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Serum proteome profiling reveals HGFA as a candidate biomarker for pulmonary arterial hypertension

Respiratory Research volume 25, Article number: 418 (2024) Cite this article

Abstract

Background

Identification and validation of potential biomarkers could facilitate the identification of pulmonary arterial hypertension (PAH) and thus aid to study their roles in the disease process.

Methods

We used the isobaric tag for relative and absolute quantitation approaches to compare the protein profiles between the serum of PAH patients and the controls. Bioinformatics analyses and enzyme-linked immunosorbent assay (ELISA) identification of PAH patients and the controls were performed to identify the potential biomarkers. The receiver operating characteristic curve (ROC) analysis was used to evaluate the diagnostic performance of these potential biomarkers. Mendelian randomization (MR) analysis further clarified the relationship between the potential biomarkers and PAH. Additionally, the expression levels of the potential biomarkers were further validated in two PAH animal models (monocrotaline-PH and Sugen5416 plus hypoxia-PH) using ELISA and reverse transcription-quantitative PCR (RT-qPCR).

Results

We identified significant changes in three proteins including heparanase (HPSE), gelsolin (GSN), and hepatocyte growth factor activator (HGFA) in PAH patients. The ROC analysis showed that the areas under the curve of HPSE, GSN, and HGFA in differentiating PAH patients from controls were 0.769, 0.777, and 0.964, respectively. HGFA was correlated with multiple parameters of right ventricular functions in PAH patients. Besides proteomic analysis, we also used MR method to demonstrate the causal link between genetically reduced HGFA levels and an increased risk of PAH. In subsequent validation study in PAH animal models, the mRNA expression levels of HGFA in the lung tissues were significantly lower in PAH rat models than in controls. In the rat models, serum levels of HGFA were lower compared to the control group and showed a negative correlation with right ventricular systolic pressure.

Conclusion

The study demonstrated that HGFA might be a promising biomarker for noninvasive detection of PAH.

Introduction

Pulmonary arterial hypertension (PAH) is a life-threatening disease characterized by a sustained rise in mean pulmonary artery pressure (mPAP) due to increased pulmonary vascular resistance (PVR) [1]. Symptoms are always nonspecific and usually include dyspnea and fatigue on exertion. In addition, PAH is a disease characterized by the progressive occlusion of small pulmonary arteries and a lack of compensatory pulmonary angiogenesis [2], thus resulting in right ventricular hypertrophy, and vascular remodeling [3] with poor outcomes including right ventricular failure [4] and death [5].

Recent advances in therapeutic interventions have shown promise in extending the active life span of affected individuals. Despite these advances, the diagnosis of PAH remains challenging. Early symptoms are atypical and may mimic other cardiorespiratory ailments, often resulting in misdiagnosis or delayed diagnosis. Moreover, accurate diagnosis of PAH is contingent upon invasive cardiac catheterization. Overall, there is a critical need for an accurate biomarker for the early detection of PAH.

Recent developments in the field of proteomics have paved the way for the discovery of novel disease biomarkers. Specifically, the isobaric tags for relative and absolute quantitation (iTRAQ)-based proteomic analysis have emerged as a potent tool for biomarker identification in various diseases [6, 7]. However, the application of proteomics in the context of PAH is lacking, with limited evidence focusing on this field [8, 9]. This approach holds the potential to not only enhance our understanding of PAH pathophysiology but also to revolutionize its diagnostic landscape.

Our study used proteomics to explore the plasma proteome features of PAH patients. Bioinformatics analyses and enzyme-linked immunosorbent assay (ELISA) identification of PAH patients and the controls were performed to identify the potential biomarkers. Subsequent validation study was conducted in PAH animal models.

Materials and methods

Study cohort and ethics statement

PAH patients were retrospectively recruited from respiratory inpatients in the China–Japan Friendship Hospital and the healthy controls were recruited from physical examination center during the same time period. The population we included was divided into two parts. First, nine PAH patients were included in the iTRAQ study to discover differential proteins in 2020. Next, the other group of PAH patients (28 PAHs) was used for subsequent ELISA validation in 2023. According to 2022 ESC/ERS Guidelines for the diagnosis and treatment of pulmonary hypertension (PH) [10], subjects enrolled with PAH at the time of recruitment, as defined by the Group 1 classification, were required to have a right heart catheterization (RHC) demonstrating mPAP > 20 mmHg, pulmonary arterial wedge pressure (PAWP) ≤ 15 mmHg and PVR > 2 Wood units [10]. The study was approved by the Research Ethics Committee of China-Japan Friendship Hospital and written informed consent were obtained from all patients before enrollment.

