Comprehensive analysis of ferritinophagy-related genes and immune infiltration landscape in diabetic retinopathy

Fenfen Yu; Congyao Wang; Yihua Su; Tingting Chen; Wenhui Zhu; Xia Dong; Wanyi Ke; Leqi Cai; Shasha Yang; Pengxia Wan

doi:10.3389/fendo.2023.1177488

Comprehensive analysis of ferritinophagy-related genes and immune infiltration landscape in diabetic retinopathy

Front Endocrinol (Lausanne). 2023 Jul 14:14:1177488. doi: 10.3389/fendo.2023.1177488. eCollection 2023.

Authors

Fenfen Yu¹, Congyao Wang¹, Yihua Su¹, Tingting Chen¹, Wenhui Zhu¹, Xia Dong¹, Wanyi Ke¹, Leqi Cai¹, Shasha Yang², Pengxia Wan¹

Affiliations

¹ Department of Ophthalmology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
² Department of Ophthalmology, Guangzhou First People's Hospital, The Second Affiliated Hospital, South China University of Technology, Guangzhou, China.

Abstract

Background: Diabetic retinopathy (DR) is deemed a microangiopathy and neurodegenerative disorder, which is a primary reason of visual impairment in the world. Ferritinophagy is a critical regulator of ferroptosis and has a vital part in the etiopathogenesis of DR. Nevertheless, its molecular mechanism in DR remains to be expounded.

Methods: The GSE146615 dataset was adopted to identify ferritinophagy-related differentially expressed genes (FRDEGs). The interactions and biological functions of the genes were described by means of functional enrichment analysis (FEA). The enriched gene sets were analyzed utilizing gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA). Identification of hub genes was performed utilizing protein-protein interaction (PPI) analysis. mRNA-miRNA, mRNA-transcription factors (TF), mRNA-drugs, mRNA-RNA-binding proteins (RBP) interaction networks were constructed. In addition, datasets GSE60436 and GSE94019 were utilized for validation. The diagnostic performance of FRDEGs was assessed by means of receiver-operating characteristic curve monofactor analysis, followed by immune infiltration analysis. Lastly, quantitative real-time polymerase chain reaction (qRT-PCR) was implemented to analyze the validation of genes.

Results: In total, the identification of eight FRDEGs was completed utilizing differential expression analysis. FEA mainly implicated the autophagy of mitochondrion, mitochondrion disassembly, autophagosome assembly, and organization pathways. GSEA and GSVA mainly implicated the interferon alpha response, ultraviolet response up, interferon gamma response, apical junction, pical surface, and allograft rejection pathways. BECN1 and HERC2 displayed high diagnostic accuracies in validation sets. Immune infiltration analysis revealed that several immune cells related to ferritinophagy may be play potential roles in DR. Finally, qRT-PCR was utilized to validate the upregulated expression of BECN1 as well as the downregulated expression of BCAT2 and ATG7 in the DR model.

Conclusion: BECN1, HERC2, ATG7, and BCAT2 act as potential biomarkers for DR and might regulate ferritinophagy and the immune microenvironment to influence its development and progression. This research can provide new insights into pathogenesis of DR related to ferritinophagy.

Keywords: ATG7; BCAT2; BECN1; HERC2; diabetic retinopathy; differentially expressed genes; ferritinophagy; immune landscape.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Autophagy / genetics
Diabetes Mellitus*
Diabetic Retinopathy* / genetics
Ferroptosis*
Humans
Interferon-gamma
MicroRNAs*

Substances

Interferon-gamma
MicroRNAs

Grants and funding

This study was financially supported by the Medical Science and Technology Research Foundation of Guangdong Province (A2022164), Natural Science Foundation of Guangdong Province (2021A1515010372), Research Project of Traditional Chinese Medicine Bureau of Guangdong Province (20221072 & 20221298), and Guangdong Basic and Applied Basic Research Foundation, China (2019A1515110012).