Bioinformatics analysis of cellular senescence-related mitochondrial autophagy genes in diabetic retinopathy_Chinese Journal of Ocular Fundus Diseases

Authors：

Liang Na ^1,2 , Wang Wenting ^1,2 , Song Xin ² , Ha Wenjing ² ,  Peng Shaomin ¹

1. Aier Eye Medical Center of Anhui Medical University, Hefei 230022, China;
2. Ningxia Aier Eye Hospital, Yinchuan 750000, China;

Corresponding author：

Peng Shaomin, Email: pengshaomin@aierchina.com

Keywords：

Biological aging; Diabetic retinopathy; Mitochondria; Autophagy gene

DOI：

10.3760/cma.j.cn511434-20241203-00463

Video：

Export PDF Favorites Scan Get Citation

Abstract Full text Figures/Tables Video References Cited by

Objective To investigate the potential mechanism of cellular senescence-related mitochondrial autophagy genes in diabetic retinopathy (DR). Methods The DR gene datasets GSE53257 and GSE60436 from the GEO database and screened the differentially expressed genes (DEG) were downloaded. Cellular senescence-related genes and mitochondrial autophagy-related genes from the GeneCards database, and the intersection of the two to obtain the DR-related differentially expressed genes (CSRMRDEG) were collected. The obtained CSRMRDEG was subjected to Gene Ontology (GO) functional enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway enrichment analysis, protein-protein interaction network (PPI) analysis, and hub gene identification using Maximal Clique Centrality (MCC), Degree, Maximum Neighborhood Component (MNC)、Edge Percolated Component (EPC) and Closeness algorithms. Gene Set Enrichment Analysis (GSEA) was conducted to obtain the enriched pathways of DEG, and ssGSEA immune infiltration analysis was performed to screen the correlation between immune cells and DR. The diagnostic efficacy of hub genes for DR was evaluated by drawing the receiver operating characteristic (ROC) curve and calculating the area under the curve (AUC). Meanwhile, the Wilcoxon rank sum test was used to compare the differences in the infiltration level of immune cells between the DR Group and the control group. Results 23 DR-related CSRMRDEG were obtained. GO analysis showed that they were mainly enriched in the pathways of dicarboxylic acid, biosynthetic process of folate-containing compounds, tetrahydrofolate conversion, mitochondrial matrix, mitochondrial endomembrane, structural components of ribosomes, and glutamate transmembrane transporter protein activity. The results of KEGG pathway enrichment analysis showed that CSRMRDEG was highly enriched in pathways such as the folate carbon pool, biosynthesis of cofactors, and pyruvate metabolism. The PPI analysis results show that there are 16 related CSRMRDEG. Five algorithms (MCC, Degree, MNC, EPC, Closeness) obtained the nine Hub genes. The results of ROC curve analysis showed that the AUC of the expression levels of 9 hub genes for diagnosing DR ranged from 0.7-0.9. The ssGSEA results showed that there were statistically significant differences in Wilcoxon of central memory CD4⁺ T cells, macrophages, natural killer cells, and helper T cell 1 between the DR group and the control group (Z=−2.85, −2.23, −2.10, −2.52; P＜0.05). Conclusion Mitochondrial autophagy genes related to cellular senescence are potential diagnostic targets for DR.

Citation： Liang Na, Wang Wenting, Song Xin, Ha Wenjing, Peng Shaomin. Bioinformatics analysis of cellular senescence-related mitochondrial autophagy genes in diabetic retinopathy. Chinese Journal of Ocular Fundus Diseases, 2025, 41(9): 697-706. doi: 10.3760/cma.j.cn511434-20241203-00463 Copy

