Introduction: Chronic rhinosinusitis with nasal polyps (CRSwNP) is a serious inflammatory condition. Nasal fluids (NFs) present a noninvasive alternative to nasal biopsy for studying CRSwNP pathogenesis. We aimed to compare the protein and mRNA inflammation signature between nasal polyps (NPs) and NFs. Method: The performance of polyvinyl alcohol (PVA) sponges and NFs absorbable device (NFAD) for collecting NFs from 20 patients with CRSwNP was compared using the Luminex assay. The other group consisted of four healthy controls and an additional 21 CRSwNP patients (including eosinophilic CRSwNP [ECRSwNP] and non-eosinophilic CRSwNP [NECRSwNP]) for protein quantification by Olink platform and gene expression evaluation by RNA-sequencing. Spearman’s analysis was performed to detect correlations between protein expression levels in NFs and clinical assessment variables. Results: NFAD-collected NFs contained at least a 2-fold higher concentration of cytokines than that obtained using PVA sponge, and these cytokines levels are significantly associated with NPs (ρ > 0.45, p < 0.05). Differentially expressed proteins between NFs and NPs were significantly correlated in the ECRSwNP subgroup compared with controls (ρ = 0.41, p < 0.01). Levels of Th2/IL-13, MCP4, and CCL4, characteristic of eosinophilic infiltration, were increased in ECRSwNP patients. A significant correlation between gene and protein expression was observed (ρ = 0.34, p < 0.01). PDL2 levels in NFs were positively correlated with ECRSwNP postoperative recurrence, the nasal VAS, and SNOT-22 scores (ρ > 0.68, p < 0.05 for all). Conclusion: Our study revealed similarities and discrepancies in inflammatory signatures between NPs and NFs in the same CRSwNP patient.

Chronic rhinosinusitis with nasal polyps (CRSwNP) is a heterogeneous condition which encompasses various endotypes. The pathogenic mechanism of CRSwNP has not been fully elucidated in terms of biomarkers and observable characteristics (i.e., phenotypes) [1, 2]. In Western countries, 67–88% of patients with CRSwNP exhibit mucosal eosinophilic infiltration [3, 4], while this figure is much lower (21.7–59.6%) in East Asian countries, such as China, South Korea, and Japan. However, the incidence of mucosal eosinophilia in the East Asian population is on the rise [4, 5]. This subtype of CRSwNP, commonly referred to as eosinophilic CRSwNP (ECRSwNP), is characterized by nasal polyps (NPs) with strong eosinophilic infiltration and overproduction of multiple pro-inflammatory type 2 T-helper cell (Th2)-related cytokines. ECRSwNP is particularly challenging to manage as it displays rapid postoperative recurrence and is resistant to conventional treatment strategies [6]. Currently, the disease is managed using steroids, which are considered the most effective treatment for achieving remission; however, their long-term use is costly and is associated with serious side effects [7]. Thus, profiling inflammatory patterns in the nasal mucosal is crucial for understanding ECRSwNP pathogenesis and developing novel treatment strategies.

Microarray analyses investigating the immune endotypes [8, 9] and pathogenesis [10] of CRSwNP typically rely on invasive nasal biopsies. By contrast, nasal lavage and sponge adsorption are noninvasive and cost-effective means of collecting nasal fluids (NFs) [11, 12]. However, the primary limitation of NFs samples is the overdilution of the fluids, causing the cytokines values to drop below the lower limit of detection. Additionally, previous proteomics studies of NFs have predominantly employed mass spectrometry (MS), which may not offer the required levels of sensitivity in this setting. For instance, Yoshikawa et al. reported that MS was unable to detect the cytokines and growth factors, which are typically highly elevated in CRSwNP with NPs involvement but which often go undetected due to the small size and low abundance of these molecules [13, 14].

The aim of this study was therefore to compare the protein expression profiles of NFs obtained by modified and conventional adsorption methods (NFs absorbable device [NFAD] and polyvinyl alcohol [PVA] sponge, respectively) with those of matched nasal biopsies using the high-throughput Olink platform, which is capable of detecting 92 markers. The technique uses a multiplex immunoassay to perform relative quantitative proteomics and requires only 1 μL of undiluted raw NFs to determine the proteomic inflammatory signatures of CRSwNP patients. In addition, because protein and mRNA transcript levels may differ due to posttranslational processing or posttranscriptional regulation, we both explored the relationship between mRNA and protein expression in CRSwNP.

