Screening of Primary Open-Angle Glaucoma Diagnostic Markers based on Immune-related Genes and Immune Infiltration

doi:10.21203/rs.3.rs-1513297/v1

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Screening of Primary Open-Angle Glaucoma Diagnostic Markers based on Immune-related Genes and Immune Infiltration

https://doi.org/10.21203/rs.3.rs-1513297/v1

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PURPOSE. Primary open-angle glaucoma (POAG) continues to be a poorly understood disease. Performing bioinformatics analyses of optic nerve (ON) and trabecular meshwork (TM) gene expression data in order to further elucidate the immune-related genes of POAG and identify candidate target genes for treatment.

METHODS. We performed a gene analysis of publicly available microarray data, namely, the GSE27276-GPL2507, GSE2378-GPL8300, GSE9944-GPL8300, GSE9944-GPL571 datasets from Gene Expression Omnibus database. The obtained datasets were used as input for parallel pathway analyses. Based on Random Forest and Support vector machine (SVM) analysis to screen the key genes, significantly changed pathways were clustered into functional categories and the results were further investigated. CIBERSORT was used to evaluate infiltration of immune cells in POAG tissues. A network visualizing the differences between the POAG and normal was created. GO and KEGG enrichment analyses were performed using the Metascape database. We divided the differential mRNA into up-regulated and down-regulated groups, and predicted the drug targeting of the differential genes through the Connectivity Map (CMap) database.

RESULTS. A total of 49 differentially expressed genes, including 19 downregulated genes and 30 upregulated genes, were detected. Five genes (KRT14, HBB, ACOX2, HEPH and KRT13) were significantly changed. The results showed that the expression profiles of drug disturbances such as avrainvillamide-analysis-3, cytochalasin-D, NPI-2358, oxymethylone and vinorelbine were negatively correlated with the expression profiles of disease disturbances. It indicated that these drugs may reduce or even reverse the POAG disease state.

CONCLUSION. This study provides an overview of the processes involved in the molecular pathogenesis of POAG in the ON and TM. The findings provide a new understanding of the molecular mechanism of POAG from the perspective of immunology.

bioinformatics analysis

Primary open-angle glaucoma

optic nerve

trabecular meshwork

key genes

biomarker

Glaucoma is the leading cause of irreversible blindness worldwide. With the growing number and proportion of older persons in the population, it is projected that 111.8 million people will have glaucoma in 2040[1]. Primary open-angle glaucoma (POAG) is the most common type of glaucoma, accounting for 60–70% all glaucoma[2]. In POAG, the anterior and posterior segments of the eye are affected, and serious damage may be detected in the trabecular meshwork (TM) and optic nerve (ON)[2–4]. The TM is a specialized eye tissue essential for regulation of the aqueous humor outflow and control of the intraocular pressure (IOP), disturbances of which may lead to elevated IOP and glaucoma[5]. In general, due to POAG is insidious onset and develops painlessly and quietly, visual problems often late in the course of the disease, when significant and irreversible ON damages[1]. Neuroprotective therapies are not available and current treatments are limited to lowering IOP, which can slow disease progression at early diseases stages. However, over 50% of glaucoma is not diagnosed until irreversible ON damage has occurred[6].

The numerous POAG patients’ data that has been collected in research[7–9], but the molecular pathogenesis of POAG remains largely obscure. Therefore, an effective treatment option addressing these molecular changes is also still missing. In recent years, accumulating evidence have showed that immune cell infiltration plays an important role in POAG development[10]. Zhang et al., generalized that POAG may be associated with systemic disorders, mainly those related to the nervous system, endocrine system and immune systems. It has been firmly established that the neuroendocrine system and immune system closely interact through mediators such as hormones, neuropeptides, neurotransmitters and cytokines[11]. Cytokines mediate the biological effects of the immune system, and our previous study revealed an imbalance of T-helper (Th) 1-derived and Th2-derived cytokines in the serum of patients with glaucoma[12]. We also collected irises from normal individuals and POAG or chronic angle-closure glaucoma (CACG).[13]