1.	Teo ZL, Tham YC, Yu M, et al. Global prevalence of diabetic retinopathy and projection of burden through 2045: systematic review and meta-analysis[J]. Ophthalmology, 2021, 128(11): 1580-1591. DOI: 10.1016/j.ophtha.2021.04.027.
2.	Hammes HP. Diabetic retinopathy: hyperglycaemia, oxidative stress and beyond[J]. Diabetologia, 2018, 61(1): 29-38. DOI: 10.1007/s00125-017-4435-8.
3.	Soni D, Sagar P, Takkar B. Diabetic retinal neurodegeneration as a form of diabetic retinopathy[J]. Int Ophthalmol, 2021, 41(9): 3223-3248. DOI: 10.1007/s10792-021-01864-4.
4.	Hombrebueno JR, Cairns L, Dutton LR, et al. Uncoupled turnover disrupts mitochondrial quality control in diabetic retinopathy[J/OL]. JCI insight, 2019, 4(23): e129760[2019-12-05]. https://pubmed.ncbi.nlm.nih.gov/31661466/. DOI: 10.1172/jci.insight.129760.
5.	Kowluru RA, Mohammad G. Mitochondrial fragmentation in a high homocysteine environment in diabetic retinopathy[J/OL]. Antioxidants (Basel), 2022, 11(2): 365[2022-02-11]. https://pubmed.ncbi.nlm.nih.gov/35204246/. DOI: 10.3390/antiox11020365.
6.	Kowluru RA, Alka K. Mitochondrial quality control and metabolic memory phenomenon associated with continued progression of diabetic retinopathy[J/OL]. Int J Mol Sci, 2023, 24(9): 8076[2023-04-29]. https://pubmed.ncbi.nlm.nih.gov/37175784/. DOI: 10.3390/ijms24098076.
7.	Lam CH, Cheung JK, Tse DY, et al. Proteomic profiling revealed mitochondrial dysfunction in photoreceptor cells under hyperglycemia[J/OL]. Int J Mol Sci, 2022, 23(21): 13366[2022-11-01]. https://pubmed.ncbi.nlm.nih.gov/36362154/. DOI: 10.3390/ijms232113366.
8.	Lam CH, Zou B, Chan HH, et al. Functional and structural changes in the neuroretina are accompanied by mitochondrial dysfunction in a type 2 diabetic mouse model[J/OL]. Eye Vis (Lond), 2023, 10(1): 37[2023-09-01]. https://pubmed.ncbi.nlm.nih.gov/37653465/. DOI: 10.1186/s40662-023-00353-2.
9.	Palmer AK, Gustafson B, Kirkland JL, et al. Cellular senescence: at the nexus between ageing and diabetes[J]. Diabetologia, 2019, 62(10): 1835-1841. DOI: 10.1007/s00125-019-4934-x.
10.	Barrett T, Wilhite SE, Ledoux P, et al. NCBI GEO: archive for functional genomics data sets--update[J]. Nucleic Acids Res, 2013, 41(Database issue): 991-995. DOI: 10.1093/nar/gks1193.
11.	Govindarajan G, Mathews S, Srinivasan K, et al. Establishment of human retinal mitoscriptome gene expression signature for diabetic retinopathy using cadaver eyes[J]. Mitochondrion, 2017, 36: 150-181. DOI: 10.1016/j.mito.2017.07.007.
12.	Ishikawa K, Yoshida S, Kobayashi Y, et al. Microarray analysis of gene expression in fibrovascular membranes excised from patients with proliferative diabetic retinopathy[J]. Invest Ophthalmol Vis Sci, 2015, 56(2): 932-946. DOI: 10.1167/iovs.14-15589.
13.	Ritchie ME, Phipson B, Wu D, et al. Limma powers differential expression analyses for RNA-sequencing and microarray studies[J/OL]. Nucleic Acids Res, 2015, 43(7): e47[2015-04-20]. https://pubmed.ncbi.nlm.nih.gov/25605792/. DOI: 10.1093/nar/gkv007.
