Identification of Immune-related Core Genes Based on Immune Signatures of Age-related Macular Degeneration

doi:10.21203/rs.3.rs-3419525/v1

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Identification of Immune-related Core Genes Based on Immune Signatures of Age-related Macular Degeneration

https://doi.org/10.21203/rs.3.rs-3419525/v1

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Background

Age-related macular degeneration (AMD) is a disease that affects the retina and choroid and is the leading cause of blindness in older adults. Despite the prevalence of this disease, its etiology and pathogenesis are unknown, and many studies have shown that the immune system plays an important role in the development and progression of age-related macular degeneration. Here, we aimed to explore the immune profile of different stages of age-related retinopathy and to identify immune-related biomarkers that could improve the treatment of patients with age-related retinopathy.

Methods

Publicly available gene expression profiles of AMD samples at different stages of the disease were downloaded from the GEO database (GSE115828 dataset). The activity of 19 immune signatures in AMD retinal samples was assessed using ssGSEA, and the changes in immune levels at different stages of AMD were compared. Differential expression analysis was performed on advanced AMD samples, and the screened immune-related differentially expressed genes (DEGs) were used as a candidate gene set. We constructed an AMD-related model based on AMD immune-related DEGs by logistic regression and least absolute shrinkage and selection operator (LASSO), which was also verified by ROC curve. Finally, protein-protein interaction (PPI) networks were mapped using STRING and further analyzed using Cytoscape software.

Results

As AMD progresses to advanced stages, the activity of 13 immune signatures gradually increases. A total of 91 immune-related DEGs were identified by differential expression analysis and further submitted to LASSO regression model for screening, and a total of 12 core genes were identified (AUC = 0.827). Three important clusters with a high degree of intermolecular interactions were identified in the PPI network, revealing three major molecular mechanisms, including MHC class II antigen processing and presentation, microglia activation and complement activation.

Conclusion

Multiple immune signatures play an important role in the development of age-related macular degeneration. Twelve core immune-related genes were identified that warrant investigation in experimental and clinical studies. PPI network analysis revealed three immune-related molecular mechanisms of AMD.

age-related macular degeneration

transcriptome

retina

immune-related genes

immune estimation

protein-protein interaction

gene set enrichment analysis

Age-related macular degeneration (AMD) is an ocular disease that can lead to severe vision loss and blindness in older adults, with an average age of onset around 60 years, and it is the leading cause of vision loss in developed countries. Currently, nearly 200 million people worldwide have some form of AMD, and the number of people with AMD is expected to reach 288 million by 2040[1]. AMD is a disease in which multiple risk factors come together, and in addition to age factors the risk factors for AMD include genetic factors and environmental aggressions (e.g., smoking, ultraviolet light)[2][3][4].

Age-related macular degeneration (AMD) is a complex ophthalmic disease affecting the Bruch's membrane, retinal pigment epithelium, and choroid, characterized by photoreceptor death and degeneration of the retinal pigment epithelium and choroidal capillaries [5]. In the early and intermediate stages of AMD there are no symptoms or mild symptoms and vision is usually unaffected, characterized by increased deposits called vitreous warts and pigment abnormalities [6]. As the disease progresses to advanced stages, patients experience symptoms such as distorted vision, blurred vision, and impaired vision. There are two forms of advanced AMD: choroidal neovascularization (CNV) also called wet AMD, and geographic atrophy (GA) of the retinal pigment epithelium and overlying photoreceptors also called advanced dry AMD [7].

Several studies have demonstrated the role of inflammation and the immune response in the pathogenesis and severity of AMD. Cellular and humoral immunity correlate with the formation of geographic atrophy and choroidal neovascularization [8]. Genetic and epidemiologic studies have found that multiple genetic variants contributing to AMD risk are associated with the complement system [9]. Many therapeutic options and clinical trials are currently focused on modulating the complement system as a strategy for treating AMD. A trial of geographic atrophy suggested that research into the mechanisms of complement-mediated pathology could help in the development of AMD treatments [10]. Innate immune cells such as resident immune reactive cells (microglia), macrophages, dendritic cells, neutrophils and natural killer cells also play different roles in the pathogenesis of AMD, respectively [11]. In conclusion, there is growing evidence that inflammation and immune responses play an integral role in the pathogenesis of AMD, and anti-inflammatory and immunotherapies are expected to be important directions in addressing AMD.

