Introduction

Chronic Obstructive Pulmonary Disease (COPD) is a heterogeneous lung condition characterized by chronic respiratory symptoms (dyspnea, cough, sputum production, and/or exacerbations) due to abnormalities of the airways (bronchitis, bronchiolitis) and/or alveoli (emphysema) that cause persistent, often progressive, airflow obstruction1. Beyond respiratory morbidity, COPD imposes a substantial global health burden with impaired quality of life, high healthcare utilization, and premature mortality. It is currently the third leading cause of death worldwide2, and model-based projections indicate continued growth in prevalence and mortality over the coming decades, driven by population aging and persistent tobacco exposure in many regions. Estimates suggest that the number of COPD cases among adults may approach 600 million by 2050, with annual deaths exceeding 5.4 million3. Despite comprehensive management—including pharmacotherapy, smoking cessation, pulmonary rehabilitation, and psychosocial support—pharmacologic disease modification remains limited and airway remodeling is largely irreversible. This underscores the need to elucidate upstream mechanisms and to develop robust biomarkers for diagnosis, risk stratification, and personalized therapeutic guidance.

Airway and systemic inflammation are central to COPD pathogenesis. Multiple innate and adaptive immune cells sustain a chronic inflammatory milieu accompanied by elevated pro-inflammatory cytokines such as IL-1β, TNF-α, IL-6, and IL-8, which drive epithelial injury, mucus hyper-secretion, protease–antiprotease imbalance, and oxidative stress, thereby accelerating airflow limitation and predisposing to acute exacerbations (AECOPD)4,5,6,7. Respiratory infections represent the predominant trigger of AECOPD, yet cytokine-directed therapeutic strategies have been constrained by redundancy in inflammatory networks, highlighting the need to identify upstream regulators that integrate multiple signaling pathways and provide clinically actionable readouts.

MicroRNAs (miRNAs), small non-coding RNAs that post-transcriptionally repress gene expression, regulate critical processes including inflammation and immune responses8. Among these, miR-155 is an NF-κB-regulated immunomodulatory miRNA implicated in hematopoiesis, inflammatory amplification, and host defense9. Although multiple microRNAs (e.g., miR-21, miR-146a, miR-223, miR-126) have been confirmed to be associated with chronic obstructive pulmonary disease (COPD), the present study focused on miR-155 for the following reasons. First, its expression is regulated by NF-κB. As an NF-κB-regulated miRNA, miR-155 acts as a key mediator of proinflammatory signaling10,11, which can directly amplify the production of TNF-α, IL-6, and IL-8—cytokines that are central to the progression of COPD. Second, as the primary risk factor for COPD, cigarette smoke-induced pulmonary inflammation and COPD pathogenesis are critically mediated by miR-155, and inhibition of miR-155 has been shown to alleviate cigarette smoke-induced lung inflammation in mice12,13. Furthermore, among patients with COPD, the expression level of miR-155 is significantly higher in those with GOLD stage III–IV disease than in those with GOLD stage II disease. Notably, miR-155 expression is significantly negatively correlated with the severity of airflow limitation [measured as the percentage of predicted forced expiratory volume in 1 s (FEV1) and post-bronchodilator FEV₁/forced vital capacity (FVC)] as well as with lung diffusing capacity for carbon monoxide (DLCO)12. This mechanistic relevance supports miR-155 as a promising candidate target for validation as a COPD biomarker.

Furthermore, emerging data reveal differential microRNA expression patterns across pathogen classes. Gupta et al.14 demonstrated that Aspergillus fumigatus germ tubes induce miR-132 but not miR-155 in peripheral blood mononuclear cells (PBMCs), a response distinct from bacterial triggers like LPS (which potently upregulates miR-155)15. This pathogen-specificity raises the intriguing possibility that circulating miR-155 could serve as a discriminative biomarker for bacterial versus fungal infections in AECOPD, particularly for invasive pulmonary aspergillosis (IPA)-a life-threatening complication in severe COPD associated with delayed diagnosis and mortality exceeding 70%16,17.

Despite the promising preclinical evidence supporting miR-155 as a potential biomarker in COPD, critical gaps remain in the clinical literature. Specifically, most prior studies have been preclinical in nature, with few clinical investigations concurrently evaluating miR-155 expression levels alongside systemic cytokine profiles, multidimensional disease severity indices (e.g., GOLD staging, exacerbation history), and long-term prognostic outcomes. Most importantly, the core potential of miR-155 to differentiate infection subtypes—especially life-threatening IPA—during AECOPD, a key unmet clinical need for improving diagnosis and treatment, has not been explored in clinical cohorts. These existing limitations hinder the translation of preclinical findings on miR-155 into clinical practice, highlighting the necessity of the present study.

