Identification of human age-associated gene co-expressions in functional modules using liquid association
Metrics: PDF 1232 views | HTML 1738 views | ?
Jialiang Yang1,*, Yufang Qin2,*, Tiantian Zhang1, Fayou Wang3, Lihong Peng1, Lijuan Zhu4, Dawei Yuan5, Pan Gao6, Jujuan Zhuang6, Zhongyang Zhang7,8, Jun Wang9 and Yun Fang9
1College of Information Engineering, Changsha Medical University, Changsha, Hunan, P. R. China
2Department of Mathematics, Shanghai Ocean University, Shanghai, China
3School of Mathematics and Information Science, Henan Polytechnic University, Henan, P. R. China
4Department of Mathematics, Hebei University of Science and Technology, Shijiazhuang, Hebei, China
5Geneis (Beijing) Co. Ltd., Beijing, P. R. China
6Department of Mathematics, Dalian Maritime University, Dalian, Liaoning, P. R. China
7Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
8Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
9Department of Mathematics, Shanghai Normal University, Shanghai, P. R. China
*These authors contributed equally to this work
Yun Fang, email: [email protected]
Keywords: aging; anti-aging drug prediction; gene co-expression; liquid association; GTEx
Received: August 23, 2017 Accepted: November 17, 2017 Published: December 08, 2017
Aging is a major risk factor for age-related diseases such as certain cancers. In this study, we developed Age Associated Gene Co-expression Identifier (AAGCI), a liquid association based method to infer age-associated gene co-expressions at thousands of biological processes and pathways across 9 human tissues. Several hundred to thousands of gene pairs were inferred to be age co-expressed across different tissues, the genes involved in which are significantly enriched in functions like immunity, ATP binding, DNA damage, and many cancer pathways. The age co-expressed genes are significantly overlapped with aging genes curated in the GenAge database across all 9 tissues, suggesting a tissue-wide correlation between age-associated genes and co-expressions. Interestingly, age-associated gene co-expressions are significantly different from gene co-expressions identified through correlation analysis, indicating that aging might only contribute to a small portion of gene co-expressions. Moreover, the key driver analysis identified biologically meaningful genes in important function modules. For example, IGF1, ERBB2, TP53 and STAT5A were inferred to be key genes driving age co-expressed genes in the network module associated with function “T cell proliferation”. Finally, we prioritized a few anti-aging drugs such as metformin based on an enrichment analysis between age co-expressed genes and drug signatures from a recent study. The predicted drugs were partially validated by literature mining and can be readily used to generate hypothesis for further experimental validations.
All site content, except where otherwise noted, is licensed under a Creative Commons Attribution 3.0 License.