PDF(Pubmed)
Abstract：
In this study, using RNA-Seq gene expression data and advanced machine learning techniques, we identified distinct gene expression profiles between male and female pancreatic ductal adenocarcinoma (PDAC) patients. Building upon this insight, we developed sex-specific 3-year survival predictive models along with a single comprehensive model. These sex-specific models outperformed the single general model despite the smaller sample sizes. We further refined our models by using the most important features extracted from these initial models. The refined sex-specific predictive models achieved improved accuracies of 92.62% for males and 91.96% for females, respectively, versus an accuracy of 87.84% from the refined comprehensive model, further highlighting the value of sex-specific analysis. Based on these findings, we created Gap-App, a web application that enables the use of individual gene expression profiles combined with sex information for personalized survival predictions. Gap-App, the first online tool aiming to bridge the gap between complex genomic data and clinical application and facilitating more precise and individualized cancer care, marks a significant advancement in personalized prognosis. The study not only underscores the importance of acknowledging sex differences in personalized prognosis, but also sets the stage for the shift from traditional one-size-fits-all to more personalized and targeted medicine. The GAP-App service is freely available at www.gap-app.org.
摘要：
在这项研究中,使用RNA-Seq基因表达数据和先进的机器学习技术，我们在男性和女性胰腺导管腺癌(PDAC)患者之间确定了不同的基因表达谱.基于这种洞察力，我们开发了性别特异性的3年生存预测模型，男性准确率为88.47%，女性准确率为88.94%，分别。尽管样本量较小，但这些模型的性能优于单个通用模型，强调性别特异性分析的价值。基于这些发现,我们创建了Gap-App,一个Web应用程序，可以将个体基因表达谱与性别信息结合使用，用于个性化的生存预测。Gap-App,第一个在线工具，旨在弥合复杂的基因组数据与临床应用之间的差距，并促进更精确和个性化的癌症护理，标志着个性化预后的显著进步。这项研究不仅强调了在个性化预后中承认性别差异的重要性，但也为从传统的一刀切向更个性化和有针对性的医学转变奠定了基础。GAP-App服务可在www上免费获得。gap-app.org。