Blood sample processing and proteomics profiling

Blood samples were collected at the time of initial diagnosis and centrifuged shortly after clot formation. All samples were stored at – 80 °C in aliquots and thawed only before the test. To identify specific proteins of PAH, equal volumes of serum samples from 9 patients with PAH and 9 control subjects were pooled for iTRAQ analysis. The ProteoMiner was used to remove the high abundance proteins from the samples. Peptide mixtures from each group were labeled using the iTRAQ Reagent-8 Plex Multiplex Kit (Applied Biosystem). The digested peptides were labeled with iTRAQ tags (reagent 114: PAH; reagent 118: Control). The labeled peptides of each group were separated by using a high-performance liquid chromatography (HPLC) system. Then liquid chromatography-mass spectrometry/mass spectrometry (LC–MS/MS) analysis was performed on Q–Exactive MS (Thermo Fisher Scientific, Waltham, MA, USA).

Database searching and bioinformatics analysis

The original data of iTRAQ assay were collected by mass spectrometry, and the search of the database was conducted using the Proteome Discoverer software (version 1.3). The primary quality deviation was 15 ppm, and the secondary quality deviation 20 mmu. The database was UniProt-Swiss human database (including 20,443 annotated proteins, released on 2019.07). Log2 transformation of the original quantitative value was carried out to make it into a normal distribution.

To identify differentially expressed proteins (DEPs) between the PAH and control groups, proteins with fold change ≥ 1.20 and P value < 0.05 by t test were defined as upregulated; proteins with fold change ≤ 0.83 and P value < 0.05 by t test were defined as downregulated. Further bioinformatics analysis was performed using R studio and other necessary websites. Volcano plots were performed by R studio with the R package “ggplot2”. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotation analyses were generated by R studio with the R packages “clusterProfiler”. The DEPs were submitted to STRING 11.5 database for protein–protein interaction (PPI) network.

Verification of differential expression by ELISA

The identification of candidate proteins for further validation was based on (1) differential expression in PAH patients and controls; and (2) potential functional or pathological significance in PAH. The candidate biomarkers heparanase (HPSE), gelsolin (GSN), hepatocyte growth factor activator (HGFA), serum amyloid A1 protein (SAA1) and extracellular matrix protein 1 (ECM1) were further validated by using commercially available sandwich ELISA kits (Abcam, Waltham, MA, USA or R&D Systems, Minneapolis, MN, USA) following the manufacturer’s instructions. The results were interpolated from the standard reference curve provided with each kit.

Proteome-wide Mendelian randomization (MR) analysis

MR was used to verify whether HGFA was associated with PAH [11]. First, we acquired the largest PAH Genome-Wide Association Study (GWAS) summary statistics. This PAH GWAS used data from four international case–control studies across 11,744 individuals with European ancestry [12]. We next obtained serum protein quantitative trait loci [13]. In the MR analysis, protein-relevant single nucleotide polymorphisms (SNPs) were used as instrumental variables (IVs) to test the causal effect of the exposure (HGFA expression) on the outcome (PAH). For MR analysis, the inverse variance weighted (IVW) method [14] was used as the main MR analysis. A P value < 0.05 was set as the significance level.

Validation of gene expression in dataset GSE113439

For in-depth validation of the accuracy of key biomarkers, GSE113439 [15] was used to compare gene expression between the PAH and control groups. GSE113439 is a public dataset derived from the GEO database. GSE113439 is based on the GPL6244 (Affymetrix Human Gene 1.0 ST Array) platform and contained 6 idiopathic PAH patients and 11 controls (lung tissues) for transcriptomic information. We compared the gene expression of key biomarkers between PAH and controls. The receiver operating characteristic (ROC) curve and the area under the curve were utilized to assess the diagnostic value of the identified protein for PAH.