Patients and Tissue Sample Collection

The study protocols were approved by the Ethics Committee of the First Affiliated Hospital of Sun Yat-sen University (No. 2023-211). The diagnosis of CRSwNP was based on history taking, physical examination, nasal endoscopy, and sinus computed tomography by reference to the EPOS 2020 criteria [15]. Patients meeting the following criteria were excluded from the study: (1) age below 14 years and (2) presence of unilateral NPs, antrochoanal polyps, allergic fungal sinusitis, cystic fibrosis, or immotile ciliary disease. The demographic characteristics of the participants are summarized in Table 1. Healthy controls were collected from patients with a deviated nasal septum without sinonasal disease. The performance of PVA sponge and NFAD sampling methods in collecting NFs was compared in 20 CRSwNP patients. Another group consisting of 4 healthy controls and an additional 21 CRSwNP patients underwent protein quantification and gene expression assessment. NPs specimens obtained during endoscopic sinus surgery were promptly stored at −80°C in a refrigerator for subsequent analysis.

Table 1.

Demographic and disease characteristics of CRSwNP patients

ECRSwNP (n = 12)NECRSwNP (n = 9)p value
Preoperative features 
 Age, mean (range), years 43.6 (25.0–74.0) 34.3 (18.0–66.0) 0.21 
 Sex, male/female 7/5 5/4 1.00 
 Smoke, N (%) 1 (8.33) 4 (44.4) 0.11 
 Allergy, N (%) 7 (58.3) 1 (11.1) 0.06 
 Asthma, N (%) 3 (33.3) 0 (0.00) 0.10 
 Total nasal VAS, mean (±SD) 6.92 (1.51) 6.33 (2.69) 0.57 
 Total SNOT-22 score, mean (±SD) 48.3 (25.3) 26.6 (17.2) 0.03* 
 LK score, mean (±SD) 10.5 (2.94) 9.56 (4.69) 0.60 
 LM score, mean (±SD) 16.2 (3.76) 10.1 (5.09) <0.01** 
 Anosmia score, mean (±SD) 4.25 (0.45) 2.89 (0.33) <0.01** 
 History of surgical revision, N (%) 4 (33.3) 2 (22.2) 0.65 
Postoperative features 
 EOS counts, mean (±SD) 125 (88.1) 11.0 (9.58) <0.01** 
 Follow-up time, mean (±SD), days 230 (40.5) 206 (25.3) 0.14 
 Total nasal VAS, mean (±SD) 2.00 (1.53) 2.22 (2.91) 0.84 
 Total SNOT-22 score, mean (±SD) 18.0 (23.9) 20.3 (25.2) 0.85 
 LK score, mean (±SD) 6.00 (4.86) 3.43 (2.76) 0.25 
 Recurrence, N (%) 6 (60.0) 1 (11.1) 0.05 
ECRSwNP (n = 12)NECRSwNP (n = 9)p value
Preoperative features 
 Age, mean (range), years 43.6 (25.0–74.0) 34.3 (18.0–66.0) 0.21 
 Sex, male/female 7/5 5/4 1.00 
 Smoke, N (%) 1 (8.33) 4 (44.4) 0.11 
 Allergy, N (%) 7 (58.3) 1 (11.1) 0.06 
 Asthma, N (%) 3 (33.3) 0 (0.00) 0.10 
 Total nasal VAS, mean (±SD) 6.92 (1.51) 6.33 (2.69) 0.57 
 Total SNOT-22 score, mean (±SD) 48.3 (25.3) 26.6 (17.2) 0.03* 
 LK score, mean (±SD) 10.5 (2.94) 9.56 (4.69) 0.60 
 LM score, mean (±SD) 16.2 (3.76) 10.1 (5.09) <0.01** 
 Anosmia score, mean (±SD) 4.25 (0.45) 2.89 (0.33) <0.01** 
 History of surgical revision, N (%) 4 (33.3) 2 (22.2) 0.65 
Postoperative features 
 EOS counts, mean (±SD) 125 (88.1) 11.0 (9.58) <0.01** 
 Follow-up time, mean (±SD), days 230 (40.5) 206 (25.3) 0.14 
 Total nasal VAS, mean (±SD) 2.00 (1.53) 2.22 (2.91) 0.84 
 Total SNOT-22 score, mean (±SD) 18.0 (23.9) 20.3 (25.2) 0.85 
 LK score, mean (±SD) 6.00 (4.86) 3.43 (2.76) 0.25 
 Recurrence, N (%) 6 (60.0) 1 (11.1) 0.05 

VAS, visual analog scale; SNOT-22, Sino-Nasal Outcome Test-22; LK score, Lund-Kennedy score; LM score, Lund-Mackay score; EOS, eosinophils; SD, standard deviation.

