Peripheral blood cytokine expression levels and their clinical significance in children with influenza

Introduction

Influenza A virus causes the zoonotic acute respiratory infection known as influenza A. The primary subtypes infecting humans are H3N2 and H1N1, with outbreaks mainly occurring in the spring and winter. During these seasons, there is an increase in epidemic frequency, higher morbidity rates, greater contagiousness, and rapid transmission, all of which negatively impact human health. Children are especially vulnerable to influenza, with 20–30% of children worldwide contracting seasonal influenza annually. This high prevalence places a significant financial burden on families and societies and leads to a substantial number of pediatric consultations and hospital admissions (1-3).

Influenza virus infection activates the host immune system, stimulating the expression of various inflammatory and immunomodulatory cytokines (4). Children are more susceptible to influenza infections because their immune systems are not yet fully developed, making them prone to multiple complications and severe illnesses (5,6), for example, one study reported that out of 722 children notified, 613 had laboratory-confirmed influenza and at least one severe complication (7). Therefore, our research emphasizes understanding the pathogenesis of influenza in children, particularly severe cases, as well as the associated immune responses.

Interferons (IFNs), a family of cytokines, play a critical role in the innate immune response to viruses and other microbial pathogens (8). During viral infection, interferon alpha (IFN-α) activates macrophages, stimulates dendritic cell maturation, and induces cytokine and chemokine production in epithelial cells (9,10). Additionally, IFN-α isoforms also play distinct roles in immune responses, with certain isoforms linked to the pathogenicity of influenza strains (11). One study found that interferon alpha-2 (IFN-α2) expression was elevated in the lungs of mice infected with H1N1 and H3N2 (12). Therefore, we hypothesize that IFN-α2 may hold significant research potential in studying H1N1 and H3N2 infections in children.

The chemokine family, consisting of approximately 40 proteins, shows diverse chemokine receptor expression across different leukocyte subpopulations. Upon viral infection of epithelial cells, chemokines such as interleukin-8 (IL-8) and monocyte chemoattractant protein-1 (MCP-1) are secreted, attracting specific inflammatory cells to the infection site via the bloodstream, thereby exacerbating the infection (13,14). Interestingly, there are ongoing efforts to explore anti-MCP-1 antibodies or drugs as potential treatments for influenza A virus infections, although these attempts have not yielded significant results (15,16). Further investigation into the role of MCP-1 in childhood influenza infections could pave the way for the development of effective therapeutic drugs. Similarly, interleukin-6 (IL-6) and interleukin-10 (IL-10) may share molecular similarities, as they primarily operate through the JAK-STAT signaling pathways (17). Previous studies have demonstrated that mice (18) infected with influenza A exhibit significantly elevated IL-6 levels. However, limited research has been conducted to determine whether cytokine levels differ among children with influenza of varying severities.

This study aims to evaluate IFN-α2 and MCP-1 as biomarkers for early detection and severity prediction in pediatric influenza. Specifically, we examined the levels of cytokines, including IFN-α2, MCP-1, IL-6, IL-8, and IL-10, among others, and evaluated their expression in severe pediatric influenza cases, along with their correlation to disease progression, with the expectation of identifying early warning indicators of severe influenza. We present this article in accordance with the STARD reporting checklist (available at https://tp.amegroups.com/article/view/10.21037/tp-2024-534/rc).

Methods

General clinical data

This study recruited 119 patients from the Department of Respiratory Medicine at the Children’s Hospital of Soochow University between February and March 2023. All subjects were evaluated and screened for study eligibility by the first author prior to study entry. This was a convenience sample of children with influenza A; the subjects were enrolled when the author was present in the respiratory department. While this approach facilitates efficient data collection, it inherently carries the risk of selection bias, as the sampled population may not fully represent the broader target population. Efforts were made to mitigate this bias by ensuring that the enrolled subjects represented a diverse age range and by matching the control group for age and gender in a 3:1 ratio.