Using omics technologies, we are able to measure the expression of several thousands of molecules from one sample of affected tissue, leading to an exponential increase in data[14]. The data was used in bioinformatics analyses to identify key transcription factors (TFs) associated with POAG, to examine the pathogenesis of glaucoma and may provide a basis for the diagnosis of glaucoma and drug development. CIBERSORT is a method to describe the composition of immune cells in complex tissues based on their gene expression profiles[15]. Few studies have used CIBERSPORT to analyze immune cell infiltration in POAG. In this study, we identified the key genes from TM tissue and ON tissue in patients with POAG compared with that of normal controls. The aim of this study was to have a deeper understanding of the molecular pathogenesis of POAG by applying integrative bioinformatics analysis on the available human gene expression data of the TM and ON tissues in patients with POAG and controls. The obtained results enable us to identify possible drug targets to modulate the disease outcome.

Systematic search

After the systematic search, the datasets of the four different human microarray studies were selected for further analyses. After correcting the batch effect, we combined the four GEO data sets of GSE27276, GSE2378, GSE9944 (GPL8300) and GSE9944 (GPL571) into the expression profiles of 110 samples (control group: 67 cases; POAG group: 43 cases) (Fig. 1A-B). The difference analysis was performed by limma package. The screening conditions of difference genes were P value < 0.05 and |logFC| > 0.585. Finally, 49 difference genes were screened, including 30 up-regulated genes and 19 down-regulated genes (Fig. 1C).

Pathway analysis

We further analyzed the pathway of these 49 candidate genes through Metascape database. The results showed that these candidate genes were mainly enriched in structural molecular activity, epidermis development, extractive matrix, oxidoreductase activity, aminoglycan metabolic process, aging and other pathways (Fig. 2A). Moreover, we analyzed the protein-protein interaction (PPI) network of genes in different gene sets by Cytoscape software (Fig. 2B).

Key genes

We analyzed the above 49 differential genes by Random Forest and SVM respectively to screen the key genes (Fig. 3A-3B). According to the comprehensive scores of the two machine learning methods, we obtained the top 5 genes as the key gene sets, which are KRT14, HBB, ACOX2, HEPH and KRT13 (Fig. 3C). The expression of five key genes in the POAG group and normal group is shown in the Fig. 4.

Immune cell infiltration

Microenvironment is mainly composed of immune cells, extracellular matrix, a variety of growth factors, inflammatory factors and special physical and chemical characteristics, which significantly affects the sensitivity of disease diagnosis and clinical treatment. By analyzing the relationship between key genes and immune infiltration in POAG data set, we further explore the potential molecular mechanism of key genes affecting the progress of POAG. The results show that the proportion of immune cells in each patient and the correlation between immune cells are shown in the Fig. 5A-5B Compared with normal group, the T cells regulatory (Tregs) level of samples in POAG group was significantly higher (Fig. 5C).

We further explored the relationship between key genes and immune cells. The five key genes were highly correlated with immune cells. KRT14 was positively correlated with plasma cells, neutrophils, and negatively correlated with T cells regulatory (Tregs), mast cells resetting. HBB was positively correlated with NK cells activated and monocytes, and negatively correlated with mast cells resting and dendritic cells resting. ACOX2 was positively correlated with T cells CD4 memory resting and monocytes, and negatively correlated with T cells cellular helper and T cells CD4 naïve. HEPH was positively correlated with T cells CD4 memory resetting and T cells regulatory (Tregs), and negatively correlated with T cells CD4 naive and T cells follicular helper. KRT13 is positively correlated with T cells follicular helper and plasma cells, and negatively correlated with T cells regulatory (Tregs) and mast cells resting (Fig. 6A). We further obtained the correlation between these key genes and different immune factors from TISIDB database, including immune modulators, chemokines and cell receptors (Fig. 6B-E). These analyses confirmed that these key genes are closely related to the level of immune cell infiltration and play an important role in the immune microenvironment.