*p < 0.05.

**p < 0.01.

NFs Collection

In this study, we compared two methods for collecting NFs within the same group: the modified NFAD and the traditional PVA sponge, as described in previous [16‒18]. Each CRSwNP patient from whom NFs were collected underwent endoscopic nasal polypectomy. NPs tissue and NFs in the subsequent analysis were all from the same individual.

NFAD: The NFAD is made of polyester fiber (Xinweike Anti-STATIC Technology, Guangdong, China). A CO2 laser cutter (Universal Laser Systems) was then used to make 10 × 20 mm rectangular-shaped thin stripes. We utilized the NFADs by placing them between the inferior turbinate and nasal septum on both sides, securing them in place with a plastic clip for 4 min to collect NFs. Subsequently, the NFADs were placed back in filter centrifuge tubes for centrifugation (16,000 g, 4°C, 10 min).

PVA sponge: The 9 × 24 mm PVA sponge (YINGJIA Medical Materials Co., Beijing, PVF-JB45) was halved along its long axis and placed between the inferior turbinate and nasal septum on both sides for 4 min. Subsequently, the sponges were removed using forceps and transferred to two 15 mL tubes, each containing 3 mL of saline. After soaking at 4°C for 2 h, the contents of the tubes were transferred into a 5 mL syringe, and the fluids were recovered from the sponges using the plunger, followed by centrifugation at 1,500 g at 4°C for 10 min. Supernatant collected by both methods were divided into aliquots and then stored at −80°C until further analysis.

Immunohistochemical Assessment of Eosinophil Inflammation

The NPs specimens were fixed and processed for paraffin embedding. Standard 5 µm sections were prepared and stained for eosinophils (Anti-Ribonuclease 3/eosinophil cationic protein antibody, Abcam, Ab207429). The presence of ECRSwNP was determined by counting the number of eosinophils per high-power field (HPF). The histological definition of ECRSwNP was based on the presence of ≥55 eosinophils per 400 × high-power field, as previously described [19]. Typical histological findings of eosinophilic and noneosinophilic polyps are shown in online supplementary Figure 1 (for all online suppl. material, see https://doi.org/10.1159/000534226).

Nasal Tissues and NFs Protein Measurement

An array of growth factors, cytokines, and chemokines was selected for analysis based on previous evidence suggesting their involvement in CRS, olfactory dysfunction, or inflammation/remodeling. To comprehensively evaluate these indicators as possible, we chose to measure protein levels using the Olink Immuno-oncology panel. This panel employs the Proximity Extension Assay (PEA) technology from Olink Proteomics AB (Uppsala, Sweden). The protein levels were determined following the manufacturer’s instructions and are presented in normalized protein expression units as NPX values [20]. Subsequent differential protein analysis was based on an NPX matrix representing relative quantitative protein levels. Some values including high proportions (>33%) below the lower limit of detection were excluded from these analyses (see Table 2).

Table 2.

Detection values of 92 proteins from the Olink immuno-oncology panel

Table 2.

Detection values of 92 proteins from the Olink immuno-oncology panel

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Furthermore, selected markers were quantified using a user-mixed magnetic Luminex assay kit obtained from R&D Systems (Minneapolis, MN, USA). The total protein concentration in the samples was determined using the Bradford assay (Thermo Fisher Scientific).

Gene Expression Analysis Using RNA Sequencing

RNA was extracted from the same NPs biopsies and RNA-Seq was performed using Illumina HiSeq4000, as described [21].

Statistics

All statistical analyses were performed using R (Version R-4.2.2) and Bioconductor Project packages (www.bioconductor.org). The comparisons of continuous variables were analyzed using the Student’s t test or the Mann-Whitney U test when the assumptions of a t test could not be met. Categorical variables were analyzed using the Fisher’s exact test or the χ2 test. The Mann-Whitney U test with Bonferroni correction was used for multiple comparisons.