PAH animal model construction

Adult male Sprague–Dawley (SD) rats (6 weeks old, 150–170 g) were purchased from Beijing Vital River Laboratory Animal Technology Co., Ltd. Experimental protocols were approved by the China-Japan Friendship Hospital Laboratory Animal Ethics Committee (IACUC Issue No.: ZRDWLL240016). In this study, the SD male rats were initially randomized into two groups: the monocrotaline (MCT)-PH group was subjected to a single subcutaneous injection in the back of the neck with MCT (C-2401, Sigma, 60 mg/kg). The control group received an equivalent amount of saline. After 21 days, the MCT-induced rat PAH model was established [16]. In the Sugen5416 plus hypoxia (SuHx)-PH group, the SD male rats were initially randomized into two groups: the SuHx-PH group was given a single subcutaneous injection in the back of the neck with vascular endothelial growth factor receptor 1/2 (VEGFR1/2) receptor antagonist Sugen5416 (APExBIO, 20 mg/kg). Immediately after the injection, animals were exposed to normobaric hypoxia (10% O2) in the ventilated hypoxia chamber for three weeks, followed by re-exposure to normoxia (21% O2) for another three weeks [17, 18]. The control group received an equivalent amount of saline followed by 6 weeks of normoxia (21% O2). Animals were housed at 24 °C in a 12-h light/12-h dark cycle where food and water were freely available.

Invasive hemodynamic measurements and specimen storage of PAH rat models

Invasive hemodynamic methods to measure right ventricular systolic pressure (RVSP) was performed in a blinded manner to all the rats. Rats were anesthetized using isoflurane (2.5% induction, 0.5–2.5% maintenance). RVSP was measured via a curved tip 2 French pressure transducer catheter (Millar, SPR-513) inserted into the right ventricle through the right internal jugular vein [19]. Subsequent to RVSP assessment, rats were executed by bloodletting, and blood was withdrawn from the abdominal aorta and collected in blood collection tubes. The serum was then centrifuged to separate the serum and rapidly frozen. Whole lungs were perfused with saline via the right ventricle to clear blood cells from the pulmonary circulation. The right lower lobe of the lung was then excised and rapidly frozen.

Measurement of H gfa expression by reverse transcription-quantitative PCR (RT-qPCR) in PAH rat models

Total RNA was extracted from isolated lung tissues using TRIzol reagent, and then reverse transcribed into cDNA using the PrimeScript™ RT reagent kit (TaKaRa, Japan) according to the manufacturer’s instructions. RT-qPCR was conducted using SsoAdvanced™ Universal SYBR Green Supermix (Bio-Rad, USA) and a CFX96™ real-time PCR detection system (Bio-Rad Laboratories, USA). The 2−ΔΔCt method was used to analyze the relative expression levels. Each group with n = 6. The primer sequences were as follows: HGFA forward, GAACCCAGACAAGGACGAGAG and reverse, AGACGATGAGCCACCAATGA; and β-actin forward, TGTCACCAACTGGGACGATA and reverse, ACCCTCATAGATGGGCACAG.

Measurement of HGFA expression by ELISA in PAH rat models

To ascertain the reproducibility and consistency of our findings, serum levels of HGFA were quantified using a commercial ELISA kit (Rat HGFAC/HGFA ELISA Kit, LSBio, cat. LS-F25540) in both the MCT-PH (n = 10) and control groups (n = 10), as well as the SuHx-PH (n = 10) and their respective controls (n = 10), following the manufacturer's instructions.

Statistical analysis

The Kolmogorov–Smirnov test was used to test the normal distribution of the variables. In the case of a normal distribution, continuous data are expressed as mean ± standard deviation, while skewed data are expressed as the median and corresponding inter-quartile range. We compared continuous variables based on the results of the normality distribution using the Student’s t test for two independent samples or the nonparametric test. Categorical data were expressed as numbers and percentages and were compared using the Chi-Square test. Since the three proteomic indicators for which we performed correlation analysis were skewed, we evaluated the correlation between continuous variables using Spearman's correlation coefficient. The ROC curve and the area under the curve were utilized to assess the diagnostic value of the identified protein for PAH. All analyses were performed with the SPSS (version 22.0) and R Project (version 4.1.1).

Results

Clinical characteristics of the patients enrolled in this study

The clinical characteristics of PAH patients and controls are summarized in Table 1. In the iTRAQ assay, 9 PAH patients and 9 age- and sex-matched controls were involved. Among the 9 PAH patients, 6 were female. The mean age was 39.3 ± 16.9 years. Among the included patients, 1 patient was at low risk, 6 at intermediate risk, and 2 at high risk. The median mPAP was 54.0 (42.0, 90.5) mmHg, and the mean PVR was 1324.4 ± 721.2 dyn•s•cm−5. 2 patients received calcium channel blockers (CCBs) treatment, 2 patients received initial monotherapy, 4 patients received initial combination therapy and 1 patient who was diagnosed with pulmonary veno-occlusive disease (PVOD) did not receive any specific-PAH therapy (Table 1).

