Landscape of infltrating immune cells and related genes in Infectious Mononucleosis and its Associated Characteristics in Children with Primary Epstein-Barr Virus Infection

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Our aim is to investigate the incidence, characteristics, and potential risk factors of IM to facilitate early risk prediction. Methods: We downloaded data from the Gene Expression Omnibus (GEO) database. The limma package in R software was used to identify differentially expressed genes (DEGs). We used Xiantao Academic's software to analyze differences in the immune microenvironment between EBV-infected patients and controls. we examined the correlation between diagnostic markers and inffltrating immune cells to better understand the molecular immune mechanism.Finally, We conducted a retrospective analysis of all immunocompetent patients diagnosed with EBV infection at a tertiary Traditional Chinese Medicine over a three-year period. We evaluated their demographic, clinical, and laboratory characteristics. Results: We demonstrated the landscape of infltrating immune cells in patients with EBV-infected and identifed the top 17 hub immune regulatory genes. Five of the core genes (OAS2,PARP9,IFIT5,ISG15,ICAM1) were signifcantly correlated with the estimated EBV-infected. We verifed that macrophage numbers were remarkably elevated, whereas Treg and Th17 cells were remarkably reduced in the EBV-infected. Th1 and Th2 cells were abundant in the EBV-infected. pDC,DC and NK56 cells were abundant in the EBV-infected. The expression of immune cells is also demonstrated by clinical patient results. Conclusions: The changes in the number and function of immune cells in children with IM in different sexes and ages have certain reference significance for evaluating the overall treatment of IM and their ability to be combined with other pathogenic bacteria. Epstein-Barr virus (EBV) Infectious mononucleosis pathogenic bacteria Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Introduction Infectious mononucleosis is a clinical condition characterized by fever, sore throat, and swollen lymph nodes in the neck. Various pathogens contribute to the development of this disease, but this article specifically focuses on the illness caused by primary Epstein-Barr virus (EBV) infection ( 1 , 2 ) . EBV infection can enhance the body's immune response. However, the immune response mechanism in children, especially in young children, is not yet fully understood. Primary Epstein-Barr virus (EBV) infection in childhood often presents atypically, which can result in misdiagnosis or underdiagnosis ( 3 ) . Moreover, there is a lack of comprehensive studies examining the spectrum of diseases in children with primary EBV infection and infectious mononucleosis (IM). The diagnosis of EBV infection primarily relies on serological and molecular biology tests ( 4 , 5 , 6 ) . It is often necessary to determine the immune status of patients with Epstein-Barr virus (EBV)-infected infectious mononucleosis (IM) in order to diagnose complications associated with EBV infection ( 7 ) . Both innate and adaptive immunity are involved in host infection with EBV. However, the overall immune response of the innate immune system during infectious mononucleosis has not been thoroughly studied ( 8 ) . Only in humanized mice has it been shown that NK cells are crucial for early control of EBV, particularly in eliminating lysed infected B cells ( 9 ) . The immune response to EBV is initiated when the viral load becomes measurable in the oropharynx and peripheral blood ( 10 ) . By the time patients begin to experience symptoms related to the acute illness, this response has significantly diminished, transitioning to a signature more closely associated with hemophagocytic syndromes due to the rapid expansion of CD8 + Tcells (11)(12) . In this study, a combination of serology and molecular biology techniques was used to track the disease spectrum and changes in immune function during primary Epstein-Barr virus (EBV) infection in children. Additionally, the study aimed to evaluate the potential factors that may influence the development of infectious mononucleosis (IM) and its prognosis. Materials and methods Microarrays and Clinical Data Collection Microarray datasets GSE45919 and GSE46519 were downloaded from the Gene Expression Omnibus ( https://www.ncbi.nlm.nih.gov/geo/ ). Both datasets are based on the GPL10558 (Illumina HumanHT-12 V4.0 expression beadchip) and GPL570 [HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array. Array platforms. contains includes EBV transcripts from EBV-infected samples 9 control transcripts, and while contains transcripts of from EBV EBV-infected samples 2 control. Identification of Differentially Expressed Genes (DEGs) To identify the differences between the EBV infectioned group and the control group, we utilized the R package to standardize the datasets and detect differentially expressed genes (DEGs) in GSE45919 and GSE46519. Genes that met the screening criteria of |log2FC| ≥ 1 and an adjusted P-value < 0.05 were classified as DEGs. Volcano plots generated using the package illustrated the differential expression of these DEGs. Additionally, heatmaps were created to display the top 82 DEGs with the highest and lowest expression levels. Finally, we identified DEGs of significant interest in GSE45919 and GSE46519 through Venn diagram analysis. Protein-Protein Interaction Network Generation and Subnetwork Analysis The STRING database (version 12.0; http://string-db.org/ ) was utilized to integrate the DEG-encoded proteins and their associations, facilitating a comprehensive characterization of the query proteins. The protein-protein interaction (PPI) network was imported into Cytoscape software (version 3.9.1) for visualization. CytoHubba, a plugin for Cytoscape, was utilized to calculate the degree of each protein node within the PPI network. The genes with the top 17 node degrees were identified as hub genes. Clinical significance - Prognostic class - Prognostic Lasso coefficient screening We employed the Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression for feature selection to identify diagnostic markers in the EBV-infected group. The LASSO algorithm was implemented using the glmnet package (version 4.1.7, Xiantao Academic) to reduce the number of genes in the model and to address multicollinearity issues in the regression analysis. Subsequently, we utilized multivariable logistic regression with a backward elimination method to identify independent diagnostic biomarkers and develop a multimarker diagnostic model. Infltrating immune