After screening and exclusion, a total of 57 children diagnosed with influenza A were enrolled. The cohort consisted of 30 males (52.64%) and 27 females (47.36%), with a male-to-female ratio of 1.11:1. The ages of the children with influenza A ranged from 1 month to 18 years. These children exhibited influenza-like symptoms, including fever, sore throat, runny nose, nasal congestion, rhinitis, mental changes, and shortness of breath. Based on clinical symptoms and imaging findings, the children were divided into two groups: 32 cases in the mild-to-moderate group and 25 cases in the severe group. The influenza virus subtypes (H1N1 or H3N2) were identified through nucleic acid detection of viral antigens, with 49 cases of H1N1 and 8 cases of H3N2. Additionally, 20 children undergoing elective surgery in the Department of Otorhinolaryngology were selected as the control group. For children tested for cytokines and laboratory tests after hospitalization, the tests were performed within the first 48 hours of admission. We reviewed the database of patients, and patient charts were subsequently examined retrospectively following the influenza A study by an author with over five years of experience.

Inclusion and exclusion criteria

Inclusion criteria: according to the Expert Consensus on the Diagnosis and Treatment of Influenza in Children (2020 edition) (19), patients diagnosed with influenza A met the following criteria: (I) a temperature of 38 ℃ or higher, accompanied by chills and local clinical symptoms such as sore throat, nasal congestion, runny nose, cough, and systemic symptoms like fatigue and generalized muscle aches; (II) a positive test for influenza A virus nucleic acid antigen. Severe influenza cases were classified based on one or more of the following conditions: (I) dyspnea or increased respiratory rate; (II) Altered mental status, including unresponsiveness, lethargy, agitation, or convulsions; (III) severe vomiting and diarrhea with signs of dehydration, oliguria or acute renal failure, respiratory failure, septic shock, and multiple organ dysfunction; (IV) a significant worsening of pre-existing underlying conditions; (V) other clinical conditions necessitating hospitalization. The control group included individuals with no recent history of infection who underwent elective surgery during the same period, matched for age and gender.

Exclusion criteria: individuals were excluded if they had: (I) illnesses caused by non-influenza A infections affecting the digestive, immune, or hematologic systems; (II) mental illness or cognitive dysfunction; (III) genetic disorders or malignant tumors; (IV) a history of chronic lung diseases.

Plasma or serum specimen analysis

The Human Inflammation Panel 1 (BioLegend, USA) is a bead-based multiplex assay using fluorescence-encoded beads, suitable for use on various flow cytometers (20). Standards were prepared according to the kit instructions, after which standards and test samples were added to the 96-well plate, followed by the addition of capture microspheres. The mixture was incubated at room temperature with shaking, shielded from light, for 2 hours, washed, and then the detection antibody was added and incubated for 1 hour. After a second wash, the microspheres were transferred to flow tubes for on-line detection. The results were analyzed by two trained researchers, who were blinded to the patients’ information, the sample groupings, and the study hypothesis. To ensure blinding, all samples were assigned anonymized codes by a third-party individual who was not involved in the data analysis. Additionally, the study hypothesis and expected outcomes were not disclosed to the analysts during the experimental or analysis phases. Abnormal or inconclusive results were repeated for validation and included in the final analysis.

Statistical analysis

Data analysis was performed using GraphPad Prism 8.0 and SPSS Statistics 26.0. Measurement data were tested for normality and presented as mean ± standard deviation (x± s) for normally distributed data. One-way analysis of variance (ANOVA) was used for comparisons between multiple groups, and a t-test was applied for comparisons between two groups. Data with skewed distributions were expressed as median (interquartile range) [M (P25, P75)], and the Kruskal-Wallis H test was used for comparisons between three groups. Pairwise comparisons were performed using the two-by-two method with the Bonferroni correction applied for multiple testing. Mann-Whitney U tests were used for comparisons between two groups. Categorical data were expressed as counts and percentages, and the χ² test or Fisher’s exact test was used for group comparisons. In correlation analysis, Spearman’s rank correlation coefficients were used to assess relationships between ordered and continuous variables with non-normal distributions. Logistic regression analysis was performed to calculate the odds ratio (OR) and 95% confidence interval (CI). A two-tailed significance level of α = 0.05 was used for all tests.

Ethics statement

The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by the Institutional Review Board of the Children’s Hospital of Soochow University (Approval No. 2021CS164). Written informed consent was obtained from the guardians of all participants.