Table 1 Clinical characteristics of the patients enrolled in this study
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In the subsequent ELISA verification, 28 PAH patients and 28 age- and sex-matched controls were included in the ELISA assay. 22 patients were female. The median age was 39.0 (29.3, 60.5) years. Most patients (19, 67.9%) were at intermediate risk. 5 (17.9%) patients were at low risk and 4 (14.3%) at high risk. The mean mPAP and PVR were 47.2 ± 15.4 mmHg and 746.4 ± 435.1 dyn•s•cm−5, respectively. Among the included patients, 2 patients (7.1%) received initial CCBs, 10 (35.7%) received initial monotherapy, 15 (53.6%) patients received initial combination therapy and 1 (3.6%) patient who was diagnosed with PVOD did not receive any specific-PAH therapy (Table 1).

Schematic workflow of screening PAH proteins

We collected 9 serum samples from each group and conducted proteomics (Fig. 1A). A total of 362 proteins were identified in proteomic analysis, and 256 of them contained quantitative information (Fig. 1B). 85 serum proteins showed differential expression, with 41 proteins upregulated and 44 downregulated in PAH patients compared to healthy controls (Fig. 1C, D).

Fig. 1
figure 1

Proteomics analysis revealed differential protein expression in the serum of PAH patients. A Flow chart. B Total number of identified (green bar) and quantified (red bar) proteins in the iTRAQ experiment. C Volcano plots for the expression of differentially expressed proteins. D A total of 41 upregulated proteins and 44 downregulated proteins were identified

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Functional enrichment analyses of DEPs

To exploit the potential functions of DEPs, we analyzed GO function and KEGG pathway enrichment. In GO Biological Process (BP) enrichment analysis, DEPs were centered on “platelet degranulation”, “humoral immune response”, “coagulation”, “hemostasis”, “protein activation cascade”, and “fibrin clot formation” (Fig. 2A). As for the Cellular Component (CC) category, the core DEPs were significantly enriched related to “blood microparticle”, “secretory granule lumen”, “cytoplasmic vesicle lumen”, “vesicle lumen”, “collagen-containing extracellular matrix”, and “platelet alpha granule” (Fig. 2B). In addition, DEPs were enriched in the Molecular Function (MF) category focused on “glycosaminoglycan binding”, “serine-type endopeptidase activity”, “heparin binding”, “serine-type peptidase activity”, “serine hydrolase activity”, and “sulfur compound binding” (Fig. 2C).

Fig. 2
figure 2

Functional enrichment analyses of differentially expressed proteins. A The top 10 enrichment GO Biological Process (BP) pathways ranked by enrichment score. B The top 10 enrichment GO Cellular Component (CC) pathways ranked by enrichment score. C The top 10 enrichment GO Molecular Function (MF) pathways ranked by enrichment score. D The top 9 enrichment KEGG pathways ranked by enrichment score

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Moreover, we were able to obtain a global perspective of the changes in protein expression patterns. The top enriched KEGG pathways of DEPs were involved in complement and coagulation cascades, platelet activation, neutrophil extracellular trap formation, hypertrophic cardiomyopathy, and dilated cardiomyopathy (Fig. 2D).

PPI network analysis of DEPs

To identify hub proteins that may serve as biomarkers or therapeutic targets for PAH, a protein–protein co-expression network was constructed for the DEPs. The PPI network contained a total of 80 nodes and 1196 edges (Fig. 3). Subsequently, the co-expression network was further analyzed to detect potential critical modules, and three significant modules (HPSE, HGFA, and GSN) were determined. HPSE interacted with SERPINF2. HGFA interacted with CPB2, FGB, FGG, F2, PROC, SERPINA5, KRT1, SPP1 and HABP2. GSN interacted with ALB, A2M, ACTN1, A1BG, ACTB, APCS, CP, HP, HPX, TPM3, TPM4, TLN1, TF, FN1, LTF, LYZ, GIG25, and SAA1. Four proteins (FGA, GC, APOA4, TTR) were noteworthy and interacted with GSN and HGFA at the same time.

