title: Tissue Non-Specific Genes and Pathways Associated with Diabetes: An Expression Meta-Analysis.
abstract: We performed expression studies to identify tissue non-specific genes and pathways of diabetes by meta-analysis. We searched curated datasets of the Gene Expression Omnibus (GEO) database and identified 13 and five expression studies of diabetes and insulin responses at various tissues, respectively. We tested differential gene expression by empirical Bayes-based linear method and investigated gene set expression association by knowledge-based enrichment analysis. Meta-analysis by different methods was applied to identify tissue non-specific genes and gene sets. We also proposed pathway mapping analysis to infer functions of the identified gene sets, and correlation and independent analysis to evaluate expression association profile of genes and gene sets between studies and tissues. Our analysis showed thatPGRMC1andHADHgenes were significant over diabetes studies, whileIRS1andMPSTgenes were significant over insulin response studies, and joint analysis showed that HADH and MPST genes were significant over all combined data sets. The pathway analysis identified six significant gene sets over all studies. The KEGG pathway mapping indicated that the significant gene sets are related to diabetes pathogenesis. The results also presented that 12.8% and 59.0% pairwise studies had significantly correlated expression association for genes and gene sets, respectively; moreover, 12.8% pairwise studies had independent expression association for genes, but no studies were observed significantly different for expression association of gene sets. Our analysis indicated that there are both tissue specific and non-specific genes and pathways associated with diabetes pathogenesis. Compared to the gene expression, pathway association tends to be tissue non-specific, and a common pathway influencing diabetes development is activated through different genes at different tissues.
authors.affiliation: Department of Data Science, School of Population Health, University of Mississippi Medical Center, Jackson, MS 39216, USA. hmei@umc.edu.|Shanghai Children's Medical Center, School of Public Health and School of Medicine, Shanghai Jiaotong University, Shanghai 200127, China. hmei@umc.edu.
authors.forename: Hao
authors.initials: H
authors.lastname: Mei
authors.affiliation: Department of Biology, Tougaloo College, Jackson, MI 39216, USA. lli@tougaloo.edu.
authors.forename: Lianna
authors.initials: L
authors.lastname: Li
authors.affiliation: Shanghai Children's Medical Center, School of Public Health and School of Medicine, Shanghai Jiaotong University, Shanghai 200127, China. arrow64@163.com.
authors.forename: Shijian
authors.initials: S
authors.lastname: Liu
authors.affiliation: Shanghai Children's Medical Center, School of Public Health and School of Medicine, Shanghai Jiaotong University, Shanghai 200127, China. jiangfan@scmc.com.cn.
authors.forename: Fan
authors.initials: F
authors.lastname: Jiang
authors.affiliation: Department of Data Science, School of Population Health, University of Mississippi Medical Center, Jackson, MS 39216, USA. mgriswold@umc.edu.
authors.forename: Michael
authors.initials: M
authors.lastname: Griswold
authors.affiliation: Department of Neurology, University of Mississippi Medical Center, Jackson, MS 39216, USA. tmosley@umc.edu.
authors.forename: Thomas
authors.initials: T
authors.lastname: Mosley
keywords: diabetes; gene expression; pathway