To address these gaps, our study quantified serum miR-155 levels and key cytokines (IL-1β, IL-6, IL-8, TNF-α) in COPD patients, heavy smokers, and controls. Tobacco smoke is the primary risk factor for COPD, yet not all heavy smokers develop COPD. We included heavy smokers as a separate group to specifically dissect the independent impact of chronic smoke exposure on miR-155 expression and systemic inflammatory profiles, distinct from the pathological alterations inherent to established COPD. We examined associations with disease status, inflammation, clinical severity, and pathogen-specific infection subgroups (including IPA diagnosis). Furthermore, a prospective cohort design evaluated miR-155’s predictive value for exacerbation frequency over one year. By situating miR-155 at the interface of smoke-induced inflammation, systemic cytokine networks, and infection-specific responses, this work aims to validate its utility as a biomarker for identifying disease activity, prognosis, and pathogen discrimination in COPD.

Materials and methods

Study design

This study was designed as a prospective cohort study. The study protocol was approved by the Ethics Committee of Longyan First Affiliated Hospital of Fujian Medical University. Written informed consent was obtained from all participants. The study was conducted in accordance with the principles of the Declaration of Helsinki.

Study population

We consecutively enrolled participants from December 2022 to June 2024 at the Pulmonary and critical care medicine Department, Longyan First Affiliated Hospital of Fujian Medical University. Eligible participants included outpatients and inpatients who were diagnosed with COPD, heavy smokers, and healthy individuals. The diagnosis of COPD was based on a comprehensive assessment, including a documented history of risk factor exposure, clinical symptoms and signs, and pulmonary function testing, while excluding other diseases that could cause similar symptoms and persistent airflow limitation.

Exclusion criteria included: (1) coexisting asthma; (2) bronchiectasis; (3) active pulmonary tuberculosis; (4) malignancy; (5) newly diagnosed pneumonia; (6) pulmonary fibrosis; (7) endobronchial foreign body; (8) severe immunosuppression, such as organ transplantation or acquired immuno-deficiency syndrome (AIDS); and (9) uncontrolled chronic systemic diseases, such as severe hypertension, uncontrolled diabetes mellitus, significant hepatic or renal impairment, and neurological disorders.

Data collection and definitions

For all enrolled participants, demographic and anthropometric data were recorded, including age, sex, height, weight, and body mass index (BMI). For patients with COPD, additional information was collected, comprising smoking history, medical history, symptom profile, long-term medication use, comorbidities, pulmonary function test results, sputum culture findings. Disease-specific assessments included the modified Medical Research Council (mMRC) dyspnea scale, the COPD Assessment Test (CAT), and the St George’s Respiratory Questionnaire (SGRQ). Pulmonary function was graded according to the Global Initiative for Chronic Obstructive Lung Disease (GOLD) criteria, based on the percentage of predicted forced expiratory volume in one second (FEV₁), with disease severity categorized from stage I to IV. Clinical grouping was also performed according to the GOLD 2023 COPD assessment framework (GOLD ABE Assessment Tool). Patients categorized as group A have modified Medical Research Council (mMRC) dyspnea scale scores of 0–1, a COPD Assessment Test (CAT) score < 10, and a history of zero or one moderate exacerbation not leading to hospitalization. Patients in group B have mMRC scores ≥ 2, a CAT score ≥ 10, and a history of zero or one moderate exacerbation not leading to hospitalization. Patients in group E have a history of ≥ 2 moderate exacerbations or ≥ 1 exacerbation leading to hospitalization, irrespective of their mMRC or CAT scores. For heavy smokers, smoking history, imaging studies, and pulmonary function test reports were recorded. For healthy controls, imaging studies and pulmonary function test reports were documented. For patients with AECOPD, we additionally documented the presence or absence of concomitant invasive pulmonary aspergillosis (IPA).

All patients with COPD, were followed up for one year to evaluate clinical outcomes, including changes in respiratory symptoms (assessed by the mMRC dyspnea scale and the CAT), pulmonary function decline, occurrence of comorbidities, health-related quality of life (assessed by the SGRQ), frequency of acute exacerbations or hospitalizations, medication use, and mortality, if applicable.

An exacerbation of COPD is defined as an event characterized by dyspnea and/or cough and sputum that worsen over < 14 days. Exacerbations of COPD are often associated with increased local and systemic inflammation caused by airway infection, pollution, or other insults to the lungs1.

Heavy smokers were defined as individuals aged 35–80 years who smoked at least 20 cigarettes per day and had a smoking index ≥ 400. The smoking index was calculated as the number of cigarettes smoked per day multiplied by the number of years of smoking.