Fig. 3
figure 3

PPI network for differentially expressed proteins and key module analysis

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Verification of significantly dysregulated proteins between the PAH and reference groups

According to the results of PPI analysis, potential functional and pathological significance, we selected some candidate proteins for in-depth research, including HPSE, GSN, HGFA, SAA1, and ECM1. To validate the expression level of these candidate proteins, serum derived from another 28 PAH patients and 28 healthy individuals were determined by ELISA. The ELISA results showed that the levels of GSN [PAH: 10,771.8 (4950.5, 14,198.0) vs. control: 14,482.5 (13,283.7, 15,494.1) ng/mL, P < 0.001] and HGFA [PAH: 8652.8 (6941.9, 10,931.5) vs. control: 21,850.9 (16,660.7, 26,080.6) ng/mL, P < 0.0001] were lower in PAH patients compared to normal controls according to ELISA, whereas HPSE was higher [PAH: 81.5 (52.9, 148.5) vs. control: 24.5 (12.9, 55.1) ng/mL, P < 0.001] (Fig. 4A–C). No significant differences were observed for SAA1 [PAH: 157.7 (85.9, 188.3) vs. control: 133.3 (88.4, 150.0) ng/mL, P > 0.05] and ECM1 [PAH: 9450.1 (9158.6, 10,236.9) vs. control: 8997.2 (8547.5, 9329.1) ng/mL, P > 0.05] (Fig. 4D, E).

Fig. 4
figure 4

Verification of differentially expressed proteins by ELISA in validation cohort. A GSN, B HFGA, C HPSE, D SAA1, and E ECM1 in pulmonary arterial hypertension patients and healthy controls. F Receiver operating characteristic (ROC) results of different proteins between the PAHs and healthy controls. As the result of significance test, *P value < 0.05; **P value < 0.01; ***P value < 0.001; ****P value < 0.0001; ns P value > 0.05

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Diagnostic performance of candidate serum biomarkers

Given the observed differences of serum HPSE, GSN, and HGFA concentrations between PAH patients and healthy controls, we intended to test the diagnosis performance of candidate serum biomarkers to help diagnose PAH. In Fig. 4F, the AUC of HGFA was 0.964 and the AUC of HPSE and GSN proteins were in the range of 0.7–0.8 in the PAH diagnosis. The sensitivity and the specificity of HPSE were 75.0% and 75.0%, respectively, at the cutoff value of 54.5 ng/mL; the sensitivity and the specificity of GSN were 67.9% and 78.6%, respectively, at the cutoff value of 13,145.1 ng/mL; the sensitivity and the specificity of HGFA were 89.3% and 89.3%, respectively, at the cutoff value of 14,675.0 ng/mL.

Correlation analysis between clinical data and candidate biomarkers

Spearman’s correlation test was employed to investigate the correlation of serum HPSE, GSN, and HGFA levels with a cluster of clinical parameters including hemodynamic parameters, 6-min walking distance (6MWD), echocardiographic parameters, WHO functional class (WHO-FC), and laboratory tests. In Fig. 5, serum HPSE concentrations were inversely correlated with the tricuspid annular plane systolic excursion (TAPSE). Furthermore, serum HPSE concentrations were positively correlated with pericardial effusion (PE) and main pulmonary artery internal diameter (MPA). Serum GSN concentrations were negatively correlated with 6MWD. HGFA was negatively correlated with right atrial transverse diameter (RA-t), WHO-FC, MPA, and N-terminal pro-brain natriuretic peptide (NT-proBNP). Serum HGFA concentrations were also significantly lower in patients with a high risk-stratification of the 2018 world symposium on PH (WSPH) [4].

Fig. 5
figure 5

Correlation network of three biomarkers and clinical indicators in pulmonary arterial hypertension patients. Correlations are indicated in red for positive correlations and in blue for negative correlations. As the result of significance test, *P value < 0.05

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MR verified the causal relationship of HGFA with PAH using plasma pQTL

We identified 34 independent SNPs as instrumental variables that exhibited significant associations with HGFA levels. As HGFA levels were genetically reduced, the risk of PAH increased using IVW (OR 0.70, 95% CI 0.55–0.89; P value = 0.003). These findings were consistent with the result of MR Egger analyses. Collectively, our data suggested a causal association of genetically reduced HGFA levels with increased risk of PAH (Fig. 6).

Fig. 6
figure 6

Two-sample Mendelian randomization reveals causal evidence for HGFA on pulmonary arterial hypertension. A The forest plots illustrate the standardized beta (95% confidence interval) for each two-sample Mendelian randomization method; B Scatter plot to visualize the causal effect of HGFA on the risk of pulmonary arterial hypertension. The slope of the straight line indicates the magnitude of the causal association. IVW inverse-variance weighted

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Validation of HGFA expression in PAH in GSE113439

HGFA expression in PAH tissues was verified. The results demonstrated that HGFA expression was downregulated in PAH compared to the controls in the GSE113439 datasets (P < 0.0001; Fig. 7A). Next, to assess the potential predictive value of key gene, ROC curves were generated. The AUC (0.903) suggested that HGFA had a high accuracy and good predictive value for PAH (Fig. 7B).

