RESUMO
Meibomian gland dysfunction (MGD) is an epidemic chronic ocular inflammation. However, little is known about its effective treatment. Here, this study identified important MGD-related genes, core regulators, and potential drugs and their targets though integrating a series of bioinformational analyses. First, there were 665 differentially expression genes (DEGs) were identified. Then, 56 coexpression modules were exacted based on the expression of DEGs and their interactors. Moreover, core transcription factors (TF) significantly regulated modules were identified, including RELA, HIF1A, SIRT1, and MYC, which related to variety of eye diseases. Finally, the prediction of potential drugs and the identification of their target were performed. The results showed that artenimol, copper, and glutathione may have the remarkable curative effect or the toxicology to MGD. Moreover, their targets module gene LDHA (lactate dehydrogenase A), ENO1 (enolase 1), ALB (albumin), and PKM (pyruvate kinase M) are play important role in eye diseases. It suggests that these potential drugs may be useful for the treatment of MGD by acting on their targets. It provides valuable references for drug redirection and new drug development for drug developers, and provides individualized treatment strategies for tarsal gland dysfunction.
Assuntos
Artemisininas/farmacologia , Biomarcadores/análise , Regulação da Expressão Gênica/efeitos dos fármacos , Disfunção da Glândula Tarsal/patologia , Medicina de Precisão , Biomarcadores/metabolismo , Biomarcadores Tumorais/genética , Cobre/farmacologia , Proteínas de Ligação a DNA/genética , Perfilação da Expressão Gênica , Glutationa/farmacologia , Humanos , L-Lactato Desidrogenase/genética , Disfunção da Glândula Tarsal/classificação , Disfunção da Glândula Tarsal/tratamento farmacológico , Disfunção da Glândula Tarsal/genética , Fosfopiruvato Hidratase/genética , Piruvato Quinase/genética , Albumina Sérica Humana/genética , Proteínas Supressoras de Tumor/genéticaRESUMO
Uveitis is the inflammation of the uvea and is a serious eye disease that can cause blindness for middle-aged and young people. However, the pathogenesis of this disease has not been fully uncovered and thus renders difficulties in designing effective treatments. Completely identifying the genes related to this disease can help improve and accelerate the comprehension of uveitis. In this study, a new computational method was developed to infer potential related genes based on validated ones. We employed a large protein-protein interaction network reported in STRING, in which Laplacian heat diffusion algorithm was applied using validated genes as seed nodes. Except for the validated ones, all genes in the network were filtered by three tests, namely, permutation, association, and function tests, which evaluated the genes based on their specialties and associations to uveitis. Results indicated that 59 inferred genes were accessed, several of which were confirmed to be highly related to uveitis by literature review. In addition, the inferred genes were compared with those reported in a previous study, indicating that our reported genes are necessary supplements.