cell analysis Based on ssGSEA algorithm provided by R-package-GSVA, this paper utilizes Immunity article provided markers for 24 immune cells to calculate the immune infiltration of the uploaded data.The ssGSEA algorithm was used to calculate the relative proportions of 24 immune cell types between normal and EBV-infected groups, which were exhibited in a bar plot and a heatmap.To improve the reliability of the deconvolution method, samples with a ssGSEA P < 0.05 were selected for further analysis. The number of permutations was set at 100. In addition, immune cell profles of the normal and DKD groups were subjected to principal component analysis (PCA). Correlation analysis We frst conducted a Pearson correlation between the top 3 hub genes and immune cell subtypes of interest, to explore their regulatory networks. Further, we analyzed the association between 3 hub genes and EBV-infected-IM clinical features (PCT,Monocyte and CD56). Participants and Methods Study Site and Patient Specimens A total of 106 patients, comprising 57 males and 49 females, were included in the research conducted at the Hospital. This retrospective study analyzed data collected over a span of three years. All patients diagnosed with IM were categorized into three age groups: Group A (1–3 years old), Group B (3–6 years old), and Group C (6–13 years old). Each group was further divided into subgroups based on gender (A-F/A-M, B-F/B-M, C-F/C-M). We combined variables from hospital admissions to analyze test results and describe primary Epstein-Barr virus (EBV) infection and IM in children. This is a retrospective study.This study was approved by the Scientific Research Ethics Committee of Hangzhou Hospital of Traditional Chinese Medicine (2023KLL101) (Fig. 7 A). IM is defined by the following criteria: (1) the presence of fever accompanied by symptoms typical of infectious mononucleosis, such as a sore throat, swollen lymph nodes, or enlargement of the liver and spleen, along with a positive VCA-IgM test or the detection of EBV-DNA in the peripheral blood; (2) the presence of at least 50% lymphocytes or at least 10% atypical lymphocytes; and (3) the exclusion of other infections. All three criteria must be satisfied ( 4 ) . Experimental Methods The 17-year-old was admitted to the hospital on 21/7/2022. We collected patient materials from19/7/2019 to 17/8/2023.The ELISA method was employed to detect antibodies specific to the EBV. Peripheral venous blood samples (3ml) were collected, centrifuged, and the serum was extracted for further experiments. Serum levels of VCA-IgM, VCA-IgG, EA-IgG, and EBNA-IgG were measured using ELISA kits sourced from China. Peripheral blood and bone marrow samples were treated with Baso Liu A and B solutions (Catalog No. C220101). Real-time quantitative PCR (CSTB) was utilized to determine the EBV-DNA copy number in EBV-infected whole blood. All reagents were obtained from Shengxiang Biology, and amplification was performed using the ABI 7500 instrument. The flow cytometer used was the Beckman Coulter NAVIOS, and the antibodies were provided by Beckman Company. Hemolysin was sourced from QIAGEN Sciences. The number of cells collected and analyzed ranged from 20,000 to 50,000, targeting the markers CD45, SSC, CD3, and CD56.DetectedantibodiesCD3/CD8/CD3/KAPPA/LAMBDA/CD38/CD20/CD56/CD5/CD20/CD29/CD57/FMC-7/CD200/CD79b/CD23/CD103/CD11c/CD22/CD45/158.Specimens were prepared by whole blood erythrolysis method.Biochemical indicators were determined using chemical methods, while immune function was assessed using flow cytometry. Bacterial identification and antimicrobial susceptibility test: Strain identification was performed by matrix-assisted laser desorption ionization flight mass spectrometry on VITEK mass spectrometer. Statistical Analysis All statistical analyses were conducted using IBM SPSS Statistics version 20 (IBM). The Kruskal-Wallis rank-sum test was employed to compare the clinical characteristics within each group in the study of infectious mononucleosis (IM). The Mann-Whitney U test, also known as the Wilcoxon rank-sum test, was utilized to compare clinical indicators between groups with and without comorbid bacterial infections. For correlation analysis, the Pearson correlation coefficient was applied, while the chi-square test was used to compare pre- and post-treatment indicators. The statistical method employed for comparing clinical characteristics across different groups was the Pearson chi-square test. A p-value of less than 0.05 was considered statistically significant. Results Principal Component Analysis and Identification of Differentially Expressed Genes With |log2FC| ≥ 1 and an adjusted P-value < 0.05, we identified 348 differentially expressed genes (DEGs) from the GSE45919 dataset (2281 up-regulated and 2039 down-regulated) and 460 DEGs from the GSE46519 dataset (702 up-regulated and 386 down-regulated). The volcano plots illustrated the significantly different distributions of DEGs in each dataset (Fig. 1 A, C). The heat maps displayed the DEGs for each dataset (Fig. 1 B, D). The Venn diagram revealed that there were 57 continuously up-regulated DEGs and 25 down-regulated DEGs common to both datasets (Fig. 1 E, F).Furthermore, the genes of 82 DEGs were further analyzed, and the genes with relatively high expression were revealed. Functional enrichment analyses GO and KEGG pathway analyses were conducted to evaluate the systematic functional pathway annotations of commonly differentially expressed genes (DEGs). The top 17 GO-enriched biological processes (BPs) related to body systems are illustrated in Fig. 2 A,B. According to the KEGG pathways, the common DEGs were enriched in fluid shear stress and atherosclerosis, TNF signaling pathway and NF-kappa B signaling pathway.GSEA showed that the signifcantly enriched pathways in EBV-infectioned were the regulation of cellular DNA damage and mitosis,coagulation cascades pid integrin3 pathway,mapk targets pathway in Fig. 2 C,D,E. Identification and Analysis of Hub Genes We generated the PPI network based on a combined score > 0.4.The PPI network consisted of 82 nodes and 88 edges. Five highly connected subnetworks were identiled by the MCODE plug-in algorithm.The graphical visualization of the generated PPI network was performed by Cytoscape software.we identified 17 hub genes: ICAM1,ISG15,FOS,OAS2,SOD2,VCAM1,TNFRSF9,PCLAF,LAG3,PTX3,IFIT5,HERC6,GBP1,TRIM21,PARP9,GBP3,ZC3HAV1(Fig. 3 A-E). In the five highly connected subnetworks, We found that only 3 target genes coexisted with each other, namely ICAM1,ISG15,and OAS2(Fig. 4 A). Construction of the diagnostic risk model To solve the