Differential expression analysis was performed using Limma (version 3.54.2). Proteins with |logFC| > 0.585 and FDR <0.1 were considered as differentially expressed proteins (DEPs). FDR corrections were implemented using the Benjamini-Hochberg method. Cluster analysis and correlation analysis between DEPs and clinical parameters were performed using Spearman’s rank correlation analysis. Correlations between nasal tissue and fluids were computed using the coefficient of determination (R squared) after fitting a linear model.

Comparison of Cytokines Concentrations in NFs Collected from Patients with CRSwNP Using a NFAD or a PVA sponge

We analyzed additional group from 20 CRSwNP patients who met the same inclusion criteria. The Luminex assay was used to measure cytokines levels in NFs collected using both NFAD and PVA sponge, by placing them between the inferior turbinate and nasal septum (shown in Fig. 1a. We found a significant difference in the levels of all markers (with the exception of interferon γ) between the NFs obtained using the two sampling methods (p < 0.05) (shown in Fig. 1b). The mean concentrations of cytokines collected using NFAD were at least 2-fold higher than those collected using a PVA sponge; the levels of TNF, IL-1b, and IL-5 exhibited 16.4-, 10.3-, and 7.3-fold increases, respectively. Approximately 28.6% and 14.3% of sponge samples had GM-CSF (0.108 pg/mL) and IL-17A (0.163 pg/mL) levels below the detection limit; however, 100% of the NFs collected using the NFAD had detectable levels of both biomarkers. Thus, the NFAD increased the detectability of the analyzed biomarkers by 14.3–28.6%.

Fig. 1.

Two methods of NFs collection were compared. a Endoscopic placement of two materials in the nasal cavity position. b Concentration of selected cytokines in NFs using the two methods. c Comparison of cytokines correlations between tissue homogenates and fluids from the same CRSwNP patient. NFAD is represented in blue, while PVA sponge is represented in gray. **p < 0.01, *p < 0.05 compared with PVA sponge. The mean value and standard error bars were shown. NFs, nasal fluids; PVA, polyvinyl alcohol; NFAD, NFs absorbable device.

Fig. 1.

Two methods of NFs collection were compared. a Endoscopic placement of two materials in the nasal cavity position. b Concentration of selected cytokines in NFs using the two methods. c Comparison of cytokines correlations between tissue homogenates and fluids from the same CRSwNP patient. NFAD is represented in blue, while PVA sponge is represented in gray. **p < 0.01, *p < 0.05 compared with PVA sponge. The mean value and standard error bars were shown. NFs, nasal fluids; PVA, polyvinyl alcohol; NFAD, NFs absorbable device.

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Based on the current understanding of inflammatory CRS endotypes, we next analyzed the correlation between the cytokines signatures associated with the Th1, Th2, and Th17 responses in NFs and those in nasal tissue homogenates (shown in Fig. 1c). The levels of cytokines, such as IL-5 (ρ = 0.59, p < 0.01), interferon-γ (ρ = 0.55, p < 0.01), IL-17A (ρ = 0.54, p = 0.01), and IL-1b (ρ = 0.45, p = 0.04), in NFs collected using NFAD correlated more strongly with those detected in the nasal tissue homogenates than when the PVA sponge was used to collect NFs. NFs concentrations were standardized to pg/mg of total protein to alleviate the impact of variations in sample volume. The NFs collected by NFAD provide a more accurate representation of the mucosal state. Hence, for the subsequent analysis, NFs were obtained using optimized NFAD.

The Differential Protein Expression Profiles of NPs and NFs in Patients with ECRSwNP Exhibited Similar Inflammatory Patterns

We next performed differential protein expression analysis to further characterize the pathological mechanisms underlying the immunophenotypes of the ECRSwNP and NECRSwNP. Overall, the DEPs in ECRSwNP patients versus controls were similar between the nasal tissues and NFs (ρ = 0.41, p < 0.01, Fig. 2a), which was in agreement with previous research [22, 23]. However, the number of DEPs obtained under the same criteria in the NECRSwNP group was too small to conduct further analysis (shown in Fig. 2b).

Fig. 2.

Differential expression protein analysis. a Spearman’s correlation scatter plots depict the log2FCH proteomic differences in ECRSwNP patients compared to healthy controls. The Y-axis represents NPs, and the X-axis represents NFs; the size of the circles represents the absolute difference (in log2FCH) between protein expressions of ECRSwNP versus control subjects in NPs and NFs. b Venn diagram shows differentially expressed proteins among the four groups. c Heatmap represents the DEPs in ECRSwNP and NECRSwNP compared to healthy controls. The left graph represents fluids, while the right graph represents nasal tissue. The red area and blue area indicate up-regulated or down-regulated markers compared with controls, respectively. DEPs, differentially expressed proteins; ECRSwNP, eosinophilic CRSwNP; NECRSwNP, non-eosinophilic CRSwNP; FCH, fold change.