With the advancement of high-throughput technologies, genome sequencing has been widely used in the study of various diseases, and a large amount of transcriptomics data has been generated and made publicly available, which can help to explain the key biological functions contributing to the development of diseases in order to improve the diagnosis and treatment of the diseases. The first comparative transcriptomic analysis of AMD and normal human donor eyes was performed by Newman and colleagues [12]. Multiple studies. In recent years, several genetic and genomic studies have utilized bioinformatics based on genome sequencing to study the mechanisms of AMD development and to identify genetic risk loci [13][14][15]. Genetic studies with larger sample sizes of AMD and comprehensive analysis of the genome will increase the overall understanding of the mechanisms of AMD occurrence and development.

In this study, the publicly available AMD dataset GSE115828 was collected from the GEO database for analysis. First, the activity of the immune signature was estimated using single-sample genome enrichment analysis (ssGSEA) for samples at four different stages of AMD. Second, to explore and identify potential immune-related biomarkers in AMD, we constructed logistic regression and least absolute shrinkage with selection operator (LASSO) models to screen for immune-related core DEGs. the screened genes may be important for the diagnosis and treatment of AMD patients. Finally, we analyzed the PPI network constructed by immune-related DEGs and investigated the biological functions and pathways of genes in the key modules of this network.

2.1 Data Collection

GEO is a public genomics database containing high-throughput gene expression data submitted by researchers worldwide. The AMD retinal gene expression profiling dataset used in this study was downloaded from the GEO public database (www.ncbi.nlm.nih.gov/geo/).The GSE115828 dataset contains transcriptional profiles of retinal tissue from 453 cases of postmortem AMD. The samples in the dataset were characterized using the Minnesota Grading System (MSG), which is similar to the criteria of the Age-Related Eye Disease Study (AREDS), containing 105 MGS1, 175 MGS2, 112 MGS3, and 61 MGS4 samples [13]. The MGS1 retinal samples did not show characteristics of senile retinopathy and could be used as a control group, whereas samples from MGS2 to MGS4 represented progressively more aggressive disease stages.

2.2 Immune Estimation

The 17 immune signature gene sets were obtained from the Immunology Database and Analysis Portal (ImmPort), and the gene sets related to retinal microglia and the complement system were obtained from the Molecular Signatures Database (MSigDB) [16]. These 19 immune signatures represent different immune cell types, functions and pathways. Enrichment scores for the immune signatures were calculated for each AMD retinal sample by Single Sample Gene Set Enrichment Analysis (ssGSEA) [17], where each ssGSEA enrichment score represents the extent to which genes in a particular immune gene set are coordinately up- or down-regulated within the sample. In this study, we used the ssGSEA enrichment scores of a set of genes included in the immune signature to quantify the level of immune signature activity.

2.3 Identification of DEGs

The probes in the dataset were first converted to gene symbols for further analysis. The processed data were normalized to remove low expressed genes. Differentially expressed genes (DEGs) between control and AMD tissues were screened using the "limma" package and the wilcox-test function, with filters |log2FoldChange| > 0.5 and p-value < 0.05.

2.4 Protein-Protein Network Analysis

The STRING database is a database for searching for known and predicted interactions between proteins [18]. We used the STRING database (version 11.0, http://www.string-db.org/) to construct Protein-Protein Network (PPI) networks to analyze the interactions between proteins encoded by immune-related differentially expressed genes. A confidence level greater than 0.7 was considered significant. In a PPI network, proteins are represented as nodes and edges represent interactions between nodes. We used Cytoscape software (version 3.8.0, http://www.cytoscape.org/) to visualize and analyze the constructed PPI networks [19]. We performed a modular analysis of the PPI network using the Cytoscape MCODE plug-in with default parameters (degree cutoff = 2, node score cutoff = 0.2, node density cutoff = 0.1, K-score = 2, and maximum depth = 100) in order to find highly interconnected clusters.