For the definition of COPD complicated by IPA, we referred to the Bulpa criteria16: Proven IPA was diagnosed by existence of mycelium and related tissue damage in histopathological examination of lung tissue and was accompanied by any of the following: (1) isolation of Aspergillus in the lower respiratory tract (LRT) samples; (2) positive serum Aspergillus antigen or antibody; (3) direct molecular immunology or culture methods observed that the mycelium was Aspergillus filaments. Probable IPA was diagnosed by the coexistence of host factors (severe COPD patients according to GOLD stage and usually treated by steroids), clinical manifestations (COPD patients had a recent exacerbation of dyspnea and suggestive chest imaging, and poor response to regular treatment) and microbiological evidence (Aspergillus isolation in LRT sample or two consecutive positive serum galactomannan [GM] tests). As for possible IPA, it required host factors but without microbiological evidence. Colonization was defined as isolation of Aspergillus in LRT samples without any symptom or new pulmonary infiltrate16. In this study, Proven/probable IPA were taken as IPA; possible IPA with positive response to antifungal therapy were also considered as IPA. Since pathological confirmation was not available, all patients in our study were clinically diagnosed. We applied slight modifications to the Bulpa criteria. For suspected cases, we additionally performed bronchoalveolar lavage (BAL) GM testing. If a patient’s BAL GM test was positive while the serum GM test was negative, we considered the BAL GM positivity as supportive evidence for the diagnosis of IPA.

All the above subgroup classification processes were independently determined by two senior respiratory physicians to ensure the reliability of the classification.

Sample collection and assays

After rigorous screening and enrollment, peripheral blood samples were collected from all included participants—COPD patients, heavy smokers, and healthy controls. PBMCs were isolated to assess miR-155 expression, and serum samples were used to measure the levels of inflammatory cytokines, including IL-1β, IL-6, IL-8, and TNF-α. The expression levels of miR-155 and the aforementioned inflammatory cytokines were compared among the three study groups. Correlation analyses were performed to evaluate the relationships between miR-155 and serum inflammatory cytokines. Receiver operating characteristic (ROC) curve analysis was conducted to assess the predictive value of miR-155 expression levels for disease progression in patients with COPD.

Experimental methods

miR-155 expression analysis

A 5 mL peripheral venous blood sample was collected from each participant. PBMCs were isolated using Percoll density gradient centrifugation. The expression level of miR-155 in PBMCs was determined using RT-PCR. Experimental procedures included the following steps: First, total RNA was extracted from samples using TRIzol reagent (Invitrogen, USA), and the purity (A260/A280 ratio was between 1.8 and 2.0) and concentration of RNA were detected using Nanodrop 2000 (Thermo Fisher Scientific, USA). Subsequently, reverse transcription reaction was performed according to the instructions of the miScript Reverse Transcription Kit (Qiagen, Germany) to reverse transcribe RNA into cDNA. In the PCR amplification stage, U6 small nuclear RNA (U6 snRNA) was used as the reference miRNA (it is a commonly used reference gene in miRNA detection due to its stable expression in various tissue samples), and real-time quantitative PCR was performed using the miScript SYBR Green PCR Kit (Qiagen, Germany). The 2^(−ΔΔCt) method was used for normalization calculation: first, the Ct value difference between the target gene (miR-155) and the reference gene (U6 snRNA) was calculated (ΔCt = Ct miR-155 − Ct U6 snRNA), then the difference was calculated based on the ΔCt values of the control group and the patient group (ΔΔCt = ΔCt patient group - ΔCt control group), and finally the relative expression level of miR-155 was expressed as 2^(−ΔΔCt).

Measurement of inflammatory cytokines (TNF-α, IL-8, IL-6, IL-1β)

Serum was separated from whole blood by centrifugation. The concentrations of serum inflammatory cytokines (including TNF-α, IL-8, IL-6, IL-1β) were determined using ELISA. All procedures were conducted strictly in accordance with the manufacturer’s instructions provided with the commercial ELISA kits. Specifically, the units of cytokines (TNF-α, IL-8, IL-6, IL-1β) detected in this study are all unified as “pg/mL”; the information of the ELISA kits used is as follows: the detection range of the TNF-α kit (Elabscience, Cat. No. E-EL-H0109c) is 1.24–50 pg/mL, with a lower limit of detection (LLOD) of 0.56 pg/mL; the detection range of the IL-8 kit (Elabscience, Cat. No. E-EL-H6008) is 1.24–50 pg/mL, with an LLOD of 0.56 pg/mL; the detection range of the IL-6 kit (Elabscience, Cat. No. E-EL-H6156) is 1.36–100 pg/mL, with an LLOD of 0.84 pg/mL; the detection range of the IL-1β kit (Elabscience, Cat. No. E-EL-H0149c) is 1.56–100 pg/mL, with an LLOD of 0.82 pg/mL.