14.	Ben Salem K, Ben Abdelaziz A. Principal component analysis (PCA)[J]. Tunis Med, 2021, 99(4): 383-389.
15.	Stelzer G, Rosen N, Plaschkes I, et al. The GeneCards Suite: from gene data mining to disease genome sequence analyses[J]. Curr Protoc Bioinformatics, 2016, 54: 1-33. DOI: 10.1002/cpbi.5.
16.	Szklarczyk D, Gable AL, Lyon D, et al. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets[J]. Nucleic Acids Res, 2019, 47(D1): 607-613. DOI: 10.1093/nar/gky1131.
17.	Shannon P, Markiel A, Ozier O, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks[J]. Genome Res, 2003, 13(11): 2498-2504. DOI: 10.1101/gr.1239303.
18.	Chin CH, Chen SH, Wu HH, et al. cytoHubba: identifying hub objects and sub-networks from complex interactome[J/OL]. BMC Syst Biol, 2014, 8(Suppl 4): S11[2014-12-08]. https://pubmed.ncbi.nlm.nih.gov/25521941/. DOI: 10.1186/1752-0509-8-S4-S11.
19.	Li JH, Liu S, Zhou H, et al. starBase v2.0: decoding miRNA-ceRNA, miRNA-ncRNA and protein-RNA interaction networks from large-scale CLIP-Seq data[J]. Nucleic Acids Res, 2014, 42(Database issue): 92-97. DOI: 10.1093/nar/gkt1248.
20.	Zhang Q, Liu W, Zhang HM, et al. hTFtarget: a comprehensive database for regulations of human transcription factors and their targets[J]. Genomics Proteomics Bioinformatics, 2020, 18(2): 120-128. DOI: 10.1016/j.gpb.2019.09.006.
21.	Nawaz IM, Rezzola S, Cancarini A, et al. Human vitreous in proliferative diabetic retinopathy: characterization and translational implications[J/OL]. Prog Retin Eye Res, 2019, 72: 100756[2019-04-02]. https://pubmed.ncbi.nlm.nih.gov/30951889/. DOI: 10.1016/j.preteyeres.2019.03.002.
22.	Tang H, Luo N, Zhang X, et al. Association between biological aging and diabetic retinopathy[J/OL]. Sci Rep, 2024, 14(1): 10123[2024-05-02]. https://pubmed.ncbi.nlm.nih.gov/38698194/. DOI: 10.1038/s41598-024-60913-x.
23.	Mortuza R, Chen S, Feng B, et al. High glucose induced alteration of SIRTs in endothelial cells causes rapid aging in a p300 and FOXO regulated pathway[J/OL]. PLoS One, 2013, 8(1): e54514[2013-01-16]. https://pubmed.ncbi.nlm.nih.gov/23342163/. DOI: 10.1371/journal.pone.0054514.
24.	Kern TS. Contributions of inflammatory processes to the development of the early stages of diabetic retinopathy[J/OL]. Exp Diabetes Res, 2007, 2007: 95103[2007-10-01]. https://pmc.ncbi.nlm.nih.gov/articles/PMC2216058/. DOI: 10.1155/2007/95103.
25.	Caspi RR. Ocular autoimmunity: the price of privilege?[J]. Immunol Rev, 2006, 213: 23-35. DOI: 10.1111/j.1600-065X.2006.00439.x.
26.	Chen M, Luo C, Zhao J, et al. Immune regulation in the aging retina[J]. Prog Retin Eye Res, 2019, 69: 159-172. DOI: 10.1016/j.preteyeres.2018.10.003.
