡事件的公开全血转录组队列统一到一个共享的7660基因特征空间，并使用逻辑回归、随机森林、XGBoost和LightGBM在留一队列交叉验证下进行评估。除了AUROC和AUPRC，还通过共形预测、校准分析、选择性预测和提出的通路稳定性指数来评估模型行为。发现基因水平和混合表征通常具有最强的判别性能，而通路水平表征在不同模型家族中表现出更大的稳健性，在跨队列转移下具有更可靠的不确定性行为，并且通过基因本体和KEGG富集分析确定了富含免疫和宿主防御过程的稳定分子特征。这些发现表明分子表征不仅影响预测判别，还影响外部验证下的校准、不确定性可靠性、生物学一致性和可转移性。
英文摘要
Blood transcriptomic profiling enables prognostic modeling by capturing the host immune response at the molecular level. Yet, the within-cohort evaluation strategies employed by many transcriptomic models inadequately reflect deployment across independent hospitals. Outside deployment scenarios introduce a cohort shift that can substantially degrade predictive performance and reliability of uncertainty estimates. We present a framework for evaluating transcriptomic sepsis mortality prediction under realistic cross-cohort deployment, systematically comparing gene-level, pathway-level and hybrid molecular representations. Four publicly available whole-blood transcriptomic cohorts consisting of 936 patients and 248 mortality events were harmonized into a shared 7,660-gene feature space and evaluated under leave-one-cohort-out validation using logistic regression, random forests, XGBoost and LightGBM. Beyond AUROC and AUPRC, model behavior was evaluated via conformal prediction, calibration analysis, selective prediction and the proposed Pathway Stability Index. Gene-level and hybrid representations were found to generally achieve the strongest discriminative performance, whereas pathway-level representations exhibited greater robustness across model families, more reliable uncertainty behavior under cross-cohort shift and stable molecular signatures enriched for immune and host-defense processes identified through Gene Ontology and KEGG enrichment analyses. These findings demonstrate that molecular representation influences not only predictive discrimination but also calibration, uncertainty reliability, biological coherence and transferability under external validation.
