# Identification of Molecular Mechanism of OSA with Hypertension Based on Multiple Microarray Analysis

Authors: Xizhu Zhang; Gang Jing; Liqing Qi; Mingzhu Ma; Liting Li; Ningning Shen; Zheng Guo; Xiaoling Gao
Journal: Mathematical Problems in Engineering
 (2022)

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## Abstract

Purpose. OSA is closely associated with hypertension, and both epidemiological and experimental studies have confirmed that OSA is one of the most important independent risk factors for hypertension. The pathological mechanisms by which OSA causes hypertension are not well understood, and in this paper, we explored the molecular mechanisms by which OSA may mediate hypertension at the bioinformatics level. Materials and Methods. We downloaded disease-related datasets from the GEO public database, calculated the differential genes between the two groups of patients by the limma package, and then further constructed the WGCNA network based on the clinical characteristics of patients to explore the important regulatory genes in the disease. Subsequently, ssGSEA was used to explore the potential molecular mechanisms of disease progression and GSVA was applied to analyze the specific signaling pathways. Finally, we performed real-time quantitative PCR (qRT-PCR) to validate these pivotal genes. Results. Three genes were selected as target genes, namely, GPR179, RNF150, and JPH4. The results showed they were strongly correlated with immune cell content that high expression of the three core genes was associated with myogenesis, angiogenesis, oxidation, metabolism, and PI3K/AKT/mTOR pathways. qRT-PCR validated that all three genes have statistically significant differences between the OSA group and OSA combined with hypertension group. Conclusion. Our study provides new evidence for the potential molecular mechanisms of OSA combined with hypertensive disease as well as diagnosis and treatment.


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## Body

## 1. Introduction

OSA (obstructive sleep apnea) is a common sleep disorder characterized by chronic intermittent nocturnal hypoxia. Patients can have recurrent complete and/or incomplete upper airway obstruction during sleep, resulting in apnea or hypoventilation with intermittent nocturnal hypoxia or sleep fragmentation, which is the main pathophysiological heterogeneity of the disease. OSA is now widely recognized as an independent risk factor for hypertension [1, 2], and is associated with the development of multiple systemic diseases, including cardiovascular diseases [3], metabolic syndrome [4], and cognitive dysfunction [5].Epidemiology shows a prevalence of arterial hypertension between 35% and 80% in patients with OSA, and the degree of elevated blood pressure is positively correlated with its severity [6]. When focusing on hypertensive patients, the prevalence of OSA patients is approximately 40%, rising to nearly 90% in patients with intractable hypertension [7]. OSA is closely related to hypertension through a variety of pathophysiological factors, and the current possible causative factors, including activation of neural sympathetic nerves, oxidative stress, endothelial dysfunction, insulin resistance, and inflammatory response [8, 9]. In addition, hypertension itself may promote vascular remodeling and endothelial dysfunction, forming a vicious circle [10].However, hypertension is a complex disease and mechanisms may be affected by genetic factors, dietary factors, and ethnic differences. Disease may be involved in the development of immune activation, inflammation, and alterations in the levels of various cytokines. It is necessary to explore the pathogenesis of OSA at the molecular level, developing new biomarkers, and exploring potential targets to prevent and treat hypertension.In recent years, bioinformatics analysis has been widely used to analyze microarray data to identify differentially expressed genes (DEGs) and perform various analyses. In our study, we found the genomes of OSA patients and normal subjects from the GEO database and investigated the pathway enrichment of possible differentially expressed genes by gene ontology (GO) annotation, Kyoto Encyclopedia of genes and genomes (KEGG), then we looked up the gene sets of hypertensive patients from the GEO database, which were analyzed by the WGCNA coexpression network to find their core genes. Ultimately, a total of three core genes were screened for possible prospective diagnostic biomarkers and therapeutic targets for OSA combined with hypertensive disease.


## 2. Materials and Methods

### 2.1. Data Download

Microarray data were downloaded from GEO (https://www.ncbi.nlm.nih.gov/geo), a public genomic database containing sufficient high-throughput gene expression data [11]. We downloaded Series Matrix File data files for GSE75097 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE75097), as well as annotation platform GPL10904, with 28 transcriptome data sets, including the package OSA group (n = 19), OSA combined hypertension group (n = 9); we download Series Matrix File data files for GSE74144 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE74144), as well as annotation platform GPL13497, a total of 22 transcriptomic data sets, including the normal group (n = 8) and the disease group (n = 14).