Fig. 7
figure 7

Validation of HGFA expression. A Gene expression of HGFA in GSE113439 datasets; B receiver operating characteristic (ROC) results of HGFA between the PAHs and healthy controls in GSE113439 datasets; C Hgfa mRNA expression via RT-qPCR in lung tissues from SuHx-PH rat models or control; D Hgfa mRNA expression via RT-qPCR in lung tissues from MCT-PH rat models or control; E HGFA expression via ELISA in serum from SuHx-PH rat models or control; F HGFA expression via ELISA in serum from MCT-PH rat models or control; G relationship between HGFA expression and RVSP in SuHx-PH rat models or control; H relationship between HGFA expression and RVSP in MCT-PH rat models or control. **P value < 0.01. ***P value < 0.001. ****P value < 0.0001

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The expression of HGFA in the lung tissues and serum from different PAH rat models

To investigate the role of HGFA in PAH, we determined HGFA expression levels in the lung tissues and serum from MCT-PH and SuHx-PH rat models using RT-qPCR and ELISA, respectively. The mRNA expression levels of Hgfa were significantly lower in both PAH rat models than in the control group (Fig. 7C, D). Additionally, HGFA was found to be downregulated in the serum of PAH rat models relative to controls (Fig. 7E, F). Serum HGFA concentrations negatively correlated with RVSP (Fig. 7G, H).

Discussion

The present study profiled the serum proteome of PAH patients and identified 41 upregulated proteins and 44 downregulated proteins. Subsequently, we identified three candidate biomarkers according to the results of PPI analysis and the clinical relevance, including 1 upregulated protein (HPSE) and 2 downregulated proteins (GSN and HGFA), which were accurate in PAH early identification from healthy controls. Furthermore, serum HGFA concentration was correlated with WHO-FC and NT-proBNP. Besides proteomic analysis, we also used MR study to demonstrate the causal link between genetically reduced HGFA levels and increased risk of PAH.

Our research showed that the enriched KEGG pathways of DEPs such as platelet activation, neutrophil extracellular trap (NET) formation, hypertrophic cardiomyopathy, and dilated cardiomyopathy might be associated with the progression of PAH. PAH is a well-known and documented thrombogenic disease. Previous studies observed that platelet functional impairment was attributed to chronic activation and degranulation, increased thrombopoietin expression in the pulmonary artery, and elevated the mean platelet volume [20,21,22]. The findings suggested that platelet activation, aggregation, and consumption in the pulmonary circulation might be increased in patients with PAH, which was similar to the changes in MCT-PH models [23]. NETs have an important role in thrombotic pathogenesis of cardiovascular diseases [24,25,26]. It has been reported that markers of NET formation are increased in plasma and lung tissues from PAH patients. However, not much is yet known about its role in the pathophysiology of PAH [27]. NETs may have a key role in PAH through activating the platelets and endothelial cells and promoting the proliferation of pulmonary artery smooth muscle cells (PASMCs) [26, 28]. NET formation may be induced by endothelial signaling and/or cell–cell interactions between platelets and primed neutrophils, creating a positive feedback loop [26]. Additionally, a recent study indicated that NETs could promote the cytoskeletal remodeling, phenotypic transformation and proliferation of PASMCs mediated by CCDC25 [28]. The pathways linked to hypertrophic and dilated cardiomyopathy in PAH patients point to cardiac remodeling as a significant aspect of the disease. Hypertrophic cardiomyopathy, characterized by thickened heart muscle, can lead to heart failure, a severe PAH complication. Dilated cardiomyopathy, which involves heart muscle dilation and weakening, also contributes to heart failure in PAH [29]. These associations underscore the importance of cardiac involvement in PAH and emphasize the need for therapeutic approaches that address these diverse yet interconnected pathways to manage PAH effectively. In short, our KEGG analysis of DEPs could provide deep insights into the pathophysiology of PAH, which provides potential targets for intervention.