2.5 Selection of Characteristic Genes

To understand the functional significance of key modules in the PPI network. Gene Ontology (GO) enrichment analysis was performed using the R package clusterProfilter [20]. GO enrichment analysis included three categories: cellular component (CC), molecular function (MF) and biological process (BP). P value < 0.05 was used as the critical value to be considered statistically significant.

2.6 Gene Ontology Enrichment Analysis

Identification of AMD retina-associated biomarkers using an algorithm based on LASSO model. Lasso logistic regression is a machine learning method that identifies variables by finding the lambda value that minimizes the classification error [21]. The algorithm is commonly used to screen for important disease molecular features and prognostic variables. A LASSO model is built from the expression matrix of the candidate genes using the "glmnet" package in R software. The best characterized genes for inclusion in the model are then determined using the minimum lambda value as a reference. Receiver operating characteristic curves (ROC) and area under the curve (AUC) were used to assess the specificity and sensitivity of the Lasso model in identifying classes of AMD retinal samples. Receiver operating characteristic curves were implemented using the pROC package in version 3.6.0 of the R software [22].

2.7 Statistical Analysis

Analysis of variance (ANOVA) was applied to compare the differences in immune enrichment scores among different stages of AMD samples. The gene expression differences between the control group and the late-stage AMD group were compared using the Wilcoxon test. Analysis results with P-value <0.05 indicated statistical significance. Correlation between the enrichment scores of immune gene sets was analyzed using Pearson analysis. All statistical analyses were performed using R software (version 3.6.0).

3.1 AMD immune activity analysis

To dissect differences in immune characteristics in tissues at different stages of AMD, we used ssGSEA to analyze the enrichment scores of the 19 immune signatures in the GSE115828 dataset, which were used to determine the activity levels of the immune signatures. The violin plot shows that antigen processing and expression (p<0.001), antimicrobials (p<0.001), retinal microglia(p<0.001), complement activation (p<0.001), TNF family members (p<0.01), natural killer cells (p<0.01), cytokines (p<0.01), cytokine receptors (p<0. 01), chemokines (p <0.01), chemokine receptor (p<0.05), TGF-b family member (p<0.05), TGF-b family member receptor (p<0.05), and interleukin (p<0.05) in AMD retinal samples differed significantly across MSG stages. (“***”, “**”, “*”, “ns” are “P<0.001” “P<0.01” “P<0.05” “no significance”) (Fig. 1). The level of activity of all these immune signatures was higher in late AMD retinal samples than in early AMD retinal samples.

3.2 Correlation analysis of immune signature

Notably, the immune system in retinal tissue requires the concerted activity of multiple immune cells or pathways. Therefore, we performed a correlation analysis of 19 immune signatures in the AMD retina. The results showed that the different immune signatures exhibited strong correlations with each other. Antigen processing and expression, antimicrobials, retinal microglia, complement activation, TNF family members, natural killer cells, cytokines, and cytokine receptors were positively correlated with other immune signatures to varying degrees, suggesting that these immune signatures cooperate with each other in the immune response (Fig. 2).

3.3 Identification of immune-related DEGs

To identify genes that have a significant effect on AMD, we first obtained genes from the GSE115828 dataset that are differentially expressed between advanced AMD and controls. DEGs were defined as p-values < 0.05 and |log2 fold change| > 0.5, and 203 DEGs were identified. 2135 immune-related gene were obtained from 19 immune signatures, and after overlapping DEGs and immune-related genes, 91 immune-related DEGs were identified (Table 1).