### 2.2. GO and KEGG Functional Annotation

To obtain biological functions and signaling pathways involved with disease development, gene ontology (GO) analysis, and Kyoto Encyclopedia of genes and genomes (KEGG) pathway analysis were performed for specific genes using the Metascape database (https://www.metascape.org/) for annotation and visualization [12]. Min overlap ≥3 and p ≤ 0.01 was considered statistically significant.


### 2.3. WGCNA Analysis

The weighted gene coexpression network was constructed to find coexpressed gene modules and to explore the association between gene networks and phenotypes, as well as the core genes in the network. The coexpression network of all genes in the dataset was constructed separately using the WGCNA-R package [13], and the genes with the top 5000 variance were screened for further analysis using this algorithm, where the soft threshold was set to 8. The weighted adjacency matrix was transformed into a topological overlap matrix (TOM) to estimate the network connectivity, and the hierarchical clustering method was applied to construct the clustering tree structure of the TOM matrix. Different branches of the clustering tree represent different gene modules, and different colors represent different modules. Based on the weighted correlation coefficients of genes, which are classified according to expression patterns, and genes with similar patterns are grouped into one module, and tens of thousands of genes are divided into multiple modules by expression patterns [14].


### 2.4. Immunogenetic Correlation

To evaluate the effect of genes on immune infiltration, ssGSEA was used to quantify the level of immune cell infiltration in each sample, and Spearman correlation analysis was performed for gene expression as well as immune cell content [15].


### 2.5. GSVA (Gene Set Variation Analysis)

Gene set variation analysis (GSVA) is a nonparametric unsupervised method to assess the enrichment of transcriptomic gene sets. GSVA converts gene level changes into pathway level changes by composite scoring of gene sets of interest, and then determines the biological function of the samples. In this study, gene sets will be downloaded from the molecular signatures database (v7.0), and the GSVA algorithm [16] will be used to score each gene set comprehensively to assess the potential biological functional changes of different samples.


### 2.6. GeneMANIA Analysis

GeneMANIA (https://www.genemania.org) is a flexible and a user-friendly PPI network building database for visualizing functional networks between genes and analyzing gene functions as well as interaction relationships. The site allows setting up data sources for gene nodes with various bioinformatics analysis methods, such as physical interaction, gene coexpression, gene colocalization, gene enrichment analysis, and site prediction [17]. In this study, the core gene network was generated by GeneMANIA to explore its possible mechanisms of action in patients.


### 2.7. Reverse Transcription and Quantitative Real-Time PCR (qRT-PCR)

Total RNA was isolated from PMBC of normal human models (n = 10), OSA patients (n = 10), and OSA combined hypertensive patients (n = 10), respectively, using RNAiso Plus (Takara, Japan). The quality and quantity of RNA were measured by using the spectrophotometer according to the manufacturer’s instructions. The RNA was then reverse transcribed into cDNA using the PrimeScript™ RT kit (No. RR047 A, Takara, Japan) and qRT-PCR was performed on a LightCycler 480 (Roche). qRT-PCR was performed using the TB Green Premix Ex Taq™ kit (No. RR820, Takara, Japan) to analyze gene expression levels, and the relative expression of genes was calculated by the 2(−ΔΔCt) method with HS-GAPDH as the internal reference. The thermal cycling program was set to predenaturation: 95°C, 30 s, 1 cycle; quantitative analysis: 95°C, 5 s, 60°C, 30 s, 40 cycles; melting: 95°C, 5 s, 60°C, 1 min, 1 cycle; cooling: 50°C, 30 s, 1 cycle. Three biological replicates were performed for our experiments, and all primer sequences are shown in Table 1.Table 1
Primer sequences for qRT-PCR.
No.GenePrimers sequences (5′ to 3′)1GPR179 forwardCTTCCCTAAATCCCACAGCCTCAAG2GPR179 reverseGACTCTCCTCCACACTCTCCTTCTC3JPH4 forwardAACTAGCTGGCTATGAGGCTGAGG4JPH4 reverseTCCTCCTCTCGAAGACTTCCTGAAC5RNF150 forwardACTTCGGTTGTGTTTGTCTCCATCTC6RNF150 reverseGCTGGTTCCTATCCCTGGCATTTG


### 2.8. Statistical Analysis

Statistical analysis was performed using theR language (version 3.6). All statistical tests were two-sided, and p < 0.05 was statistically significant.