The level of HPSE was elevated in PAH patients, whereas serum GSN and HGFA levels decreased. Besides proteomic analysis, we also used an MR study to demonstrate the causal link between genetically reduced HGFA levels and the increased risk of PAH. Thus, we focused on the role of HGFA in PAH. The involvement of HGFA in PAH is rooted in its role in activating HGF. ProHGF is the inactive precursor form of HGF that is cleaved into its active form (HGF) by specific enzymes, such as HGFA and cellular type II transmembrane serine proteinases [21]. In PAH, the potential protective role of HGF has been reported in several studies. HGF enhanced endothelial cell repair and regeneration, potentially countering the endothelial dysfunction commonly seen in PAH [30]. Additionally, HGF has anti-inflammatory and anti-fibrotic properties, which are crucial in combating the inflammatory and fibrotic components of PAH pathology [31]. Several animal pulmonary hypertension (PH) models (e.g. rats with MCT-induced PH, rabbits with shunt flow-induced PH) have shown that exogenous HGF or Hgf gene transfection reduced the development of PH, which was associated with less marked right heart hypertrophy and lower inflammatory profiles than shams [32,33,34]. High plasma levels of HGF have been detected in PAH patients, which were associated with right heart maladaptive phenotype in PAH and predictive of 3-year clinical worsening [35, 36]. However, the serum HGFA levels of PAH have never been reported in previous studies. Our data, for the first time, showed that serum HGFA concentrations were lower in PAH patients than in control subjects. The difference between the levels of HGF and HGFA in circulation may be related to their expression in different tissues. In MCT-PH models, mRNA levels of Hgf were increased in the right ventricle and decreased in lungs and liver, whlie expression of Hgfa was significantly suppressed in lung tissue, and no change was observed in right ventricle/left ventricle (RV/LV) and liver tissues [37]. This may be the reason for the decrease of circulating HGFA levels. However, the timely and direct effect of HGFA in PAH remains to be elucidated. HGFA, through its activation of HGF, may be essential in modulating the complex pathophysiological processes of PAH, making it a potential target for therapeutic intervention.

To further investigate the role of HGFA in PAH, we evaluated its clinical value including its diagnostic performance. HGFA showed good performance for the PAH diagnosis with 89.3% specificity and 89.3% sensitivity. To further demonstrate the role of HGFA in clinical practice, we analyzed the relationship between HGFA and a cluster of clinical parameters. We discovered that serum HGFA concentrations were further decreased in high risk-stratification PAH patients, suggesting HGFA may potentially be an indicator to evaluate disease severity. Furthermore, serum HGFA concentrations were also negatively correlated with RA-t, WHO-FC, and NT-proBNP. WHO-FC and NT-proBNP are both the important markers of right heart failure and disease progression in PAH [38, 39], suggesting the potential role of HGFA in the right heart maladaptation. The diagnostic accuracy of HGFA and its correlation with right heart function parameters and risk-stratification confirmed that HGFA has the potential to be a stable and independent serum biomarker for PAH diagnosis, and may be a potential target for exploring the pathogenesis of PAH.

Our study had certain limitations. First, the sample size of serum specimens was limited, and further validation of the results will be necessary in a larger population. Second, some PAH patients received the medication, we cannot completely rule out the effects of the drugs. Third, the results have not been validated at the cellular level, which is currently an ongoing study. Fourth, due to the limited observation time, our study lacked of the prognosis and survival data. Lastly, we used the datasets of GSE113439, and the sample is Caucasian people. Further validation of our findings is needed in lung tissue samples from PAH patients in Asian populations.

Conclusions

In conclusion, we comprehensively profiled the serum proteomic feature of PAH. Significantly altered proteins and biological processes were found. More importantly, our findings may have identified HGFA as an early diagnosis biomarker for PAH. Further experimental validation is warranted to confirm the role of HGFA in future studies.

Availability of data and materials

The raw data of proteomics and quantitative analysis in the current study are available from the corresponding author upon reasonable request. No datasets were generated or analysed during the current study.

Abbreviations

DEPs:

Differentially expressed proteins

ECM1:

Extracellular matrix protein 1

GO:

Gene Ontology

GSN:

Gelsolin

HGFA:

Hepatocyte growth factor activator

HPSE:

Heparanase

iTRAQ:

Isobaric tags for relative and absolute quantitation

IVW:

Inverse variance weighted

KEGG:

Kyoto Encyclopedia of Genes and Genomes

6MWD:

6-Min walking distance

LC–MS/MS:

Liquid chromatography–mass spectrometry/mass spectrometry

MPA:

Main pulmonary artery internal diameter

mPAP:

Mean pulmonary artery pressure

MR:

Mendelian randomization

NT-proBNP:

N-terminal pro-brain natriuretic peptide

PAH:

Pulmonary arterial hypertension

PAWP:

Pulmonary arterial wedge pressure

PE:

Pericardial effusion

PPI:

