Abstract
Background
Asthma is a heterogeneous chronic inflammatory disease of the airway, and its development is the result of genetic factors, environmental factors, immune dysfunction, and other factors. This study aimed to identify biomarkers of asthma.
Methods
A differential gene expression (DGE) analysis and a weighted gene co-expression network analysis (WGCNA) were conducted to identify the asthma-related genes in the GSE67472 dataset, and these genes were intersected with immune genes from the Immuport database to identify the asthma-associated immune genes. Least absolute shrinkage and selection operator (LASSO) regression was used to identify the key asthma-associated immune genes, and receiver operating characteristic (ROC) curves were used to assess the diagnostic potential of these genes. Subsequently, the mechanism of action of the inducible factor interleukin 13 (IL-13) in human bronchial epithelial (HBE) cells and its effect on markers of asthma were verified in vitro.
Results
The DGE analysis and WGCNA identified 62 asthma-related genes, and eight asthma-associated immune genes were identified after the intersection of the asthma-related genes and the immune-related genes. Utilizing LASSO regression, five key immune genes linked to asthma were uncovered. These included S100 calcium binding protein A16 (S100A16), Lactotransferrin (LTF), BPI fold containing family A member 1 (BPIFA1), cystatin 4 (CST4), and growth-related protein (GRP). The area under the curve (AUC) values of S100A16 and CST4 were greater than 0.8. The single-cell analysis showed that S100A16 was highly expressed in asthmatic neutrophils. Finally, the results of the in vitro experiments showed that IL-13 not only induced lipid peroxidation to promote iron death in the HBE cells, but also promoted the expression of S100A16.
Conclusions
This study identified a new biomarker, S100A16, for the diagnosis of asthma. S100A16 may serve as a new potential target for asthma treatment, and may be an important regulator of ferroptosis.
Keywords: Asthma, weighted gene co-expression network analysis (WGCNA), S100A16, ferroptosis
Highlight box.
Key findings
• Our study is the first to identify S100A16 as a diagnostic marker for asthma and to verify that S100A16 can be promoted by the inducer IL-13. Moreover, we implicated a novel mechanistic link between T2 inflammation in asthma and ferroptosis-driven airway epithelial injury, positioning ferroptosis modulation as a promising therapeutic strategy for T2-inflammatory airway diseases.
What is known, and what is new?
• S100A16 has been found to be widely expressed in a variety of tissues, although at varying levels, and is particularly highly expressed in tissues rich in epithelial cells. S100A16 is associated with inflammation, fat formation, and glucose metabolism. The mRNA and protein levels of S100A16 have been reported to be differentially expressed in most human cancers.
• This study identified potential biomarkers of asthma, and analyzed the biological mechanism of asthma via a bioinformatics analysis and in vitro experiments.
What is the implication, and what should change now?
• S100A16 has high diagnostic value for asthma. Moreover, S100A16 is involved in interleukin-13 (IL-13)-induced airway epithelial cell activity injury and the inflammation mechanism. Our findings provide novel insights for individualized asthma treatment.
Introduction
Asthma is a heterogeneous disease, and its development is the result of genetic factors, environmental factors, immune dysfunction, and other factors (1). The airway epithelium plays a crucial role in the pathophysiology of asthma, serving as a structural and immune barrier against the external environment. In patients with asthma, abnormal immune responses and repair processes can lead to recurrent or chronic inflammation and damage to the airway epithelium, leading to structural changes in the airway. These structural changes, which are collectively referred to as airway remodeling, lead to airflow restriction, followed by the aggravation of clinical symptoms and the deterioration of lung function (2). Genetic variations in the airway epithelium can trigger or exacerbate asthma, and affect remodeling (3). At present, the main treatment for asthma in clinical practice is to control airway inflammation; however, this treatment cannot truly reverse the pathological changes caused by airway remodeling. Thus, it is particularly important to further explore the biomarkers of airway damage and remodeling in neutrophil asthma caused by multiple factors such as immunity and genetics.