Table 1 DEGs for immune signatures

Immune Signature	Count	Immune-related DEGs
Antigen Processing and Presentation	14	HLA-DQA1/HLA-DOA/HLA-DMB/HLA-DRA/HLA-B/HLA-DMA/ICAM1/ HLA-DRB1/CD74/MR1/CIITA/B2M/HLA-DPA1/HLA-F
Chemokine Receptors	2	CXCR4/FPR1
TCR Signaling Pathway	1	PTPRC
Cytokines	10	SEMA3C/C3/LTBP1/CSF1/LEFTY2/HAMP/ADM/IGF2/TNC/LIF
Interferons Receptors	0	-
BCR Signaling Pathway	3	INPP5D/FCGR2B/PLCG2
Interleukins	0	-
Natural Killer Cell	5	HLA-B/ICAM1/TYROBP/FCGR3A/PLCG2
Interferons	0	-
TNF Family Members	0
Chemokines	3	SEMA3C/C3/TNC
TGF-b Family Members Receptors	0	-
TGF-b Family Members	1	LEFTY2
Interleukins Receptors	1	IL10RA
TNF Family Members Receptors	1	TNFRSF1B
Cytokine Receptors	9	MCHR1/IL10RA/C3AR1/TNFRSF1B/FGFR2/CXCR4/CSF1R/FPR1/ANGPTL4
Antimicrobials	16	TLR2/LTBP1/HLA-B/S100B/GFAP/B2M/CD86/SERPINA3/RBP7/SOCS3/GBP2/ CYBB/HAMP/CXCR4/OLR1/PLTP
Retina Microglia	46	SRGN/PARVG/DOCK2/CD74/HLA-DRB1/PLCB2/PTPRC/C1QA/INPP5D/FPR1/ HLA-DMB/IL10RA/GPR183/RNASE6/FCGR3A/MS4A7/SELPLG/CYBB/ TNFRSF1B/CSF1R/RGS1/C3/ATF3/HLA-DRA/VSIG4/AIF1/C3AR1/LAPTM5/ CD86/CD300A/HLA-DPA1/FCGR2A/TREM2/FCGR1A/OLR1/LAIR1/ HLA-B/HLA-DMA/CD84/B2M/ITGAM/ITGAX/TYROBP/C1QB/FYB1/CLEC7A
Complement Activation	12	C3/SERPING1/CFB/C7/C4A/C1R/C1S/TREM2/C1QA/C1QB/C2/C4B

3.4 Screening and identification of immune-related hub genes in AMD

Further screening of AMD immune-related signature genes using LASSO regression algorithm. The expression profiles of the selected immune-related DEGs were extracted and standardized, and then used to establish the LASSO model (Fig. 3A). 91 immune-related DEGs further subjected to a LASSO regression analysis based on the value of lambda.min = 0.3653487, and 12 genes (C1R, C1S, C3, C7, CLEC7A, GFAP, HLA-DMA, HLA-DMB, HLA-DQA1, PLCG2, S100B, TNC) were identified. Additionally, we assessed the accuracy of the LASSO model by creating ROC curve, with the AUC value used for designation. As shown in Fig. 3B, the AUC value of the 12-gene-based model was 0.827, indicating that these immune-related DEGs may serve as potential biomarkers of AMD for further research.

3.5 PPI network construction and analysis of modules

Ninety-one immune-related DEGs were selected to construct the PPI network, 89 nodes and 289 edges were included in the network (PPI enrichment p-value < 1.0e-16). Import network data into Cytoscape software to visualize PPI networks. A module analysis of the target network and protein clustering were performed using the Cytoscape MCODE plug-in, three important modules in the PPI network were identified. The result of MCODE analysis shows that cluster1 consists of 12 nodes and 62 edges, cluster2 consists of 15 nodes and 54 edges and cluster3 consists of 11 nodes and 21 edges (Fig. 4).