multicollinearity problem in regression analysis, LASSO analysis was used to further narrow down the differentially expressed DGEs as candidate diagnostic biomarkers for IM. The LASSO model included 20 genes and basic phenotype information (age and sex). We identiffed 4 genes (ICAM1,OAS2,ISG15 and PARP9) based on lambda.1se (Fig. 4 B, C).However, only 3 genes (ICAM1, ISG15 and IFIT5) were found to be different in the normal and EBV-infected groups((Fig. 4 D). Landscape of infltrating immune cell We performed an analysis on the infltrating immune cells and immune-related DEGs based on 11 normal controls and 18 EBV-infected sample with a ssGSEA algorithm P-value of < 0.05 were entered into the immune infltration analysis. The distribution of 24 immune cells is shown in a bar plot (Fig. 5 A) and heatmap (Fig. 5 C).Wilcoxon rank sum test was used to compare the changes of immune cells in each group. The results showed that iDC,NK CD56bright cells,Tcm and Th2 cells were increased in EBV infection group, while Th1, Tem, Eosinophils, Macrophages in the infection group were lower (Figuer 5B).Distinctly different immune cells were identified by ROC analysis, NK CD56bright cells (AUC = 0.904), iDC(AUC = 0.7934) ,Th1 cells (AUC = 0.783),and Tcm(AUC = 0.788)(Fig. 5 D). Correlation analysis between ICAM1, IFIT5 and ISG5 and inffltrating immune cells and EBV-infected-IM clinical features (PCT,Monocyte and CD56) Correlation analysis revealed that ICAM1 was positively correlated with Cytotoxic cells (r = 0.870,p < 0.01),pDCcells (r = 0.847,p < 0.01) and negatively correlated with Eosinophils (r=-0.779,p < 0.01) and Neutrophils (r=-0.752, p < 0.01).Correlation analysis revealed that IFIT5 was positively correlated with Tcm(r = 0.909, p < 0.01), NK CD56bright cells(r = 0.904, p < 0.01) and negatively correlated with Tem (r=-0.856, p < 0.01),CD8 T cells(r=-0.809, p < 0.01).Correlation analysis revealed that ISG15 was positively correlated with T helper cells(r = 0.503,p < 0.01), DC cells(r = 0.847,p < 0.01) and negatively correlated with CD8 T cells(r=-0.608, p < 0.01),Tcells (r=-0.586, p < 0.01)(Fig. 6 A-D).Correlation analysis revealed that ISG15 was positively correlated with CD56(r = 0.398,p < 0.05)and ICAM1 was positively correlated with PCT(r = 0.588,p < 0.05) and IFIT5 was positively correlated with Monocyte(r = 0.381,p < 0.05)(Fig. 6 E,F). The course of diagnosis and treatment and the evaluation of treatment The 17-year-old had no underlying medical conditions. Ten days ago, he had a fever of 37.9°C for no apparent reason, and the fever occurred non-stop after taking ibuprofen.(Fig. 7 B). Admission test analysis: The total number of white blood cells is 22.30 (4–10) ×10 9 /l, with a neutrophil count of 12.90 (50–70)%, a lymphocyte count of 72.20 (20–40) %, and a monocyte count of 14.30 (3–10) %. The total platelet count is 80 (100–300) ×10 9 /l (Fig. 7 C and D). Alanine aminotransferase (ALT): 389.3 U/l (normal range: 9–50 U/l), aspartate aminotransferase (AST): 195.7 U/l (normal range: 15–40 U/l)(Fig. 7 E). On admission during the first week, test results indicated that the levels of cytomegalovirus IgM antibody (luminescence) were positive at 74.44 AU/ml (reference range: 1–10 AU/ml), and the levels of EB-DNA were measured at 1.5 x 10 5 copies/ml(Table.1). Bone marrow smear results show significant granulocyte hyperplasia with no abnormalities and no significant changes in red blood cell lines(Fig. 7 E). Lymphocyte morphological irregularities suggest impaired macrokaryocyte maturation (Fig. 7 F). Ultrasound of the lymph nodes showed bilateral inguinal lymphadenopathy, as well as lymphadenopathy in the abdominal cavity, retroperitoneum, mediastinum, and axillary regions (Figs. 8 A, B, C). Flow cytometry analysis of peripheral blood lymphoma revealed that 62.6% of the mature lymphocyte population was examined, with CD3 + CD8 + TRBC1 cells accounting for 32.6% ( Figs. 9 A, B). The flow cytometry analysis of KIR receptor expression and function in NK cells indicated that the CD3 population increased to 92.8% ( Fig. 10 A). Additionally, 2.8% of the naive cell population expressed markers consistent with acute leukemia, as determined by immunophenotyping, with CD3 + CD8 + TRBC1 + cells comprising 39.4% (Fig. 10 B). During the second week of transfer, a re-examination of EBV-DNA revealed a significant decrease to 9.07 × 10² copies/ml (Table 1). Characteristics of children with IM The research involved 106 children who had Epstein-Barr virus-specific antibodies and underwent plasma immune function tests. The median age of the enrolled children was 5.34 years, with an age range of 1 to 13 years. The male-to-female ratio was 1.16, consisting of 57 males and 49 females. Among the 106 hospitalized patients, 96(90.57%) tested positive for EBV antibodies, while 10 (9.43%) tested negative for EBV antibodies, including those not detected. All 106 hospitalized patients exhibited clinical symptoms associated with infectious mononucleosis (IM). Distribution of Epstein-Barr virus (EBV)-specific antibodies by age and gender. We retrospectively studied 106 subjects who had a confirmed diagnosis of IM. Of the 106 hospitalized patients, 96(90.57%) tested positive for EBV antibodies, while 10 (9.43%) tested negative on the EBV antibody test. The serum antibody titers of VCA-IgM antibody were significantly higher in groups B and C compared to group A. The serum antibody titers of VCA-IgM antibody in the C-M group showed a consistent trend, similar to the previous pattern. VCA-IgG was not statistically significant in any of the groups, with a seropositivity rate of only 22 out of 106 (20.75%) during the acute phase of infection. At the same time, the inflammatory indicators of the patients were different, which was manifested as CRP and PCT were significantly higher in group B than in its group, and groups A-F and C-F were significantly higher than those in group B-F(Fig. 11 A,B,C).Therefore, VCA-IgM is the primary indicator for children who have recently had an Epstein-Barr virus infection (Fig. 11 D,E,F). Further study showed that the antibody titers of EA-IgM and VCA-IgM antibodies in the serum were significantly higher than those of non-pathogenic bacteria, indicating that other pathogens may also affect the lymphocyte changes of the body through other forms (Fig. 11 G,H,I). Atypical lymphocytes were found to be greater than 10% in 106 cases (46.22%).The proportion of atypical lymphocytes in group C is relatively high(Table 1). Distribution of patients with infectious mononucleosis based on age and gender We gathered data from 106 children and found that the serum mean value of ALT in the 