Statistical analysis

Statistical analyses were performed using SPSS version 27.0 and R software version 4.4.0. Continuous variables with a normal distribution were expressed as the mean ± standard deviation (SD), whereas those with a non-normal distribution were reported as the median with interquartile range (P25, P75). Categorical variables were presented as frequencies and percentages. For comparisons between two groups, the independent-samples t-test was applied for normally distributed data, and the Mann–Whitney U test was used for non-normally distributed data. For comparisons among multiple groups, one-way analysis of variance (ANOVA) was applied for normally distributed data, and the Kruskal–Wallis rank-sum test was used for non-normally distributed data. After confirming a statistically significant overall difference among groups, the Bonferroni method was used for multiple comparison correction to control the Type I error caused by multiple testing. Differences in categorical variables were assessed using the χ2 test or Fisher’s exact test (if the presence of cells with expected counts were less than 5). Multivariate regression adjusted for smoking status (current/former) was performed to compare miR-155 expression levels between the AECOPD and the stable COPD. The diagnostic value of miR-155 expression for acute exacerbations of COPD was evaluated using receiver operating characteristic (ROC) curve analysis, with the optimal cut-off value, sensitivity, and specificity determined at the point of maximum Youden index. For correlation analyses, Pearson’s correlation coefficient was used for normally distributed data and Spearman’s rank correlation coefficient for non-normally distributed data. A two-tailed P value of < 0.05 was considered statistically significant.

Results

Baseline characteristics of study groups

The demographic characteristics and lung function parameters of the study groups are summarized in Table 1. Participants were categorized as follows: acute exacerbation of COPD (AECOPD, n = 29), stable COPD (n = 30), heavy smokers without COPD (n = 31), and healthy controls (n = 27).T he mean age ± standard deviation (SD) was 70.03 ± 8.11 years for the AECOPD group, 69.57 ± 6.76 years for the stable COPD group, 67.52 ± 7.72years for the heavy smoker group, and 67.44 ± 6.70 years for the healthy control group. Sex distribution was predominantly male across groups: AECOPD (93.1%, 27 M/2F), stable COPD (93.3%, 28 M/2F), heavy smokers (90.3%, 28 M/3F), and healthy controls (77.8%, 21 M/6F). The mean body mass index (BMI) was 23.38 ± 2.88, 22.80 ± 2.61, 23.74 ± 2.40, and 24.51 ± 2.36 kg/m2 for the AECOPD, stable COPD, heavy smoker, and control groups, respectively. Importantly, no significant differences were observed among groups in age, sex distribution, or BMI (all P > 0.05). However, the distribution of smoking status (never, former, and current smokers) differed significantly among groups (P < 0.05). Furthermore, lung function assessed by median (interquartile range [IQR]) percent predicted forced expiratory volume in 1 s (FEV1%) differed significantly (P < 0.05). FEV1% values were 30.0 (25.7–43.6) in AECOPD, 65.7 (40.9–86.1) in stable COPD, 101.5 (91.8–112.3) in heavy smokers, and 97.5 (87.0–109.5) in healthy controls. In contrast, no significant differences were observed in the distribution of comorbidities, GOLD stages, or ABE groups across groups (P > 0.05) (Table 1).

Table 1 The basic characteristics of the AECOPD group, stable COPD, heavy—smoking group, and healthy control group.
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Influence of baseline factors on miR-155 and cytokines

miR-155 levels did not differ significantly by age, BMI, or major comorbidities (P > 0.05). In stable COPD, current smokers had higher miR-155 than former smokers (P < 0.05), while no such difference was observed in AECOPD. Smoking cessation duration (< 5 vs. ≥ 5 years) did not affect miR-155.

For cytokines: IL-1β, IL-6, and IL-8 were higher in males (due to the relatively small sample size of females, the clinical significance of this difference needs further verification). IL-1β and IL-8 also increased in those with cardiovascular comorbidities (all P < 0.05). IL-8 was higher in AECOPD patients with longer smoking cessation history (≥ 5 years). TNF-α levels showed no differences across age, BMI, smoking status, or comorbidities (Table 2)

Table 2 Expression levels of miR-155, IL-1β, IL-6, IL-8, and TNF-α across subjects with different baseline characteristics.
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miR-155 expression across groups

Serum miR-155 levels were significantly elevated in the heavy-smoking group, AECOPD group, and stable COPD group compared to the healthy control group (all P < 0.01). Furthermore, miR-155 levels in the heavy-smoking group were significantly higher than those in the stable COPD group (P < 0.01). Within the COPD cohort, patients with AECOPD exhibited higher miR-155 levels than those with stable COPD (P < 0.01). Given the exclusive smoking exposure history in COPD patients and the need to isolate acute exacerbation effects, we restricted multivariate linear regression analyses to the COPD cohort (n = 59, including stable and AECOPD groups). After adjusting for smoking status(former/current), miR-155 levels remained significantly elevated in the AECOPD group compared to the stable COPD group (P < 0.01). ROC curve analysis demonstrated that serum miR-155 had considerable diagnostic utility for AECOPD, with an area under the curve (AUC) of 0.872 (95% CI 0.782–0.963), sensitivity of 68.97%, and specificity of 96.67%. Furthermore, within the AECOPD cohort, no significant differences were observed among IPA patients (n = 8) and non-IPA patients (n = 21) in age, sex distribution, smoking status, or comorbidities (all P > 0.05). However non-IPA patients showed significantly higher serum miR-155 levels than IPA patients (P < 0.05). ROC curve analysis demonstrated that serum miR-155 had considerable diagnostic utility for IPA, with an area under the curve (AUC) of 0.899 (95% CI 0.699–1.000), sensitivity of 100.00%, and specificity of 87.50%. (Tables 3 and 4; Figs. 1 and 2).