Fig. 2.

Differential expression protein analysis. a Spearman’s correlation scatter plots depict the log2FCH proteomic differences in ECRSwNP patients compared to healthy controls. The Y-axis represents NPs, and the X-axis represents NFs; the size of the circles represents the absolute difference (in log2FCH) between protein expressions of ECRSwNP versus control subjects in NPs and NFs. b Venn diagram shows differentially expressed proteins among the four groups. c Heatmap represents the DEPs in ECRSwNP and NECRSwNP compared to healthy controls. The left graph represents fluids, while the right graph represents nasal tissue. The red area and blue area indicate up-regulated or down-regulated markers compared with controls, respectively. DEPs, differentially expressed proteins; ECRSwNP, eosinophilic CRSwNP; NECRSwNP, non-eosinophilic CRSwNP; FCH, fold change.

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In the NFs of the ECRSwNP group versus those of the control group, we found 28 DEPs, of which 11 were up-regulated and 17 were down-regulated. Using the same criteria for differential analysis, we identified 35 DEPs (12 up-regulated and 20 down-regulated) in the nasal tissue of ECRSwNP versus that of the control group (shown in Fig. 2c). The results of the differential expression analysis are shown in online supplementary Table 1. Of note, the same 11 markers were observed in both the DEPs datasets of NFs and NPs, and their relative protein quantitative expression levels are shown in Table 3. We found that increased levels of certain proteins, such as Th2/IL-13, CCL4, and MCP4, in fluids often corresponded to increased levels of these proteins in tissues, which aligns with eosinophilic inflammatory characteristics. By contrast, a decrease in the NFs of the following significantly down-regulated proteins was observed: Th1/CXCL9; ANGPT1 and HGF, angiogenic regulatory proteins and tissue repair proteins, respectively; IL-18 and IL-33, activators of the NF-κB and MAPK signaling pathways; granzyme A (a cytotoxic protein produced by natural killer cells) and CD40L (an important regulator of B- and T-cell activation). These findings suggest that the proteins in NFs primarily originate from nasal structural cells and immune cells.

Table 3.

Normalized concentrations of 11 specific differentially expressed proteins in nasal fluids of patients with ECRSwNP and NECRSwNP

Concentration (NPX), median (IQR)ECRSwNP (n = 12)NECRSwNP (n = 9)
ANGPT1 8.34 (8.03–8.78) 7.65 (7.49–8.43) 
CCL4 11.10 (9.54–11.80) 11.60 (9.63–11.9) 
CD40L 0.35 (0.18–0.61) 0.61 (0.35–0.86) 
CXCL9 10.10 (9.06–10.70) 9.98 (8.78–10.6) 
Gal9 7.99 (7.33–9.10) 7.89 (7.15–9.10) 
GZMA 4.22 (4.12–4.49) 4.86 (4.22–5.69) 
HGF 9.28 (8.93–9.79) 9.79 (9.79–10.8) 
IL13 2.18 (1.71–3.02) 0.61 (0.18–2.05) 
IL18 10.50 (10.20–11.40) 11 (9.45–11.60) 
IL33 −0.06 (−0.50 to 0.35) 0.35 (0.18–0.86) 
MCP4 11 (9.63–11.70) 8.43 (7.65–11.80) 
Concentration (NPX), median (IQR)ECRSwNP (n = 12)NECRSwNP (n = 9)
ANGPT1 8.34 (8.03–8.78) 7.65 (7.49–8.43) 
CCL4 11.10 (9.54–11.80) 11.60 (9.63–11.9) 
CD40L 0.35 (0.18–0.61) 0.61 (0.35–0.86) 
CXCL9 10.10 (9.06–10.70) 9.98 (8.78–10.6) 
Gal9 7.99 (7.33–9.10) 7.89 (7.15–9.10) 
GZMA 4.22 (4.12–4.49) 4.86 (4.22–5.69) 
HGF 9.28 (8.93–9.79) 9.79 (9.79–10.8) 
IL13 2.18 (1.71–3.02) 0.61 (0.18–2.05) 
IL18 10.50 (10.20–11.40) 11 (9.45–11.60) 
IL33 −0.06 (−0.50 to 0.35) 0.35 (0.18–0.86) 
MCP4 11 (9.63–11.70) 8.43 (7.65–11.80) 

NPX, normalized protein expression; IQR, interquartile range.

