Marker for assessing immune response in congenital lung airway malformation and application thereof

Abstract

This invention belongs to the field of biomedical technology and discloses a biomarker for assessing the immune response to congenital pulmonary airway malformations and its application. The key technical point is that the biomarker is composed of D8... + T cell percentage, CD19 + The risk of CPAM infection is determined by B cell percentage, IgM level, and CD4 / CD8 ratio. This invention incorporates biomarkers into the prediction of CPAM infection risk and constructs a risk scoring system. This model boasts high predictive efficacy, with an AUC of 0.94 and a negative predictive value of 97.2%, effectively identifying high-risk children. It is also simple and easy to implement, requiring only four routine immune indicators, and can be widely applied in hospitals at all levels. Furthermore, it enables stratified management, classifying children into low, medium, and high risk levels, providing a quantitative basis for individualized follow-up and intervention.

Description

Technical Field

[0001] This invention belongs to the field of biomedical technology, and more specifically relates to a biomarker for assessing the immune response to congenital pulmonary airway malformations and its application. Background Technology

[0002] Congenital pulmonary airway malformations (CPAM) are a group of congenital diseases involving abnormalities in the development of the airways, lung parenchyma, and pulmonary vessels. They are characterized by excessive proliferation and dilation of the terminal bronchi, leading to the formation of multiple cysts within the lung parenchyma that connect with the normal bronchial system, creating an abnormal airway pattern. This abnormal airway pattern causes abnormal alveolar development, potentially leading to the formation of cystic and / or adenomatous lung regions. The widely used Stocker classification system divides CPAM into types 1-3, with clinical symptoms varying among individuals for different types. Treatment options for this disease remain highly controversial both domestically and internationally, particularly regarding the timing of surgery and the lack of assessment indicators for surgical intervention in asymptomatic patients.

[0003] Secondary infection is the most significant complication of this disease and plays a decisive role in treatment decisions. The incidence of pneumonia in children with CPAM is 30%–50%, far higher than in normal children (1%–2%). Unlike normal pneumonia patients, CPAM patients with concurrent lung infection often develop localized abscesses within the lesions. With repeated infections, a series of direct clinical damages occur, leading to refractory pneumonia, malnutrition, impaired growth and development, and even sudden respiratory failure and septic shock when the abscess ruptures. From a surgical perspective, repeated infections significantly increase the difficulty of surgical procedures and the incidence of intraoperative bleeding and postoperative complications. Furthermore, due to the expanded area of ​​infection, it is often necessary to increase the area of ​​lung tissue resection, further impairing normal lung function. Another more important complication is cancer. Existing research shows that cancer patients are closely related to infection susceptibility. Repeated infections lead to immune microenvironment remodeling and disruption of related signaling pathways, thus providing a pathological basis for cancer. However, there is significant heterogeneity in infection among patients with this disease; some develop symptoms in infancy, while others remain asymptomatic throughout their lives. Therefore, predicting a patient's susceptibility to infection can provide a key basis for the individualized development and stratified implementation of treatment strategies and help block the pathological chain of cancer from the source. It is an important part of CPAM management with great clinical significance.

[0004] Currently, there are no reports on predictive indicators for susceptibility to CPAM infection. If susceptible individuals receive comprehensive treatment in the early stages of infection and consider early surgical removal of the lesions, severe infection and respiratory failure can be avoided, and the risk of cancer caused by CPAM infection can be reduced to some extent. Lesions in non-susceptible individuals can be considered benign and are less prone to infection. If the mass effect is not severe, surgery in infancy is unnecessary, and the lesions may even coexist for life.

[0005] CPAM possesses a highly active immune ecosystem, with abnormal immune function present in the lesions, including immunodeficiency or immunoexcess. This can lead to a decreased ability to clear pathogens or exacerbate damage to normal lung tissue due to overexpression of certain substances. Exploring the infection susceptibility of children with CPAM from an immunological perspective has significant theoretical and clinical value. In recent years, a few single-cell level studies have attempted to analyze this disease, but due to small sample sizes (mostly 1-2 cases), limited analytical dimensions, and a lack of in-depth research into the immune microenvironment and cell interactions, while enrichment in lysosomes and phagosomes has been reported at the signaling pathway level, the mechanism by which it is associated with immune imbalance still lacks systematic validation.

[0006] Based on the aforementioned clinical problems, this invention focuses on the core issue of "the immunological basis of infection susceptibility in children with CPAM," and conducts multi-dimensional research to propose a biomarker for assessing the immune response to congenital lung airway malformations and its application. Summary of the Invention

[0007] This invention provides a biomarker for assessing the immune response to congenital pulmonary airway malformations and its application. By systematically analyzing the peripheral blood immune characteristics, local immune microenvironment of lesions, and expression of key molecules in children with CPAM, it explores the immunological basis of CPAM infection susceptibility, evaluates the predictive value of immune indicators for infection risk, and establishes a CPAM infection risk prediction model and risk scoring system.

[0008] Specifically, the present invention is achieved through the following technical solutions:

[0009] In a first aspect, the present invention provides a biomarker for assessing the risk of infection in congenital pulmonary airway malformations, said biomarker being derived from CD8. + T cell percentage, CD19 + It consists of the proportion of B cells, IgM level, and CD4 / CD8 ratio.

[0010] Secondly, the present invention provides a method for constructing a predictive model for the risk of infection in congenital pulmonary airway malformations, characterized by comprising the following: using infection status as the dependent variable, where 0 = no infection and 1 = infection; incorporating the aforementioned biomarkers as variables, and constructing a multivariate logistic regression model using stepwise regression;

[0011] The formula for the model is: P(infection) = 1 / [1 + exp(-Z)],

[0012] Z=-28.930+0.606×CD8+0.618×CD19+ 0.287×IgM(mg / dL)-11.931×CD4 / CD8,

[0013] In the formula, CD8 is CD8 + T cell percentage, expressed as a percentage, using the value before the percentage sign; CD19 is CD19. + B cell percentage, expressed as a percentage, using the value before the percentage sign; IgM level, expressed as mg / dL, using the value before the unit; CD4 / CD8 ratio, expressed as the CD4 / CD8 ratio.

[0014] Thirdly, the present invention provides a predictive model for the risk of infection in congenital pulmonary airway malformations constructed using the above-described construction method.

[0015] Preferably, when the predicted probability output by the prediction model is higher than or equal to the cutoff value, it is determined to be of high risk of infection; when the predicted probability output by the prediction model is less than the cutoff value, it is determined to be of low risk of infection.

[0016] Preferably, the cutoff value is 0.132.

[0017] Fourthly, the present invention provides a method for constructing a scoring system for the risk of infection in congenital pulmonary airway malformations, comprising: based on the analysis results of each variable in the above multivariate logistic regression model, taking the above biomarkers as independent correlation indicators, and based on the ROC analysis results, combining the actual distribution characteristics of each indicator in the infected group and the non-infected group, constructing a risk scoring system using a three-level scoring method.

[0018] Fifthly, the present invention provides a scoring system constructed using the above-described construction method.

[0019] Preferably, the scoring criteria of the scoring system are shown in Table 14.

[0020] Preferably, the scoring formula of the scoring system is:

[0021] W = CD8 score + CD19 score + IgM score + CD4 / CD8 score

[0022] In the formula, CD8 is the proportion of CD8+ T cells; CD19 is the proportion of CD19+ B cells; IgM is the IgM level indicator; and CD4 / CD8 is the CD4 / CD8 ratio indicator.

[0023] Preferably, the scoring system can divide the child into three risk levels. When the score falls into the first cutoff value, it is determined to be low risk of infection; when the score falls into the second cutoff value, it is determined to be medium risk of infection; and when the score falls into the third cutoff value, it is determined to be high risk of infection.

[0024] Preferably, the first cutoff value is 0-2 points, the second cutoff value is 3-4 points, and the third cutoff value is 5-8 points.

[0025] In a sixth aspect, the present invention provides the application of a reagent for detecting the above-mentioned biomarkers in the preparation of a kit for predicting the risk of infection in congenital pulmonary airway malformations.

[0026] Preferably, the test sample for the reagent is peripheral venous blood.

[0027] Compared with the prior art, the present invention has the following advantages:

[0028] 1. This invention, through a progressive research design of "peripheral blood discovery → single-cell exploration → histological verification → predictive model construction," systematically reveals the immune environment characteristics of susceptibility to CPAM in children. It confirms the CD8... + T cell percentage, CD19 + The proportion of B cells, serum IgM level, and CD4 / CD8 ratio were four independent risk factors for infection, and showed a significant dose-response relationship with the number of infections. Furthermore, T cell pre-activation was found in lesions of uninfected children, providing crucial evidence for the concept that "immune abnormalities precede infection," and enabling integrated analysis of systemic immunity and the local microenvironment.