Protein–protein interaction

PVR:

Pulmonary vascular resistance

RA-t:

Right atrial transverse diameter

RHC:

Right heart catheterization

ROC:

Receiver operating characteristic

RVSP:

Right ventricular systolic pressure

SAA1:

Serum amyloid A1 protein

TAPSE:

Tricuspid annular plane systolic excursion

WHO-FC:

WHO functional class

WSPH:

World symposium on pulmonary hypertension

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Funding

This study was supported by the Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences (2021-I2M-1-061, 2021-I2M-1-049), National High Level Hospital Clinical Research Funding, Elite Medical Professionals Project of China-Japan Friendship Hospital (ZRJY2023-QM31), Institute of Respiratory Medicine, Chinese Academy of Medical Sciences Foundation for Young Scholars (2023-ZF-22), and National High Level Hospital Clinical Research Funding (2022-NHLHCRF-LX-01-0203).

Author information

Author notes

  1. Meng Zhang, Haobo Li, Shuangshuang Ma and Xincheng Li contributed equally.

Authors and Affiliations

  1. Department of Pulmonary and Critical Care Medicine, Beijing Anzhen Hospital, Capital Medical University, Beijing, China

    Meng Zhang & Jun Wan

  2. National Center for Respiratory Medicine; State Key Laboratory of Respiratory Health and Multimorbidity; National Clinical Research Center for Respiratory Diseases; Institute of Respiratory Medicine, Chinese Academy of Medical Sciences; Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China-Japan Friendship Hospital, Beijing, China

    Haobo Li, Shuangshuang Ma, Xincheng Li, Linfeng Xi, Yishan Li, Zhu Zhang, Shuai Zhang, Qian Gao, Qiang Huang, Wanmu Xie, Yunxia Zhang & Zhenguo Zhai

  3. China-Japan Friendship Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China

    Haobo Li

  4. Beijing University of Chinese Medicine, Beijing, China

    Shuangshuang Ma

  5. Harbin Medical University, Harbin, China

    Xincheng Li

  6. China-Japan Friendship Hospital, Capital Medical University, Beijing, China

    Linfeng Xi

  7. The First Clinical Medical College, Shanxi Medical University, Taiyuan, China

    Yishan Li

  8. Department of Respiratory and Critical Care Medicine, Beijing Chao-Yang Hospital, Capital Medical University; Beijing Key Laboratory of Respiratory and Pulmonary Circulation Disorders, Beijing Chao-Yang Hospital, Capital Medical University; Beijing Institute of Respiratory Medicine, Beijing Chao-Yang Hospital, Capital Medical University; Department of Respiratory Disease, Capital Medical University, Beijing, China

    Jifeng Li

  9. State Key Laboratory of Respiratory Health and Multimorbidity, Department of Physiology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and School of Basic Medicine, Peking Union Medical College; National Center for Respiratory Medicine; Institute of Respiratory Medicine, Chinese Academy of Medical Sciences; National Clinical Research Center for Respiratory Diseases, Beijing, China

    Peiran Yang

Authors
  1. Meng Zhang
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  2. Haobo Li
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  7. Zhu Zhang
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  11. Jun Wan
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  15. Yunxia Zhang
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  16. Zhenguo Zhai
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Contributions

Study concept and design: Yunxia Zhang and Zhenguo Zhai. Data acquisition: Yunxia Zhang, Shuangshuang Ma, Jifeng Li, and Yishan Li. Drafting of the manuscript: Meng Zhang, Haobo Li, Yunxia Zhang, and Linfeng Xi. Statistical analysis: Yunxia Zhang, Haobo Li, and Xincheng Li. Technical support and contributed to the discussion: Zhu Zhang, Shuai Zhang, Qian Gao, Qiang Huang, Jun Wan, Wanmu Xie, and Peiran Yang. All authors provided final approval to the version submitted for publication.

Corresponding authors

Correspondence to Yunxia Zhang or Zhenguo Zhai.

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Ethics approval and consent to participate

The study's ethical approval (patients and animals) was obtained from the Ethics Committee in China-Japan Friendship Hospital. All the participants provided informed consent and agreed to participate in this research.

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The authors declare no competing interests.

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Zhang, M., Li, H., Ma, S. et al. Serum proteome profiling reveals HGFA as a candidate biomarker for pulmonary arterial hypertension. Respir Res 25, 418 (2024). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12931-024-03036-1

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  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12931-024-03036-1

Keywords

Respiratory Research

ISSN: 1465-993X

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