Annals of human genetics · 2019
Shared gene signature between pterygium and meibomian gland dysfunction uncovered through gene-expression meta-analysis.
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Zhang Jing, Xiang Jun, Wu Dan, Dai Yiqin, Xu Jianjiang
Аннотация
BACKGROUND: Pterygium and meibomian gland dysfunction (MGD) are two clinically correlated ocular diseases. We propose to investigate the shared gene signature between pterygium and MGD.
METHODS: Microarray datasets were retrieved from the Gene Expression Omnibus (GEO) database. Initial processing of the data was performed using the R programming package. Gene-expression values were log2 transformed and normalized by quantile normalization. The differentially expressed genes (DEGs) in each individual dataset were analyzed by the limma package. The integration of different pterygium datasets and gene-expression meta-analysis was conducted by the NetworkAnalyst package. A Venn diagram was created to find the overlapped DEGs between MGD and pterygium datasets. Gene ontology enrichment and pathway analysis were performed using the ToppGene Suite.
RESULTS: We found 193 DEGs significantly up-regulated in pterygium, with the combined effect sizes ranging from 1.53 to 3.78. A gene signature consisting of 11 DEGs were found to be shared by pterygium and MGD (SPRR3, SERPINB13, NMU, KRT10, IL37, KRT6B, PI3, S100A2, MAL, AURKA, and RGCC), and bioinformatics analyses showed that these overlapped DEGs were significantly enriched in pathways related to keratinization, cell-cycle regulation, and formation of the cornified envelope.
CONCLUSION: We identified a shared gene signature between pterygium and MGD through gene-expression meta-analysis. The analysis of this signature underlined that keratinization-related pathways may play important roles in the development of these two clinically correlated pathologies.
METHODS: Microarray datasets were retrieved from the Gene Expression Omnibus (GEO) database. Initial processing of the data was performed using the R programming package. Gene-expression values were log2 transformed and normalized by quantile normalization. The differentially expressed genes (DEGs) in each individual dataset were analyzed by the limma package. The integration of different pterygium datasets and gene-expression meta-analysis was conducted by the NetworkAnalyst package. A Venn diagram was created to find the overlapped DEGs between MGD and pterygium datasets. Gene ontology enrichment and pathway analysis were performed using the ToppGene Suite.
RESULTS: We found 193 DEGs significantly up-regulated in pterygium, with the combined effect sizes ranging from 1.53 to 3.78. A gene signature consisting of 11 DEGs were found to be shared by pterygium and MGD (SPRR3, SERPINB13, NMU, KRT10, IL37, KRT6B, PI3, S100A2, MAL, AURKA, and RGCC), and bioinformatics analyses showed that these overlapped DEGs were significantly enriched in pathways related to keratinization, cell-cycle regulation, and formation of the cornified envelope.
CONCLUSION: We identified a shared gene signature between pterygium and MGD through gene-expression meta-analysis. The analysis of this signature underlined that keratinization-related pathways may play important roles in the development of these two clinically correlated pathologies.