To further prove that NFs were a suitable substitute for nasal tissue biopsies, we conducted Spearman’s correlation analyses (online suppl. Table 2). The top three markers were as follows: MCP4 (ρ = 0.63, q < 0.01), IL-13 (ρ = 0.57, q < 0.01), and CCL20 (ρ = 0.55, q < 0.01).

Correlation Analysis of DEPs and the Clinical Characteristics of ECRSwNP Patients

To further explore the relationship between protein expression and the clinical parameters of ECRSwNP patients, we next investigated the relationships between the 28 DEPs in NFs and the 35 DEPs in NPs (shown in Fig. 3a, online suppl. Table 3). We performed hierarchical clustering analysis of ECRSwNP patient markers and subsequently grouped these markers into several distinct clusters. In NFs, one group characterized by CCL3, CCL4, and CCL23 expression showed a significant correlation among these markers (p < 0.01). Another group, characterized by CXCL23, IL-8, and MUC16 expression also exhibited a significant correlation among these markers (p < 0.05).

Fig. 3.

Spearman’s correlation analysis of DEPs of ECRSwNP and clinical parameters in fluids. The correlation between 28 DEPs in fluids (a) and between DEPs and clinical parameter (b). The red area and blue area indicate positive or negative correlation, respectively.

Fig. 3.

Spearman’s correlation analysis of DEPs of ECRSwNP and clinical parameters in fluids. The correlation between 28 DEPs in fluids (a) and between DEPs and clinical parameter (b). The red area and blue area indicate positive or negative correlation, respectively.

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Additionally, we performed Spearman’s correlation analysis to evaluate the relationship between the clinical parameters and the markers (shown in Fig. 3b; online suppl. Table 4). We found that the expression levels of CCL3, CCL4, MCP3, CCL20, IL6, and TNFRSF12A were positively correlated with the LM (Lund-Mackay) score (p < 0.05). Meanwhile, the expression levels of IL-13, MCP4, CCL4, and TNFRSF12A were positively correlated with the LK (Lund-Kennedy) score (p < 0.05). Furthermore, CCL20 and TNFRSF12A showed positive correlations, while ADA, CASP8, Gal9, and CXCL13 showed negative correlations with the allergy status (p < 0.01). Notably, TNFRSF12A levels were significantly positively correlated with six out of the eight clinical parameters (p < 0.05). Furthermore, we observed that some markers, especially KLRD1, IL33, and Gal9, were negatively correlated with the clinical parameters. However, we found that only a few markers were positively correlated with clinical parameters, and most were negatively correlated on nasal tissue (shown in online suppl. Fig. 2).

Correlation between Biomarkers in NFs and Prognosis of ECRSwNP Patients

To further assess the predictive value of NFs, we investigated the correlation between NFs biomarker levels and postoperative outcomes in the ECRSwNP group of patients as shown in Figure 4a. All patients were followed up for at least 6 months after endoscopic sinus surgery. Surprisingly, the results showed that the levels of IL-6, PDL2, and CCL20 in the NFs were significantly correlated with ECRSwNP recurrence (ρ > 0.80, p < 0.01). In addition, each prognostic indicator corresponds to a specific biomarker. For instance, the CCL19 level was correlated with the nasal visual analog scale (VAS) score (ρ = 0.83, p < 0.01), the PDL2 level was correlated with the LK score (ρ = 0.93, p < 0.01), the CCL4 level was correlated with the SNOT-22 score (ρ = 0.94, p < 0.01), the MCP4 level was correlated with loss of smell or taste (ρ = 0.84, p < 0.01), and the CCL4 level was correlated with facial pain/pressure (ρ = 0.67, p < 0.05). PDL2 levels in NFs were positively correlated with postoperative recurrence, the nasal VAS and SNOT-22 scores (ρ > 0.68, p < 0.05 for all). In addition, we included an additional validation cohort of 30 ECRSwNP patients who underwent the same experimental procedures and analyses as our initial study group. The results demonstrated a positive correlation between the level of CCL4 and the postoperative total SNOT22 score (ρ = 0.62, p < 0.01, shown in Fig. 4b). Similarity, the level of CCL19 exhibited a positive correlation with the nasal VAS score (ρ = 0.76, p < 0.01, shown in Fig. 4c).