Table 3 Comparison of miR-155 levels between the AECOPD group, stableCOPD, the heavy—smoking group, and the healthy control group.
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Table 4 The basic characteristics of the IPA group and the n-IPA group.
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Fig. 1
Fig. 1The alternative text for this image may have been generated using AI.
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Expression of miR-155 in the AECOPD, stable COPD, heavy-smoking, and healthy control groups and its diagnostic value for AECOPD. There was a statistically significant difference in the expression of miR-155 among the four groups (a). Further analysis using the ROC curve demonstrated that miR-155 had a relatively high sensitivity and specificity for diagnosing AECOPD (b). “*” indicates significant difference compared to healthy controls (P < 0.05). “#” indicates significant difference compared to heavy-smoking (P < 0.05). “&” indicates significant difference compared to Stable COPD (P < 0.05).

Fig. 2
Fig. 2The alternative text for this image may have been generated using AI.
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Expression of miR-155 in the IPA group and the non- IPA group and its diagnostic value for IPA. There was a statistically significant difference in the expression of miR-155 among the IPA patients and the non-IPA patients (a). Further analysis using the ROC curve demonstrated that miR-155 had a relatively high sensitivity and specificity for diagnosing IPA (b).

Comparison of inflammatory cytokine levels and their correlation with miR-155

The IL-1β level in the AECOPD group was significantly higher than that in the stable COPD group, heavy smokers, and healthy controls (all P < 0.05). Similarly, IL-6 levels in the AECOPD group were markedly elevated compared to the stable COPD group, heavy smokers, and healthy controls (all P < 0.05). The IL-8 level in the AECOPD group was also significantly higher than in the stable COPD group, heavy smokers, and healthy controls (all P < 0.05). Notably, within the stable COPD group, IL-8 levels were higher than those in heavy smokers and healthy controls (P < 0.05). Additionally, TNF-α levels in the AECOPD group were greater than in heavy smokers and healthy controls (P < 0.05), while TNF-α levels in the stable COPD group were higher than in healthy controls (P < 0.05) (Fig. 3). Additionally, we also performed partial Spearman correlation analysis to evaluate the independent correlation between miR-155 expression and inflammatory cytokines, with adjustment for potential confounding factors including age, gender, and comorbidities (e.g., diabetes mellitus and cardiovascular diseases). The results demonstrated that serum miR-155 levels were weakly but significantly positively correlated with IL-1β (R = 0.22, 95% CI 0.03–0.39, P < 0.05), IL-6 (R = 0.20, 95% CI 0.01–0.37, P < 0.05), IL-8 (R = 0.20, 95% CI 0.02–0.37, P < 0.05), and TNF-α (R = 0.22, 95% CI 0.04–0.39, P < 0.05) (Fig. 4).

Fig. 3
Fig. 3The alternative text for this image may have been generated using AI.
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Comparative analysis of serum inflammatory factors between the AECOPD group, the COPD stable group, the heavy smoking group and the healthy control group. There was a statistically significant difference in the expression of Serum Inflammatory Factors among the four groups. “*” indicates significant difference compared to healthy controls (P < 0.05). “#” indicates significant difference compared to heavy- smoking (P < 0.05). “&” indicates significant difference compared to Stable COPD (P < 0.05).

Fig. 4
Fig. 4The alternative text for this image may have been generated using AI.
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The correlation between miR-155 and serum inflammatory factors. The results showed that miR-155 was significantly weakly correlated with these inflammatory markers.

miR-155 and COPD severity

miR-155 expression showed positive correlations with both GOLD stage (R = 0.35, 95% CI 0.10–0.55, P < 0.01; Fig. 5a) and ABE clinical grouping (R = 0.66, 95% CI 0.49–0.79, P < 0.01; Fig. 5b), indicating association with increasing disease severity.

Fig. 5
Fig. 5The alternative text for this image may have been generated using AI.
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The correlation between miR-155 and the severity of COPD in patients. The results revealed that serum miR-155 levels were positively correlated with both the GOLD stages I–IV (a) and the ABE groups (b).

miR-155 and frequency of exacerbations

COPD patients with frequent exacerbations (FE, ≥ 2/year) had significantly higher serum miR-155 than non-frequent exacerbators (NFE) (P < 0.01; Table 5). miR-155 correlated positively with the number of acute exacerbations over 1 year (R = 0.63, 95% CI 0.45–0.76, P < 0.01; Table 6; Fig. 6).

Table 5 Comparative analysis of miR-155 Levels Between the frequent acute exacerbation groups and the non-frequent acute exacerbation groups.
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Table 6 The distribution of the number of acute exacerbations in COPD patients within 1 year.
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Fig. 6
Fig. 6The alternative text for this image may have been generated using AI.
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The correlation between miR-155 and the number of acute exacerbations in COPD patients within 1 year. The results revealed that serum miR-155 levels were positively correlated with the number of AECOPD.