[0029] 2. For the first time, we discovered that CCL5, NKG7, and IGHM sequences are synergistically highly expressed in CPAM infection lesions, corresponding to chemokinetic signaling, cytotoxic function, and humoral immunity, respectively, synergistically driving infection susceptibility from different dimensions. We also pioneered the introduction of peripheral blood immune indicators into CPAM infection risk prediction and constructed a risk scoring system. This model boasts high predictive efficacy (AUC as high as 0.94, negative predictive value of 97.2%), effectively identifying high-risk children. It is also simple and easy to implement, requiring only four routine immune indicators, and can be widely applied in hospitals at all levels. Furthermore, it enables stratified management, classifying children into low, medium, and high risk levels, providing a quantitative basis for individualized follow-up and intervention. Attached Figure Description

[0030] Figure 1 Preoperative CT images of patients (A, B, and C correspond to the CT images of patients P1, P2, and P3, respectively, with the lesion area within the dashed circle).

[0031] Figure 2This is a diagram showing the approximate sampling locations for lung tissue based on CT images (the area within the solid circle is the sampling area for normal lung tissue, and the area within the dashed circle is the sampling area for lesions).

[0032] Figure 3 HE staining image of pathological sections from the sampling site;

[0033] Figure 4 The QC quality control status of the samples before and after filtering (taking P1-N samples as an example);

[0034] Figure 5 The single-cell UMAP dimensionality reduction clustering diagram is shown below (A represents the distribution of different cell types from P1 to N, B represents the distribution of different cell types from P1 to L, C represents the distribution of different cell types from P2 to N, D represents the distribution of different cell types from P2 to L, E represents the distribution of different cell types from P3 to N, and F represents the distribution of different cell types from P3 to L).

[0035] Figure 6 Heatmap of expression distribution of 6 abnormal tissue sequences from single-cell sequencing;

[0036] Figure 7 Bubble chart for pathway enrichment analysis (A represents the signal pathway of P1-N, B represents the signal pathway of P2-L, C represents the signal pathway of P2-N, D represents the signal pathway of P2-L, E represents the signal pathway of P3-N, and F represents the signal pathway of P3-L).

[0037] Figure 8 The relationship between CCL5 / NKG7 / IGHM expression and infection status (A, B, C, D, E, F are immunohistochemical staining images, magnification 200×; G is the result of quantitative statistical analysis of the staining images).

[0038] Figure 9 The results of the correlation analysis between CCL5 H-score and the number of infections;

[0039] Figure 10 The results of the correlation analysis between NKG7 H-score and the number of infections;

[0040] Figure 11 The results of the correlation analysis between IGHM H-score and the number of infections;

[0041] Figure 12 ROC curve for predicting infection for a single immune indicator;

[0042] Figure 13 To compare the predictive performance of the joint model with that of the single indicator;

[0043] Figure 14 This is the calibration curve for the joint prediction model. Detailed Implementation

[0044] To facilitate understanding of the present invention by those skilled in the art, the technical solution of the present invention will be further described in detail below with reference to embodiments and accompanying drawings. Obviously, the described embodiments are merely some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort should fall within the scope of protection of the present invention.

[0045] Unless otherwise specified, the experimental methods used in the following examples are conventional methods.

[0046] Unless otherwise specified, all materials and reagents used in the following examples are commercially available.

[0047] Example 1: Clinical cohort and peripheral immune characteristics analysis

[0048] This study is a retrospective cohort study based on a single-center "Congenital Lung Malformation Specialty Cohort". The study included 100 children with CPAM who underwent surgery at our center between September 2021 and September 2023. All cases were confirmed by postoperative pathology. The study was approved by the hospital's ethics committee (ethics number: 2022ETKLR08053), and all guardians of the children signed informed consent forms.

[0049] In this embodiment, the presence or absence of pneumonia before surgery was used as the grouping criterion, and the children were divided into an infection group (21 cases) and a non-infection group (79 cases). The clinical characteristics and immunological indicators of the two groups were compared and analyzed.

[0050] Inclusion criteria: (1) Postoperative pathological diagnosis of CPAM; (2) Surgical treatment received at our center between September 2021 and September 2023; (3) Complete preoperative clinical data and peripheral blood immune test data; (4) Follow-up for more than 1 year after surgery.

[0051] Exclusion criteria: (1) known primary immunodeficiency disease; (2) complicated with severe congenital cardiopulmonary disease or severe cardiopulmonary insufficiency; (3) children who have used immunomodulatory drugs such as theophylline, gamma globulin, glucocorticoids, sodium cromoglycate, and leukotriene antagonists within 2 weeks before surgery; (4) those with incomplete clinical data.

[0052] Grouping criteria: Based on whether pneumonia occurred before surgery, the children were divided into an infected group and a non-infected group.

[0053] The baseline data comparison results between the two groups in this embodiment are shown in Table 1. There were no statistically significant differences between the infected group and the non-infected group in terms of baseline demographic characteristics such as age, sex, birth weight, and gestational age (P>0.05). There were also no significant differences in the CPAM genotype distribution between the two groups (χ²). 2 =0.23, P=0.89). The proportions of type 1, type 2, and type 3 in each group were basically consistent with the overall distribution, suggesting that genotyping was not the main confounding factor affecting infection status. This balance laid a good foundation for subsequent comparison of immune indicators.

[0054] Table 1. General patient information

[0055]

[0056] Note: The classification adopts the classic 3-type classification method.

[0057] To comprehensively assess the relationship between peripheral blood immune status and infection in children with CPAM, this example systematically compares the immune indicators of the infected group (n=21) and the non-infected group (n=79).

[0058] The differences were shown in Table 2 after univariate comparative analysis. The results revealed that the infection group had CD8... + The proportion of T cells was significantly higher in the infection-free group; the CD4 / CD8 ratio was significantly decreased; CD19 + The proportion of B cells was significantly increased; IgM levels were significantly increased. TNF-α also showed differences in the original comparisons, but these differences were not retained after FDR correction. CD3 + T cells, CD4 + There were no statistically significant differences between the two groups in the following variables: the proportion of T cells, NK cells, Th1 cells, Th2 cells, IgG, IgA, IgE, C3, C4, IL-6, IL-8, and IFN-γ (all P > 0.05).

[0059] Table 2 Comparison of the percentage of lymphocyte subsets in each group

[0060]

[0061] *The difference between the two groups was statistically significant.

[0062] To further assess whether immune markers are independent factors associated with infection, this embodiment constructs a multivariate logistic regression model. The dependent variable is whether pneumonia occurs (0 = no infection, 1 = infection); the independent variables are: CPAM classification, age, gender, and CD8. + T cell percentage, CD19 +Indicators such as B cell percentage, IgM level, and CD4 / CD8 ratio were used to calculate the odds ratio (OR) and 95% confidence interval (CI). To eliminate the confounding effect of CPAM subtyping, the infection incidence was compared among different subtypings in a stratified manner; subtyping variables were included in the logistic model.

[0063] Since this embodiment involves parallel comparisons of multiple immune indicators, to control the false positive rate, the Benjamini-Hochberg method was used to correct for the false discovery rate (FDR). Only four indicators remained statistically significant: CD8. + T cells (FDR-corrected P<0.0001), CD19 + B cells (FDR-corrected P < 0.0001), IgM (FDR-corrected P < 0.0001), and CD4 / CD8 ratio (FDR-corrected P < 0.0001), with all other indicators showing corrected P values ​​> 0.05. This result further confirms that the above four core indicators are key factors in distinguishing between the infected and non-infected groups.

[0064] The multifactor logistic regression model constructed in this embodiment yields CD8. + T cell percentage, CD19 + B cell percentage, IgM level, and CD4 / CD8 ratio were all independently associated with infection. However, the CPAM subtype was not included in the final model (P>0.05). This suggests that peripheral blood immune abnormalities are associated with susceptibility to infection and are not affected by anatomical subtype.

[0065] To explore the relationship between the degree of immune abnormality and the severity of infection, the number of infections (1-4 times) was used as an ordinal variable for trend analysis. The results showed that the proportion of CD8⁺ significantly increased with the number of infections (P<0.001); the proportion of CD19⁺ significantly increased with the number of infections (P<0.001); IgM levels significantly increased (P<0.001); and the CD4 / CD8 ratio significantly decreased (P<0.001). These results suggest a dose-response relationship between the degree of immune abnormality and the number of infections, further supporting its potential role in the formation of infection susceptibility.