3.6 Gene Ontology Enrichment Analysis of the Identified Modules

GO pathway enrichment analysis showed that DEGs in cluster 1 are mainly involved in biological processes such as antigen processing and presentation of exogenous peptide antigen via MHC class II, antigen processing and presentation, MHC class II protein complex assembly (Fig. 5A). These DEGs in cluster 1 are mainly involved in cellular components such as MHC protein complex and endoplasmic reticulum membrane. Their molecular functions include MHC class II protein complex binding, antigen binding and immune receptor activity binding. The DEGs in cluster 2 are mainly involved in biological processes such as leukocyte-mediated immunity, immunoglobulin-mediated immune response, lymphocyte-mediated immunity, immune receptor reorganization, macrophage activation, and microglia activation (Fig. 5B). The DEGs in cluster 2 are primarily located in the external side of plasma membrane, protein complex involved in cell adhesion. The molecular functions include IgG binding, immunoglobulin binding, amyloid-β binding, and immunoreceptor activity. DEGs in cluster 3 is mainly involved in biological processes such as complement activation and humoral immune response. The molecular functions include complement binding, peptidase modulator activity and enzyme inhibitor activity (Fig. 5C).

Age-related macular degeneration (AMD) is considered a disease of aging and chronic inflammation, which has been studied for decades, but there is no complete cure for this eye disease. Due to its unique anatomical and physiological characteristics, the retina is considered an immune-privileged tissue. As the disease worsens in patients with AMD, the immune system in the retina becomes dysregulated and compromises the blood-retinal barrier (BRB), leading to a disruption of the immune privilege of the retina, which in turn leads to further progression of retinopathy [23][24]. Although the exact pathogenesis of AMD remains unclear, several lines of evidence suggest that inflammation and immune responses are involved in the development of AMD and that the immune system plays a crucial role in the etiology of AMD [25]. With the development of high-throughput sequencing technologies, microarray and RNA-Seq transcriptomic datasets are increasing, providing an important resource for exploring the pathogenic mechanisms of AMD at the molecular level using bioinformatics approaches. However, few have used transcriptomic data to study changes in immune activity levels at different stages of AMD. Therefore, our aim was to analyze the changes in immune levels at different stages of AMD and to identify biomarkers associated with AMD immunity and to explore the mechanisms of AMD immunity.

In this study, we analyzed the immune activity levels of retinal tissues at different AMD stages in the GSE95095 dataset using ssGSEA. 13 immune signatures were significantly different at different stages, and most of the immune signatures had higher immune activity levels in retinal tissues of advanced AMD than in retinal tissues of early AMD. Several studies have elucidated that the complement system, natural killer cells, cytokines, and microglia play important roles in the development of AMD [24][25][26], so further studies are needed to confirm the effects of other immune signatures on AMD. The results of the study show that the level of inflammation and immune response increases progressively as the disease worsens. Since the immune system is a synergy of multiple pathways or cells, we performed a correlation analysis and found a strong correlation between different immune signatures. The therapeutic approach to AMD has been a major outstanding issue, and various medications can only be used to improve the clinical symptoms and slow down the progression of the disease in patients with AMD, rather than to cure it. Therefore, it is particularly important to find potential therapeutic targets. The immune-related DEG between advanced AMD tissues and control tissues was analyzed using LASSO regression method. Lasso regression model was constructed for further screening of 91 immune-related DEGs, and a total of 12 immune signature genes were identified (C1R, C1S, C3, C7, CLEC7A, GFAP, HLA-DMA, HLA-DMB, HLA-DQA1, PLCG2, S100B, TNC). C1R, C1S, and C7 are mainly involved in complement activation function, and the effect of the C3 gene on geographic atrophy (GA) has been elucidated [25][28]. CLEC7A is a marker for microglia in the retina, which are resident immune cells in the retina and are important participants in the immune response to AMD [27]. GFAP is a marker for astrocytes, astrocyte is a retinal glial cell [29][30]. S100B is an active neural distress biomarker that has been associated with many neurodegenerative diseases [31]. These evidences prove that the genes we identified are of high research value.