The phenotypes of asthma mainly include type 2 (T2) and non-T2 asthma. Non-T2 asthma is usually seen in severe and steroid-resistant asthma patients (4). Research has shown that 50% of severe asthma patients who are resistant to corticosteroid treatment have T2 asthma (5). The Chinese cohort revealed that over 55.8% of severe asthma patients exhibit T2 asthma (6). The T2 pathway is characterized by T helper 2 (Th2) cells and innate lymphoid cells type 2 (ILC2), which secrete key cytokines including interleukin 4 (IL-4), interleukin 5 (IL-5), interleukin 13 (IL-13) (7). IL-13 induces fibroblast and epithelial cell activation through direct modulation of extracellular matrix synthesis and deposition, leading to progressive airway wall thickening and subepithelial fibrosis that drive structural remodeling in asthma. This cytokine-mediated profibrotic mechanism has been demonstrated to correlate with disease severity, particularly in steroid-resistant phenotypes characterized by persistent type 2 inflammation (8). Recent advances in deciphering type 2 inflammatory endotypes have driven the development of targeted biologics against key bronchial inflammation mediators. While these monoclonal antibodies demonstrate significant efficacy in symptom control and steroid-sparing effects, in severe asthma phenotypes characterized by persistent type 2 inflammation, combination therapy targeting multiple inflammatory pathways may be required to achieve comprehensive suppression of the heterogeneous inflammatory cascade (9). There are currently no monoclonal antibodies or other specific treatment options for non-T2 asthma (4). Thus, research needs to be conducted into the pathological mechanisms of non-neutrophilic asthma to provide direction for the development of new treatment strategies.
This study used a differential analysis and co-expression networks to identify potential biomarkers of neutrophil asthma, and analyzed the expression of potential biomarkers in asthmatic neutrophils. It also examined the effect of IL-13 on potential biomarkers in human bronchial epithelial (HBE) cells through in vitro experiments, and explored the regulatory mechanism of IL-13 in HBE cells. We present this article in accordance with the MDAR reporting checklist (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-874/rc).
Methods
Data acquisition
Asthma airway epithelial cell microarray assay datasets were collected and downloaded from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/). GSE67472, which comprised the data of 43 control subjects and 62 asthmatic patients, was used as the training set. GSE64913, which comprised the data of 23 control subjects and 17 asthmatic patients, was used as the validation set. PRJCA005877, a single-cell dataset for asthma that comprised the neutrophilic sequencing data of three asthma mice, was obtained from the China National Center for Bioinformation database (https://ngdc.cncb.ac.cn/search/specific?db=bioproject&q=PRJCA005877). The data of 1,793 immune genes related to asthma were obtained from the Immuport database (https://www.immport.org/resources). The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.
Differential analysis
The GEO2R online tool was used in the GEO database to identify the differentially expressed genes (DEGs) in the normal control subjects and asthmatic patients based on the following criteria: |log2 fold change| >0.5 and P<0.05.
Weighted gene co-expression network analysis (WGCNA)
WGCNA can be used for identifying clusters of highly correlated genes, summarizing such clusters utilizing the module eigengene or an intramodular hub gene, relating modules to one another and to external sample traits (using eigengene network methodology), and calculating module membership measures (10). WGCNA was used to identify the gene modules that were highly associated with the disease and to determine the candidate biomarkers of the disease. The R package “WGCNA” (10) was used to identify the genes most associated with asthma.
Least absolute shrinkage and selection operator (LASSO)
LASSO is a regression analysis method that performs variable selection by shrinking the coefficients of less influential predictors to zero via increasing the value of a penalty parameter, λ. The optimal value of λ was selected based on the maximum value criterion. Using the default parameters, LASSO regression was employed to identify the key genes for asthma, and an optimal LASSO logistic regression model was constructed through a 10-fold cross-validation using the R package “glmnet”.
Assessment of the diagnostic value of the key genes
The diagnostic performance of the candidate genes was evaluated using the areas under the curve (AUCs) from the receiver operating characteristic (ROC) curve analysis. The “pROC” R package was used to generate the curves.