Key genes related pathway

We used these five key genes in the gene set of this analysis to further explore the transcriptional regulatory network involved in key genes. Relevant transcription factors were predicted through Cistrome DB online database, including 55 transcription factors predicted by KRT14, 92 transcription factors predicted by HBB, 71 transcription factors predicted by ACOX2, 106 transcription factors predicted by HEPH and 57 transcription factors predicted by KRT13, Finally, a comprehensive transcriptional regulatory network of POAG key genes was constructed by visualization through Cytoscape (Fig. 7).

We studied the specific signal pathways enriched by five key genes to explore the potential molecular mechanism of key genes affecting the progress of POAG. We selected the significantly enriched pathways which showed in Fig. 8 and Fig. 9. The pathways enriched with KRT14 by GO analysis include cell substrate junction assembly, cell junction assembly and other pathways. The pathways enriched by KEGG include ladder, cancel and so on Butanoate metabolism and other channels. The pathways enriched with HBB by GO analysis included brown fat cell differentiation, corporate cytoskeleton organization. The pathways enriched by KEGG include Angel processing and presentation, focal adhesion. The pathways enriched with ACOX2 by GO analysis include spindle localization and transitional initiation. The pathways enriched by KEGG include promote metabolism and pyruvate metabolism. The pathways enriched with HEPH by GO analysis include numeric expression repair DNA recognition, lamellipodium organization. The pathways enriched by KEGG include glycerophospholipid metabolism, beta alanine metabolism. The pathways enriched with KRT13 by GO analysis include autophagosome organization, column cuboidal epithelial cell differentiation. The pathways enriched by KEGG include circuit cycle, TCA cycle, cytokine receptor interaction (Fig. 9).

Gene regulatory network analysis of key genes in POAG

We predicted and analyzed the five key genes through miRWalk database and ENCORI database to obtain their possible miRNA and lncRNA. Firstly, the mRNA-miRNA relationship pairs related to these five key mRNAs were extracted from the miRWalk database. We only retained 35 mRNA-miRNA relationship pairs with TargetScan of 1 or miRDB of 1 (including 4 mRNAs and 13 miRNAs). Then we predicted the interacting lncRNA according to these miRNAs, in which 1112 pairs of interactions (including 2 miRNA and 823 lncRNA) were predicted. Finally, we constructed the ceRNA network through Cytoscape (V3.7) (Fig. 10).

POAG biomarkers

We discussed the prediction efficiency of key genes through the ROC curve verified by diagnostic efficiency. The results showed that the area under the AUC curve of KRT14 is 0.8254; The area under the AUC curve of HBB is 0.7404; The area under the AUC curve of ACOX2 is 0.7782; The area under AUC curve of HEPH is 0.8008; The area under the AUC curve of KRT13 is 0.816. These results show that these five key genes have good prediction efficiency for POAG and may better predict the occurrence and development of diseases (Fig. 11A -11E).

Drug targeting prediction in POAG

We divided the differential mRNA into up-regulated and down-regulated groups, and predicted the drug targeting of the differential genes through the Conectivity Map database. The results showed that the expression profiles of drug disturbances such as avrainvillamide-analysis-3, cytochalasin-D, NPI-2358, oxymethylone and vinorelbine were negatively correlated with the expression profiles of disease disturbances (Fig. 12). It indicated that these drugs may reduce or even reverse the POAG disease state.

POAG is a chronic retinal neurodegeneration disease characterized by the anterior and posterior segments of the eye being affected, and serious damage may be detected in the TM and ONH[17–19]. Lowering IOP using drugs or surgery is the only intervention available[6]. However, clinical evidence indicates that lowering IOP does not prevent progression in all POAG patients. Consequently, non-IOP factors are involved in the disease[2]. With the rapid development of science and technology, bioinformatics provides a powerful strategy for the screening of molecular markers[20]. Biomarkers reflect changes at the molecular level and can accurately monitor pathological changes in TM and ONH and provide important information for the diagnosis of POAG[5, 14, 21–25]. Nevertheless, detection of POAG lesions using molecular biology methods is suboptimal, and treatments were currently in a limited pharmacotherapy phase[6]. In the present study, to screen the pathogenic genes involved in POAG, an integrated analysis was performed by using the microarray datasets in glaucoma derived from the GEO database. The functional annotation and potential pathways of DEGs were additionally examined by GO and KEGG enrichment analyses. A POAG-specific transcriptional regulatory network was constructed to identify crucial transcriptional factors that target the key genes in POAG. We used Random Forest and SVM method to screen out five potential key genes in both human TM and ONH tissue. A growing number of researchers realize that immune infiltration is related to the diagnosis in POAG[11–13]. Therefore, analyzing the pattern of POAG immune cell infiltration and finding specific diagnostic marker have profound significance for POAG patients. Subsequently, the CIBERSORT algorithm performed deconvolution analysis on the immune microenvironment to assess the proportion of the immune cells in POAG.