Fig. 4.

a Heatmap illustrating the significance of markers with prognostic outcomes in nasal fluids from ECRSwNP patients. For visual simplicity, markers with less than 1 significantly important prognostic outcome indicator in both nasal fluids and tissues are not shown. The red area and blue area indicate positive or negative correlation, respectively. The right side (b, c) represents the correlation coefficient. **p < 0.01, *p < 0.05.

Fig. 4.

a Heatmap illustrating the significance of markers with prognostic outcomes in nasal fluids from ECRSwNP patients. For visual simplicity, markers with less than 1 significantly important prognostic outcome indicator in both nasal fluids and tissues are not shown. The red area and blue area indicate positive or negative correlation, respectively. The right side (b, c) represents the correlation coefficient. **p < 0.01, *p < 0.05.

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Integrated Protein and mRNA Expression in ECRSwNP

Considering that previous studies on the pathogenic mechanisms of CRS mainly relied on tissue transcriptomics, we conducted RNAseq analysis on the tissue biopsy of the same patient to examine the connection between CRS proteomic and transcriptomic gene markers. With RNAseq covering a broader range of genes, we specifically analyzed 92 markers that overlapped with Olink measurements [24]. Our findings revealed a significant correlation between protein and mRNA differences in CRS compared to healthy tissue biopsy, especially in ECRSwNP (ρ = 0.34, p < 0.01, shown in Fig. 5). While no significant correlation was observed in CRSwNP (p > 0.05). Notably, genes with increased mRNA expression often exhibited a concordant rise in protein levels, suggesting a localized translation process into proteins.

Fig. 5.

Olink proteomics compared with RNAseq in nasal tissues from patients with ECRSwNP. Spearman’s correlation scatterplots depict the log2FCH change genomic difference in nasal polyps compared with normal nasal mucosa (x-axis) versus the log2FCH proteomic differences in ECRSwNP patients compared with controls in protein expression (y-axis). The size of the circles represents the absolute difference (in log2FCH) between protein or gene expressions of ECRSwNP versus control participants.

Fig. 5.

Olink proteomics compared with RNAseq in nasal tissues from patients with ECRSwNP. Spearman’s correlation scatterplots depict the log2FCH change genomic difference in nasal polyps compared with normal nasal mucosa (x-axis) versus the log2FCH proteomic differences in ECRSwNP patients compared with controls in protein expression (y-axis). The size of the circles represents the absolute difference (in log2FCH) between protein or gene expressions of ECRSwNP versus control participants.

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To the best of our knowledge, the present study was the first to conduct a proteomic comparison of NPs obtained by biopsy and NFs from CRSwNP patients. Previous studies have established relationships between nasal mucosa status and NFs [22], serum [25], and exhaled breath condensate [26]. However, these connections were established based on a limited set of markers. In our study, the sampling and protein quantification methods were carefully selected for optimization. We used the modified NFAD because of its flexibility and ability to fit the unique anatomy of the abnormal nasal cavities, including nasal septum deviation and reverse turbinate. In addition, NFAD that have just adsorbed NFs can be directly centrifuged without excessive dilution of the protein elution buffer. This results in generally higher detected values in NFAD-collected NFs compared to those in sponges [11]. Further, NFAD enables the collection of the entire nasal cavity between the nasal septum and inferior turbinate. This includes a broader sampling site to obtain a more comprehensive profile of nasal cavity immune and inflammatory markers. These advantages provide a more accurate reflection of the actual condition of the nasal mucosa. On the other hand, we preferred the Olink multiplex immunoassay panels due to its higher throughput capabilities compared to Elisa. By utilizing human-specific secondary proximity probes, Olink technology minimizes nonspecific signal interference compared to Luminex, resulting in improved detection sensitivity and specificity. Moreover, the Olink panels require smaller sample sizes compared to other proteomics platform, such as MS. These advantages make the Olink platform suitable for analyzing indicators in NFs.

The collection of NFs provides a noninvasive method for assessing the levels of inflammatory markers in the nasal mucosa, which allows researchers and clinicians to explore these indicators in nonsurgical patients and trace their dynamics during treatment. However, it is important to note that some indicators found in NFs will not directly reflect the conditions within the nasal tissue. This is because nasal tissue and NFs may be associated with different immune cell compartments and biological processes. For instance, by contrast to tissue homogenates, NFs contain no epithelial cells and are strongly influenced by the rate of nasal mucociliary clearance [27, 28]. Moreover, NFs represent the inflammatory state of the entire nasal cavity, while tissue samples highlight local pathological characteristics such as inflammatory infiltration, epithelial injury, and fibrosis. This difference has been confirmed by other studies, which suggest that the pathogenesis of certain immune imbalanced states develops either locally or systemically [29, 30].

