Wang Yin, Huang Tao, Xie Lu, Liu Lei
Shanghai Public Health Clinical Center and Institutes of Biomedical Sciences, Fudan University, Shanghai, China.
Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, 200031, People's Republic of China.
BMC Syst Biol. 2016 Dec 23;10(Suppl 4):132. doi: 10.1186/s12918-016-0354-4.
Aging is a complex process relating multi-scale omics data. Finding key age markers in normal tissues could help to provide reliable aging predictions in human. However, predicting age based on multi-omics data with both accuracy and informative biological function has not been performed systematically, thus relative cross-tissue analysis has not been investigated entirely, either.
Here we have developed an improved prediction pipeline, the Integrating and Stepwise Age-Prediction (ISAP) method, to regress age and find key aging markers effectively. Furthermore, we have performed a serious of network analyses, such as the PPI network, cross-tissue networks and pathway interaction networks.
Our results find important coordinated aging patterns between different tissues. Both co-profiling and cross-pathway analyses identify more thorough functions of aging, and could help to find aging markers, pathways and relative aging disease researches.
衰老过程复杂，涉及多尺度组学数据。在正常组织中寻找关键衰老标志物有助于实现对人类衰老的可靠预测。然而，基于多组学数据准确且具有生物学功能信息地预测年龄尚未得到系统开展，因此相关的跨组织分析也未得到充分研究。
在此，我们开发了一种改进的预测流程，即整合逐步年龄预测（ISAP）方法，以有效回归年龄并找到关键衰老标志物。此外，我们还进行了一系列网络分析，如蛋白质-蛋白质相互作用（PPI）网络、跨组织网络和通路相互作用网络。
我们的研究结果发现了不同组织之间重要的协同衰老模式。共分析和跨通路分析都能更全面地确定衰老功能，有助于找到衰老标志物、通路以及相关衰老疾病的研究。