Interactions between proteins and genes play a critical role in executing important cellular functions, and PPI networks can be used to understand cellular functions and disease mechanisms, and to discover new therapeutic targets [32][33]. To more deeply investigate the immune-related molecular functions and disease mechanisms of AMD, we used PPI networks to systematically probe the contribution of gene expression and interactions in AMD. A PPI network was constructed using 91 immune-related DEGs, and the PPI network was analyzed using the MCODE algorithm, and three important clusters were analyzed, which contained 12, 15 and 11 nodes, respectively. The results of the functional annotated cluster analysis of the three clusters showed that the DEGs in cluster 1 were mainly involved in biological processes such as antigen processing and presentation of MHC class II and the production of MHC protein complexes. The DEGs in cluster 2 were mainly involved in biological processes such as leukocyte-mediated immunity, immunoglobulin-mediated immune response, macrophage activation and microglia activation. DEGs in cluster 3 are mainly involved in complement activation.

Although this study analyzed the immune profile of AMD at different stages, constructed LASSO model to screen 12 biomarkers with potential research value, constructed a PPI network based on immune-related DEGs and analyzed the molecular mechanism of clustering in the network, there are many limitations of this study to be addressed. First, the use of ssGSEA to analyze the proportion of immune cells in AMD tissues at different stages of the disease requires experiments to verify the validity of the results. Second, we only studied 19 immune-related signatures, and some other immune cells and pathways need to be further analyzed. Finally, the immune signature genes that were screened using a single dataset need to be validated in future studies.

The present study provided insight into the landscape of immune signatures underlying AMD and identified 12 core immune-related genes. The PPI network constructed on the basis of immune- related DEGs revealed 3 three molecular mechanisms. However, future studies will require clinical trials analyzing additional samples and clinical information to validate these findings.

AMD: Age-related Macular Degeneration

AUC: Area Under Curve

BP: biological process

CC: Cell composition

DEGs: differentially Expressed Genes

GEO: Gene Expression Omnibus

GO: Gene Ontology

LASSO: Least Absolute Shrinkage and Selection Operator

MF: Molecular function

MSG: Minnesota Grading System

PPI: Protein-Protein Interaction

ROC: Receiver Operating Characteristic

ssGSEA: Single-sample Gene Set Enrichment Analysis

Ethics approval and consent to participate

GEO are public databases. Our research is based on open source data, so there are no ethical issues or other conflicts of interest.

Consent for publication

Not applicable.

Data Availability Statement

Publicly available datasets were analyzed in this study. This data can be found here: The GEO database (https://www.ncbi.nlm.nih.gov/geo/). The Molecular Signatures Database (https://www.gsea-msigdb.org/gsea/msigdb). The ImmPort database (http://www.immport.org).

Competing interests

The authors declare no competing interests.

Funding

Not applicable.

Author Contributions

CG conceived of the presented idea. GP contributed to the data collection and performed the analysis. GP and KZ wrote the manuscript. All the authors have read and approved the final manuscript.

Acknowledgements

Thanks to the editors and reviewers for their hard work and important comments.

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No competing interests reported.

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Identification of Immune-related Core Genes Based on Immune Signatures of Age-related Macular Degeneration

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Version 1

Abstract

Background

Methods

Results

Conclusion

Figures

1 Introduction

2 Materials and Methods

2.1 Data Collection

2.2 Immune Estimation

2.3 Identification of DEGs

2.4 Protein-Protein Network Analysis

2.5 Selection of Characteristic Genes

2.6 Gene Ontology Enrichment Analysis

2.7 Statistical Analysis

3 Result

3.1 AMD immune activity analysis

3.2 Correlation analysis of immune signature

3.3 Identification of immune-related DEGs

3.4 Screening and identification of immune-related hub genes in AMD

3.5 PPI network construction and analysis of modules

3.6 Gene Ontology Enrichment Analysis of the Identified Modules

4 Discussion

5 Conclusions

Abbreviations

Declarations

Ethics approval and consent to participate

Consent for publication

Data Availability Statement

Competing interests

Funding

Author Contributions

Acknowledgements

References

Additional Declarations

Supplementary Files

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