研究目的
To compute a global triple network of competitive endogenous RNAs (ceRNAs) in order to pinpoint essential molecules related to diabetic retinopathy (DR).
研究成果
The study highlighted specific lncRNAs and miRNAs related to the pathogenesis of DR, which might be used as novel diagnostic biomarkers and therapeutic targets for DR.
研究不足
A major concern is the insufficient data and a relatively small sample size. Owing to the limited sample size, the miRNA-lncRNA-mRNA network might be restricted. Moreover, the type of diabetes was not reported by study participants. Another major concern of this research study is that computational results might be noises and false-positive results.
1：Experimental Design and Method Selection:
The study aimed to explore the crosstalk between lncRNA-miRNA-mRNA complexities in DR using ceRNA theory. Key molecules were identified by performing Gene Ontology (GO) analysis and Weighted Gene Co-expression Network Analysis (WGCNA).
2：Sample Selection and Data Sources:
Using eight week-old C57BL/6 mice, a mouse model of streptozotocin (STZ)-induced diabetes was constructed. Total RNAs were isolated from the retinas of the diabetic mice. Agilent Mouse Gene Expression Microarrays were used to generate microarray data from mouse mRNA and lncRNA.
3：List of Experimental Equipment and Materials:
Agilent Mouse Gene Expression Microarrays (Product Number G4852A; Agilent Technologies, Santa Clara, CA, USA), TRIzol reagent.
4：Experimental Procedures and Operational Workflow:
Differential expression analysis was performed to determine the key RNA molecules. The interactions between lncRNAs and miRNAs were predicted using miRanda. The target mRNAs of miRNAs were identified using miRTarBase, miRecords, and starBase version 2.0. The ceRNA network was constructed and visualized with Cytoscape software.
5：The ceRNA network was constructed and visualized with Cytoscape software.
Data Analysis Methods:
5. Data Analysis Methods: GO analysis and WGCNA analysis were performed to determine the functions of differentially expressed lncRNAs and to identify clusters of highly correlated genes.
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