Discussion

Our study systematically evaluated serum miR-155 and key inflammatory cytokines (IL-1β, IL-6, IL-8, TNF-α) across COPD clinical status and heavy smokers, evaluated diagnostic performance for AECOPD, examined associations with disease severity, and assessed predictive value for future exacerbations over 1 year. Our findings demonstrate that miR-155 is not only elevated in COPD patients and heavy smokers compared with healthy controls, but also further increased during acute exacerbations. Furthermore, miR-155 levels correlate with systemic inflammatory cytokines and COPD severity, and are independently associated with frequent exacerbations. These results highlight miR-155 as a potential biomarker reflecting both disease activity and progression in smoking-related COPD. Notably, miR-155 was lower in AECOPD with IPA than in non-IPA infections, suggesting etiologic specificity. Particularly importantly, the multidimensional value of miR-155 spanning diagnosis, stratification and prognosis provides a feasible molecular target for the personalized management of COPD, which is expected to address the current clinical challenges of insufficient accuracy in disease assessment and homogeneity of treatment regimens.

Smoking-related upregulation and implications for COPD pathogenesis

Smoking is the primary risk factor for COPD, inducing cellular apoptosis, airway remodeling, and emphysema18,19. miR-155, a multifunctional microRNA on chromosome 21, is implicated in pulmonary inflammatory disorders20. Previous studies demonstrate miR-155 upregulation in cigarette smoke-induced COPD models and human lung tissues, with highest expression in smoking COPD patients13,21. Mechanistically, cigarette smoke extract induces miR-155 in a dose- and time-dependent manner, while miR-155 inhibition attenuates smoke-induced inflammation and apoptosis12,13.

Our findings confirm significantly elevated miR-155 in both heavy smokers and COPD patients versus controls (P < 0.01). However, COPD patients did not consistently exceed heavy smokers in miR-155 levels, likely reflecting the high smoking cessation rate (78%) in our COPD cohort. Supporting this interpretation, current smokers with stable COPD had higher miR-155 than former smokers (P < 0.05). These data suggest that miR-155 reflects ongoing and cumulative smoke exposure rather than disease presence alone. The lack of difference between smoking cessation durations (< 5 vs. ≥ 5 years) may indicate either prolonged miR-155 elevation post-cessation or insufficient power to detect temporal changes. Overall, miR-155 appears to be a smoke-responsive biomarker involved in COPD pathogenesis, with levels modulated by current exposure status. Notably, miR-155 is a pan-inflammatory mediator elevated in asthma, ILD, pneumonia, autoimmune diseases and malignancies22,23,24,25,26. Because we did not include non-COPD respiratory disease controls, we cannot determine whether the observed increases reflect COPD-specific pathobiology or simply mirror smoking-associated inflammation. Indeed, our heavy-smoker group already displays marked miR-155 up-regulation. Thus, elevations should be interpreted as quantifying inflammatory burden rather than confirming COPD diagnosis, and future work must compare COPD patients with age-matched smokers, asthmatics, pneumonia cases and fibrotic ILD to establish true diagnostic specificity.

Diagnostic utility in acute exacerbations and etiologic differentiation

AECOPD is a critical event associated with deteriorating health status, accelerated lung function decline, and increased mortality risk27. However, due to AECOPD heterogeneity, reliable biomarkers for accurate diagnosis remain lacking28. Our study demonstrated significantly elevated miR-155 levels in AECOPD versus stable COPD (P < 0.01), with good diagnostic performance (AUC = 0.8724; sensitivity 68.97%, specificity 96.67%), Taking into account the potential confounding effect of smoking, we conducted a multiple linear regression analysis. After adjusting for smoking status, miR-155 levels remained significantly higher in the AECOPD group than in the stable COPD group, suggesting miR-155’s potential in distinguishing acute exacerbations.

Respiratory infections are the primary AECOPD trigger, with viral infections predominating (~ 50% of cases)29, followed by bacterial infections (37.4% culture-positive in Chinese cohorts)30. Invasive pulmonary aspergillosis (IPA) also occurs in severe COPD patients, with high mortality rates (up to 77%) due to delayed diagnosis16,17. Identifying biomarkers to distinguish causative pathogens holds substantial clinical significance for targeted therapy. Previous studies show miR-155 upregulation in viral (COVID-19, influenza, RSV) and bacterial (LPS, Staphylococcus) infections15,31,32,33,34, while Aspergillus exposure induces miR-132 but not miR-15514. Based on this differential response, we compared miR-155 levels between IPA and non-IPA infections in AECOPD patients. miR-155 was significantly lower in the IPA group (P < 0.01), suggesting its potential value in pathogen differentiation. Unfortunately, the sample size of IPA cases in this study was relatively small. Future studies should include larger sample sizes and further differentiate between bacterial and viral infections to clarify the diagnostic utility of miR-155 in targeted anti-infective therapy for AECOPD. From the perspective of clinical applicability, the high specificity of miR-155 for AECOPD (96.67%) can effectively reduce overdiagnosis and unnecessary antibiotic administration. Moreover, its ability to differentiate between invasive pulmonary aspergillosis (IPA) and non-IPA infections enables the guidance of early clinical initiation of antifungal therapy, thereby lowering the mortality rate of patients with severe AECOPD. Considering the current limitations of AECOPD aetiological identification, which relies on either time-consuming, low-yield pathogenic culture or high-cost nucleic acid testing, miR-155, as a serological marker, offers the advantages of convenient detection and rapid result turnaround, making it more suitable for widespread application in primary healthcare settings.