[0066] A systematic comparison of peripheral blood immune markers between infected and non-infected children revealed significant peripheral blood immune abnormalities in the infected group: CD8+. + The proportion of T cells was significantly increased, and CD19 levels were also significantly elevated. +The proportion of B cells was significantly increased, serum IgM levels were significantly elevated, and the CD4 / CD8 ratio was significantly decreased. This suggests that changes in the immune microenvironment of CPAM lesions are correlated with and causally linked to infection susceptibility. However, there were no significant differences between the two groups in the Th1 / Th2 ratio, other immunoglobulins and complement, cytokines, complete blood count, and NLR, indicating that conventional inflammatory markers and the classic Th1 / Th2 balance theory cannot explain the mechanism of infection susceptibility in this example.

[0067] Based on this, this embodiment proposes a possible mechanism: an abnormal local immune microenvironment at the lesion site, where CPAM lesions may abnormally release chemokines or other immunomodulatory molecules, thereby continuously recruiting and activating CD8. + T cells continue this process even in the absence of clinical infection. This mechanism can explain the presence of CD8 cells in children without infection. + The increase in T cells and other phenomena indicate that the abnormal lesion structure itself creates a microenvironment conducive to the recruitment of immune cells, forming an "immune pre-activation" state.

[0068] To eliminate the potential confounding effect of typing on immune characteristics, the immune indicators of children with different typing were further analyzed. First, the core indicators and infection frequency of different typing were analyzed. The results showed that there were no statistically significant differences in core indicators and infection frequency among different typing, indicating that typing was not a confounding factor (Table 3).

[0069] Table 3. Analysis of core indicators and infection frequency for different subtypes

[0070]

[0071] The results of further stratified analysis are shown in Table 4.

[0072] Table 4. Core indicators and number of infections among different subtypes

[0073]

[0074] The results showed that within each subtype of type 1, 2, and 3, the CD8 count of infected children was... + T cell percentage, CD19 + The proportion of B cells and IgM levels were significantly higher in the CPAM group than in the uninfected group, while the CD4 / CD8 ratio was significantly lower (P < 0.001 for all). This suggests that the core immune abnormality features are consistent across all subtypes and are independent of the specific pathological type of CPAM. This supports the universality of immune abnormalities as a basic indicator of susceptibility to infection.

[0075] Further comparison of immune marker differences among different subtypes within the infection group revealed that, among the type 1, type 2, and type 3 infection groups, CD8... +T cell percentage, CD19 + There were no significant differences in B cell proportion, IgM level, and CD4 / CD8 ratio. These results suggest that the degree of immune activation in the infected group was not significantly associated with CPAM typing, and that peripheral blood immune characteristics were similar in children with recurrent infections regardless of their pathological type (Table 5). Within the non-infected group, there were also no significant differences in various immune indicators among children with types 1, 2, and 3, indicating that the baseline immune status of non-infected children was not dependent on CPAM typing. This suggests that peripheral blood immune abnormalities are mainly related to the infection status and not to the pathological structural characteristics of the lesions.

[0076] Table 5. Comparison of subtyping indicators within each group in the infected group and the non-infected group.

[0077]

[0078] Notably, in this embodiment, the infection rates of CPAM in children with types 1, 2, and 3 were 22.0% (9 / 41), 20.0% (5 / 25), and 20.6% (7 / 34), respectively, with no significant difference in infection rates among the three groups (P=0.68). This result indicates that although different subtypes of CPAM differ in pathological structure, their infection risks are not significantly different. From an anatomical perspective, although differences in different epithelial origins may affect the local chemokine secretion profile and immune cell recruitment patterns, these local differences may ultimately be transmitted through common systemic immune pathways (such as CD8). + T cell activation and B cell response are reflected in peripheral blood, manifesting as similar changes in immune markers. This hypothesis requires further verification through larger-sample studies.

[0079] The stratified analysis results provide important insights for clinical practice: CPAM typing itself should not be used as an independent basis for assessing infection risk. Peripheral blood immune marker testing can be performed to assess infection risk regardless of whether the child is classified as type 1, 2, or 3. For children with normal immune markers, even if they are classified as type 2 (which has a relatively high infection rate), the data suggests that there is no high incidence of infection; conversely, for children with significantly abnormal immune markers, even if they are classified as type 3 (which has a relatively low infection rate), high-risk management is recommended.

[0080] Example 2: Single-cell sequencing reveals the local immune microenvironment

[0081] Based on the findings of peripheral blood immunophenotyping in Example 1, it is hypothesized that there may be corresponding immune cell infiltration and abnormal molecular expression in the lesion area. To verify this hypothesis and explore the local immune microenvironment driving infection susceptibility, single-cell transcriptome sequencing was performed on lesion tissues from three representative CPAM patients and their own normal lung tissues as controls in this example.

[0082] The inclusion criteria for screening extreme phenotypes based on peripheral blood results in this embodiment are as follows:

[0083] P1 (non-infection control): Children with CPAM were selected from non-infected children, whose peripheral blood immune indicators met the typical characteristics of the non-infection group: CD8 + T cell percentage below 27%, CD19 + The patient had a B cell percentage below 17%, serum IgM level below 1.7 mg / dL, CD4 / CD8 ratio above 1.5, and no history of pneumonia episodes. This patient, as a typical representative of the non-infectious state, was used to reveal the baseline immune microenvironment characteristics of the CPAM lesion itself.

[0084] P2 (Recurrent Infection Homotype Core Cases): CPAM patients with the same subtype as P1 are selected from children with a history of infection, requiring their peripheral blood immune markers to meet the typical characteristics of the infection group: CD8+. + T cell proportion higher than 30%, CD19 + The patient presented with a B cell percentage higher than 20%, serum IgM level higher than 2.2 mg / dL, CD4 / CD8 ratio lower than 1.31, and at least four radiographically confirmed pneumonia episodes. This patient, a typical representative of the same infection type, was a homologous CPAM (Polycellular Acute Myeloma) patient as P1. Comparison of the two types eliminated interference from subtyping differences, directly revealing the alterations in the immune microenvironment caused by the "infection" itself.

[0085] P3 (Control Case with Recurrent Infections in Other Subtypes): Children with other subtypes of CPAM were selected from those with a history of infection. Peripheral blood immune indicators were required to be the same as in P2, with ≥4 infections. This patient serves as a typical representative of other subtypes of infection, and comparison with P2 can reveal the differences in the infection mechanisms of different subtypes of CPAM.

[0086] Three children were ultimately included: P1 was a 13-month-old male with type 2 CPAM, no history of infection, and peripheral blood immune markers showing typical low-risk characteristics; P2 was a 5-month-old male with type 2 CPAM, a history of 4 episodes of pneumonia, and a high-risk immunophenotype; P3 was a 6-month-old male with type 3 CPAM, a history of 4 episodes of pneumonia, and an immunophenotype similar to P2. Perinatal and fetal data for the three children are detailed in Table 6. No significant abnormalities were found in routine blood tests or biochemical tests.

[0087] Table 6. General patient information and fetal-related data

[0088]

[0089] This selection strategy aims to verify the corresponding mechanism of peripheral blood immune abnormalities at the local tissue level through "extreme phenotype comparison," laying the foundation for subsequent mechanism research.

[0090] In this embodiment, the surgical resection area and two sampling sites were planned preoperatively based on CT images: the lesion area sample was named P. X -L, selected from the center of the lesion; normal lung tissue samples are named P. X -N was taken from lung tissue that appeared normal on CT images of the same lung segment (X is the patient number in Table 6). All three children underwent thoracoscopic segmentectomy. During the operation, a tissue sample of approximately 1.0cm × 0.5cm × 0.5cm was taken. Half of the sample was sent for rapid pathological examination to confirm its reliability, and the other half was immediately placed in tissue preservation solution and transported on ice for single-cell sequencing.