6–13 years old (C) group was 170.93 U/l, surpassing the upper limit of detection. This disparity was statistically significant compared to the other groups (p < 0.001). In the ADA test results, the values in the 6–13 years old (C) group were relatively higher (49.49 U/l) than in the other groups. γ-GT had the highest values of 80.25 U/l and 64.31 U/l in the 6–13 years old (C) group and the 6–13 years old male (C-M) group. We observed that CK and CK-MB levels were notably higher in the 1–3 years group, measuring 1106.63 U/l and 51.15 U/l, respectively. The levels of CK and CK-MB in the 1–3 years old female (A-F) and 1–3 years old male (A-M) groups exhibited a significant increase of 160.63 U/l, 46.37 U/l, 235.61 U/l, and 54.93 U/l. There was no significant difference in liver function between comorbid and non-concomitant bacteria in this group (Fig. 12 A,B,C). Distribution of IgG, IgM, IgA, and Ig E in children following EBV infection, categorized by age and gender We investigated and found that out of 106 children, 6.60% (7/106) had total IgG levels that exceeded the upper limit of detection (1600 mg/dl), 15.09% (16/106) had IgM antibody levels that surpassed the upper limit of detection (230 mg/dl), and 33.96% (36/106) had IgE antibody levels that exceeded the upper limit of detection (165 mg/dl). IgG and IgA levels were higher in group C compared to other groups. However, 14.15% of the children had IgM values higher than the detected levels, and 32.3% of the children had IgE values higher than the detected levels. There was no significant change in immune indexes between concurrent and non-concurrent bacteria (Figure.12D,E,F). When a child with IM is infected with the Epstein-Barr virus, the body's immune cells mainly respond to B and T cells, resulting in cell proliferation and the presence of atypical lymphocytes. Distribution of T, B, and NK cells in peripheral blood by age and gender We retrospectively counted that 52.83% (56/106) of CD3 + T cells in 106 IM peripheral blood samples had relative numbers higher than the upper limit of detection (81.22%). Additionally, 83.96% (89/106) of CD8 + T lymphocytes had relative numbers higher than the upper limit of detection, which was 38.24%. Furthermore, 2.83% (3/106) of CD19 + B lymphocytes had relative numbers that exceeded the upper limit of detection (18.23%). However, 50.94%(54/106) of CD19 + B lymphocytes the number is below the lower limit of detection(5.389%). And the proportion of NK cells (CD56+/CD3-) was lower than the upper limit of detection (6.37%) in 29.24% (31/106)of cases. The relative numbers of CD3 + T cells and CD8 + T cells were significantly increased, and the number of children in groups B and C was also significantly higher (Fig. 12 G,H,I). The proportion of CD4 + lymphocytes in Group A was significantly higher than that in groups B and C. Additionally, the lymphocyte count in groups A-M was consistent. Nevertheless, we did find that the relative number of CD19 + B lymphocytes in the blood was significantly higher in Group A compared to Group B and C, and this trend was consistent across different age groups and genders. According to the data changes, we found a positive correlation between CD4 + and CD19 + B by spearman correlation analysis, and the correlation coefficient R was 0.733 (Fig. 12 J). Distribution of Bacteria, Viruses, and Fungi in Immunocompromised Patients. There were 8 cases of gram-negative bacteria, including 2 cases of Moraxella catarrhalis, 1 case of Acinetobacter baumannii, 1 case of Haemophilus haemolyticus, 1 case of Haemophilus influenzae, 1 case of Enterobacter cloacae, 1 case of Klebsiella pneumoniae, and 14 cases of Gram-positive bacteria. Additionally, there were 9 cases of Gram-positive bacteria combined with viruses, 5 cases of Staphylococcus aureus and cytomegalopathy, 1 case of Staphylococcus aureus and Mycoplasma pneumoniae, 1 case of Staphylococcus aureus + influenza A virus, 1 case of Staphylococcus aureus + herpes simplex virus, and 1 case of rubella virus + Herpes simplex virus + cytomegalovirus + Staphylococcus aureus. The total number of viruses was 14: 10 cases of CMV, 1 case of herpes simplex virus, 1 case of cytomegalovirus and herpes simplex virus, 1 case of rubella + herpes simplex virus, and 1 case of respiratory adenovirus. There was also 1 case of Candida albicans fungus. Other findings included Mycoplasma pneumoniae in 3 cases, rheumatoid factor in 1 case, antinuclear antibody (ANA) in 1 case, and antistreptococcal "O" hemolysin in 1 case (Fig. 13 A). The total number of children was 106, with 52 cases having pathogenic bacteria and 54 cases without pathogens. In group A, there were 19 cases of combinations, totaling 31 cases; in group B, there were 18 cases of combinations, totaling 39 cases; and in group C, there were 15 cases of combinations, totaling 33 cases. Furthermore, there were 8 cases of combined bacteria in group A-F, totaling 16 cases; 11 cases of combined bacteria in A-M combination, totaling 15 cases; 7 cases of combined bacteria in B-F combination, totaling 15 cases; 11 cases of combined bacteria in B-M combination, totaling 24 cases; 6 cases of combined bacteria in C-F combination, totaling 18 cases; and 9 cases of combined bacteria in C-M combination, totaling 15 cases (Fig. 13 B). Clinical manifestations of children with and without co-orbidiobacteria First EBV infection is more common in young children, and the vast majority present with asymptomatic or atypical infection. Mild overt manifestations may include symptoms of upper respiratory tract infection, such as fever, nasal congestion, pharyngitis, etc., and can also be accompanied by superficial lymph node enlargement. Symptoms of upper respiratory tract infection such as fever, sore throat, and palpable swelling of superficial lymph nodes in the neck, armpit, or groin are typical clinical manifestations of IM. Chi-square analysis of IM and related diseases showed that the typical clinical manifestations of non-co-bacteria were more obvious than those of co-orbids. According to the table, among the clinical signs and symptoms of IM without pathogenic bacteria, fever, nasal congestion, pharyngeal redness and swelling with tonsils II and signs, cervical lymphatic swelling, and splenomegaly were more obvious than those in children with pathogenic bacteria, but pharyngeal redness and swelling with tonsillar I were more obvious in comorbid pathogens. Comparison of clinical antibiotic administration between the two groups showed that the co-pathogenic bacteria were much higher than the non-co-pathogenic bacteria. Discussion In the present study, we demonstrated the