Integrative analysis of PANoptosis-related genes in diabetic retinopathy: machine learning identification and experimental validation
Han Chen1,2† , Enguang Chen1,2† , Ting Cao1† , Feifan Feng1 , Min Lin1 , Xuan Wang1 and Yu Xu1 *
Background: Diabetic retinopathy (DR) is a major complication of diabetes, leading to severe vision impairment. Understanding the molecular
mechanisms, particularly PANoptosis, underlying DR is crucial for identifying potential biomarkers and therapeutic targets. This study aims to identify differentially expressed PANoptosis-related genes (DE-PRGs) in DR, offering insights into the disease’s pathogenesis and potential diagnostic tools.
Methods: DR datasets were obtained from the Gene Expression Omnibus (GEO) database, while PANoptosis-related genes were sourced from the GeneCards database. Differentially expressed genes (DEGs) were identified using the DESeq2 package, followed by functional enrichment analysis through DAVID and Metascape tools. Three machine learning algorithms—LASSO regression, Random Forest, and SVM-RFE—were employed to identify hub genes. A diagnostic nomogram was constructed and its performance assessed via ROC analysis. The CIBERSORT algorithm analyzed immune cell infiltration. Hub genes were validated through RT-qPCR, Western blotting, immunohistochemistry, and publicly available datasets. Additionally, the impact of FASN and PLSCR3 knockdown on HUVECs behavior was validated through in vitro experiments.
Results: Differential expression analysis identified 1,418 DEGs in the GSE221521 dataset, with 39 overlapping DE-PRGs (29 upregulated, 10 downregulated). Functional enrichment indicated that DE-PRGs are involved in apoptosis, signal transduction, and inflammatory responses, with key pathways such as MAPK and TNF signaling. Machine learning algorithms identified six PANoptosis-related hub genes (BEX2, CASP2, CD36, FASN, OSMR, and PLSCR3) as potential biomarkers. A diagnostic nomogram based on these hub genes showed high diagnostic accuracy. Immune cell infiltration analysis revealed significant differences in immune cell patterns between control and DR groups, especially in Activated CD4 Memory T Cells and Monocytes. Validation confirmed the diagnostic efficiency and expression patterns of the PANoptosis-related hub genes, supported by in vitro and the GSE60436 dataset analysis. Furthermore, experiments demonstrated that knocking down FASN and PLSCR3 impacted HUVECs behavior.
Conclusion: This study provides valuable insights into the molecular mechanisms of DR, particularly highlighting PANoptosis-related pathways, and
identifies potential biomarkers and therapeutic targets for the disease.
KEYWORDS
diabetic retinopathy, PANoptosis, machine learning, bioinformatics analysis, differentially expressed genes, biomarkers
https://doi.org/10.3389/fimmu.2024.1486251