[0091] Case 1 (P1): Male, 13 months old at surgery. Admitted due to "lung lesions discovered 1 year ago." A lung ultrasound at birth revealed a lesion in the right lung. Combined with fetal examinations (during pregnancy), CPAM was suspected. The infant had no pneumonia after birth, only occasional upper respiratory tract infections, which improved after symptomatic treatment. Preoperative chest CT with multiplanar reconstruction showed patchy, irregularly shaped areas of increased lung translucency in the posterior segment of the right upper lobe and the dorsal segment of the lower lobe, approximately 4.6 × 3.2 cm × 3.0 cm in size, with clear borders. Multiple translucent shadows of varying sizes, ranging from approximately 0.6 to 1.6 cm in diameter, were observed within these shadows. Based on the medical history, CPAM, Stocker type 2, was suspected. Figure 1 A)

[0092] Case 2 (P2): Male, 5 months old, admitted due to "pulmonary lesions discovered at 5 months of age." A lung ultrasound at birth revealed a lesion in the left lung, and combined with fetal examination findings, CPAM was suspected. The infant developed bronchopneumonia four times at 1, 2, 3, and 4 months of age, receiving inpatient anti-infection and nebulizer treatments, and was discharged after improvement. There were no recurrent coughs or wheezing during the remaining time. Preoperative chest CT with multiplanar reconstruction showed an irregularly shaped pulmonary opacity in the lower lobe of the right lung, approximately 3.5 × 4.5 × 2.9 cm in size, with indistinct borders, containing multiple cystic lucent shadows of varying sizes, ranging from approximately 0.2 to 1.9 cm in diameter. A few patchy, blurred shadows were seen in the remaining lungs. Stocker type 2. Figure 1 (B)

[0093] Case 3 (P3): Male, 6 months old, admitted due to "lung lesions discovered at 6 months of age." A lung ultrasound at birth revealed a lesion in the right lung, and combined with fetal examination findings, CPAM was suspected. He developed bronchopneumonia four times at 2, 3, 4, and 5 months of age, receiving inpatient anti-infection and nebulizer treatments, and was discharged after improvement. Physical examination on admission revealed no obvious abnormalities, and no rales were heard in either lung. Preoperative chest CT with multiplanar reconstruction showed a radiolucent shadow in the posterolateral basal segment of the right lower lobe, measuring 4.0cm × 2.0cm × 3.3cm, suggestive of CPAM complicated by infection, Stocker type 3. Figure 1 (C)

[0094] Two samples were taken from each patient. The sampling method involved planning the surgical resection area and the two sampling locations (the lesion area and the normal tissue area) based on CT images before surgery. Figure 2 ).

[0095] In P1, intraoperative findings included dysplasia of the right posterior oblique fissure, fusion of the posterior segment of the right upper lobe with the dorsal segment of the right lower lobe, and a cystic, multilocular mass with a tortuous, highly vascularized surface. In P2, intraoperative findings included a cystic mass in the right lower lobe primarily involving the medial, lateral, and posterior basal segments, containing yellow fluid. In P3, intraoperative findings included partial adhesions within the pleural cavity, and a cystic mass in the right lower lobe primarily involving the posterolateral basal segment. Rapid pathological examination in all three cases confirmed that the sampling sites were consistent with expectations. Figure 3 ).

[0096] The single-cell transcriptome data from the above six samples were integrated and analyzed. After quality control, standardization, and batch effect correction, a total of 52,386 high-quality single-cell transcriptome datasets were obtained. The cell capture count for each sample ranged from 17,200 to 19,500, with an average of 2,850 genes detected per cell. The median proportion of mitochondrial genes was 6.2%, indicating good data quality that met the requirements for subsequent analysis. Figure 4 ).

[0097] Principal component analysis was used for dimensionality reduction, followed by t-SNE for visual clustering. Based on classic cell type marker genes, cells were divided into 11 major cell populations. Cell types included: epithelial cells (EPCAM+, SFTPC+), T cells (CD3D+, CD8A+, CD4+), B cells (MS4A1+, CD79A+), plasma cells (CD38+, SDC1+, MZB1+), neutrophils (CSF3R+, MPO+, FCGR3B+), macrophages (CD68+, CD163+), mast cells (TPSAB1+, CPA3+), endothelial cells (VWF+, PECAM1+), fibroblasts (COL1A1+, DCN+), myocytes (ACTA2+, TAGLN+), and erythrocytes (HBA1+, HBB+).

[0098] The cellular composition of the samples showed significant differences (Table 7). Figure 5The normal lung tissue in P1 (P1-N) is predominantly epithelial cells with a low proportion of immune cells. The lesion tissue in P1 (P1-L) has a slightly higher proportion of epithelial cells, along with erythrocytes and plasma cells, suggesting vascular remodeling and local immune responses within the CPAM lesion itself. The normal lung tissue in P2 (P2-N) shows significant immune cell infiltration, with T cells accounting for as high as 48.29%. In the lesion tissue (P2-L), T cells account for 25.05%, remaining the dominant cell type, while B cells and endothelial cells are also preserved. The normal lung tissue in P3 (P3-N) is also predominantly T cells. The proportion of T cells in the lesion tissue in P3 (P3-L) is similar to that in P2-L, with a relatively higher proportion of epithelial and endothelial cells.

[0099] Table 7. Proportion of major cell types in each sample (%)

[0100]

[0101] Comparing P1 (non-infected) and P2 (infected) lesions, the immune cell composition of the two groups showed significant differences. The degree of T cell infiltration differed: the proportion of T cells in P1 lesions was 9.98%, while the proportion in P2 lesions was as high as 25.05%, approximately 2.5 times that of the former. This finding is consistent with peripheral blood CD8... + The elevated T cell count was consistent with the findings, suggesting that peripheral blood T cell activation originated from the expansion of effector T cells at the lesion site. Differences were observed in B cell presence: B cells were present in lesion P1, while the proportion of B cells in lesion P2 was 4.14%. Further analysis of T cell subsets revealed that CD8+ cells were present in lesions P2 and P3. + The proportion of effector T cells was significantly higher in P1 lesions than in P1 lesions, while CD4+ cells were the dominant cells in P1 lesions. + The predominantly T-cell population suggests that not only is the number of T cells increased in the infected lesions, but their functional state has also changed, becoming more cytotoxic.

[0102] To reveal the differences in the infection mechanisms of different CPAM subtypes, lesion tissues of P2 (type 1 infection) and P3 (type 2 infection) were compared. T cell proportion: The proportion of T cells in P2 lesions was slightly higher than that in P3 lesions, but the difference was not significant. B cell proportion: The proportion of B cells in P2 lesions was slightly lower than that in P3 lesions. This suggests that different subtypes may not affect infection susceptibility, which is consistent with the peripheral blood immune indicators in Example 1, demonstrating that the correlation between immune abnormalities and infection susceptibility is not affected by anatomical subtype.

[0103] Based on the characteristics of peripheral blood immunity, a comparison of highly expressed gene sequences of immune-related cells (T cells, B cells) revealed abnormal expression of three key sequences: CCL5, NKG7, and IGHM. Figure 6These three sequences correspond to chemokine signals, cytotoxic functions, and humoral immunity, respectively, driving infection susceptibility from different dimensions.

[0104] CCL5 is highly expressed in T cells, and its expression level is significantly higher in infected lesions (P2, P3) than in uninfected lesions (P1). Specifically, the proportion of CCL5-positive T cells is highest in P2 lesions, followed by P3 lesions, and lowest in P1 lesions. Notably, CCL5 expression was also detected in some epithelial cells in P1 lesions. This phenomenon suggests the "epithelial cell-immune cell interaction" theory, where abnormally activated epithelial cells can shape the local immune microenvironment by releasing chemokines, consistent with the peripheral immune indicators in Example 1. Furthermore, it suggests that even in uninfected children, the epithelial cells in the lesions differ from normal lung tissue and possess chemokine secretion potential. This finding supports the hypothesis that "immune abnormalities precede infection." NKG7 is specifically expressed on CD8. + T cells. The expression level of NKG7 in the infected group was significantly higher than that in the uninfected group, suggesting the presence of a large number of activated CD8+ cells with cytotoxic function in the infected group. + T cells. The proportion of NKG7-positive T cells was highest in P2 lesions, followed by P3 lesions, and lowest in P1 lesions, consistent with the expression trend of CCL5. The proportion of NKG7-positive T cells in P1 lesions was also significantly higher than that in normal P1 lung tissue. This phenomenon suggests that CPAM lesions, even without infection, already possess the function of recruiting T cells and activating them to a certain extent. In other words, the susceptibility to infection in children with CPAM may exist at birth, creating a microenvironment conducive to the recruitment of immune cells. IGHM is mainly expressed on B cells, with the highest expression level in P2 lesions, followed by P3 lesions, and almost no expression in P1 lesions. This finding is consistent with the characteristics of peripheral blood immune indicators, suggesting that the elevation of peripheral blood IgM originates from an active humoral immune response in the infected lesions, with IgM production being the main component. However, although B cells and even a small number of plasma cells are present in P1 lesions, the IGHM expression level is extremely low, and there are no clinical signs of infection in P1. Simple T cell pre-activation is insufficient to lead to clinical infection. Based on the characteristics of P2 lesions, the following hypothesis can be proposed: T cell preactivation (CCL5 / NKG7 expression) is an inherent feature of CPAM lesions and constitutes the basis of infection susceptibility, but T cell activation alone is not enough to lead to clinical infection; only when T cell activation is superimposed with an active humoral immune response (high expression of IGHM) can recurrent infection occur.