We identified key differential genes between POAG and normal by performing a combine analysis of TM and ONH. A total of 49 DEGs were identified, including 19 downregulated genes and 30 upregulated genes. These 49 differential genes further analyzed by Random Forest and SVM respectively to screen the key genes. Five genes (KRT14, HBB, ACOX2, HEPH and KRT13) were significantly changed. We found that these key genes were highly correlated with immune cells.

KRT14 is a member of the type I keratin family of intermediate filament proteins[26]. KRT 14 is expressed in a variety of cell in human. It is recovered as a heterodimer with KRT5 and form the cytoskeleton of epithelial cells[27, 28]. Gautam et al., using multi-species single-cell transcriptomic analysis of human eye, KRT 14 was expressed in corneal epithelial cells[29]. However, previous studies did not perform deeper investigation in POAG. In our study, we found that KRT14 was expressed in both TM and ON tissues in human. KRT14 was highly correlated with immune cells (plasma cells, neutrophils, etc.), and negatively correlated with T cells regulatory (Tregs), mast cells resetting. HBB (Hemoglobin subunit beta) is encoded by the HBB gene on human chromosome 11[30]. HBB was positively correlated with NK cells activated and monocytes, and negatively correlated with mast cells resting and dendritic cells resting. ACOX2 gene encodes in human autosome 3 for the enzyme Acyl-CoA oxidase 2, oxidizing the Coenzyme A esters of bile acid and di-trihydroxycholestanoic acid intermediates. It is located in the peroxisomes of cells and has a tripeptide at the C-terminal end of the protein formed by serine—lysine-leucine that serves as a peroxisome localization signal. The deficiency of this enzyme causes an accumulation of fatty acid and bile intermediates, generating Zellweger syndrome. ACOX2 was positively correlated with T cells CD4 memory resting and monocytes, and negatively correlated with T cells cellular helper and T cells CD4 naïve. HEPH (Hephaestin) is involved in the metabolism and homeostasis of iron and possibly copper[31]. It is a transmembrane copper-dependent ferroxidase responsible for transporting dietary iron from intestinal enterocytes into the circulatory system. HEPH was positively correlated with T cells CD4 memory resetting and T cells regulatory (Tregs), and negatively correlated with T cells CD4 naive and T cells folgular helper. KRT13 (Keratin 13) gene encodes a type I cytokeratin which is expressed in the differentiated cells of non-cornified stratified squamous epithelia[32, 33]. KRT13 is positively correlated with T cells folgular helper and plasma cells, and negatively correlated with T cells regulatory (Tregs) and mast cells resting.

The ocular innate immune response relevant to glaucoma involves the complement cascade, microglia, astrocytes, and Muller cells. Autoantibodies, tumor necrosis factor (TNF)-alpha, and other products of adaptive B and T cells are also featured prominently in the glaucomatous adaptive immune response. However, research has not provided clear immune infiltration among these cells[10]. Thus, in our study, based on the immune infiltration analysis between key genes and immune cells, we divided the differential mRNA into up-regulated and down-regulated groups, and predicted the drug targeting of the differential genes through the Conectivity Map database. The results showed that the expression profiles of drug disturbances such as avrainvillamide-analysis-3, cytochalasin-D, NPI-2358, oxymethylone and vinorelbine were negatively correlated with the expression profiles of disease disturbances. NPI-2358 and vinorelbine are Tubulin inhibitor. cytochalasin-d is Actin polymerization inhibitor. Oxymetholone is Androgen receptor agonist. avrainvillamide-analog-3 is nucleophosmin inhibitor. It indicated that these drugs may reduce or even reverse the POAG disease state.