landscape of infltrating immune cells in patients with EBV-infected group and identifed the top 17 hub immune-regulatory genes. Three of the core genes (ICAM1, ISG15, and IFIT5) were signifcantly correlated with EBV. Through multiplex immunofuorescence staining, we verifed the diferentially NK CD56bright cells, iDC, and Th1 subsets. We found that the macrophage count was remarkably elevated, whereas NK CD56bright cells were remarkably.We analyze further that correlation analysis between ICAM1, IFIT5 and ISG5 and inffltrating immune cells and EBV-infected-IM clinical features (PCT,Monocyte and CD56). The young child was admitted to the hospital with a diagnosis of infectious mononucleosis (IM), presenting with persistent fever, a sore throat, and significant cervical lymphadenopathy, which ruled out other hematologic disorders. The child exhibited a notable increase in liver injury markers, lymph node enlargement, and alterations in the immune cell count. Based on this information, we reviewed relevant studies and found that liver impairment was more pronounced in patients aged 6 to 13 years. However, damage to cardiac enzymes, specifically creatine kinase (CK) and CK-MB, was more significant in the younger age group of 1 to 3 years, which aligns with the findings of Chijioke O ( 13 ) . In this review, the diagnosis of infectious mononucleosis (IM) was confirmed in 90.57% of cases, with 46.22% of patients exhibiting atypical lymphocytes in relevant examinations ( 14 ) . Liver injury caused by IM is a common early complication, resulting from the infiltration of lymphocytes infected with the EBV ( 15 , 16 , 17 , 18 ) . Retrospective studies have indicated that liver function impairment is more severe in patients older than 7 years of age. Symptomatic treatment with liver protection has shown significant improvement. For patients aged 1 to 3 years, myocardial enzyme injury was more pronounced, with elevated levels of CK and CK-MB observed in the peripheral blood of both girls and boys (P < 0.05). While cardiac complications are uncommon in infectious mononucleosis, the first association between acute pericarditis and EBV infection was described by researchers ( 19 ) . Innate and adaptive immunity play crucial roles in mediating host responses to EBV infections. NK cells are significant contributors to the pathophysiology of infectious mononucleosis ( 20 ) . In an analysis of NK cell counts in the peripheral blood of 106 patients with infectious mononucleosis, we observed that 29.24% (31 out of 106) of the children exhibited a decrease in NK cell counts. As noted in the literature, NK cell depletion following EBV infection does not significantly differ from the depletion observed prior to infection ( 21 , 22 ) . EBV-activated lymphocytes initiate adaptive immunity, and NK cells are emerging as critical players in the context of infectious mononucleosis, as they preferentially kill EBV-infected cells when the virus enters the lytic cycle, underscoring the importance of NK cells ( 23 ) . NK cells are continuously depleted during the immune response to EBV, leading to a reduced population that reflects the overall immune status of the body ( 24 ) . Interestingly, unlike the depleting effects observed with co-pathogenic bacterial infections, the depletion of NK cells in EBV-infected individuals does not appear to have a significant impact ( 25 ) . During primary EBV infection, there is a notable increase in the number of peripheral blood T cells. This population consists of a mixture of CD8 + cytotoxic T cells, NK cells, and CD4 + helper T cells, with a significantly higher proportion of these cells observed in children aged 1 to 3 years compared to other age groups ( 26 , 27 ) . Although there is no significant increase in the total number of CD4 + T cells during infectious mononucleosis, evidence suggests that CD4 + T cells become activated and play a role in controlling infected B cells ( 28 ) . Studies indicate that during acute infection, CD4 + T cells can recognize multiple lytic antigens ( 29 , 30 ) . Our data reveal that the number of B lymphocytes decreased in 51% (49 out of 106) of the children, with a significant decline noted in those aged 6 to 13 years. This finding aligns with previous studies conducted on healthy children ( 31 , 32 ) . As a child's immune system matures with age, the importance of B lymphocytes in defending against viral infections becomes increasingly evident ( 33 ) . Research has shown that the predominant pathogenic bacteria in children with IM and tonsillitis are Staphylococcus aureus and anaerobic bacteria ( 34 , 35 ) . However, there is a significant lack of studies analyzing pathogenic bacteria in children with IM across different age groups. Our findings indicate that 29.82% (17/57) of IM cases were complicated by cytomegalovirus and Staphylococcus aureus, 8.77% (5/57) were associated with the herpes simplex virus, 4.02% (4/57) tested positive for anti-streptococcal type O, 5.26% (3/57) were allergic to household dust mites, 5.26% (3/57) tested positive for rheumatic factor, and 3.51% (2/57) tested positive for the rubella virus. Therefore, after children are infected with EBV and diagnosed with IM, and following appropriate symptomatic treatment, it is essential to remain vigilant for the possibility of co-infection with other strains, particularly cytomegalovirus and Staphylococcus aureus, as these are the most frequently encountered pathogens ( 36 ) . In summary, we analyzed 106 hospitalized children with IM and observed significant differences in EBV-specific antibodies, lymphocyte count, and immune function in the peripheral blood of children across various age groups and genders. These differences may make children more vulnerable to infections caused by other viruses and bacteria, ultimately leading to less effective treatment and prognosis for infectious mononucleosis IM. Declarations Ethics approval and consent to participate This study was approved by the Scientific Research Ethics Committee of Hangzhou Hospital of Traditional Chinese Medicine (2023KLL101). The committee agreed on the use of clinical specimen data. Consent for publication The authors would like to acknowledge the infrastructure support provided by Hangzhou TCM Hospital Affiliated with Zhejiang Chinese Medicine University and Department of Clinical Laboratory, Huai ， an Second People’s Hospital, The Affiliated Huai ， an Hospital of Xuzhou Medical University. The clinically collected data is accurate and reliable. Informed consent was obtained from all participants and Informed consent must have been obtained from a parent and/or legal guardian. Availability of data and materials All relevant data are within the paper and its Supporting information files. Competing Interests The authors declare no conflicts of interest. Funding Not applicable. Authors' contributions Study design, S.L; Sample and data collection, Y.Z; Case collection L.X, Q,Y. Data analysis, L.X, Q,Y. S.L wrote the first draft of the manuscript and participated in the collection and evaluation of literature. S.L supervised and critically revised the manuscript. All authors contributed to the article and approved the submitted version. The clinically collected data is true and reliable. Acknowledgements We thank all author institution for their strong support. References Balfour H H , Dunmire S K , Hogquist K A .Infectious mononucleosis[J].Clinical & Translational Immunology, 2015, 4(2).DOI:10.1038/cti.2015. Dowd JB, Palermo T, Brite J, McDade TW, Aiello A. Seroprevalence of Epstein-Barr virus infection in U.S. children ages 6–19, 2003–2010. 