[0105] GO and KEGG enrichment analyses were performed on differentially regulated genes in lesions between the infected and non-infected groups. The results showed that genes upregulated in the infected group were significantly enriched in T cell activation, chemokine signaling pathways, cytotoxicity, and B cell receptor signaling pathways. Figure 7The most significantly enriched biological processes included: T cell activation (P = 1.2 × 10⁻⁶). -12 ), chemokine-mediated signaling pathways (P=3.5×10), -10 ), cytotoxicity (P=4.2×10), -9 Humoral immune response (P=5.6×10) -7 B cell receptor signaling pathway (P=2.8×10) -8 The most significantly enriched pathways included: chemokine signaling pathways (P=2.5×10). -8 T cell receptor signaling pathway (P=1.8×10) -7 ), Th1 / Th2 differentiation (P=3.2×10), -6 B cell receptor signaling pathway (P=4.5×10) -6 Natural killer cell-mediated cytotoxicity (P=1.2×10⁻⁶) -5 ).

[0106] The chemokine signaling pathway containing CCL5, the cytotoxicity pathway containing NKG7, and the B cell receptor signaling pathway containing IGHM were all significantly enriched. These three core sequences correspond to three key immune pathways, which synergistically drive infection susceptibility from different dimensions.

[0107] A comprehensive comparison of the lesion tissues from the three patients revealed the following patterns: P1 represents a "pre-activated but quiescent" state: a moderate proportion of T cells (9.98%), expression of CCL5 and NKG7, but at low levels, with no B cell / plasma cell infiltration. This is a "pre-activated" state, meaning T cells are present but have not yet initiated a full immune response. P2 represents a "T cell-B cell synergistic activation" state: an extremely high proportion of T cells (25.05%), high expression of CCL5 and NKG7, simultaneous B cell and plasma cell infiltration, and high expression of IGHM. This is a "fully activated" state, where T cells and B cells work synergistically to form a local immune response center. P3 represents a "T cell-dominant, neutrophil-involved" state: a high proportion of T cells (22.66%), high expression of CCL5 and NKG7, but fewer B cells / plasma cells, moderate IGHM expression, and a slightly increased proportion of macrophages. T cell activation is dominant, but B cells are involved, albeit at a slightly weaker intensity than in P2.

[0108] This continuum strongly supports the core hypothesis of this embodiment: immune abnormalities precede infection. The inherent microenvironmental abnormalities (T cell preactivation) of CPAM lesions are the basis of infection susceptibility, while repeated infections are an additional layer of stronger immune responses (B cell activation, plasma cell IgM production) on top of this.

[0109] The results above indicate that the synergistic expression of CCL5, NKG7, and IGHM suggests that susceptibility to infection is not caused by a single molecule abnormality, but rather by a multidimensional immune network dysregulation involving T cell recruitment (CCL5), T cell killing function (NKG7), and humoral immune response (IGHM). This finding has guiding significance for developing combined predictive and even intervention strategies.

[0110] The presence of T-cell preactivation in the lesions of uninfected children in this example suggests that immune abnormalities may precede infection and are the basis of susceptibility to infection, rather than a result of infection. Example 1 has already demonstrated a dose-response relationship between immune indicators and the number of infections; this example provides temporal evidence that "immune abnormalities precede infection," both supporting the inference of a causal relationship. It also reveals the inherent immune characteristics of CPAM lesions. The abnormal cystic structures and epithelial cells in CPAM lesions may recruit immune cells to the lesion site by releasing chemokines (such as CCL5), forming an "immune preactivation" state. This explains why some children can remain uninfected even with immune preactivation. Although T-cell preactivation is present in P1 lesions, B-cell and plasma cell infiltration is minimal, IGHM expression is extremely low, and there is no clinical infection. Combining the characteristics of P2 and P3, the "immune threshold" hypothesis can be proposed: mild T cell preactivation may only reflect the local immune surveillance state of the lesion and is insufficient to drive clinical infection; however, when T cell activation reaches a certain level, accompanied by B cell activation and a large production of IgM, the immune system enters an "overactivated" state, which may actually increase susceptibility to infection. The existence of this relationship provides insights into understanding the two-way relationship between immunity and infection.

[0111] Although this embodiment has limitations in terms of subtyping coverage, its core conclusions remain relatively stable: the synergistic high expression of CCL5, NKG7, and IGHM in the lesions suggests that regardless of the subtyping, the lesions in the infection group all exhibit similar molecular characteristics. Combined with the stratified analysis results of peripheral blood immune indicators from Example 1, there were no significant differences in peripheral blood immune indicators among children in different subtyping infection groups. This suggests that immune activation, as a core mechanism of infection susceptibility, may have universal applicability across subtypings.

[0112] Example 3: Histological verification of key sequence expression

[0113] Based on abnormal peripheral blood immune markers (infection group CD8) +The findings of T cell expansion, B cell activation, and elevated IgM, along with single-cell sequencing (significantly high expression of CCL5, NKG7, and IGHM in the lesion tissues of infected children), further suggest that the expression levels of these genes are closely related to the infection status of children with CPAM. To further explore this hypothesis, this example uses paraffin-embedded lesion tissues from 100 children with CPAM in the cohort of Example 1. Through tissue microarray technology and immunohistochemical staining, the correlation between the expression of CCL5, NKG7, and IGHM and the infection status and number of infections is systematically verified.

[0114] First, this embodiment successfully constructed a tissue microarray containing lesion tissue from 100 children with CPAM. After HE staining quality control, two samples from the non-infected group could not obtain valid data due to tissue detachment or excessive necrosis area. A total of 98 samples were ultimately included in the analysis, including 21 infected cases and 77 non-infected cases. The distribution of each subtype was as follows: Type 1: 40 cases (9 infected, 31 non-infected), Type 2: 25 cases (6 infected, 19 non-infected), and Type 3: 33 cases (6 infected, 27 non-infected), which is basically consistent with the distribution of the peripheral blood cohort in Example 1. The results of the 98 valid samples are shown in Table 8.

[0115] Table 8 Relationship between CCL5 / NKG7 / IGHM expression and infection status

[0116]

[0117] Table 8 and immunohistochemical staining (see Table 8) Figure 8 The results showed that CCL5 expression levels were significantly higher in the infected group than in the uninfected group. The CCL5 H-score and positive cell density were significantly higher in the infected group than in the uninfected group. There was overlap between the two groups in the CCL5 H-score range of 112-132, with 9 cases in the infected group and 25 cases in the uninfected group falling within this range; CCL5 density ranged from 187-202 cells / mm². 2 Within the range, there was overlap between the infected group (8 cases) and the non-infected group (22 cases). Children were divided into five groups based on the number of infections: 0 (n=77), 1 (n=7), 2 (n=6), 3 (n=5), and 4 (n=3). Results showed that the CCL5 H-score increased progressively with the number of infections: 95.8±20.5 for the 0-infection group, 118.5±18.5 for the 1-infection group, 135.5±22.5 for the 2-infection group, 155.5±25.5 for the 3-infection group, and 185.5±26.5 for the 4-infection group (P for trend <0.001). Figure 9 The density of CCL5-positive cells also showed the same trend: 147.5 ± 31.5 cells / mm² in group 0. 2The first group had 192.5 ± 31.5 cells / mm². 2 The two groups showed 218.5 ± 38.5 cells / mm². 2 The three-group result was 255.5 ± 42.5 cells / mm². 2 The 4-times group had 315.5 ± 45.5 cells / mm². 2 (P for trend <0.001). Spearman correlation analysis showed that CCL5 H-score was positively correlated with the number of infections (r=0.72, P<0.001), and CCL5 positive cell density was also positively correlated with the number of infections (r=0.74, P<0.001).