27.	Xi J, Rong Y, Zhao Z, et al. Scutellarin ameliorates high glucose-induced vascular endothelial cells injury by activating PINK1/Parkin-mediated mitophagy[J/OL]. J Ethnopharmacol, 2021, 271: 113855[2021-05-10]. https://pubmed.ncbi.nlm.nih.gov/33485979/. DOI: 10.1016/j.jep.2021.113855.
28.	Yoo YA, Kim MJ, Park JK, et al. Mitochondrial ribosomal protein L41 suppresses cell growth in association with p53 and p27Kip1[J]. Mol Cell Biol, 2005, 25(15): 6603-6616. DOI: 10.1128/MCB.25.15.6603-6616.2005.
29.	Conde JA, Claunch CJ, Romo HE, et al. Identification of a motif in BMRP required for interaction with Bcl-2 by site-directed mutagenesis studies[J]. J Cell Biochem, 2012, 113(11): 3498-508. DOI: 10.1002/jcb.24226.
30.	Chintharlapalli SR, Jasti M, Malladi S, et al. BMRP is a Bcl-2 binding protein that induces apoptosis[J]. J Cell Biochem, 2005, 94(3): 611-626. DOI: 10.1002/jcb.20292.
31.	Subramanian A, Kuehn H, Gould J, et al. GSEA-P: a desktop application for gene set enrichment analysis[J]. Bioinformatics, 2007, 23(23): 3251-3253. DOI: 10.1093/bioinformatics/btm369.
32.	You J, Qi S, Du Y, et al. Multiple bioinformatics analyses of integrated gene expression profiling data and verification of hub genes associated with diabetic retinopathy[J/OL]. Med Sci Monit, 2020, 26: e923146[2020-04-15]. https://pubmed.ncbi.nlm.nih.gov/32294661/. DOI: 10.12659/MSM.923146.
33.	Liu J, Yang J. Mitochondria-associated membranes: a hub for neurodegenerative diseases[J/OL]. Biomed Pharmacother, 2022, 149: 112890[2022-03-31]. https://pubmed.ncbi.nlm.nih.gov/35367757/. DOI: 10.1016/j.biopha.2022.112890.
34.	Zhang XY, Han C, Yao Y, et al. Current insights on mitochondria-associated endoplasmic reticulum membranes (MAMs) and their significance in the pathophysiology of ocular disorders[J/OL]. Exp Eye Res, 2024, 248: 110110[2024-09-24]. https://pubmed.ncbi.nlm.nih.gov/39326773/. DOI: 10.1016/j.exer.2024.110110.
35.	Forini F, Nicolini G, Amato R, et al. Local modulation of thyroid hormone signaling in the retina affects the development of diabetic retinopathy[J/OL]. Biochim Biophys Acta Mol Basis Dis, 2024, 1870(1): 166892[2023-09-25]. https://pubmed.ncbi.nlm.nih.gov/37758065/. DOI: 10.1016/j.bbadis.2023.166892.
36.	Yamakawa N, Komatsu H, Usui Y, et al. Immune mediators profiles in the aqueous humor of patients with simple diabetic retinopathy[J/OL]. J Clin Med, 2023, 12(21): 6931[2023-11-05]. https://pubmed.ncbi.nlm.nih.gov/37959396/. DOI: 10.3390/jcm12216931.
37.	Xu H, Chen M. Diabetic retinopathy and dysregulated innate immunity[J]. Vision Res, 2017, 139: 39-46. DOI: 10.1016/j.visres.2017.04.013.
38.	Kinuthia UM, Wolf A, Langmann T. Microglia and inflammatory responses in diabetic retinopathy[J/OL]. Front Immunol, 2020, 11: 564077[2020-11-06]. https://pubmed.ncbi.nlm.nih.gov/33240260/. DOI: 10.3389/fimmu.2020.564077.
39.	Wu H, Wang M, Li X, et al. The metaflammatory and immunometabolic role of macrophages and microglia in diabetic retinopathy[J/OL]. Hum Cell, 2021, 34(6): 1617-1628. DOI: 10.1007/s13577-021-00580-6.