Results

Altered cytokine gene expression in children with influenza A

We first conducted RNA sequencing analysis on three blood samples from children with influenza-related pneumonia: the severe group, the mild-to-moderate group and healthy controls, to identify differentially expressed genes (DEGs) among these groups. A total of 1,454 DEGs were identified across the three groups, with 822 genes upregulated and 632 genes downregulated (Figure 1A). Venn diagrams derived from pairwise comparisons of the three groups revealed 31 genes that were differentially expressed in all three groups (Figure 1B). These DEGs were primarily involved in immune response regulation, inflammatory response, and other biological processes through signaling pathways such as Th1/Th2 cell differentiation, cytokine-cytokine receptor interactions, and more (Figure 1C,1D). We then constructed a protein-protein interaction (PPI) network and analyzed it to identify key genes and co-expression networks associated with influenza. Based on the relative expression heatmap of cytokines and their receptor genes, we selected six cytokines—IFN-α2, MCP-1, IL-6, IL-8, IL-10, and interleukin-18 (IL-18)—for further investigation (Figure 1E).

Figure 1 The RNA sequencing analysis. (A) Cluster analysis heatmap: three samples per group with genes represented horizontally and one sample per column. (B) Venn diagram of DEGs. (C) GO analysis identifying the biological functions of DEPs. (D) KEGG pathway analysis of DEPs. (E) PPI network analysis of DEGs. Con, control; SG, the severe group; MMG, the mild-to-moderate group; DEGs, differentially expressed genes; GO, gene ontology; DEPs, differentially expressed proteins; ECM, extracellular matrix; KEGG, Kyoto Encyclopedia of Genes and Genomes; PD-1, programmed cell death protein 1; PD-L1, programmed death-ligand 1; PPI, protein-protein interaction; NF, nuclear factor.

Clinical and laboratory characteristics of influenza A patients

Clinical data were collected through the electronic medical record system, including age, sex, and the number of hospital days. There were no statistically significant differences in gender and age between the influenza and control groups (P>0.05, Table 1). A two-by-two comparison of six cytokines revealed significant elevations in IFN-α2, MCP-1, IL-6, IL-8, IL-10, and IL-18 in the influenza A group compared to the control group (P≤0.001).

In children with influenza A, WBC, AST, and ESR did not show significant correlations with plasma levels of IFN-α2, MCP-1, IL-6, IL-8, IL-10, or IL-18 (P>0.05). However, IFN-α2 exhibited a negative correlation (r<0, P<0.05) with LDH and globulin, and a positive correlation (r>0, P<0.05) with CD3⁺CD4⁺, CD4/CD8, and CD19⁺CD23⁺. MCP-1 showed a negative correlation (r<0, P<0.05) with globulin and a positive correlation (r>0, P<0.05) with CD3⁺CD4⁺ and CD4/CD8. IL-6 negatively correlated with ALT (r<0, P<0.05) and positively correlated with CRP and HBP (r>0, P<0.05). IL-8 positively correlated with CD3⁺CD4⁺, CD19⁺CD23⁺, and HBP (r>0, P<0.05). A strong positive correlation (r>0, P<0.05) was observed between IL-18 and CD3⁺CD4⁺, CD4/CD8, CD19⁺CD23⁺, and HBP, while a negative correlation was found with CRP (r<0, P<0.05) (Figure 2).

Figure 2 Correlation analysis between plasma cytokine levels and laboratory examination in influenza A. *, P<0.05; **, P<0.01. IFN-α2, interferon alpha-2; MCP-1, monocyte chemoattractant protein-1; IL-6, interleukin-6; IL-8, interleukin-8; IL-10, interleukin-10; IL-18, interleukin-18; WBC, white blood cells; AST, aspartate aminotransferase; ALT, alanine aminotransferase; LDH, lactate dehydrogenase; CRP, C-reactive protein; HBP, heparin-binding protein; ESR, erythrocyte sedimentation rate.

Comparison of cytokine levels in children infected with influenza A and co-infected with other pathogens

Among the 57 children tested, 17 (29.82%) were found to have one or more co-infections. Of these, 10 (58.82%) had mycoplasma, and 7 (42.18%) had other pathogens, including Moraxella catarrhalis, Streptococcus pneumoniae, Viridans Streptococci, and Staphylococcus aureus. Statistically significant differences in IFN-α2 levels (P=0.04) were observed, with the Mycoplasma co-infection group showing significantly higher levels compared to the influenza-only infection group [138.5 (64.1, 297.1) vs. 106.9 (51.2, 240.7)] (Table 2). It is important to note that although co-infections, particularly with Mycoplasma, were associated with higher IFN-α2 levels, these differences were specific to the co-infected group and did not influence the overall cytokine trends observed in the study.