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Cross-reactive influenza virus-specific CD8+ T cells contribute to lymphoproliferation in EpsteinBarr virus-associated infectious mononucleosis. J Clin Invest. 2005; 115:3602–3612.10.1172/ JCI25078 [PubMed: 16308574] Cohen JI, Fauci AS, Varmus H, Nabel GJ. Epstein-Barr virus: an important vaccine target for cancer prevention. Sci Transl Med. 2011; 3:107fs107.10.1126/scitranslmed.3002878. Yong-Wei W , Ying-Chao W , Pediatrics D O .Progress in Diagnosis and Treatment of Epstein-Barr Virus Associated Disease in Children[J].Medical Recapitulate, 2013. Andrew D ,Hislop,Umaimainthan,et al.Impaired Epstein-Barr virus-specific CD8+ T-cell function in X-linked lymphoproliferative disease is restricted to SLAM family-positive B-cell targets.[J].Blood, 2010.DOI:10.1182/blood-2009-09-238832. Azzi T, Lunemann A, Murer A, Ueda S, Beziat V, Malmberg KJ, Staubli G, Gysin C, Berger C, Munz C, Chijioke O, Nadal D. Role for early-differentiated natural killer cells in infectious mononucleosis. Blood. 2014; 124:2533–2543.10.1182/blood-2014-01-553024 [PubMed: 25205117] Jenson, Hal B .Acute complications of Epstein-Barr virus infectious mononucleosis.[J].Current Opinion in Pediatrics, 2000, 12(3):263.DOI:10.1097/00008480-200006000-00016. Danstrup C S , Klug T E .Low rate of co-infection in complicated infectious mononucleosis[J].Danish medical journal, 2019, 66(9). Klug T E .Incidence and microbiology of peritonsillar abscess: the influence of season, age, and gender[J].European Journal of Clinical Microbiology, 2014, 33(7).DOI:10.1007/s10096-014-2052-8. Sakahashi H , Takazawa A , Toyama A ,et al.Active infective endocarditis due to methicillin-resistant Staphylococcus aureus in the acute phase of infectious mononucleosis[J].Jpn J Thorac Cardiovasc Surg, 2002, 50(6):249-251.DOI:10.1007/BF03032154. Table Table 1. Young children exhibit unique distributions and heightened levels of Epstein-Barr virus (EBV)-specific antibodies and viral DNA in their plasma. virus capsid antigen(VCA), nuclear antigen(NA),Early antigens(EA),EBNA, Epstein-Barr nuclear antigen.Cytomegalovirus(CMV),Herpes simplex virus type 1(HSV1),Mycoplasma pneumoniae (MP). Time 23-Jul 28-Jul 29-Jul 1-Aug 24-Aug 1-Oct Test items CMV-IgM Positive Positive HSV-1IgM Positive Positive MP-IgM Positive Not detected EB-VCA-IgG Positive Positive EB-VCA-IgM Positive Positive EBNA1-IgG Positive Positive EA-IgM Positive Positive EBV DNA (<4.0×10 2 copies/mI） 1.5 ×10 5 2.57×10 5 9.02×10 2 Below the lower detection limit EBV-T DNA (<4.0×10 2 copies/mI） 4.48×10 4 Below the lower detection limit EBV-B DNA (<4.0×10 2 copies/mI） 7.19×10 4 Below the lower detection limit EBV-NK DNA (<4.0×10 2 copies/mI） 8.54×10 3 Below the lower detection limit Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6211732","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":434331379,"identity":"8730f78b-94cc-43d8-81bd-05ab84365085","order_by":0,"name":"Sujuan LI","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIie3QsQrCMBCA4ZNA4nCQTQIVfIVIoXQQfJWI4BTEBxAUXOtu8SV8hEqgLroLLgUXhw4ZHRyMm4s2o2D+4SBw33ABCIV+MLZ0o5JzMjysisLePQgWbihZMjiWo32eeRMgHM46Nm3qQ9jJ3NSMRq3saA0g9HinaCA4naRKdmPC1jszS6Gfb9V3MgSdSCXpmOJpZzbo7ro0EOT1i5BFJnRlkPoQoePKESKEBl9SJ69PJhJL6T5ZNN+CXMfWPuZEstXV2vugx6MG4qLi7SE+rr1HrNdaKBQK/W9PTptEHdntU/QAAAAASUVORK5CYII=","orcid":"","institution":"Hangzhou Hospital of Traditional Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Sujuan","middleName":"","lastName":"LI","suffix":""},{"id":434331380,"identity":"33bb82bc-1e74-43e0-8cbe-d5181c0928de","order_by":1,"name":"Yuanhang Zhang","email":"","orcid":"","institution":"Hangzhou Municipal Hospital of Traditional Chinese Medicine: Hangzhou Hospital of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yuanhang","middleName":"","lastName":"Zhang","suffix":""},{"id":434331381,"identity":"ac4b55e0-9c58-4a51-8b31-b23c9aa44dbe","order_by":2,"name":"Lei Xu","email":"","orcid":"","institution":"Huaian College of Information Technology","correspondingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"Xu","suffix":""}],"badges":[],"createdAt":"2025-03-12 11:40:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6211732/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6211732/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":80665776,"identity":"391aef0f-1e91-4fd4-a026-ab99a0c6a627","added_by":"auto","created_at":"2025-04-15 17:34:15","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3482062,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentiffcation of the hub genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A, B) Volcano maps of DEGs in GSE46519 . The volcano graphs show the DEGs expression distribution in each dataset. Based on adj. P \u0026lt; 0.05 and |logFC| ≥1 cut-off criteria, blue dots indicate down-regulated genes, while red dots represent the up-regulated genes.\u003c/p\u003e\n\u003cp\u003e(C,D) Heat maps of DEGs in GSE45919. The heat maps display the top 20 up-regulated and top 20 down-regulated genes in each dataset. Each column represents a sample and each row represents a gene. Red indicates up-regulation, while blue indicates down-regulation.(rest:DKD,ref:Control)\u003c/p\u003e\n\u003cp\u003e(E, F) Expression of differential genes in Venn diagram in 2 databases. 57 up-regulated DEGs and 25 down-regulated DEGs are identiffed from both GSE46519 and GSE45919.