Cell culture
The control group of the HBE cell model ICELL-H321 (iCell Bioscience, Shanghai, China) was cultured in an original T25 cell culture vial, which was sterilized with 70% ethanol and transferred to a 5% carbon dioxide incubator (Thermo Fisher Scientific, Marietta, USA). The cells were warmed to 37 ℃. After 2–4 hours of stabilization, the cells were cultured at a complete medium exchange of 10% fetal bovine serum (GEMINI, Woodland, USA) plus Dulbecco’s Modified Eagle Medium (Hyclone, South Logan, USA). The other group of ICELL-H321 was treated with 10 ng/mL of IL-13 (Novoprotein, Shanghai, China) in the same culture environment as the control group.
Cell Counting Kit 8 test
The cell density was adjusted by HBE cells to 3×104 cells/mL, and the cells were inoculated into 96-well cell culture plates with 100 µL of the cell suspension and three multiple pores per concentration gradient. The optical density (OD) value at the same time point was measured by Diatek (Wuxi, China) at 450 nm wavelength, and cell proliferation was analyzed and the OD value was measured.
5-Ethynyl-2'-deoxyuridine (EdU) detection
According to the manufacturer’s instructions for the C0071S EdU DNA cell proliferation kit (Beyotime Biotechnology, Shanghai, China), the cells were fixed in 4% paraformaldehyde for 15 minutes after EdU labeling, catalyzed with Click reaction solution (Lindu, Najing, China), and stained with Hoechst 33342 solution (Beyotime Biotechnology, Shanghai, China). An inverted photographic microscope DMI8 (Leica, Wetzlar, Germany) was used to photograph the cells.
Reverse transcription quantitative polymerase chain reaction (RT-qPCR)
Total RNA was extracted by TRIzol reagent (Biosharp, Hefei, China). The concentration of the RNA samples was measured using a NanoDrop microvolume spectrophotometer (Thermo Fisher Scientific). The complementary DNA (cDNA) was synthesized using the qScript cDNA Synthesis Kit (Quanta BioSciences, Gaithersburg, MD, USA), and cDNA reverse transcription was performed using the reverse transcription kit (Cronda, Shanghai, China). Subsequently, RT-qPCR was performed using a thermal cycler (Thermo Fisher Scientific) and a real-time detector (Amolarray, Suzhou, China). The results were analyzed using 2 to the power of minus delta delta Ct (2−ΔΔCt) to calculate the relative gene expression levels.
ELISA detection
The cell density of the HBE cells was adjusted, and the cells were inoculated into six-well plates. The enzyme-linked immunosorbent assay (ELISA) kit (CSB-EL019131HU, Cusabio Biotech, Wuhan, China) was used to collect cell particles or supernatant for ELISA detection in accordance with the manufacturer’s instructions. After the operation was complete, the OD value at 450 nm or the corresponding wavelength per well was measured by enzyme-linked immunosorbent assay (Diatek, San Diego, CA, USA).
Immunofluorescence
The cell density was adjusted, and the cells were inoculated into a 24-well cell culture plate, and then cultured in an incubator (Thermo Fisher Scientific). After cell processing, the supernatant was aspirated, the cells were washed with phosphate buffered saline (Hyclone, South Logan, USA), fixed with 4% paraformaldehyde (Sigma, Taufkirchen Germany), blocked with 5% bovine serum albumin (BSA) solution (BioFROXX, Einhausen, Germany), and incubated with 4-hydroxynonenal (4-HNE) (Abclonal, Wuhan, China) primary antibody diluted with 1% BSA. Goat anti-rabbit immunoglobulin G (Servicebio, Wuhan, China) was labeled with secondary antibody Cy3 diluted with 1% BSA, after which, Hoechst dye solution was added (Beyotime Biotechnology, Shanghai, China), and the cells were incubated at room temperature for 10 minutes without light. After washing, anti-fluorescence quenching sealing solution was used to seal the glass slide, which was then placed under an inverted fluorescence microscope IX71 (Olympus, Tokyo, Japan) for observation, and photographs were taken for preservation.