## 2.1. Data Download

Microarray data were downloaded from GEO (https://www.ncbi.nlm.nih.gov/geo), a public genomic database containing sufficient high-throughput gene expression data [11]. We downloaded Series Matrix File data files for GSE75097 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE75097), as well as annotation platform GPL10904, with 28 transcriptome data sets, including the package OSA group (n = 19), OSA combined hypertension group (n = 9); we download Series Matrix File data files for GSE74144 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE74144), as well as annotation platform GPL13497, a total of 22 transcriptomic data sets, including the normal group (n = 8) and the disease group (n = 14).


## 2.2. GO and KEGG Functional Annotation

To obtain biological functions and signaling pathways involved with disease development, gene ontology (GO) analysis, and Kyoto Encyclopedia of genes and genomes (KEGG) pathway analysis were performed for specific genes using the Metascape database (https://www.metascape.org/) for annotation and visualization [12]. Min overlap ≥3 and p ≤ 0.01 was considered statistically significant.


## 2.3. WGCNA Analysis

The weighted gene coexpression network was constructed to find coexpressed gene modules and to explore the association between gene networks and phenotypes, as well as the core genes in the network. The coexpression network of all genes in the dataset was constructed separately using the WGCNA-R package [13], and the genes with the top 5000 variance were screened for further analysis using this algorithm, where the soft threshold was set to 8. The weighted adjacency matrix was transformed into a topological overlap matrix (TOM) to estimate the network connectivity, and the hierarchical clustering method was applied to construct the clustering tree structure of the TOM matrix. Different branches of the clustering tree represent different gene modules, and different colors represent different modules. Based on the weighted correlation coefficients of genes, which are classified according to expression patterns, and genes with similar patterns are grouped into one module, and tens of thousands of genes are divided into multiple modules by expression patterns [14].


## 2.4. Immunogenetic Correlation

To evaluate the effect of genes on immune infiltration, ssGSEA was used to quantify the level of immune cell infiltration in each sample, and Spearman correlation analysis was performed for gene expression as well as immune cell content [15].


## 2.5. GSVA (Gene Set Variation Analysis)

Gene set variation analysis (GSVA) is a nonparametric unsupervised method to assess the enrichment of transcriptomic gene sets. GSVA converts gene level changes into pathway level changes by composite scoring of gene sets of interest, and then determines the biological function of the samples. In this study, gene sets will be downloaded from the molecular signatures database (v7.0), and the GSVA algorithm [16] will be used to score each gene set comprehensively to assess the potential biological functional changes of different samples.


## 2.6. GeneMANIA Analysis

GeneMANIA (https://www.genemania.org) is a flexible and a user-friendly PPI network building database for visualizing functional networks between genes and analyzing gene functions as well as interaction relationships. The site allows setting up data sources for gene nodes with various bioinformatics analysis methods, such as physical interaction, gene coexpression, gene colocalization, gene enrichment analysis, and site prediction [17]. In this study, the core gene network was generated by GeneMANIA to explore its possible mechanisms of action in patients.