1. Teo ZL, Tham YC, Yu M, et al. Global prevalence of diabetic retinopathy and projection of burden through 2045: systematic review and meta-analysis[J]. Ophthalmology, 2021, 128(11): 1580-1591. DOI: 10.1016/j.ophtha.2021.04.027.
2. Hammes HP. Diabetic retinopathy: hyperglycaemia, oxidative stress and beyond[J]. Diabetologia, 2018, 61(1): 29-38. DOI: 10.1007/s00125-017-4435-8.
3. Soni D, Sagar P, Takkar B. Diabetic retinal neurodegeneration as a form of diabetic retinopathy[J]. Int Ophthalmol, 2021, 41(9): 3223-3248. DOI: 10.1007/s10792-021-01864-4.
4. Hombrebueno JR, Cairns L, Dutton LR, et al. Uncoupled turnover disrupts mitochondrial quality control in diabetic retinopathy[J/OL]. JCI insight, 2019, 4(23): e129760[2019-12-05]. https://pubmed.ncbi.nlm.nih.gov/31661466/. DOI: 10.1172/jci.insight.129760.
5. Kowluru RA, Mohammad G. Mitochondrial fragmentation in a high homocysteine environment in diabetic retinopathy[J/OL]. Antioxidants (Basel), 2022, 11(2): 365[2022-02-11]. https://pubmed.ncbi.nlm.nih.gov/35204246/. DOI: 10.3390/antiox11020365.
6. Kowluru RA, Alka K. Mitochondrial quality control and metabolic memory phenomenon associated with continued progression of diabetic retinopathy[J/OL]. Int J Mol Sci, 2023, 24(9): 8076[2023-04-29]. https://pubmed.ncbi.nlm.nih.gov/37175784/. DOI: 10.3390/ijms24098076.
7. Lam CH, Cheung JK, Tse DY, et al. Proteomic profiling revealed mitochondrial dysfunction in photoreceptor cells under hyperglycemia[J/OL]. Int J Mol Sci, 2022, 23(21): 13366[2022-11-01]. https://pubmed.ncbi.nlm.nih.gov/36362154/. DOI: 10.3390/ijms232113366.
8. Lam CH, Zou B, Chan HH, et al. Functional and structural changes in the neuroretina are accompanied by mitochondrial dysfunction in a type 2 diabetic mouse model[J/OL]. Eye Vis (Lond), 2023, 10(1): 37[2023-09-01]. https://pubmed.ncbi.nlm.nih.gov/37653465/. DOI: 10.1186/s40662-023-00353-2.
9. Palmer AK, Gustafson B, Kirkland JL, et al. Cellular senescence: at the nexus between ageing and diabetes[J]. Diabetologia, 2019, 62(10): 1835-1841. DOI: 10.1007/s00125-019-4934-x.
10. Barrett T, Wilhite SE, Ledoux P, et al. NCBI GEO: archive for functional genomics data sets--update[J]. Nucleic Acids Res, 2013, 41(Database issue): 991-995. DOI: 10.1093/nar/gks1193.
11. Govindarajan G, Mathews S, Srinivasan K, et al. Establishment of human retinal mitoscriptome gene expression signature for diabetic retinopathy using cadaver eyes[J]. Mitochondrion, 2017, 36: 150-181. DOI: 10.1016/j.mito.2017.07.007.
12. Ishikawa K, Yoshida S, Kobayashi Y, et al. Microarray analysis of gene expression in fibrovascular membranes excised from patients with proliferative diabetic retinopathy[J]. Invest Ophthalmol Vis Sci, 2015, 56(2): 932-946. DOI: 10.1167/iovs.14-15589.
13. Ritchie ME, Phipson B, Wu D, et al. Limma powers differential expression analyses for RNA-sequencing and microarray studies[J/OL]. Nucleic Acids Res, 2015, 43(7): e47[2015-04-20]. https://pubmed.ncbi.nlm.nih.gov/25605792/. DOI: 10.1093/nar/gkv007.