Comparison of plasma cytokine levels in children with different types of influenza A

Analysis of 13 respiratory pathogen nucleic acids in sputum samples from children with influenza A identified 8 (14.03%) cases of H3N2 and 49 (85.97%) cases of H1N1. Comparison between these two subtypes revealed no significant differences overall. However, IL-18 levels were significantly higher in the H3N2 group compared to the H1N1 group [1,863.9 (1,513.765, 2,030.38) vs. 1,034.83 (626.325, 1,509.57), P=0.03] (Table 3).

Comparison of plasma cytokine levels in children with different degrees of influenza severity

Based on symptoms, signs, laboratory tests, and imaging findings, the influenza A cases were categorized into two groups: mild-to-moderate and severe. The mild-to-moderate group comprised 32 cases, including 24 cases of H1N1 and 8 cases of H3N2, while the severe group included 25 cases, all of which were H1N1. Comparison between these two groups revealed that IFN-α2 levels were significantly higher in the mild-to-moderate group compared to the severe group [84 (42.065, 158.5575) vs. 41.2 (21.43, 82.575)]. Similarly, MCP-1 levels were significantly elevated in the mild-to-moderate group compared to the severe group [850.485 (348.4625, 1,905.045) vs. 336.96 (206.1, 803.155)] (Table 4).

Correlation analysis of plasma IFN-α2, MCP-1, IL-6, IL-8, IL-10, and IL-18 with influenza A severity

In the correlation analysis, plasma levels of IFN-α2 and MCP-1 showed a strong negative correlation with the severity of influenza A, with significant differences (Spearman r=−0.357, P=0.006; Spearman r=−0.329, P=0.013, respectively). In contrast, no significant associations (P>0.05) were found between the levels of IL-6, IL-8, IL-10, or IL-18 and the severity of influenza A (Table 5).

Analysis of risk factors and plasma cytokines associated with influenza A severity in children

Multifactorial logistic regression analysis identified IFN-α2 levels (OR, 0.992; 95% CI: 0.984–1) and MCP-1 levels (OR, 0.999; 95% CI: 0.998–1) as independent risk factors for the severity of influenza A (Table 6). Receiver operating characteristic (ROC) curve analysis revealed that the optimal thresholds for IFN-α2 and MCP-1, as calculated by the Youden index, were ≥53.636 pg/mL and ≥418.22 pg/mL, respectively. The corresponding area under the curve (AUC) values for predicting the severity of influenza A were 0.733 and 0.729, respectively, with a combined AUC of 0.752 (Figure 3).

Figure 3 It illustrates the ROC curve analysis of IFN-α2 (blue line), MCP-1 (red line), and their combination (green line) in predicting the severity of influenza A. The AUC values are 0.733 for IFN-α2, 0.729 for MCP-1, and 0.752 for the combined model. “Combined” refers to the predictive model integrating both IFN-α2 and MCP-1. ROC, receiver operating characteristic; IFN-α2, interferon alpha-2; MCP-1, monocyte chemoattractant protein-1; AUC, area under the curve.

Discussion

When individuals are infected with viruses, their innate immune system, the body’s first line of defense, is activated. This process triggers the release of chemotactic and pro-inflammatory cytokines, as well as interferons, which impede the pathogen’s invasion and replication (21). The production of these small-molecule proteins, secreted by cells, is significantly influenced by single nucleotide polymorphisms (SNPs) in the genes encoding cytokines. These SNPs affect messenger RNA (mRNA) stability, transcriptional efficiency, and editing, particularly in promoter and intron regions, thereby modulating cytokine production (22,23). Cytokines primarily facilitate cell-to-cell communication and initiate various immune responses by binding to specific receptors. They play critical roles in immune and inflammatory responses, regulating angiogenesis, and controlling cell proliferation and differentiation. Influenza A virus infection disrupts the regulation of tumor necrosis factor (TNF-α), interleukins (IL-1β, IL-6, IL-17A, IL-10), and interferons, leading to immune system dysregulation (11,24,25).

This study explored the relationship between cytokines secreted in response to influenza A virus infection and specific clinical markers. The analysis revealed a positive correlation between cytokines such as IFN-α2, MCP-1, and IL-8, and immune cells like CD3⁺ T cells and the CD4/CD8 ratio. These findings suggest that the coordinated interaction between cytokines and immune cells is crucial in orchestrating an effective immune response. Therapeutically, targeting these pathways could help modulate excessive inflammation or enhance protective immunity in influenza A infections. These findings are consistent with the recruitment of viral epitope-specific CD4⁺ T cells to the infection site following influenza A virus infection (26).