## 2.7. Reverse Transcription and Quantitative Real-Time PCR (qRT-PCR)

Total RNA was isolated from PMBC of normal human models (n = 10), OSA patients (n = 10), and OSA combined hypertensive patients (n = 10), respectively, using RNAiso Plus (Takara, Japan). The quality and quantity of RNA were measured by using the spectrophotometer according to the manufacturer’s instructions. The RNA was then reverse transcribed into cDNA using the PrimeScript™ RT kit (No. RR047 A, Takara, Japan) and qRT-PCR was performed on a LightCycler 480 (Roche). qRT-PCR was performed using the TB Green Premix Ex Taq™ kit (No. RR820, Takara, Japan) to analyze gene expression levels, and the relative expression of genes was calculated by the 2(−ΔΔCt) method with HS-GAPDH as the internal reference. The thermal cycling program was set to predenaturation: 95°C, 30 s, 1 cycle; quantitative analysis: 95°C, 5 s, 60°C, 30 s, 40 cycles; melting: 95°C, 5 s, 60°C, 1 min, 1 cycle; cooling: 50°C, 30 s, 1 cycle. Three biological replicates were performed for our experiments, and all primer sequences are shown in Table 1.Table 1
Primer sequences for qRT-PCR.
No.GenePrimers sequences (5′ to 3′)1GPR179 forwardCTTCCCTAAATCCCACAGCCTCAAG2GPR179 reverseGACTCTCCTCCACACTCTCCTTCTC3JPH4 forwardAACTAGCTGGCTATGAGGCTGAGG4JPH4 reverseTCCTCCTCTCGAAGACTTCCTGAAC5RNF150 forwardACTTCGGTTGTGTTTGTCTCCATCTC6RNF150 reverseGCTGGTTCCTATCCCTGGCATTTG


## 2.8. Statistical Analysis

Statistical analysis was performed using theR language (version 3.6). All statistical tests were two-sided, and p < 0.05 was statistically significant.


## 3. Results

(1)
We downloaded the GSE75097 disease-related dataset from the NCBI GEO public database, and included expression profile data from 28 groups of patients, including the OSA group (n = 19) and OSA combined with the hypertension group (n = 9). We further used the limma package [18] to calculate differential genes between the two groups of patients with differential gene screening conditions of p < 0.05 and |Log2 FC| > 1. A total of 808 differential genes were screened, including 344 upregulated genes and 464 downregulated genes (Figure 1). We further performed pathway analysis of differential genes, and the results showed that differential genes were mainly enriched in cell-cell adhesion via plasma-membrane adhesion molecules, neuromuscular synaptic transmission, reversible differentiation, sensory perception of taste, amoebiasis, and other pathways (Figure 2(a)). The interaction diagram between the genes is shown in the figure, with upregulated genes in red and downregulated genes in green (Figure 2(b)).(2)
We downloaded GSE74144 from the NCBI GEO public database for a total of 22 groups of patients, containing healthy controls (n = 8) and disease groups (n = 14). The WGCNA network was further constructed based on the clinical traits of patients to explore the important regulatory genes in the disease. The soft threshold β was determined by the “sft$powerEstimate” function, and the soft threshold was set to 8. Then, based on the tom matrix, 15 gene modules were detected in this analysis, namely black (221), blue (524), brown (372), cyan (363), greenyellow (342), grey (16), grey60 (162), lightgreen (148), lightyellow (97), magenta (439), midnightblue (642), pink (195), tan (177), turquoise (977), and yellow (325) modules. We further analyzed between modules and traits and found that the tan module had the highest correlation with disease phenotype (cor = −0.49, p = 0.02), therefore, the tan module will be selected for subsequent validation (Figure 3).(3)
The two datasets were selected for further intersection by difference analysis as well as WGCNA analysis to derive the module with the highest correlation, resulting in three core genes: GPR179, RNF150, and JPH4 (Figure4).(4)