Statistical analysis
The statistical analysis was conducted using R software (version 4.4.1). Component differences were assessed by a one-way analysis of variance. P value <0.05 was considered statistically significant.
Results
Identification of the key immune-related genes in asthma
A total of 1,716 DEGs were identified in GSE67472, of which 709 were significantly upregulated, and 1,007 were significantly downregulated (Figure 1A).
Figure 1.
Identification of the DEGs and construction of the co-expression network modules. (A) Volcano plot of the DEGs. (B) The optimal soft threshold of the co-expression network. (C) Modules of cluster genes. The upper portion of the figure illustrates the hierarchical clustering of data points. The lower section presents a heatmap indicating gene expression levels, with colors ranging from green to red, representing low to high expression values, respectively. (D) Heat map showing the correlation between the genes and traits. (E) Dot plot of module membership in the dark-turquoise module. (F) Venn diagram of the DEGs, dark-turquoise module genes, and immune-related genes. DEGs, differentially expressed genes; FC, fold change; ME, module eigengene.
A co-expression network was constructed between the asthma and control group using an optimal soft threshold of β=12 (Figure 1B). Dynamic hybrid cutting revealed 13 distinct modules (Figure 1C). The Pearson correlation coefficient and significance level of each module against clinical traits were calculated and displayed in a heatmap, which showed that the dark-turquoise module (R=0.53, P<0.001) had the highest correlation with asthma (Figure 1D). The genes in the dark-turquoise module were also highly correlated with disease symptoms (Figure 1E).
To identify the key immune-related genes in asthma, we downloaded a total of 1,793 immune related genes from the Immuport database and intersected them with the differential genes mentioned above and the genes in the dark-turquoise module of WGCNA. We intersected the immune-related genes, DEGs, and module genes (Figure 1F). Eight key immune-related genes in asthma were identified, i.e., chemokine (C-X-C motif) ligand 2 (CXCL2), S100 calcium binding protein A16 (S100A16), Lactotransferrin (LTF), BPI fold containing family A member 1 (BPIFA1), cystatin 4 (CST4), serine peptidase inhibitor Kazal type 5 (SPINK5), chemokine (C-C motif) ligand 26 (CCL26), and growth-related protein (GRP).
Identification of asthma diagnostic markers
To identify minimally redundant biomarkers with diagnostic potential for asthma, we applied LASSO regression for feature selection. This regularization technique performs variable selection by imposing an L1 penalty constraint, effectively shrinking coefficients of non-informative genes to zero (Figure 2A). Through 10-fold cross-validation repeated 100 times, we systematically evaluated model performance across different regularization parameters (λ values). The optimal λ was determined when AUC reached its maximum value with one standard error tolerance (Figure 2B). Five genes (i.e., S100A16, LTF, BPIFA1, CST4, and GRP) were ultimately selected as the key asthma-associated immune genes (Figure 2A,2B).
Figure 2.
Screening of the key immune genes in asthma, and an analysis of their diagnostic value. (A) Tuning of the penalty parameter (λ) in the LASSO model using 10-fold cross-validation. The AUC is plotted against the log(λ) values. The AUC on the y-axis measures the model’s performance; red dots represent the mean AUC, and grey bars indicate the standard error. The left dashed line indicates the λ value that yields the maximum AUC (λmax), while the right dashed line indicates the λ for the most parsimonious model within one standard error of the maximum AUC (λ1se). The numbers at the top represent the number of non-zero coefficients at each λ value. (B) LASSO coefficient profiles of the variables plotted against the log(λ) sequence. At the optimal λ value selected by the maximum criterion ((λmax), five variables remained with non-zero coefficients. These five genes were consequently identified as the key asthma-associated immune genes. (C-G) AUC values of the key genes in GSE67472. (H,I) Expression levels of S100A16 and CST4 in GSE64913. **, P<0.01, vs. healthy control group; ****, P<0.0001, vs. healthy control group. AUC, area under the curve; LASSO, least absolute shrinkage and selection operator.