There were several limitations to this study. First, due to the limited number of samples, there is still a need to confirm these preclinic observation in the future clinic studies for novel biomarkers. Second, CIBERSORT is based on the principle of linear support vector regression and uses gene expression data in reverse to deduce the result of immune cell infiltration. Indeed, it is not based on experimental data, and further verification of immune cell infiltration by a large number of experiments is needed. Third, we performed mining and analysis of previously published data; although some previous studies showed similar results, the related molecules and their mechanisms at the molecular, cell, and tissue levels require validation.

In conclusion, the overlap of the Random Forest and SVM algorithm was obtained, and eventually, five key genes were recognized in both human TM and ONH tissue. We found that may be used as diagnostic markers for POAG. To understand POAG development, the GO and GSEA analysis of the selected supplied a more specific molecular mechanism. So far, the relationship between key genes and immune infiltration in both TM and ONH tissues has been little reported. The mechanism of key genes and immune infiltration-related factors in the diagnosis of POAG remains to be explored. Further investigation of these immune cells may identify targets of immunotherapy for POAG and help POAG patients benefit from immunomodulatory therapy.

Material and Data

The datasets used in the present study were downloaded from the National Center of Biotechnology Information (NCBI) Gene Expression Omnibus (GEO). Four sets of POAG mRNA (GSE27276-GPL2507, GSE2378-GPL8300, GSE9944-GPL8300, GSE9944-GPL571) data were obtained. The detailed information showed in Table 1. There are in total 43 POAG patients and 67 normal individuals enrolled in our study from these four datasets.

Table 1

Characteristics of four datasets of POAG mRNA data.

Series matrix file data	Annotation file	Number of POAG group	Number of Normal group	Sample type
GSE27276	GPL2507	19	17	Trabecular meshwork
GSE2378	GPL8300	7	6	Optic nerve head
GSE9944	GPL8300	13	6	Optic nerve head
GSE9944	GPL571	6	36	Optic nerve head

Screening of Key Genes

The differential expressed genes (DEGs) in the GEO dataset (P < 0.05 and |logFC| > 0.585) are screened by difference analysis, and then the candidate gene sets are further screened by Random Forest and SVM algorithms respectively. Among them, Random Forest is an integrated learning algorithm based on decision tree. Multiple samples are selected from the sample sets as a training set by sampling with replacement. The decision tree is generated from the obtained sample sets by sampling. At each generated node, features without repetition are randomly selected. Based on optimal features to divide the sample sets, determine the prediction results. In this study, the features were evaluated by the Random Forest algorithm. Using %IncMSE to evaluate the significance, 1000 trees were build, and repeat 50 times. SVM is a machine learning method based on SVM. Optimal variables were identified by deleting SVM generated feature vectors. For the sake of further recognize the diagnostic value of disease biomarkers, a support vector machine model was established according to "e1071" software package. Finally, the top features were retained for follow-up analysis.

GO and KEGG Functional annotation

The difference genes were functionally annotated using the Metascape database (www.metascape.org) to comprehensively explore the functional correlation of these genes. For the specific genes, we performed Gene Ontology (GO) analysis and Kyoto Encyclopedia of gene genome (KEGG) pathway analysis. Minimum overlap ≥ 3 and P ≤ 0.01 was considered statistically significant.

Analysis of immune cell infiltration

The CIBERSORT is widely used in evaluating the type of immune cell in the microenvironment. The tool is based on the principle of linear support vector to perform deconvolution analysis on the expression matrix of immune cell subtypes. It contains 547 biomarkers, and 22 phenotypes human immune cell, covering plasma cells, B cells, T cells, and myeloid cell subsets. Using CIBERSORT algorithm, this study analyzed the POAG patients’ data and quantified the relative proportion of 22 infiltrating immune cells. Furthermore, this study performed Pearson correlation analysis on the immune cells and gene expression.