Disease may be involved in the development of immune activation, inflammation, and alterations in the levels of various cytokines. It is necessary to explore the pathogenesis of OSA at the molecular level, developing new biomarkers, and exploring potential targets to prevent and treat hypertension. By analyzing the relationship between core genes and immune infiltration in the disease dataset, the potential molecular mechanisms of core genes affecting disease progression were further explored. The results of the study showed that all three genes were strongly correlated with immune cell content (Figure5), and the results were as expected.(5)
Next, the specific signaling pathways involved in the three core genes were investigated to explore the potential molecular mechanisms by which the core genes affect disease progression. GSVA results showed that high expression of the GPR179 gene was associated with MYOGENESIS, ANGIOGENESIS, OXIDATIVE_PHOSPHORYLATION, XENOBIOTIC_ METABOLISM, and other pathways; high expression of JPH4 was associated with ANDROGEN_RESPONSE, ESTROGEN_RESPONSE_EARLY, PI3K_AKT_MTOR_SIGNALING, and other pathways; while high expression of RNF150 was associated with the MYC_TARGETS signaling pathway (Figure6). The protein interaction networks involved in the three core genes are shown in the figure (Figure 7).(6)
We validated these three genes by qRT-PCR. The expression of GPR179 in OSA with the hypertension group was lower than that in the OSA group, and both were higher than the normal group; the expression of JPH4 and RNF150 in OSA with the hypertension group was higher than the OSA group, while the expression of RNF150 in the OSA group was higher than the normal group (Figure8).Figure 1
The limma package was used to calculate the differential genes between the two groups of patients.p < 0.05 and |Log2 FC|>1 were used for differential gene screening, and a total of 808 differential genes were screened, including 344 upregulated genes and 464 downregulated genes.Figure 2
Gene expression, correlation, and enrichment analysis, showing the significant function related to DEGs. (a) The results showed that the differential genes were mainly enriched in cell-cell adhesion via plasma-membrane adhesion molecules, neuromuscular synaptic transmission, reversible differentiation, sensory perception of taste, amoebiasis, and other pathways. (b) The interaction between the genes is shown in the diagram, with upregulated genes in red and downregulated genes in green.
(a)(b)Figure 3
The WGCNA network was further constructed based on the clinical traits of patients to explore the important regulatory genes in the disease. (a) Heat map of clustering for all samples. (b) A scale-free index and average connectivity for each soft threshold. (c, d) A tree diagram of gene clustering, different colors represent different modules. (e) Heat map of correlation between module feature genes and hypertension, blue indicates negative correlation, red indicates positive correlation, and the tan module with the highest correlation is selected for subsequent analysis. (f) A scatter plot of tan module feature genes.
(a)(b)(c)(d)(e)(f)Figure 4
The results of DEGs were further intersected with modules with the highest correlation from WGCNA analysis, resulting in three core genes: GPR179, RNF150, and JPH4.Figure 5
All three genes showed a strong correlation with immune cell content. (a) Correlation between CIBERSORT and expression of GPR179. (b) Correlation between CIBERSORT and expression of JPH4. (c) Correlation between CIBERSORT and expression of RNF150.
(a)(b)(c)Figure 6
Using GSVA to investigate the specific signaling pathways involved in the three core genes and to explore the potential molecular mechanisms by which the core genes affect disease progression. (a) High expression of GPR179 gene was associated with MYOGENESIS, ANGIOGENESIS, OXIDATIVE_PHOSPHORYLATION, and XENOBIOTIC_ METABOLISM pathways. (b) High expression of JPH4 is associated with ANDROGEN_RESPONSE, ESTROGEN_RESPONSE_EARLY, and PI3K_AKT_MTOR_SIGNALING pathways. (c) High expression of RNF150 is associated with the MYC_TARGETS signaling pathway.
(a)(b)(c)Figure 7
Exploring the protein interaction network involved in three core genes through geneMANIA.Figure 8
The expression of GPR179, JPH4, and RNF150 determined by qRT-PCR; OSA, obstructive sleep apnea; HBP, hypertension.∗P<0.05, ∗∗P<0.01, ∗∗∗P<0.001, ∗∗∗∗P<0.0001.
(a)(b)(c)