A ROC curve analysis of the five genes was performed in the training set, and the results showed that the AUC values of all the genes were greater than 0.7. Of the five genes, S100A16 (AUC: 0.84) and CST4 (AUC: 0.86) showed the best diagnostic performance (Figure 2C-2G). Meanwhile, the validation set was used to verify the expression levels of S100A16 and CST4, and the results showed that S100A16 and CST4 were significantly overexpressed in the asthma samples (Figure 2H,2I).
S100A16 was commonly expressed in asthmatic neutrophils
Neutrophils in patients with asthma can induce mesenchymal transformation in healthy bronchial epithelial cells and the release of neutrophil elastase, worsening the patient’s condition (11,12). To identify the high expression biomarkers in the asthmatic neutrophils, a follow-up analysis was conducted using neutrophils from a mouse asthma model (PRJCA005877). As Figure 3A shows, the cells in PRJCA005877 were categorized into the following two groups: the ovalbumin (OVA)-induced group, and the saline-treated group. As CST4, which encodes the cystatin S protein in humans, does not have a mouse counterpart (13), we focused on the expression of S100A16. The results indicated that S100A16 was predominantly expressed in the asthmatic neutrophils of the mice (Figure 3B).
Figure 3.
Analysis of neutrophils in asthma. (A) Neutrophils from asthmatic mice that received different treatments in PRJCA005877. (B) Distribution of S100A16 expression in mice neutrophils. OVA, ovalbumin; UMAP, Uniform Manifold Approximation and Projection.
Increased expression of S100A16 and HRD1 in the IL-13-induced injured HBE cells
IL-13 is a pleiotropic T2 cytokine that plays an important role in the pathogenesis of asthma, and biologics targeting IL-13 can be used to treat asthma (14). To observe the effect of IL-13-induced tracheal injury on S100A16, cultured ICELL-H321 was used for the follow-up cell experiments (Figure 4A). After RT-qPCR and western blot detection, the results showed that the messenger RNA (mRNA) and protein levels of S100A16 were significantly increased in the IL-13 group compared with the control group (Figure 4B-4D).
Figure 4.
Effect of induction factor IL-13 on S100A16. (A) Cultured HBE cells (magnification, 100×). (B) The mRNA expression of S100A16 was detected by RT-qPCR after the IL-13 treatment of the HBE cells. (C,D) The protein expression level of S100A16 was detected by western blot after the IL-13 treatment of the HBE cells. ***, P<0.001. HBE, human bronchial epithelial; IL-13, interleukin 13; RT-qPCR, reverse transcription quantitative polymerase chain reaction.
IL-13 decreased the cell proliferation and cell activity of the HBE cells
In the above analysis, it was found that IL-13 significantly affected the expression of the asthma diagnostic marker S100A16. To further analyze the effect of IL-13 on asthma, the effect of IL-13 on the activity and proliferation level of the HBE cells was explored. The results showed that the proliferation level of the HBE cells was significantly decreased after IL-13 treatment (Figure 5A,5B), and the cell activity level was significantly decreased (Figure 5C).
Figure 5.
Detection of the activity and proliferation of HBE cells. (A) Fluorescence staining visualization of the HBE cells after EdU labeling (magnification, 200×). (B) The proliferation level of the HBE cells was detected after EdU labeling. (C) HBE cell activity detected by the Cell Counting Kit 8. ***, P<0.001. HBE, human bronchial epithelial; IL-13, interleukin 13.
IL-13 promoted iron death in HBE cells by inducing lipid peroxidation
IL-13 significantly reduces the activity of HBE cells, and fatty-acid oxidation and iron death have been shown to be correlated with cell activity (15). Thus, we investigated the relationship between IL-13 in HBE cells and fatty-acid oxidation and iron death using ELISA and immunofluorescence assays. The expression of 4-HNE in the HBE cells of the IL-13 group was significantly increased (Figure 6A,6B), which suggested that IL-13 promoted the lipid peroxidation of the HBE cells. Additionally, the iron and malondialdehyde (MDA) levels were significantly increased and the glutathione (GSH) levels were significantly decreased in the IL-13 group (Figure 6C-6E). These findings suggested that IL-13 promoted iron death in the HBE cells by inducing lipid oxidation.