Gene regulatory network analysis of key genes

The Cistrome DB is an up-to-date, scalable, and powerful tool to process large batches of ChIP-seq and DNase-seq datasets, which map the genome-wide locations of transcription factor binding sites, histone post-translational modifications and regions of chromatin accessible to endonuclease activity[16]. Currently, the Cistrome DB contains approximately 30451 human and 26013 mouse samples. This study explored the regulatory relationships between transcription factors and key genes based on the Cistrome DB database with genome file setting as hg38 and the transcription initiation site 10KB, which was visualized through Cytoscape.

Gene set enrichment analysis (GSEA)

GSEA analysis uses a predefined gene set to rank the genes according to the degree of differential expression in the two types of samples, and then test whether the preset gene set is enriched at the top ranking or bottom ranking. This study compared the differences of KEGG and GO signaling pathways between high expression group and low expression group by GSEA to explore the molecular mechanism of key genes in the two groups, in which the number of replacement was set to 1000 and the type of replacement was set to phenotype.

Drug targeting prediction

The connectivity map (CMAP) which developed by Broad Institute is a resource created to enable data-driven studies on drug mode-of-action and drug repositioning; It is mainly used to reveal the functional relationship among small molecular compounds, genes and disease status. It contains the microarray data of 1309 small molecule drugs before and after treatment of 5 human disease cell lines. It provides various treatment conditions, including different drugs, different concentrations, different treatment duration and so force.

Statistical analysis

All statistical analyses were performed in R language (version 3.6). All statistical tests were bilateral (P < 0.05).

Ethics approval and consent to participate

Our data were downloaded from NCBI databases. No specific permits are required for sample collection in this study.

Consent for publication

Not applicable.

Competing interest

The authors have declared that no conflict of interest exists.

Acknowledgements

Not applicable.

Author contributions

Study design: LGS, XJQ; Experimentation: LGS, GLL, Data analysis: LGS, WWD, DZ, TC, TKY; figure and manuscript preparation: LGS, WWD; editing and review: SLG, XJQ, CZ; supplemental files preparation: LGS.

Funding

This study was funded by Chinese State Natural Science Foundation (Grant No. 81670851), National Science and Technology Key R&D Program (Grant No. 2019YFC0118402), Capital’s Funds for Health Improvement and Research of China (Grant No. CFH-2020-2-40911), and Peking University Medicine Fund of Fostering Young Scholars’ Scientific & Technological Innovation (BMU2022PYB018).

Author details

¹Department of Ophthalmology, Peking University Third Hospital, Beijing, China. ²Beijing Key Laboratory of Restoration of Damaged Ocular Nerve, Peking University Third Hospital, Beijing, China. ³Department of Ophthalmology, The Second Hospital of Shandong University, Shandong, China. ⁴ Clinical Stem Cell Research Center, Peking University Third Hospital, Beijing, China. ⁵ School of Basic Medicine Sciences, Peking University, Beijing, China

Availability of data and materials

The selected datasets generated and/or analyzed during the current study are available in the Gene Expression Omnibus (GEO) repository (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE27276; https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE2378; https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE9944). Based on these datasets, our data generated or analyzed during this study are available from the corresponding author on reasonable request.

Supplementary material

Supplementary material is available at the BMC Genomics Data online.

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Screening of Primary Open-Angle Glaucoma Diagnostic Markers based on Immune-related Genes and Immune Infiltration

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Abstract

Figures

Background

Results

Systematic search

Pathway analysis

Key genes

Immune cell infiltration

Key genes related pathway

Gene regulatory network analysis of key genes in POAG

POAG biomarkers

Drug targeting prediction in POAG

Discussion

Materials And Methods

Material and Data

Screening of Key Genes

GO and KEGG Functional annotation

Analysis of immune cell infiltration

Gene regulatory network analysis of key genes

Gene set enrichment analysis (GSEA)

Drug targeting prediction

Statistical analysis

Declarations

References

Additional Declarations

Status:

Version 1