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. 2023 Jan 19;23(1):27.
doi: 10.1186/s12886-023-02774-y.

Identification of immune associated potential molecular targets in proliferative diabetic retinopathy

Ying Gao #  1 , Min Xue #  2 , Bing Dai #  3 , Yun Tang #  1 , Jingyu Liu  1 , Changlin Zhao  1 , Hu Meng  1 , Feng Yan  1 , Xiaomin Zhu  1 , Yan Lu  4 , Yirui Ge  5
Affiliations

Identification of immune associated potential molecular targets in proliferative diabetic retinopathy

Ying Gao et al. BMC Ophthalmol. .

Abstract

Background: Diabetic retinopathy (DR) is one of the most common microvascular complications of diabetes and causes of blindness in developed countries. Our study was designed to identify immune-related genes involved in the progression of proliferative diabetic retinopathy (PDR).

Methods: The "GSE102485" dataset of neovascular membrane samples (NVMs) from type 1 and 2 diabetes mellitus patients was downloaded from the Gene Expression Omnibus database. Functional enrichment analyses, protein-protein interaction network (PPI) construction, and module analysis of immune pathways in NVMs and controls were conducted via Gene Set Enrichment Analysis and Metascape.

Results: The significantly upregulated hallmark gene sets in DR2 and DR1 groups were involved in five immune pathways. Only CCR4, CXCR6, C3AR1, LPAR1, C5AR1, and P2RY14 were not previously reported in the context of PDR molecular pathophysiology. Except for P2RY14, all of the above were upregulated in retinal samples from experimental diabetes mouse models and human retina microvascular endothelial cells (HRMECs) treated with high glucose (HG) by quantitative Real Time Polymerase Chain Reaction (qRT-PCR).

Conclusion: The genes identified herein provide insight into immune-related differential gene expression during DR progression.

Keywords: Biomarkers; Genes; Immune; Inflammation; Neovascular membrane; Proliferative diabetic retinopathy.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Significant immune-related gene expression in the DR2 group was analyzed via GSEA
Fig. 2
Fig. 2
Significant immune-related gene expression in the DR1 group was analyzed via GSEA
Fig. 3
Fig. 3
Overlaps including the shared term level, where the blue curves link genes with the same enriched ontology term for DR2 (A) and DR1 (B). The inner circles represent gene lists, where hits are shown along the arc. Multiple gene lists were colored in dark orange, while unique gene lists are shown in light orange
Fig. 4
Fig. 4
Enrichment analysis of immune-related gene lists in the DR2 group. A: Heatmap of enriched GO terms colored based on p-value. B: Network of enriched GO terms colored based on p-value, with terms containing more genes tending to have more significant p-values. C: Heatmap of enriched KEGG terms colored based on p-value. D: Network of enriched KEGG terms colored based on p-value
Fig. 5
Fig. 5
Enrichment analysis of immune-related gene lists in the DR1 group. A: Heatmap of enriched GO items colored based on p-value. B: Network of enriched GO terms colored based on p-value, where terms containing more genes tend to have more significant p-values. C: Heatmap of enriched KEGG items colored based on p-values. D: Network of enriched KEGG terms colored based on p-value
Fig. 6
Fig. 6
The PPI network and MCODE components for the two groups. A: PPI network for the DR2 group. B: The top three enriched MCODE components in DR2. C: PPI network for DR1. D: The top three enriched MCODE components
Fig. 7
Fig. 7
A: Differentially expressed genes in in HRMECs treated with HG and NG. B: Differentially expressed genes between DR and NR (p < 0.05)
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