Integrative analysis of methylation and transcriptional profiles to predict aging and construct aging specific cross-tissue networks. Issue 4 (December 2016)

• Record Type: Journal Article
• Authors: Wang, Yin; Huang, Tao; Xie, Lu; Liu, Lei
• Is Part Of: BMC systems biology. Volume 10:Issue 4(2016)
• Page Start: 413
• Page End: 421
• Publication Date: 2016-12
• Subjects: Biological systems -- Periodicals; Biology -- Research -- Periodicals; Cell physiology -- Periodicals; Genes -- Analysis -- Periodicals; 571
• DOI: 10.1186/s12918-016-0354-4
• Languages: English
• ISSNs: 1752-0509

Abstract

Abstract Background 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. Results 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. Conclusion 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.