Transcriptome analysis and RT-qPCR validation of mitophagy-related key genes in the progression of diabetic retinopathy.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:41601931", "kind": "science-record", "revision": 3, "content_hash": "sha256:ce268db4ada9fd88c221a8d310dddc6ae03b1b5121b716014436b94822ee9b9d", "created_at": "2026-11-01T20:31:34Z", "updated_at": "2026-11-01T22:15:23Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Transcriptome analysis and RT-qPCR validation of mitophagy-related key genes in the progression of diabetic retinopathy.", "doi": "10.3389/fendo.2025.1733368", "pmid": "41601931", "authors": ["Yifan Zhang", "Liangjie Niu", "Huika Xia", "Jianmin Wang"], "journal": "Frontiers in endocrinology", "year": 2025, "abstract": "Background: Diabetic retinopathy (DR), a prevalent microvascular complication of diabetes mellitus (DM), likely involves mitophagy in its progression. However, the exact mechanisms remain poorly understood.\nMethods: This study analyzed DR datasets GSE189005 and GSE221521 from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs 1) between patients with DR and controls were identified from GSE189005. Simultaneously, mitophagy-related genes (MRGs) were analyzed to determine DEGs 2. The datasets were integrated to obtain differentially expressed MRGs. A machine learning-based approach was used to identify key candidate genes, followed by expression validation. Additionally, a nomogram was constructed for DR risk prediction; correlation analysis was performed between key genes and immune cells; RT-qPCR analysis was conducted to verify gene expression.\nResults: Integration of datasets revealed 13 differentially expressed MRGs. Five key candidate genes were identified via machine learning, and expression validation confirmed the differential expression of SLC1A5 and RPS21 in DR. The nomogram incorporating these two genes showed high predictive accuracy for DR risk. SLC1A5 was strongly positively correlated with CD56 bright NK cells (r = 0.82) and negatively correlated with CD56 dim NK cells (r = -0.80). RPS21 exhibited the strongest positive correlation with CD56 dim NK cells (r = 0.77) and the strongest negative correlation with CD56 bright NK cells (r = -0.75). RT-qPCR analysis indicated significant upregulation of SLC1A5 and downregulation of RPS21 in DR samples.\nConclusions: This study suggests that SLC1A5 and RPS21 are involved in DR progression, offering potential therapeutic targets. However, further experimental validation is necessary to confirm their functional roles and clinical relevance.", "claims": [{"id": "claim-method", "statement": "Statistical analysis was performed using R software (v 4.1.0), and comparisons between groups were conducted using the Wilcoxon test.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41601931", "locator": "sections[14] \"Statistical analysis\" paragraph 2", "quote": "Statistical analysis was performed using R software (v 4.1.0), and comparisons between groups were conducted using the Wilcoxon test."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets presented in this study can be found in online repositories. 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