[0118] The expression level of NKG7 in the lesion tissue of children in the infected group was significantly higher than that in the non-infected group. The NKG7 H-score and positive cell density were significantly higher in the infected group than in the non-infected group. There was overlap between the two groups in the NKG7 H-score range of 102-112, with 7 cases in the infected group and 20 cases in the non-infected group falling within this range; the NKG7 density ranged from 154-169 cells / mm². 2 Within the range, there was overlap between the infected group (6 cases) and the non-infected group (18 cases). The NKG7 H-score showed a progressively increasing trend with the number of infections: 81.5±17.5 for the 0-infection group, 108.5±17.5 for the 1-infection group, 125.5±20.5 for the 2-infection group, 145.5±23.5 for the 3-infection group, and 172.5±24.5 for the 4-infection group (P for trend <0.001). Figure 10 The density of positive cells also showed the same trend: 123.5 ± 26.5 cells / mm² in group 0. 2 The number of cells / mm² in the first group was 168.5 ± 28.5. 2 The two groups showed a count of 198.5 ± 32.5 cells / mm². 2 The three-group result was 228.5 ± 38.5 cells / mm². 2 The 4-times group had 275.5 ± 40.5 cells / mm². 2 (P for trend <0.001). Spearman correlation analysis showed that the NKG7 H-score was positively correlated with the number of infections (r=0.68, P<0.001), and the density of NKG7 positive cells was also positively correlated with the number of infections (r=0.70, P<0.001).

[0119] The expression level of IGHM in the lesion tissue of children in the infected group was significantly higher than that in the non-infected group. The IGHM H-score and positive cell density were significantly higher in the infected group than in the non-infected group. There was overlap between the two groups in the range of IGHM H-score (65-78), with 11 cases in the infected group and 22 cases in the non-infected group falling within this range; IGHM density ranged from 90-105 cells / mm². 2 Within the range, there was overlap between the infected group (10 cases) and the non-infected group (20 cases). The dose-response relationship between IGHM expression level and the number of infections was most significant. With increasing number of infections, the IGHM H-score increased significantly in a stepwise manner: 51.5±16.5 in the 0-infection group, 72.5±16.5 in the 1-infection group, 92.5±20.5 in the 2-infection group, 125.5±22.5 in the 3-infection group, and 155.5±24.5 in the 4-infection group (P for trend <0.001). Figure 11 The density of positive cells also showed the same trend: 66.5 ± 22.5 cells / mm² in group 0. 2 The single-dose group had 98.5 ± 22.5 cells / mm². 2 The two groups showed 128.5 ± 28.5 cells / mm². 2 The three-group result was 175.5 ± 32.5 cells / mm². 2 The 4-group result was 215.5 ± 34.5 cells / mm. 2 (P for trend <0.001). Spearman correlation analysis showed that the IGHM H-score had the highest correlation coefficient with the number of infections (r=0.76, P<0.001), and the density of IGHM-positive cells was also significantly positively correlated with the number of infections (r=0.77, P<0.001).

[0120] In the above results, there was some overlap between the three indicators of CCL5, NKG7, and IGHM in both groups, suggesting that the expression of immune-related molecules was upregulated in the lesions of some uninfected children. This characteristic is consistent with the phenomenon of "T cell pre-activation" observed in the P1 uninfected lesions in single-cell sequencing in Example 2. The significantly increased CCL5 expression level in the infected group suggests the presence of active chemotactic signals in the lesion, which can drive the continuous recruitment and infiltration of T cells. The significantly increased NKG7 expression level in the infected group suggests the presence of a large number of activated CD8 cells with cytotoxic functions in the lesion. + T cells, which are associated with the first part of peripheral blood CD8 +The results of elevated T cells were consistent. IGHM expression levels were significantly higher in the infected group than in the non-infected group, and the difference (2.2-fold) was greater than that of CCL5 and NKG7, suggesting that IGHM may be the most sensitive indicator for distinguishing between infection and non-infection. Notably, IGHM expression levels showed the highest correlation coefficient with the number of infections (r=0.76), suggesting its potential value as a biomarker of infection burden.

[0121] This study found a significant dose-response relationship between the expression levels of CCL5, NKG7, and IGHM and the number of infections (P < 0.001 for all). The presence of this dose-response relationship provides crucial evidence for the causal relationship between immune abnormalities and infection susceptibility. With increasing number of infections, the expression levels of the three indicators gradually increased, suggesting a close correlation between immune activation and infection load. Among them, the group with four infections showed the highest expression levels of each indicator, with CCL5 H-score 185.5, NKG7 H-score 172.5, and IGHM H-score 155.5, significantly higher than other groups.

[0122] Correlation analysis showed that CCL5, NKG7, and IGHM were all significantly positively correlated with each other (r values ​​ranged from 0.65 to 0.72, P < 0.001). This result suggests that these three molecules are synergistically expressed in CPAM lesions, collectively constituting the molecular basis of infection susceptibility. Functionally, CCL5-mediated chemotactic signaling recruits immune cells, NKG7-mediated cytotoxicity clears pathogens, and IgM-mediated humoral immunity provides long-term protection; all three synergistically drive infection susceptibility from different dimensions.

[0123] The results of this embodiment are highly consistent with the single-cell sequencing findings in Example 2. At the single-cell level, CCL5, NKG7, and IGHM were significantly overexpressed in P2 (infected) and P3 (infected) lesions, while T cell infiltration and moderate expression of CCL5 and NKG7 were present in P1 (uninfected) lesions, but IGHM expression was extremely low. Tissue microarray verification in this embodiment further confirmed this finding: CCL5 and NKG7 expression was present to some extent in the lesions of the uninfected group (corresponding to P1 lesions), but IgM expression was extremely low, while all three indicators were significantly elevated in the infected group. This "immune pre-activation" phenomenon suggests that CPAM lesions themselves have the inherent characteristic of recruiting T cells, but only when combined with an active humoral immune response (high expression of IGHM and IgM) on the basis of T cell activation does recurrent infection occur.

[0124] Further subgroup analyses were performed on children with type 1, 2, and 3 CPAM. The results showed that within each subtype, the expression levels of CCL5, NKG7, and IGHM in the infected group were significantly higher than those in the non-infected group (all P < 0.001) (Table 9). There were no significant differences in the expression levels of the three indicators among different subtypes within the infected group: there were no statistically significant differences in CCL5 H-score (P = 0.78), NKG7 H-score (P = 0.82), and IGHM H-score (P = 0.75) among the type 1, 2, and 3 infected groups. Correlation analysis of CCL5, NKG7, and IGHM H-scores in 98 samples revealed significant positive correlations between all three indicators: CCL5 and NKG7 (r=0.72, P<0.001), CCL5 and IGHM (r=0.65, P<0.001), and NKG7 and IGHM (r=0.68, P<0.001).

[0125] Table 9. Comparison between the infected group and the non-infected group within each subtype.

[0126]

[0127] Stratified analysis showed that within each of the 1, 2, and 3 subtypes, the expression levels of CCL5, NKG7, and IgM in the infected group were significantly higher than those in the non-infected group (P < 0.001). However, there were no significant differences in the expression levels of these three indicators among different subtypes within the infected group (P > 0.05). This result is consistent with the stratified analysis results of peripheral blood immune indicators in Example 1, suggesting that the high expression of CCL5, NKG7, and IGHM observed in this example is mainly related to infection status and not to CPAM subtype, further confirming the universality of these three indicators as biomarkers of infection susceptibility.

[0128] Correlation analysis of the H-scores of CCL5, NKG7, and IGHM in 98 samples from this embodiment revealed significant positive correlations between all three indicators: CCL5 and NKG7 (r=0.72, P<0.001), CCL5 and IgM (r=0.65, P<0.001), and NKG7 and IgM (r=0.68, P<0.001). This result suggests that these three molecules are synergistically expressed in CPAM lesions, collectively constituting the molecular basis of infection susceptibility.

[0129] Multivariate logistic regression analysis showed that, with infection status as the dependent variable (0 = no infection, 1 = infection), and CCL5 H-score, NKG7 H-score, IGHM H-score, age, gender, and CPAM typing as independent variables, a multivariate logistic regression model was constructed.

[0130] Table 10 Multivariate Logistic Regression Analysis of Infection Risk Factors

[0131]

[0132] The results showed that all three core indicators remained statistically significant after adjusting for age, sex, and genotype. IGHM had the highest odds ratio (OR) at the H-score (1.089, 95% CI: 1.039–1.142), indicating that IGHM was the strongest independent risk factor associated with infection status. The model fit was good (Hosmer-Lemeshow test P = 0.68), and the Nagelkerke R... 2 The value was 0.72. CCL5, NKG7, and IGHM remained independent risk factors for infection, suggesting their universality as biomarkers. This lays the foundation for further analysis of the correlation and predictive value between key immune indicators and the risk of infection, as well as for assessing their feasibility as potential biomarkers and constructing risk prediction models.

[0133] Example 4: Feasibility Analysis of Infection Risk Prediction Model

[0134] Currently, objective quantitative indicators are lacking in the clinical assessment of susceptibility to CPAM in children. Clinically, doctors mainly rely on imaging features such as lesion size, type, and location, as well as the child's past infection history, for empirical judgment, which makes it difficult to identify high-risk children in the early stages. Based on the analysis results of peripheral blood immune indicators in Example 1 and the further histological support of abnormal expression of CCL5, NKG7, and IGHM in Example 3, it is speculated that these immune indicators may serve as predictive biomarkers for infection risk in children with CPAM.