## 4. Discussion

GPR179 is short for G protein-coupled receptor 179, which encodes a member of the glutamate receptor subfamily of G protein-coupled receptors. The encoded protein has an EGF-like calcium-binding structural domain and a 7-transmembrane structural domain in the N-terminal region of protein [19]. GPR179 was recently found to be required for depolarizing bipolar cell function and is mutated in autosomal recessive complete congenital resting night blindness [20]. JPH4 is a member of the junctophilin family, a family of transmembrane proteins involved in the formation of the connecting membrane complex between the plasma membrane and the endoplasmic reticulum in excitable cells that regulate Ca2+ signaling and impair the nuclear factor (NFAT) and extracellular signal-associated kinase (ERK) signaling pathways in T cells [21]. An important homolog of the RNF150 gene is ZNRF3, and the human genome encodes approximately 300 RNF proteins, most of which are thought to be E3 ubiquitin ligases, many of which are soluble proteins that play roles in tumorigenesis, development, signal transduction, cell cycle and apoptosis, protein quality control, protein transport, cell proliferation and differentiation, apoptosis, immune regulation, signaling, and mitochondrial dynamics [22, 23]. There are few literature reports on RNF150, but it has been mentioned that single nucleotide polymorphisms in RNF150 may be significantly associated with the risk of COPD (chronic obstructive pulmonary disease, COPD) development in Hainan population, and polymorphisms in RNF150 may be a novel pathogenesis of COPD [24].Hypertension is a disease with a complex pathogenesis. In addition to disorders of the sympathetic and parasympathetic nervous systems, the renin angiotensin aldosterone system and the endothelin system, genetic predisposition, and the environment, low-grade inflammation is an important factor in causing and maintaining elevated blood pressure. Studies have found that natural immune cells (monocytes/macrophages) and adaptive immune cells (T lymphocytes) are increased in number in hypertension. Natural and adaptive immune responses may be involved in the pathology of high blood pressure and target organ damage [25, 26]. Chronic immune activation of the vascular wall damages endothelial cells and inhibits nitric oxide production and release, leading to structural changes in blood vessels and diastolic dysfunction; also, immune cells can damage the kidneys, leading to more water and sodium retention, further contributing to elevated blood pressure. In addition, activation of chronic inflammation can cause end-organ damage and dysfunction, ultimately leading to hypertension related complications including coronary artery disease, heart failure, stroke, and chronic kidney disease [27].Sleep deprivation is the typical pathophysiological feature of OSA, which have been shown to affect the immune system by reducing neutrophil phagocytosis and NADPH oxidase activity, altering the balance of the associated chemokine Th1 and decreasing the levels of CD4+ T Lymphocytes thus affecting vascular endothelial function and the level of inflammation-associated lymphocytes, leaving the whole body in a state of hypo-inflammation, which may be an important factor in the development and progression of hypertension [28]. The three genes we screened have a strong correlation with immune cell content and may also be involved in the pathological course of chronic inflammation.It is also important to understand the molecular mechanisms of gene pathogenesis by exploring signaling pathways analyzing of the potential mechanisms by GSVA of three core genes affecting disease progression revealed that high expression of the GPR179 gene is associated with myogenic, angiogenic, oxidative stress, and metabolic pathways. In primary hypertension, small arterial smooth muscle cells reorganize around smaller lumens, and in some secondary hypertension, hypertrophic remodeling can be detected. Regardless of the mechanism that initiates blood pressure elevation, systemic vascular structural changes are the end result of hypertension [29]. Studies have shown that increased oxidative stress is an important mediator of endothelial injury in hypertension, which is associated with the production of prooxidants, such as superoxide peroxide, decreased synthesis of nitric oxide, and reduced bioavailability of antioxidants. Oxidative stress is associated with endothelial dysfunction, inflammation, hypertrophy, apoptosis, cell migration, fibrosis, and angiogenesis which may change vascular remodeling in hypertension [30]. High expression of JPH4 is associated with signaling pathway PI3K/AKT/mTOR. The phosphatidylinositol 3-kinases (PI3K)/serine/threonine kinase (AKt) signaling pathway is a classical transduction pathway involved in cellular activities, such as cell activation, growth, differentiation, survival, malignancy, and apoptosis, and regulates important biological processes, such as protein synthesis, energy metabolism, and angiogenesis [31]. Studies have shown that nesfatin-1 mediates the PI3K/AKt/mTOR signaling pathway, affecting phenotypic transformation and proliferation of VSMC (vascular smooth muscle cells, VSMC), and controls blood pressure changes [32]. High expression of RNF150 is associated with the MYC signaling pathway, a classical cancer cell pathway that regulates the development of cancer cells [33]. It has now been found that enhanced expression of c-Myc may be associated with the proliferation of smooth muscle cells in small arteries of spontaneous rats [34].


## 5. Conclusion

Based on a comprehensive bioinformatics analysis, the biological functional differences between OSA combined with hypertension and OSA alone were determined. The combined role of GPR179, RNF150, and JPH4 in the progression was explored. The results showed that all three core genes are associated with immune function and may be involved in the progression of the disease through signaling channels, such as myogenesis, angiogenesis, oxidative stress, PI3K/AKt/mTOR, and MYC, providing a new basis for the potential molecular mechanisms of OSA combined hypertensive as well as diagnosis and treatment.



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*Source: 1257400-2022-07-04.xml*