miR-155 and inflammatory cytokines in COPD

Systemic inflammation is central to COPD progression. We observed a stepwise increase in IL-1β, IL-6, IL-8, and TNF-α from healthy controls to heavy smokers to stable COPD, peaking in AECOPD (all P < 0.01), consistent with prior studies35. However, IL-6 levels in stable COPD did not differ from controls, contrasting with Zeng et al.36, and IL-8 levels in heavy smokers were similar to controls, differing from prior reports37. Common inhaled corticosteroids (ICS) use in our COPD cohort may explain these discrepancies, as ICS therapy can attenuate cytokine expression38, and our relatively small sample size may also contribute. Animal data indicate miR-155 upregulation augments proinflammatory cytokines, whereas its inhibition reduces them39. After adjusting for potential confounding factors including age, gender, and comorbidities (e.g., diabetes mellitus and cardiovascular diseases), our correlation analysis demonstrated that serum miR-155 levels were weakly yet significantly positively correlated with interleukin-1β (IL-1β), interleukin-6 (IL-6), interleukin-8 (IL-8), and tumor necrosis factor-α (TNF-α). The modest correlations likely reflect ICS-mediated attenuation of systemic inflammation40, as ICS therapy reduces inflammatory biomarkers including TNF-α, IL-1β, and IL-6, thereby weakening miR-155–cytokine associations. However, due to ICS use was irregular and closely tied to GOLD stage, we could not adjust for its potential confounding effect; the observed associations may therefore be modestly over- or underestimated. Overall, miR-155 appears to index systemic inflammatory burden in COPD, though effect sizes are moderated by treatment and heterogeneity. Prospective studies with strictly standardised ICS regimens (fixed dose, duration and adherence monitoring) are required in the future to isolate the drug-specific influence on miR-155 and systemic inflammation from the underlying disease severity.

miR-155 as a marker of disease severity

COPD severity is traditionally assessed by spirometric airflow limitation (GOLD stages I–IV based on FEV₁% predicted)1. Previous studies reported higher miR-155 expression in GOLD stages III–IV versus stage II12. Our study demonstrated a positive correlation between miR-155 and GOLD stages I–IV (R = 0.35, P < 0.01), indicating that miR-155 reflects airflow limitation severity. However, FEV₁-based assessment does not capture overall disease burden and systemic manifestations41,42. Current COPD evaluation incorporates symptoms and exacerbation risk to guide optimal management1. The GOLD 2023 classification uses ABE grouping, with group E including patients with ≥ 2 moderate exacerbations or ≥ 1 hospitalization1.We found that miR-155 levels were highest in group E, intermediate in group B, and lowest in group A, with strong correlation between miR-155 and ABE grouping (R = 0.66, P < 0.01). This indicates that patients with higher symptom scores and more frequent exacerbations demonstrate elevated miR-155 expression.

These results suggest that miR-155 increases with both airflow limitation and multidimensional disease severity, positioning it as a potential biomarker for comprehensive COPD severity assessment. The strong correlation between miR-155 and GOLD ABE groups (R = 0.66, P < 0.01) directly aligns with the core requirement of current personalized COPD management, namely stratified therapy based on disease severity and exacerbation risk. In clinical practice, for group E patients with high miR-155 expression, priority may be given to combined therapy with intensified anti-inflammatory agents and long-acting bronchodilators, along with increased follow-up frequency. In contrast, for group A patients with low miR-155 expression, treatment regimens can be simplified to avoid overtreatment. This biomarker-based stratification strategy is capable of optimizing the allocation of medical resources and improving the cost-effectiveness of treatment.

Prognostic value of miR-155 for AECOPD

COPD patients experience 0.5–3.5 acute exacerbations annually43. Frequent exacerbations are associated with poorer quality of life, accelerated lung function decline, and increased risk of future exacerbations, cardiovascular events, and mortality44,45. Predicting exacerbations could markedly improve patient outcomes and reduce disease burden. While FEV₁% predicted is commonly used to assess COPD severity46, Zhang et al. demonstrated weak correlation between airflow limitation and exacerbation frequency—39 patients with FEV₁ <50% had < 2 exacerbations in 12 months, while 20 patients with FEV1 >50% had ≥ 2 exacerbations47. This underscores that spirometry alone cannot capture COPD complexity. Prior exacerbation history is a strong predictor of future risk48.

