Abstract

Pterygium is a common ocular surface condition frequently associated with irritative symptoms. The precise identity of its critical triggers as well as the hierarchical relationship between all the elements involved in the pathogenesis of this disease are not yet elucidated. Meta-analysis of gene expression studies represents a novel strategy capable of identifying key pathogenic mediators and therapeutic targets in complex diseases. Samples from nine patients were collected during surgery after photo documentation and clinical characterization of pterygia. Gene expression experiments were performed using Human Clariom D Assay gene chip. Differential gene expression analysis between active and atrophic pterygia was performed using limma package after adjusting variables by age. In addition, a meta-analysis was performed including recent gene expression studies available at the Gene Expression Omnibus public repository. Two databases including samples from adults with pterygium and controls fulfilled our inclusion criteria. Meta-analysis was performed using the Rank Production algorithm of the RankProd package. Gene set analysis was performed using ClueGO and the transcription factor regulatory network prediction was performed using appropriate bioinformatics tools. Finally, miRNA-mRNA regulatory network was reconstructed using up-regulated genes identified in the gene set analysis from the meta-analysis and their interacting miRNAs from the Brazilian cohort expression data. The meta-analysis identified 154 up-regulated and 58 down-regulated genes. A gene set analysis with the top up-regulated genes evidenced an overrepresentation of pathways associated with remodeling of extracellular matrix. Other pathways represented in the network included formation of cornified envelopes and unsaturated fatty acid metabolic processes. The miRNA-mRNA target prediction network, also reconstructed based on the set of up-regulated genes presented in the gene ontology and biological pathways network, showed that 17 target genes were negatively correlated with their interacting miRNAs from the Brazilian cohort expression data. Once again, the main identified cluster involved extracellular matrix remodeling mechanisms, while the second cluster involved formation of cornified envelope, establishment of skin barrier and unsaturated fatty acid metabolic process. Differential expression comparing active pterygium with atrophic pterygium using data generated from the Brazilian cohort identified differentially expressed genes between the two forms of presentation of this condition. Our results reveal differentially expressed genes not only in pterygium, but also in active pterygium when compared to the atrophic ones. New insights in relation to pterygium’s pathophysiology are suggested.

Highlights

  • Pterygium is a common ocular surface condition frequently associated with irritative symptoms

  • Demographic and epidemiological characteristics of this cohort are in accordance with previously described features of patients with pterygium in our region, as described by Artioli-Schelini et al, that found that most of the patients had active and grade 2 ­pterygia[3]

  • SPRR3 and SPRR1B encode envelope proteins of keratinocytes, being part of the cornification process that culminates with the formation of a keratinized cell ­layer[46,47]

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Summary

Methods

Microarray data analysis of the Brazilian cohort. Sample collection. The normalization of the transcriptomic data of 9 samples from our cohort was performed by SST-RMA summarization to generate gene level expression signals using Transcriptome Analysis Console (TAC 4.0, Applied Biosystems) software after the evaluation of quality control metrics. To gain more insights into the biological processes associated with the down-regulated signature, these genes were used for an additional functional analysis based on the Functional Analysis of Individual Microarray/RNAseq Expression (FAIME) algorithm implemented in seq2pathway ­package[34]. A FAIME score is computed based on the gene expression pattern of each sample for the predicted terms or biological processes. Receiver operating characteristic (ROC) curve was computed to evaluate the discriminatory capacity of each of these enriched terms for a binary classification In this analysis, the binary classification was pterygium vs control, across FAIME scores computed for each pathway. We computed the area under (AUC) the ROC curve using pROC R ­package[38]

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Results
Discussion
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