Figure 6.
Changes in lipid oxidation and Fe2+ in the HBE cells treated with IL-13. (A) Fluorescence staining visualization of the HBE cells (magnification, 200×). (B) The detection of the 4-HNE level. (C) The detection of the Fe2+ level. (D) The detection of the MDA level. (E) The detection of the GSH level. **, P<0.01; ***P<0.001. 4-HNE, 4-hydroxynonenal; EdU, 5-Ethynyl-2'-deoxyuridine; GSH, glutathione; HBE, human bronchial epithelial; IL-13, interleukin 13; MDA, malondialdehyde.
Discussion
Asthma is a complex chronic inflammatory disease of the airway. In the inflammatory environment, many innate and adaptive immune system cells interact with airway epithelial cells, causing changes in airway structure (16), Furthermore, asthma involves the interaction of multiple genetic and environmental factors (17,18). A genome-wide association study of asthma identified thousands of genetic variants associated with the development of asthma (17). Almost all of these genetic variants were present in the non-coding genomic regions, which obscured the functional effect of asthma-related variants and their translation into disease-related mechanisms (17). A study used asthma and ferroptosis datasets for bioinformatics analyses, weighted gene co-expression network analyses to identify co-expressed genes, and gene ontology enrichment analyses to identify the potential functions of candidate genes (19). Research has shown that the ferroptosis-related gene aldo-keto reductase family 1 member C3 (AKR1C3) can be used as a diagnostic biomarker for asthma, and it regulates ferroptosis in BEAS-2B cells (19). In that study, Li et al. showed that mRNA expression levels of IL13, CCL26, and S100A16 were negatively correlated with LTF; CXCL2, BPIFA1, and GRP were positively correlated with LTF (20). All these genes might be important asthma biomarkers, and it is worth noting that, they all belong to the eight key genes identified in the current study. In this study, the potential biomarkers of asthma were identified, and the biological mechanisms of asthma were analyzed by a bioinformatics analysis and verified by in vitro experiments.
This is the first study to show the value of S100A16 in the diagnosis of asthma. S100A16 has been found to be widely expressed in a variety of tissues, although at varying levels, and is particularly highly expressed in tissues rich in epithelial cells (21). S100A16 is associated with inflammation, fat formation, and glucose metabolism (22,23). The mRNA and protein levels of S100A16 have been reported (21) to be differentially expressed in most human cancers. S100A16 has been implicated in several aspects of tumorigenesis, such as cell proliferation, differentiation, migration, invasion, and epithelial-stromal transformation (21). Our study is the first to identify S100A16 as a diagnostic marker for asthma and to verify that S100A16 can be promoted by the inducer IL-13.
Through a systematic literature review, we showed that S100A16 inhibits the expression of large tumor suppressor homolog 1 (LAST1) by regulating the ubiquitin ligase Cullin 4A (24), and degrades the expression of glycogen synthase kinase 3 beta (GSK3β) and casein kinase 1 alpha (CK1α) via the ubiquitination of HMG-CoA reductase degradation protein 1 (HRD1) (25,26). Solute carrier family 7 member 11 (SLC7A11) and glutathione peroxidase 4 (GPX4) are two crucial ferroptosis-regulating enzymes. Additionally, many signaling pathways, such as iron metabolism and lipid metabolism, precisely regulate the process of ferroptosis. In the early stage of membrane lipid peroxidation accumulation, an increase in Ca2+ flux triggers a membrane repair mechanism centered around endosomal sorting complex required for transport III (ESCRT-III) proteins, which limits lethal cell membrane permeabilization (27). S100A16 is a newly discovered calcium-binding protein that plays an important role in lipid metabolism. The knockout of S100A16 has been shown to protect mice from alcoholic liver lipid accumulation and inflammation by upregulating mesencephalic astrocyte-derived neurotrophic factor (MANF) and inhibiting stress in the endoplasmic reticulum (ER) (28). S100A16 regulates the proteins associated with the Adenosine Monophosphate-activated protein kinase pathway via an interaction with calmodulin, thereby regulating liver lipid synthesis (23). In patients with asthma, a high-fat meal triggers the increased expression of the S100A16 gene, which correlates with elevated levels of total and saturated non-esterified fatty acids in plasma. This, in turn, facilitates neutrophil migration into the airways and worsens airway inflammation (29). The above vague evidence led us to speculate that S100A16 plays a role in airway epithelial cell activity injury and inflammation by regulating ferroptosis. This hypothesis was preliminarily verified by in vitro test.