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6211732/v1/78ec03136a89e9ee8a0ab85e.png"},{"id":80666254,"identity":"4c7cda80-8cc0-4d31-8806-f55347123f97","added_by":"auto","created_at":"2025-04-15 17:42:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":5680022,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional enrichment analysis of 82 DEGs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) GO clustering tree enrichment analysis of 14 common DEGs. (B) KEGG pathways of 3 common DEGs. We considered adjusted p-value \u0026lt; 0.05 as significant. The blue triangle is the term on the right, and the red circle represents the enriched gene of the corresponding pathway.\u003c/p\u003e\n\u003cp\u003e(C,D,E)Gene set enrichment analysis (GSEA) of the identifed immune-related DEGs.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6211732/v1/bd41761d0656b54a0856c99a.png"},{"id":80665458,"identity":"fcb4d212-8e8d-4528-bfb6-5ab6803124a5","added_by":"auto","created_at":"2025-04-15 17:26:16","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":3610022,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConstruction of the PPI network and validation of hub genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePPI network, highly connected subnetworks, and enrichment analyses of genes involved in subnetworks. The graphical visualization of the generated PPI network was performed byCytoscapesoftware.(A)string_interactions_short.tsv_Degree_top10_with_neighbors_and_expanded.(B)string_interactions_short.tsv_EPC_top10_with_neighbors_and_expanded.(C) string_interactions_short.tsv_MCC_top10_with_neighbors_and_expanded.(D) string_interactions_short.tsv_MNC_top10_with_neighbors_and_expanded.(E)string_interactions_short.tsv_Stress_top10_with_neighbors_and_expanded.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-6211732/v1/0e4ceb3aa5958153d65441fa.png"},{"id":80665445,"identity":"90120c09-8b21-42c6-84f0-8ced15ae62f4","added_by":"auto","created_at":"2025-04-15 17:26:15","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2645500,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnalysis of the hub gene and Screening of the optimal differentially expressed EBV-related genes used for the construction of the diagnostic model.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A)The hub genes in the 5 groups were analyzed by UpSet.(B) Screening of the optimal parameter (using lambda.1se as the best lambda) at which the vertical lines were drawn. (C) LASSO coefficient profiles of the 20 differentially expressed autophagy-related genes plus age and sex.(D)Differentially differentiated genes were analyzed for comparison in each group.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-6211732/v1/f42b824a066feb1fdbeeaf09.png"},{"id":80665777,"identity":"e2604a42-5fff-4967-9415-e5f24e4b04f6","added_by":"auto","created_at":"2025-04-15 17:34:15","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":3019696,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEvaluation and visualization of immune cell infiltration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Heat map of the 24 immune cell proportions. (B) The violin plot exhibits the differences in ssGSEA algorithmimmune cell fractions between EBV-infected group and healthy controls.\u003c/p\u003e\n\u003cp\u003e(C)Differentially expressed 24 immune cells in different groups.(D)ROC analysis showing that this diagnostic model has good diagnostic performance.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-6211732/v1/23a9dd0aca781ad03c7081ec.png"},{"id":80665461,"identity":"025ddcaf-484a-44de-a00b-220b2d992504","added_by":"auto","created_at":"2025-04-15 17:26:16","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":5857743,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation analysis among hub genes, immune cells, and clinical features.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrelation between the top hub genes (ICAM1) and innate immune cells.(B)Correlation between the top hub genes (IFIT5 ) and innate immune cells.(C)Correlation between the top hub genes (ISG15) and innate immune cells.(D)Correlation between the top hub genes (ICAM1,IFIT5 and ISG15) and CD56 innate immune cells.(E)Correlation between the top hub genes (ICAM1,IFIT5 and ISG15) and PCT of inflammatory markers of infection.(F)Correlation between the top hub genes (ICAM1,IFIT5 and ISG15) and Monocyte of inflammatory markers of infection.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-6211732/v1/48a53d74cf1ee1aa4ad51816.png"},{"id":80665774,"identity":"45f5495e-c143-4bb4-a06c-049fe407de7a","added_by":"auto","created_at":"2025-04-15 17:34:15","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":2276486,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eChanges in body temperature of adolescent children with IM before and after admission to the hospital and the flow chart of the study and Changes in peripheral blood and serum liver function in adolescent IM patients.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA:\u003c/strong\u003eScreening and enrollment of inpatients for disease spectrum analysis. EBV,Epstein–Barr virus. RVCS:Rubella virus vesicular simplex virus CMV Staphylococcus aureus, R H: Rubella virus + herpes simplex virus, MM: Moraxella catarrhalis +monilia albicans, MR: Mycoplasma pneumoniae Respiratory adenovirus, A H: Acinetobacter calcic acetate Haemophilus influenzae\u003c/p\u003e\n\u003cp\u003eCMV: Cytomegalovirus.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB:\u003c/strong\u003eChanges in the patient's body temperature.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC:\u003c/strong\u003e Scatter plot of the peripheral blood routine.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eD:\u003c/strong\u003e Microscopic morphology of the peripheral blood smear.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eE:\u003c/strong\u003e Hepatic function indices included ALT (alanine aminotransferase), AST (aspartate aminotransferase), ALP (alkaline phosphatase), GGT (γ-glutamyl transferase), ADA (adenosine deaminase), CK (creatine kinase), and LDH (lactate dehydrogenase). Reason: The revisions improve clarity, enhance technical accuracy, and correct grammatical and punctuation errors. Additionally, the vocabulary has been refined for better readability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eF:\u003c/strong\u003e Bone marrow aspirate smear: 1. The ratio of granulocytes to red blood cells was 6.13:1. Additionally, there was a significant increase in granulogenesis, with a higher percentage of immature granulocytes observed in the middle and late stages. 2. Some granulocytes exhibited an increase in cytoplasmic granules, which stained more coarsely. 3. Erythroid hyperplasia was reduced, leading to a decrease in the proportion of intermediate and mature juvenile red blood cells. The morphology of both immature and mature red blood cells appeared generally normal. 