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. 2024 Aug 30:15:1341206.
doi: 10.3389/fendo.2024.1341206. eCollection 2024.

Identification of immune-related endoplasmic reticulum stress genes in proliferative diabetic retinopathy using bioinformatics analysis

Han Chen #  1 , Enguang Chen #  1 , Miaomiao Liu  1 , Jianhui Wang  1 , Jiawei Yin  1 , Peiquan Zhao  1 , Yu Xu  1
Affiliations

Identification of immune-related endoplasmic reticulum stress genes in proliferative diabetic retinopathy using bioinformatics analysis

Han Chen et al. Front Endocrinol (Lausanne). .

Abstract

Background: Proliferative diabetic retinopathy (PDR) is a severe complication of diabetes, and understanding its molecular mechanisms is crucial. Endoplasmic reticulum (ER) stress has been implicated in various diseases, including diabetic complications. This study aims to elucidate ER stress-related biomarkers in PDR, providing insights into the underlying molecular pathways.

Methods: We analyzed two independent PDR datasets, GSE102485 and GSE60436. The GSE102485 dataset (22 PDR and 3 normal samples) was the primary dataset for comprehensive analyses, including differential expression, functional enrichment, PPI network construction, immune cell infiltration, and drug prediction. The GSE60436 dataset (6 PDR and 3 normal samples) was used for validation. In vitro experiments using human umbilical vein endothelial cells (HUVECs) in a high-glucose environment were conducted to validate key bioinformatics outcomes. Western blotting assessed protein levels of ER stress markers (TRAM1 and TXNIP).

Results: Differential expression analysis identified 2451 genes, including 328 ER stress-related genes. Functional analysis revealed enrichment in ER stress-related processes and pathways. Hub genes (BCL2, CCL2, IL-1β, TLR4, TNF, TP53) were identified, and immune infiltration analysis showed altered immune cell proportions. Validation in GSE60436 and in vitro confirmed ER stress gene dysregulation. Drug prediction suggested potential small molecules targeting ER stress markers.

Conclusion: This study provides a comprehensive molecular characterization of ER stress in PDR, highlighting altered biological processes, immune changes, and potential therapeutic targets. The identified hub genes and small molecules offer avenues for further investigation and therapy development, enhancing understanding of PDR pathogenesis and aiding targeted intervention creation.

Keywords: bioinformatics; biomarkers; differentially expressed genes; drug prediction; endoplasmic reticulum stress; proliferative diabetic retinopathy.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.

Figures

Figure 1
Figure 1
The workflow of our research.
Figure 2
Figure 2
Identification of endoplasmic reticulum stress-related differentially expressed genes (ER stress-related DEGs). (A) Volcano plot of the DEGs in GSE102485. Blue dots represent downregulated DEGs, red dots represent upregulated DEGs and gray dots show genes with no significant difference. (B) Venn diagram of the intersection of DEGs in GSE102485 and ER stress-related genes. (C) Heatmap of the identified 51 ER stress-related DEGs in GSE102485.
Figure 3
Figure 3
Enrichment analysis of ER stress-related DEGs. (A) Bar plot of enriched GO terms. (B) Chord diagram showing the relationships between enriched GO terms and associated genes, with colors indicating gene expression changes. (C) Chordal graph depicting expression changes of genes associated with GO terms; points’ colors reflect upregulation or downregulation. The table lists GO term IDs and descriptions. (D) KEGG analyses showing the enriched associated signaling pathways. (E) Metascape bar chart of the top 20 non-redundant enrichment clusters. The x-axis represents the -log10(p) value. The y-axis lists the GO terms and KEGG pathways associated with each cluster.
Figure 4
Figure 4
Identification and analysis of ER stress-related hub genes. (A) PPI network of ER stress-related DEGs. (B) Subnetwork of hub genes from the PPI network. (C) Identification of six candidates for hub genes by four algorithms. (D) The location of the 6 hub genes on the 22 chromosomes.
Figure 5
Figure 5
The landscape of immune cell infiltration. (A) The abundance of 22 immune cells in PDR samples and control samples. (B) The fraction of each immune cell type in the two groups. (C) Correlation between ER stress-related hub gene expression and immune cells. *p < 0.05; **p < 0.01; ***p < 0.001.
Figure 6
Figure 6
External validation of TRAM1, TXNIP and ER stress-related hub genes (A) Validation of ER stress-related hub genes in the GSE60436 dataset. (B) The protein levels of TRAM1 and TXNIP were evaluated in cell samples by western blot. (C) The mRNA levels of BCL2, CCL2, IL-1Β, TLR4, TNF, and TP53 were measured in cell samples by qRT-PCR. Ctrl, control group; HG, high-glucose group. *p < 0.05; **p < 0.01; ***p < 0.001; ****p < 0.0001.
Figure 7
Figure 7
Identifying small-molecule compounds via cMAP analysis. (A) A heatmap illustrates the top 8 negatively enriched compounds. (B) The chemical structures of these 8 compounds.
See this image and copyright information in PMC

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