Identification and validation of ferroptosis-related genes for diabetic retinopathy.

Diabetic retinopathy (DR) is a leading cause of blindness, and ferroptosis may be an essential component of the pathological process of DR. In this study, we aimed to screen five hub genes (TLR4, CAV1, HMOX1, TP53, and IL-1B) using bioinformatics analysis and experimentally verify their expression and effects on ferroptosis and cell function. The online Gene Expression Omnibus microarray expression profiling datasets GSE60436 and GSE1025485 were selected for investigation. Ferroptosis-related genes that might be differentially expressed in DR were identified. Then, Gene Ontology (GO) enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, and protein-protein interaction (PPI) network analyses were conducted to characterize the differentially-expressed ferroptosis-related genes. After tissue-specific analyses and external dataset validation of hub genes, the mRNA and protein levels of hub genes in retinal microvascular endothelial cells (HRMECs) symbiotic with high glucose were verified using real-time quantitative PCR (qRT-PCR) and immunocytochemistry (ICC). Finally, hub genes were knocked down using siRNA, and changes in ferroptosis and cell function were observed. Based on the differential expression analysis, 19 ferroptosis-related genes were identified. GO and KEGG enrichment analyses showed that ferroptosis-related genes were significantly enriched in reactive oxygen species metabolic processes, necrotic cell death, hypoxia responses, iron ion responses, positive regulation of cell migration involved in sprouting angiogenesis, NF-kappa B signaling pathway, ferroptosis, fluid shear stress, and atherosclerosis. Subsequently, PPI network analysis and critical module construction were used to identify five hub genes. Based on bioinformatics analysis of mRNA microarrays, qRT-PCR confirmed higher mRNA expression of five genes in the DR model, and immunocytochemistry confirmed their higher protein expression. Finally, siRNA interference was used to verify the effects of five genes on ferroptosis and cell function. Based on bioinformatics analysis, five potential genes related to ferroptosis were identified, and their upregulation may affect the onset or progression of DR. This study sheds new light on the pathogenesis of DR.

Inferring Retinal Degeneration-Related Genes Based on Xgboost.

Retinal Degeneration (RD) is an inherited retinal disease characterized by degeneration of rods and cones photoreceptor cells and degeneration of retinal pigment epithelial cells. The age of onset and disease progression of RD are related to genes and environment. At present, research has discovered five genes closely related to RD. They are RHO, PDE6B, MERTK, RLBP1, RPGR, and researchers have developed corresponding gene therapy methods. Gene therapy uses vectors to transfer therapeutic genes, genetically modify target cells, and correct or replace disease-causing RD genes. Therefore, identifying the pathogenic genes of RD will play an important role in the development of treatment methods for the disease. However, the traditional methods of identifying RD-related genes are mostly based on animal experiments, and currently only a small number of RD-related genes have been identified. With the increase of biological data, Xgboost is purposed in this article to identify RP-related genes. Xgboost adds a regular term to control the complexity of the model, hence using Xgboost to find out true RD-related genes from complex and massive genes is suitable. The problem of overfitting can be avoided to some extent. To verify the power of Xgboost to identify RD-related genes, we did 10-cross validation and compared with three traditional methods: Random Forest, Back Propagation network, Support Vector Machine. The accuracy of Xgboost is 99.13% and AUC is much higher than other three methods. Therefore, this article can provide technical support for efficient identification of RD-related genes and help researchers have a deeper the understanding of the genetic characteristics of RD.