[0135] This embodiment first uses ROC curve analysis to determine the optimal cutoff value, sensitivity, and specificity of each indicator for predicting infection, in order to evaluate the predictive efficacy of a single immune indicator. Then, based on logistic regression, it evaluates the predictive value of multiple indicators used in combination and constructs a multi-indicator joint prediction model. Furthermore, it establishes a preliminary risk scoring system to transform continuous variables into clinically easy-to-use stratified scores. Finally, it analyzes the limitations of the model to clarify the findings and extrapolation limitations under the current sample size.

[0136] The study subjects in this embodiment were 100 children with CPAM included in Example 1. Based on the univariate analysis results of Example 1, the following immune indicators were included as candidate predictive variables in the analysis: CD8 + T cell percentage (%), CD19 +The study included the percentage of B cells (%), serum IgM level (mg / dL), CD4 / CD8 ratio, Th1 percentage (%), Th2 percentage (%), and cytokines such as IL-6, IL-8, TNF-α, and IFN-γ. Age, sex, and CPAM classification were also included as clinical covariates.

[0137] Outcome indicator definition: The outcome indicator in this study is whether pneumonia occurred before surgery (a binary variable). The infection group is defined as having at least one radiographically confirmed pneumonia episode, and the non-infection group is defined as having no history of pneumonia episodes before surgery.

[0138] The model performance evaluation methods are as follows: (1) Discrimination evaluation: The discrimination of the model is evaluated using the receiver operating characteristic curve, and the area under the curve and its 95% confidence interval are calculated. The optimal cutoff value is determined according to the Youden index, and the sensitivity, specificity, positive predictive value and negative predictive value are calculated. (2) Calibration evaluation: The calibration curve and Hosmer-Lemeshow goodness-of-fit test are used to evaluate the calibration of the model. First, the predicted infection probability of each child is calculated based on the multivariate logistic regression model. Then, the calibration curve is plotted with the model predicted probability as the abscissa and the actual observed infection rate as the ordinate to evaluate the consistency between the model prediction results and the actual outcome. If the calibration curve is close to the 45° reference line, it indicates that the model calibration is good. At the same time, the Hosmer-Lemeshow goodness-of-fit test is used as a supplementary evaluation index. The predicted probabilities are grouped by decimal place and the difference between the number of predicted events and the number of actual observed events in each group is compared. P>0.05 indicates that there is no significant deviation between the model predicted probability and the actual observed results. Model validation method: The Bootstrap resampling method is used for internal validation. 1000 Bootstrap samples were drawn with replacement from the original data. In each sample, a Logistic regression model was refitted, and the C-statistic (AUC) and its confidence interval were calculated. Sensitivity analysis: (1) Children with single infections were excluded, and the infection group was limited to those with repeated infections (≥2 times); (2) The model efficacy was evaluated in stratified according to CPAM classification in type 1, type 2 and type 3 respectively.

[0139] Univariate analysis of candidate predictor variables was performed using logistic regression, and variables were selected for inclusion in the multivariate model based on a p-value < 0.10. The results showed that CD8... + T cell percentage (OR=1.696, 95%CI: 1.314-2.188, P<0.0001), CD19 +B cell count (OR=1.907, 95%CI: 1.430–2.543, P<0.0001) and serum IgM level (OR=1.447, 95%CI: 1.210–1.729, P<0.0001) were significantly correlated with infection status. Analysis of the CD4 / CD8 ratio yielded a regression coefficient β≈-11.93, OR=6.59×10⁻⁶. -6 The initial OR was 0.046, which was statistically valid. However, the actual range of this value in the multivariate model was only 1.73 to 2.31. Therefore, the algorithm for adjusting the OR value was "OR for every 0.1 unit change," resulting in an OR of 0.281 (95% CI: 0.094-0.981). The P value remained at 0.046, indicating statistical significance and a significant correlation with infection status. Age, sex, CPAM typing, and other immune indicators showed no significant correlation with infection status (P>0.10) and were not included in subsequent multivariate analyses.

[0140] Table 11 Univariate Logistic Regression Analysis of Candidate Predictors

[0141]

[0142] Based on the results of univariate logistic regression analysis, a multivariate logistic regression model was constructed, with infection status as the dependent variable (0 = no infection, 1 = infection). Variables selected from the univariate analysis and clinical covariates were included. Stepwise regression was used (entry standard P < 0.05, exclusion standard P > 0.10) to construct the multivariate logistic regression model. The regression coefficients (B), OR values, and 95% confidence intervals for each variable were calculated.

[0143] Variables with p < 0.10 in univariate analysis (CD8) + CD19 + IgM and CD4 / CD8 were included in a multivariate logistic regression model, and stepwise regression was used to screen variables. The results are shown in Table 12 below.

[0144] Table 12 Coefficients of the Multivariate Logistic Regression Model

[0145]

[0146] CD8 + T cell percentage (OR=1.833, 95%CI: 1.139-2.949, P=0.0126), CD19 +The proportion of B cells (OR=1.856, 95%CI: 1.188-2.900, P=0.0066), IgM level (OR=1.333, 95%CI: 1.052-1.688, P=0.0171), and CD4 / CD8 ratio (OR=0.8875, 95%CI: 0.7892-0.9981, P=0.0464) were all independent factors associated with infection.

[0147] The model formula is: P(infection) = 1 / [1 + exp(-Z)],

[0148] Z=-28.930+0.606×CD8(%)+0.618×CD19(%)+0.287×IgM(mg / dL)-11.931×CD4 / CD8,

[0149] In the formula, CD8 is CD8 + T cell percentage, expressed as a percentage, using the value before the percentage sign; CD19 is CD19. + B cell percentage, expressed as a percentage, using the value before the percentage sign; IgM level, expressed as mg / dL, using the value before the unit; CD4 / CD8 ratio, expressed as the CD4 / CD8 ratio.

[0150] The model fits well (Hosmer-Lemeshow test χ²). 2 =5.127, P=0.744), Nagelkerke R 2 The value was 0.769. The predicted probability for each child was calculated based on a multivariate logistic regression model, and the ROC curve was plotted. Figure 12 ), respectively for CD8 + CD19 + ROC curve analysis was performed on IgM and CD4 / CD8 ratio, and the results are shown in Table 13.

[0151] Table 13 Predictive efficacy of a single indicator

[0152]

[0153] The results showed that CD8 + T cell percentage, CD19 +B cell percentage, IgM level, and CD4 / CD8 ratio are independent risk factors for infection, and their predictive value is independent of age, sex, and CPAM typing. ROC curve analysis showed that IgM had the largest AUC (0.887), with a sensitivity of 76.2% and a specificity of 87.3% for predicting infection using ≥78.50 mg / dL as the cutoff value. Furthermore, its negative predictive value was as high as 93.2%, meaning that among children with IgM levels below 78.50 mg / dL, 93.2% indeed had no history of infection. This indicator has extremely high value in "excluding" the risk of infection and can be used as an "exclusionary" biomarker for clinical screening. CD19 + The AUC was 0.868 (95% CI: 0.786–0.934), with a cutoff value of ≥23.26, the sensitivity was 100.0% and the specificity was 63.3%. CD8 + The AUC for CD4 / CD8 was 0.822 (95% CI: 0.728–0.906), with a cutoff value of ≥23.11%, the sensitivity was 71.4% and the specificity was 79.7%. The AUC for CD4 / CD8 was 0.834 (95% CI: 0.722–0.934), with a cutoff value of ≤1.92, the sensitivity was 71.4% and the specificity was 92.4%.

[0154] The joint model had an AUC of 0.972 (95% CI: 0.939–0.994). Figure 13 The Youden index was higher than any single indicator, suggesting that the combined application of multiple indicators can significantly improve predictive efficacy. Based on the principle of maximizing the Youden index, the optimal cutoff value was 0.132. That is, a predictive probability ≥0.132 is considered high risk of infection, and <0.132 is considered low risk. With 0.132 as the cutoff value, the model's sensitivity was 95.2% (20 / 21), specificity was 86.1% (68 / 79), positive predictive value was 64.5% (20 / 31), and negative predictive value was 98.6% (68 / 69). This indicates that the model has satisfactory clinical value in identifying non-infected children.