The current study reports the elevated levels of IL-6 and IL-10, indicating intensified inflammatory responses during the acute phase of influenza A virus infection. These findings align with results observed in human samples (18,27-29). In rat experiments, influenza virus infection stimulates the secretion of IL-8 through the activation of the epidermal growth factor receptor and the release of TGF-α via c-Met (30). Additionally, clinical studies have shown that IL-8 is associated with mortality in patients with influenza A and may serve as an early prognostic indicator (31). However, this study did not include long-term follow-up after diagnosis and treatment. Future research could explore the prognostic value of IL-8 in pediatric influenza A patients. Our study confirms and extends previous findings that IFN-α2, MCP-1, IL-6, IL-8, IL-10, and IL-18 are elevated during influenza A infection. Furthermore, IFN-α2 levels were significantly higher in individuals co-infected with Mycoplasma, suggesting that IFN-α2 could potentially be used to distinguish between influenza A virus and Mycoplasma infections. IL-18 levels were notably higher in the H3N2 group compared to the H1N1 group, highlighting its potential as a marker for H3N2 influenza A infection.

The mild-to-moderate group exhibited significantly higher levels of IFN-α2 and MCP-1 than the severe group. Correlation analysis revealed a negative association between IFN-α2 and MCP-1 levels and the severity of influenza A infection. This is consistent with the findings of Zhang et al. (32), who reported that an increase in the concentration of interferon neutralized by autoantibodies was linked to a higher risk of severe influenza. This inverse correlation may be explained by immune exhaustion or cytokine receptor desensitization in severe cases, as chronic activation of immune pathways may lead to impaired cytokine production or signaling (33). Previous studies have reported similar mechanisms in other viral infections, where prolonged inflammation dampens immune responsiveness (34). Further research is needed to explore these hypotheses. Additionally, logistic regression and ROC curve analyses indicated that IFN-α2 and MCP-1 are significant predictors of disease severity in children with influenza A. These findings suggest that IFN-α2 and MCP-1 could be valuable biomarkers for diagnosing influenza-related diseases. In clinical practice, these biomarkers could be incorporated into diagnostic panels for early risk stratification. Point-of-care testing for IFN-α2 and MCP-1 could aid in identifying high-risk patients, enabling timely interventions and reducing complications.

Conclusions

In conclusion, our findings suggest that IFN-α2, MCP-1, IL-6, IL-8, IL-10, and IL-18 have potential as biomarkers for influenza A in children, with IFN-α2 and MCP-1 showing particular promise in distinguishing between mild and severe cases. Furthermore, IFN-α2 may assist in identifying Mycoplasma co-infections, while IL-18 could serve as a supplementary marker for differentiating between H3N2 and H1N1 subtypes of influenza.

Limitations

This study has several limitations. First, the sample size was relatively small, which may limit the generalizability of the findings. Second, potential biases could arise from the single-center design. Third, the study did not include long-term follow-up data, which restricts insights into the prognostic value of the biomarkers studied over time. Future research should address these limitations by conducting larger, multi-center studies with diverse populations and incorporating long-term follow-up to validate and expand upon these findings.

Acknowledgments

We extend our heartfelt gratitude to the Children’s Hospital of Soochow University and the children for their invaluable support.

Footnote

Reporting Checklist: The authors have completed the STARD reporting checklist. Available at https://tp.amegroups.com/article/view/10.21037/tp-2024-534/rc

Data Sharing Statement: Available at https://tp.amegroups.com/article/view/10.21037/tp-2024-534/dss

Peer Review File: Available at https://tp.amegroups.com/article/view/10.21037/tp-2024-534/prf

Funding: This research was funded by the National Natural Science Foundation of China (grant numbers 82170012 and 82370020) and the Science and Technology Program of Suzhou (grant number SKY2023190).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tp.amegroups.com/article/view/10.21037/tp-2024-534/coif). The authors have no conflicts of interest to declare.

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 (as revised in 2013). The study was approved by the Institutional Review Board of the Children’s Hospital of Soochow University (Approval No. 2021CS164). Written informed consent was obtained from the guardians of all participants.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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