Iron death/ferroptosis, as a unique form of cell death, is characterized by intracellular iron accumulation and lipid peroxidation, leading to oxidative stress (15,30). Many signaling pathways, such as iron metabolism, lipid metabolism, and amino acid metabolism, precisely regulate the occurrence process of iron sag, and are closely related to the pathogenesis of asthma (30). In vitro studies have shown that ferroptosis inhibitors alleviate the inflammatory response and oxidative stress of IL-13-induced BEAS2B cells (31), and Liproxstatin 1 inhibits the ferroptosis inhibition protein of bronchial epithelial cells induced by lipopolysaccharide (LPS) and IL-13, and inhibits the expression of inflammatory factors (32). In addition, dust mite exposure induces iron apoptosis in airway epithelial cells and promotes an inflammatory response by activating ferritin phagocytosis in asthma patients (33). There are also evidences (32,33) suggesting that effectively inhibiting ferroptosis would improve airway inflammation.
Research (33) has shown that T2 inflammation in asthma is closely related to iron death. During ferroptosis, airway epithelial cells release large amounts of pro-inflammatory cytokines, leading to inflammation. The inhibition of iron sag has an important inhibitory effect on T2 inflammation (31,33). Our experimental data implicate a novel mechanistic link between T2 inflammation in asthma and ferroptosis-driven airway epithelial injury, positioning ferroptosis modulation as a promising therapeutic strategy for T2-inflammatory airway diseases.
In summary, this study is the first to report that S100A16 has high diagnostic value for asthma. Through experimental verification, we showed that S100A16 may be a promising biomarker and drug target for preventing ferroptosis in asthma patients, and may serve as a new diagnostic method. However, there is a number of limitations in this study. First, our findings are only based on bioinformatics and in vitro experiments. It has been established that IL-13-induced S100A16 expression is elevated in airway epithelial cells, and IL-13 alters airway epithelial cell activity impairment, inflammatory factor expression, and ferroptosis; however, the mode of the interaction has not yet been elucidated. Thus, these findings need to be verified in further animal experiments. Second, the expression of S100A16 at different levels of clinical asthma was not analyzed. We intend to examine this in our follow-up research. Third, the bioinformatics analysis approaches that we employed, such as WGCNA, despite being a widely used standardized tool, have inherent constraints. The algorithms used for gene selection and analysis are based on certain assumptions and criteria, which might not capture the full complexity of asthma genetics. Therefore, future studies incorporating more advanced analytical methods and experimental validation are needed to address these limitations and provide a more complete understanding of the genetic basis of asthma.
Conclusions
S100A16 has high diagnostic value for asthma. Moreover, S100A16 is involved in IL-13-induced airway epithelial cell activity injury and the inflammation mechanism. Our findings provide novel insights for individualized asthma treatment.
Supplementary
The article’s supplementary files as
Acknowledgments
The authors would like to thank the contributors to the GEO database and China National Center for Bioinformation database for sharing data.
Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.
Footnotes
Reporting Checklist: The authors have completed the MDAR reporting checklist. Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-874/rc
Funding: None.
Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-874/coif). H.Z. and S.G. are employed by ADICON Holdings Limited. The other authors have no conflicts of interest to declare.
Data Sharing Statement
Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-874/dss
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