14. Ben Salem K, Ben Abdelaziz A. Principal component analysis (PCA)[J]. Tunis Med, 2021, 99(4): 383-389.
15. Stelzer G, Rosen N, Plaschkes I, et al. The GeneCards Suite: from gene data mining to disease genome sequence analyses[J]. Curr Protoc Bioinformatics, 2016, 54: 1-33. DOI: 10.1002/cpbi.5.
16. Szklarczyk D, Gable AL, Lyon D, et al. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets[J]. Nucleic Acids Res, 2019, 47(D1): 607-613. DOI: 10.1093/nar/gky1131.
17. Shannon P, Markiel A, Ozier O, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks[J]. Genome Res, 2003, 13(11): 2498-2504. DOI: 10.1101/gr.1239303.
18. Chin CH, Chen SH, Wu HH, et al. cytoHubba: identifying hub objects and sub-networks from complex interactome[J/OL]. BMC Syst Biol, 2014, 8(Suppl 4): S11[2014-12-08]. https://pubmed.ncbi.nlm.nih.gov/25521941/. DOI: 10.1186/1752-0509-8-S4-S11.
19. Li JH, Liu S, Zhou H, et al. starBase v2.0: decoding miRNA-ceRNA, miRNA-ncRNA and protein-RNA interaction networks from large-scale CLIP-Seq data[J]. Nucleic Acids Res, 2014, 42(Database issue): 92-97. DOI: 10.1093/nar/gkt1248.
20. Zhang Q, Liu W, Zhang HM, et al. hTFtarget: a comprehensive database for regulations of human transcription factors and their targets[J]. Genomics Proteomics Bioinformatics, 2020, 18(2): 120-128. DOI: 10.1016/j.gpb.2019.09.006.
21. Nawaz IM, Rezzola S, Cancarini A, et al. Human vitreous in proliferative diabetic retinopathy: characterization and translational implications[J/OL]. Prog Retin Eye Res, 2019, 72: 100756[2019-04-02]. https://pubmed.ncbi.nlm.nih.gov/30951889/. DOI: 10.1016/j.preteyeres.2019.03.002.
22. Tang H, Luo N, Zhang X, et al. Association between biological aging and diabetic retinopathy[J/OL]. Sci Rep, 2024, 14(1): 10123[2024-05-02]. https://pubmed.ncbi.nlm.nih.gov/38698194/. DOI: 10.1038/s41598-024-60913-x.
23. Mortuza R, Chen S, Feng B, et al. High glucose induced alteration of SIRTs in endothelial cells causes rapid aging in a p300 and FOXO regulated pathway[J/OL]. PLoS One, 2013, 8(1): e54514[2013-01-16]. https://pubmed.ncbi.nlm.nih.gov/23342163/. DOI: 10.1371/journal.pone.0054514.
24. Kern TS. Contributions of inflammatory processes to the development of the early stages of diabetic retinopathy[J/OL]. Exp Diabetes Res, 2007, 2007: 95103[2007-10-01]. https://pmc.ncbi.nlm.nih.gov/articles/PMC2216058/. DOI: 10.1155/2007/95103.
25. Caspi RR. Ocular autoimmunity: the price of privilege?[J]. Immunol Rev, 2006, 213: 23-35. DOI: 10.1111/j.1600-065X.2006.00439.x.
26. Chen M, Luo C, Zhao J, et al. Immune regulation in the aging retina[J]. Prog Retin Eye Res, 2019, 69: 159-172. DOI: 10.1016/j.preteyeres.2018.10.003.
27. Xi J, Rong Y, Zhao Z, et al. Scutellarin ameliorates high glucose-induced vascular endothelial cells injury by activating PINK1/Parkin-mediated mitophagy[J/OL]. J Ethnopharmacol, 2021, 271: 113855[2021-05-10]. https://pubmed.ncbi.nlm.nih.gov/33485979/. DOI: 10.1016/j.jep.2021.113855.