We found significantly higher miR-155 in AECOPD versus stable COPD, suggesting involvement in exacerbation pathogenesis. During 1-year follow-up, frequent exacerbators (FE) had markedly elevated miR-155 compared with non-frequent exacerbators (NFE) (P < 0.01). Correlation analysis revealed a robust positive association between miR-155 expression and exacerbation number (R = 0.63, P < 0.01), indicating that higher miR-155 levels predict increased exacerbation frequency. These findings highlight miR-155 as a potential molecular biomarker for predicting AECOPD. However, our study had a relatively small sample size and lacked adjustment for confounding variables.

The predictive value of miR-155 for future acute exacerbations constitutes one of the core highlights of its clinical translation. Currently, the identification of populations at high risk of acute exacerbations in clinical practice primarily relies on retrospective reviews of past medical history, with a lack of prospective predictive biomarkers. If miR-155 levels could be measured during the stable phase of the disease, proactive interventions (e.g., influenza vaccination, optimization of inhaled medication adherence, and pulmonary rehabilitation training) could be administered to patients with high miR-155 expression, which is expected to reduce the incidence of acute exacerbations. In terms of detection feasibility and cost-effectiveness, serum miR-155 can be quantified via quantitative real-time polymerase chain reaction (qRT-PCR)- a technique that has been widely adopted in clinical laboratories across hospitals at all levels. With a single test costing approximately $7–14 (equivalent to 50–100 Chinese yuan), this approach is far more economical than genetic sequencing or protein chip assays, thus satisfying the requirements for large-scale clinical application. From the perspective of the translation timeline, following the completion of multicenter validation studies, miR-155 is expected to be incorporated into clinical management guidelines for COPD within 2–3 years and serve as a routine assessment biomarker.

Limitations and future directions

Our study has several limitations. First, this observational design cannot establish causality and the relatively short follow-up period and limited sample size, particularly for subgroup analyses (e.g., IPA vs. non-IPA), may have introduced bias and reduced precision. Second, this study only collected miR-155 samples from patients at a single time point, lacking longitudinal dynamic monitoring data throughout the entire course of acute exacerbation (i.e., stable phase → exacerbation phase → remission phase). Third, this study was conceived as a hypothesis-driven validation of miR-155, chosen because prior work links it to smoking-driven inflammation and COPD evolution. We did not perform unbiased discovery, and other miRNAs (e.g., miR-21, miR-146a, miR-223, miR-126) may provide equal, additive, or superior information. Fourth, we quantified total serum miR-155 without isolating the exosomal fraction. Consequently, we cannot determine whether the observed elevation reflects active, exosome-mediated secretion involved in disease pathobiology or passive release from damaged/apoptotic cells. Fifth, absence of other respiratory disease controls prevents conclusions about COPD specificity; miR-155 should be viewed as a general inflammatory signal whose increment may assist, but not replace, clinical diagnosis.

Large, multi-ethnic cohorts with extended follow-up should integrate genome-wide miRNA profiling to build and prospectively validate multi-marker panels, and formally test whether any miRNA signature improves prediction beyond established indices (prior exacerbations, FEV1). In particular, longitudinal repeated-measurement studies need to be conducted to dynamically track the changing patterns of miR-155 throughout the occurrence, progression, and remission of acute exacerbations, and to clarify its temporal relationship with clinical symptoms and inflammatory markers. Longitudinal data can not only further validate the reliability of miR-155 as a biomarker of disease activity, but also determine the threshold values for its application in therapeutic efficacy monitoring—for instance, whether the magnitude of the decrease in miR-155 levels after treatment can serve as an indicator for evaluating the effectiveness of anti-inflammatory therapy. Additionally, subsequent work should compare exosome-derived versus total-serum miR-155 (and other miRNAs) to establish which fraction provides superior diagnostic accuracy and biological insight in COPD; at the etiological research level, it is necessary to further distinguish between bacterial and viral infection subtypes in AECOPD to optimize the application value of miR-155 in etiology-specific diagnosis.

Conclusion

These findings support the role of miR-155 in COPD pathophysiology and clinical assessment. Key findings include: (1) miR-155 is significantly elevated in smoking-related COPD, with potential utility for identifying AECOPD and shows discriminative value in differentiating IPA from non-IPA infections; (2) miR-155 correlates positively with inflammatory cytokines and disease severity, indicating value in assessing inflammatory burden and clinical severity; (3) prospective analysis reveals miR-155’s predictive capacity for exacerbation frequency.

These findings position miR-155 as a promising molecular biomarker for enhanced risk stratification, exacerbation prediction, and personalized COPD management. In future studies, we will focus on integrating miR-155 with multi-omics data and clinical predictors to further promote the application of precision medicine in COPD management, while also employing high-throughput screening techniques for the development of multi-miRNA panels to further refine and optimize the biomarker landscape in COPD.