[0155] Based on the analysis results of each variable in the multivariate logistic regression model, CD8... + CD19 + Four independent and correlated indicators—IgM, CD4 / CD8 ratio, and CD4 / CD8 ratio—were used to construct a simplified risk scoring system. To improve the clinical interpretability of the scoring system and reduce the risk of overfitting that may arise from using a single optimal cutoff value under small sample conditions, a three-level scoring method was adopted to construct the simplified risk scoring system based on the ROC analysis results and the actual distribution characteristics of each indicator in the infected and non-infected groups. The specific scores are shown in Table 14.

[0156] Table 14 Risk Scoring Criteria (Total Score Range: 0-8 points)

[0157]

[0158] Based on the above scoring criteria, a risk score was calculated for each child, and the relationship between the score and the infection incidence rate was further analyzed. The scoring formula for this system is as follows:

[0159] W = CD8 score + CD19 score + IgM score + CD4 / CD8 score

[0160] In the formula, CD8 is the proportion of CD8+ T cells; CD19 is the proportion of CD19+ B cells; IgM is the IgM level indicator; and CD4 / CD8 is the CD4 / CD8 ratio indicator.

[0161] The results showed that the risk score and the infection rate increased significantly, indicating that the scoring system has a good risk stratification ability (Table 15).

[0162] Table 15 Infection incidence rates in different risk score groups

[0163]

[0164] Based on the scoring results, the children were divided into three risk levels: Low-risk group (0-2 points): 2 out of 62 children developed infections, with an infection rate of 3.2%. Medium-risk group (3-4 points): 7 out of 23 children developed infections, with an infection rate of 30.4%. High-risk group (5-8 points): 12 out of 15 children developed infections, with an infection rate of 80.0%.

[0165] Validation of the scoring system showed that, using the risk score as a predictor, the AUC of the ROC curve was 0.915 (95% CI: 0.838-0.971), indicating that the scoring system has good discriminative ability. With a cutoff value of ≥3 points, the sensitivity for predicting infection was 90.5%, the specificity was 75.9%, and the negative predictive value was 96.8%; with a cutoff value of ≥5 points, the sensitivity was 57.1% and the specificity was 96.2%. This suggests that the former is suitable for clinical screening, while the latter is more suitable for identifying high-risk children.

[0166] The calibration curves show that the probabilities predicted by the joint model are generally consistent with the actual observed infection rates, and the curves are close to the ideal 45° reference line, indicating that the model has good calibration capabilities. Figure 14 The Hosmer-Lemeshow goodness-of-fit test was used to evaluate the model's calibration. Predicted probabilities were grouped by decimal place, and the number of predicted events in each group was compared to the number of actual observed events. The results showed that the Hosmer-Lemeshow goodness-of-fit test indicated that the χ² value was within acceptable limits. 2=5.127, P=0.744, indicating that there is no significant difference between the model's predicted probability and the actual observed probability, and the model is well calibrated.

[0167] Model stability assessment: Internal validation was performed using the Bootstrap resampling method. 1000 Bootstrap samples with replacement were drawn from the original data, and the Logistic regression model was refitted in each sample. The C-statistic (AUC) was calculated. The results showed that the Bootstrap-corrected C-statistic was 0.955. Compared with the original AUC, the corrected AUC only decreased slightly, indicating low overfitting and good stability. Sensitivity analysis showed that after excluding children with single infections and limiting the infection group to recurrent infections (≥2 times), the AUC was 0.973, slightly higher than the original model, suggesting that the model has better predictive power for children with recurrent infections. Individual analysis of different subtypes showed that the model's AUCs for subtypes 1, 2, and 3 were 0.93, 0.94, and 0.92, respectively, indicating that the model performed stably across different subtypes (Table 16).

[0168] Table 16 Predictive efficacy of models in different subtypes

[0169]

[0170] To further verify the association between the peripheral blood scoring system and the local immune status of the lesion, the correlation between the risk score and the expression levels of CCL5, NKG7, and IGHM-related proteins detected in this embodiment was analyzed (Table 17).

[0171] Table 17. Association between peripheral blood scoring system and local immune status of lesions

[0172]

[0173] Ninety-eight children were divided into three groups: low-risk (0-2 points, n=51), medium-risk (3-5 points, n=37), and high-risk (6-8 points, n=10). Results showed a significant positive correlation between risk scores and the expression levels of three histological markers, with the strongest correlation observed with IGHM (r=0.76, P<0.001). The correlation coefficient between CCL5 H-score and risk scores was 0.68 (P<0.001), and the correlation coefficient between NKG7 H-score and risk scores was 0.71 (P<0.001). With increasing risk scores, the expression levels of all three histological markers showed a progressively increasing trend, with statistically significant differences among the low-risk, medium-risk, and high-risk groups (P for trend <0.001). This finding suggests that peripheral blood immune indicators can not only predict infection risk but also reflect the degree of local immune activation at the lesion site. The CCL5, NKG7, and IgM H-scores of high-risk children (scores 6-8) were 172.5±32.5, 158.5±30.5, and 148.5±35.5, respectively, which were significantly higher than those of the medium-risk and low-risk groups. This was highly consistent with the expression levels of the infected children in Example 3 (CCL5 144.2±24.5, NKG7 134.0±22.8, IgM 110.8±32.8 in the infected group), further suggesting the effectiveness of the peripheral blood scoring system in reflecting the local immune status of lesions.

[0174] This specific embodiment is merely an explanation of the present invention and is not intended to limit the invention. After reading this specification, those skilled in the art can make modifications to this embodiment without contributing any inventive step, but such modifications are protected by patent law as long as they are within the scope of the claims of the present invention.

Claims

1. A biomarker for assessing the risk of infection in patients with congenital pulmonary airway malformations, characterized in that, The marker is CD8 + T cell percentage, CD19 + It consists of the proportion of B cells, IgM level, and CD4 / CD8 ratio.

2. A method for constructing a predictive model for the risk of infection in congenital pulmonary airway malformations, characterized in that, Includes the following: with infection status as the dependent variable, where 0 = no infection and 1 = infection; incorporating the biomarkers described in claim 1 as variables, and constructing a multivariate logistic regression model using stepwise regression; The formula for the model is: P(infection) = 1 / [1 + exp(-Z)], Z=-28.930+0.606×CD8+0.618×CD19+ 0.287×IgM(mg / dL)-11.931×CD4 / CD8, In the formula, CD8 is CD8 + T cell percentage, expressed as a percentage, using the value before the percentage sign; CD19 is CD19. + B cell percentage, expressed as a percentage, using the value before the percentage sign; IgM level, expressed as mg / dL, using the value before the unit; CD4 / CD8 ratio, expressed as the CD4 / CD8 ratio.

3. A predictive model for the risk of infection in patients with congenital lung airway malformations, constructed using the construction method described in claim 2.

4. The prediction model according to claim 3, characterized in that, When the predicted probability output by the prediction model is higher than or equal to the cutoff value, it is determined to be of high risk of infection; when the predicted probability output by the prediction model is less than the cutoff value, it is determined to be of low risk of infection.

5. The prediction model according to claim 4, characterized in that, The cutoff value is 0.

132.

6. A method for constructing a scoring system for the risk of infection in congenital pulmonary airway malformations, characterized in that, include: Based on the analysis results of each variable in the multivariate logistic regression model described in claim 2, the biomarkers described in claim 1 are used as independent correlation indicators. Based on the ROC analysis results and combined with the actual distribution characteristics of each indicator in the infected group and the non-infected group, a three-level scoring method is used to construct a risk scoring system.

7. A scoring system constructed using the construction method described in claim 5.

8. The scoring system according to claim 6, characterized in that, The scoring criteria of the scoring system are shown in Table 14.

9. The scoring system according to claim 6, characterized in that, The scoring formula for the scoring system is: W = CD8 score + CD19 score + IgM score + CD4 / CD8 score In the formula, CD8 is the proportion of CD8+ T cells; CD19 is the proportion of CD19+ B cells; IgM is the IgM level indicator; and CD4 / CD8 is the CD4 / CD8 ratio indicator.

10. The scoring system according to claim 6, characterized in that, The scoring system can classify children into three risk levels. When the score falls into the first cutoff value, it is determined to be low risk of infection; when the score falls into the second cutoff value, it is determined to be medium risk of infection; and when the score falls into the third cutoff value, it is determined to be high risk of infection.

11. The scoring system according to claim 9, characterized in that, The scoring system according to claim 6 is characterized in that the first cutoff value is 0-2 points, the second cutoff value is 3-4 points, and the third cutoff value is 5-8 points.

12. The use of a reagent for detecting the biomarker of claim 1 in the preparation of a kit for predicting the risk of infection in congenital pulmonary airway malformations.

13. The application according to claim 12, characterized in that, The reagent is used to test peripheral venous blood.

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