28. Yoo YA, Kim MJ, Park JK, et al. Mitochondrial ribosomal protein L41 suppresses cell growth in association with p53 and p27Kip1[J]. Mol Cell Biol, 2005, 25(15): 6603-6616. DOI: 10.1128/MCB.25.15.6603-6616.2005.
29. Conde JA, Claunch CJ, Romo HE, et al. Identification of a motif in BMRP required for interaction with Bcl-2 by site-directed mutagenesis studies[J]. J Cell Biochem, 2012, 113(11): 3498-508. DOI: 10.1002/jcb.24226.
30. Chintharlapalli SR, Jasti M, Malladi S, et al. BMRP is a Bcl-2 binding protein that induces apoptosis[J]. J Cell Biochem, 2005, 94(3): 611-626. DOI: 10.1002/jcb.20292.
31. Subramanian A, Kuehn H, Gould J, et al. GSEA-P: a desktop application for gene set enrichment analysis[J]. Bioinformatics, 2007, 23(23): 3251-3253. DOI: 10.1093/bioinformatics/btm369.
32. You J, Qi S, Du Y, et al. Multiple bioinformatics analyses of integrated gene expression profiling data and verification of hub genes associated with diabetic retinopathy[J/OL]. Med Sci Monit, 2020, 26: e923146[2020-04-15]. https://pubmed.ncbi.nlm.nih.gov/32294661/. DOI: 10.12659/MSM.923146.
33. Liu J, Yang J. Mitochondria-associated membranes: a hub for neurodegenerative diseases[J/OL]. Biomed Pharmacother, 2022, 149: 112890[2022-03-31]. https://pubmed.ncbi.nlm.nih.gov/35367757/. DOI: 10.1016/j.biopha.2022.112890.
34. Zhang XY, Han C, Yao Y, et al. Current insights on mitochondria-associated endoplasmic reticulum membranes (MAMs) and their significance in the pathophysiology of ocular disorders[J/OL]. Exp Eye Res, 2024, 248: 110110[2024-09-24]. https://pubmed.ncbi.nlm.nih.gov/39326773/. DOI: 10.1016/j.exer.2024.110110.
35. Forini F, Nicolini G, Amato R, et al. Local modulation of thyroid hormone signaling in the retina affects the development of diabetic retinopathy[J/OL]. Biochim Biophys Acta Mol Basis Dis, 2024, 1870(1): 166892[2023-09-25]. https://pubmed.ncbi.nlm.nih.gov/37758065/. DOI: 10.1016/j.bbadis.2023.166892.
36. Yamakawa N, Komatsu H, Usui Y, et al. Immune mediators profiles in the aqueous humor of patients with simple diabetic retinopathy[J/OL]. J Clin Med, 2023, 12(21): 6931[2023-11-05]. https://pubmed.ncbi.nlm.nih.gov/37959396/. DOI: 10.3390/jcm12216931.
37. Xu H, Chen M. Diabetic retinopathy and dysregulated innate immunity[J]. Vision Res, 2017, 139: 39-46. DOI: 10.1016/j.visres.2017.04.013.
38. Kinuthia UM, Wolf A, Langmann T. Microglia and inflammatory responses in diabetic retinopathy[J/OL]. Front Immunol, 2020, 11: 564077[2020-11-06]. https://pubmed.ncbi.nlm.nih.gov/33240260/. DOI: 10.3389/fimmu.2020.564077.
39. Wu H, Wang M, Li X, et al. The metaflammatory and immunometabolic role of macrophages and microglia in diabetic retinopathy[J/OL]. Hum Cell, 2021, 34(6): 1617-1628. DOI: 10.1007/s13577-021-00580-6.

Chinese Journal of Ocular Fundus Diseases

Bioinformatics analysis of cellular senescence-related mitochondrial autophagy genes in diabetic retinopathy

Abstract Full text Figures/Tables Video References Cited by

Previous Article

Next Article

Format

Content