Oncotarget

Research Papers:

Identification of human age-associated gene co-expressions in functional modules using liquid association

Jialiang Yang, Yufang Qin, Tiantian Zhang, Fayou Wang, Lihong Peng, Lijuan Zhu, Dawei Yuan, Pan Gao, Jujuan Zhuang, Zhongyang Zhang, Jun Wang and Yun Fang _

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Oncotarget. 2018; 9:1063-1074. https://doi.org/10.18632/oncotarget.23148

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Abstract

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

Correspondence to:

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

ABSTRACT

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.


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