Identification of Candidate miRNAs and Predication of Their Role in Keratoconus

Background: Keratoconus (KTCN, OMIM 148300) is known as an eye degenerative disease leading to stromal thinning and conical shape of the cornea. These structural changes can be accompanied by loss of visual function in advanced cases. To date, in spite of recent advances in the investigation of molecular mechanisms which result in Keratoconus, there’s still a lack of information about the role of miRNAs in this disorder. Accordingly, this study aims to find miRNA’s aberrantly expression in KTCN suffering cases and to predict their role by investigating their possible interactions with significantly KTCN correlated genes. Material and Methods: The RNA sequencing dataset was retrieved from GEO databases (http:// www.ncbi.nlm.nih.gov/geo). The data were comprised of 25 normal and 25 KTCN cases. Weighted gene co-expression network analysis approach was used to construct a protein-coding gene coexpression network and investigate the significant modules. Gene with the higher module membership (MM) and gene significance (GS) in the selected modules were supposed to be more KTCN relevant genes. Then CluGO plugin in Cytoscape software was used for enrichment analysis of genes in the selected modules. Differentially expressed genes in KTCN cases compared with normal cases were obtained using the edgeR package in R. All experimentally recorded miRNA-mRNA interactions were downloaded from miRTarbase database. Possible interactions from differentially expressed miRNAs and genes included in significant modules were retrieved. Results: Totally 2492 protein-coding genes (PCGs) and 99 miRNAs were up-regulated and 213 PCGs and 31 miRNAs were down-regulated. Significant correlation with the KTCN was observed in three modules, including brown, green-yellow, and salmon from the total of 15 modules. Genes in significant modules have been enriched to gene expression regulation related biological processes such as negative regulation of protein secretion, intra-Golgi vesicle-mediated transport, regulation of mRNA 3’-end processing, and cytoskeleton related gene ontologies such as modulation of the mitochondrial cytoskeleton. Up-regulated miRNAs that interact with down-regulated mRNAs within significant modules include miR-1305, miR-544a, miR-1245a, miR-4635, miR-4266. Conclusion: Just as the results revealed most of the deregulated genes were involved in gene expression regulation processes. Therefore the de-regulated miRNAs might involve in the aberrant expression of their targets. In this case, mentioning miRNAs in the results section can be considered as potential diagnostic or therapeutic biomarkers for KTCN.