In our study, the most highly expressed proteins in the NFs of ECRSwNP patients were the chemotaxis-associated proteins CCL3, CCL4, CCL19, CCL20, CCL23, MCP3 (CCL7), and MCP4 (CCL13). Interestingly, the chemokines CXCL5/10/11 and CCL3/19/20 were generally found at higher levels in NFs than in nasal tissues. CCL23 overproduction in NPs may be involved in the pathogenesis of ECRSwNP through the recruitment of CCR1+ inflammatory cells, including monocytes and macrophages, and the amplification of local inflammation [31]. CCL13/MCP4 and CCL7/MCP3 are potent eosinophil-modulating chemokines. They can be secreted by monocytes, macrophages, and dendritic cells (DCs) to stimulate the degranulation of a variety of immune cells (including eosinophils), induce a cascade of Th2-type inflammatory factors and immune cells [32, 33]. CCL3 induced local T cell/macrophage and slightly granulocyte recruitment in both atopic and non-atopic skin biopsies [34]. Chu et al. [35] found that CCL4/MIP-1β-a specific ligand for CCR5 receptors-was implicated in eosinophil recruitment into the inflammatory site and was substantially released from activated eosinophils from patients with ECRS. Further, it has been shown that CCL19 levels are increased in all patients with eosinophilic pneumonia after the challenge [36]. Using an ovalbumin allergen-induced experimental allergic conjunctivitis (EAC) mouse model, CCL20, a ligand that neutralizes CCR6, was found to inhibit allergic conjunctivitis with predominantly eosinophilic infiltration [37]. In summary, the persistence of inflammatory eosinophils may in turn induce the production of other chemokines and aggravate local pathology. These cytokines can form a gradient in the extracellular space, which drives eosinophils to roll and migrate across the vascular endothelium. Subsequently, they are recruited to the mucosal surface of the sinus lumen, where they release DNA traps, leading to further inflammation [38].

The discovery of novel fluids-specific biomarkers for use in diagnostic and prognostic applications is an emerging area of research which is gaining increasing attention. For instance, Wu and colleagues [39] found that cystatin SN levels in NFs were a strong prognostic indicator of disease control status in patients with CRSwNP over a 2-year follow-up period. Meanwhile, Chen et al. [40] found that tPA in NFs was a promising noninvasive predictor of NP recurrence, with a higher predictive power than T2 biomarkers. Our study used secretory proteomics to explore prognosis-related indicators; however, it had a number of limitations, including the relatively small sample size and an analysis panel confined to 92 proteins. Despite these limitations, our research provides valuable insights into the pathogenesis of CRSwNP.

NFAD is a practical, noninvasive method for obtaining NFs for in-depth proteomics studies. The proteomic profile of NFs obtained in this study correlate with levels in sinus tissue and is elevated in ECRSwNP versus control based on Th1/Th2 skewing and eosinophil-modulating chemokines.

Study approval statement: The study protocols were approved by the Ethics Committee of the First Affiliated Hospital of Sun Yat-sen University (No. 2023-211). Prior to admission, all subjects provided written informed consent.

The authors declare no conflicts of interest.

This study was supported by the National Natural Science Foundation of China (NSFC) grants 82020108009 (W.P.W.), 81974141 (L.J.), 82171766 (W.Y.), Guangdong Natural Science Foundation of China grants 2023B1111040004 (W.P.W.), 2021A1515010273 (W.Y.), and Guangzhou Science and Technology Project of China grant 202102020498 (W.Y.).

Yilin Hou and Changhui Chen contributed to the study design and paper writing. Yilin Hou and Zhengqi Li conducted the data analysis. Tong Lu and Lin Sun were responsible for sample collection and patient follow-up. Yi Wei, Jian Li, and Weiping Wen contributed to the manuscript revision.

Additional Information

Yilin Hou, Changhui Chen, and Zhengqi Li contributed equally to this work.Edited by: H.-U. Simon, Bern.

All data generated or analyzed during this study are included in this article. Further inquiries can be directed to the corresponding author.

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