4. The proportion of lymphocytes was normal; however, irregular lymphocytes were present. 5. There were 18 megakaryocytes in the entire sample, consisting of 15 macrogranules, 3 bare nuclei, and scattered platelets.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-6211732/v1/42579da250f54a11979099db.png"},{"id":80665459,"identity":"ae1d8bf7-b812-4897-818e-10482b3e4259","added_by":"auto","created_at":"2025-04-15 17:26:16","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":13860199,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eChanges in lymph nodes in the neck and abdomen under ultrasound\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA: \u003c/strong\u003eJuly 22, 2023:Several swollen lymph nodes were observed in the bilateral inguinal region. They had clear boundaries and lymphatic hila, with the largest measuring approximately 1.7 cm x 0.7 cm on the right side. Some of the lymph nodes appeared to be enlarged, measuring approximately 2.7 cm x 1.0 cm on the left side. August 3, 2023: Several swollen lymph nodes were observed in the bilateral groin region. The nodes had clear borders, regular morphology, and a lymph hilum was present. There was also a localized thickening of the parenchyma, measuring approximately 3.4 cm x 0.8 cm on both the right and left sides.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB: \u003c/strong\u003eOn July 22, 2023, several lymph nodes were observed in the abdominal cavity and retroperitoneum. They had distinct boundaries, a consistent shape, and a significant size of approximately 1.7 cm x 0.9 cm.August 3, 2023: Several swollen lymph nodes were observed in the bilateral groin region. They had well-defined borders, regular morphology, the presence of lymph nodes, and local parenchymal thickening. The size of the lymph nodes measured approximately 3.4 cm x 0.8 cm on both the right and left sides.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC:\u003c/strong\u003eOn August 3, 2023, several swollen lymph nodes were observed on both sides of the neck. The lymph nodes had clear borders, regular morphology, and no abnormal blood flow signals. Most of the lymph nodes were present, with the largest measuring approximately 3.4 cm x 0.9 cm on the right side and 2.6 cm x 0.8 cm on the left side. Several lymph nodes were found on both clavicles, exhibiting clear borders, regular morphology, the presence of lymphatic vessels, and thickening of the local parenchymal echoes. The lymph nodes measured a maximum of 0.7 cm x 0.4 cm on the left side and 0.9 cm x 0.3 cm on the right side.\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-6211732/v1/2479d1f5f5ca1f094a4d631d.png"},{"id":80665503,"identity":"2937b5c6-8df1-4180-bf10-9431b87ed4f8","added_by":"auto","created_at":"2025-04-15 17:26:17","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":55791151,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlow cytometry, chronic lymph, and lymphoma immunophenotyping.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA:\u003c/strong\u003eOn July 22, 2023, flow cytometry was used to detect the immunophenotyping of chronic lymphoma and lymphoma in peripheral blood.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB:\u003c/strong\u003eOn August 3, 2023, flow cytometry was used to detect the immunophenotyping of chronic lymphoma and lymphoma in peripheral blood.\u003c/p\u003e\n\u003cp\u003eCD11c-APC-iFluor700:3.9/CD79b-PerCP-Cy5.5:CB3-1/CD103-BV421:Ber-ACT8/CD3-PE-Dazzle594:SK7/CD4-PE-Dazz1e594:SK3/CD45-V540:HI30/FMC7-FITC:FMC7/CD23-PE-Cy7:EBVCS-5 /CD5-PerCP-Cy5.5:UCHT2/CD56-APC:5.1H11.\u003c/p\u003e","description":"","filename":"Figure9.png","url":"https://assets-eu.researchsquare.com/files/rs-6211732/v1/b2a7241640b3657980c4c10d.png"},{"id":80665465,"identity":"1232e69d-eaad-4743-ab31-4994403c6af0","added_by":"auto","created_at":"2025-04-15 17:26:16","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":18315936,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKIR receptor and functional analysis of flow cytometry NK cells and Immunophenotyping of acute leukemia\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA: \u003c/strong\u003eKIR receptor and functional analysis of flow cytometry NK cells:CD3+:92.8,CD3-CD56+:3.4,CD158a+:22.4,CD158b+:6.1,CD158e+:17.7,CD158i+:31.2,GranzymeB:87.6,perforin:85.3.CD158ei-PE:DX9/CD158b1/b2-FITC:DX27/CD158a,h-PerCP-Cy5.5:HP-MA4/CD56-PE-Cy7:5.IH11/CDI58i-APC:DX27/CD3-APC-iF1uor700:UCHTI.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB: \u003c/strong\u003eThe analysis of 2.8% of the naïve cell population was the expression of myeloid antigens, please combine clinical and other laboratory tests to make a comprehensive judgment. CD3+CD8+TRBC1+:39.4%/TRBC1-FITC:JOV1.1/HLA-DR-mF1uor450：L243/CD13-PE:WM15CD38APC-iFluor700:OKT10/CD33-PerCP-Cy5.5:D3HL60.251/CD15-mF1uor450:FUT4/815 CD64-PE-CF594: 32.2/CD7-FITC:124-1D1/CD2-APC-iFIuor700:OKT11/CD10-APC-Cy7:FR4D11\u003c/p\u003e\n\u003cp\u003eCD117-PE-Cy7:104D2/CD14-APC-Cyanine7:26ic.\u003c/p\u003e","description":"","filename":"Figure10.png","url":"https://assets-eu.researchsquare.com/files/rs-6211732/v1/62b6c78aebdc78a07afaeda6.png"},{"id":80666258,"identity":"9d4c5d40-9689-44e5-bf67-0199a696b79e","added_by":"auto","created_at":"2025-04-15 17:42:16","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":3225588,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution of Epstein-Barr virus (EBV)-specific antibodies by age and gender.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e:These antibodies include VCA-IgM, VCA-IgG, EBNA-IgG, and EA-IgM in all Children. \u003cstrong\u003eB\u003c/strong\u003e: Changes of VCA-IgM, VCA-IgG, EBNA-IgG, and EA-IgM in female children, \u003cstrong\u003eC\u003c/strong\u003e: Changes of VCA-IgM, VCA-IgG, EBNA-IgG, and EA-IgM in male children, \u003cstrong\u003eD\u003c/strong\u003e: Changes of VCA-IgM, VCA-IgG, EBNA-IgG, and EA-IgMn with and without pathogenic bacteria, \u003cstrong\u003eE: \u003c/strong\u003eChanges in special antibodies without concomitant pathogens, \u003cstrong\u003eF:\u003c/strong\u003e Changes in antibodies against concomitant pathogens.By chemiluminescence assay, reagent, batch number global Test (Kruskal-Wallis Test) + multiple hypothesis test (Dunn\u003csup\u003e,\u003c/sup\u003es test), comparison between combined and non-combined bacteria: Mann-Whitney U test (Wilcoxon rank sum test). *p\u0026lt;0.05 **p\u0026lt;0.01，***p\u0026lt;0.01.\u003c/p\u003e","description":"","filename":"Figure11.png","url":"https://assets-eu.researchsquare.com/files/rs-6211732/v1/098d359adee04f2323efa0ff.png"},{"id":80665479,"identity":"4b8127a6-4140-4555-a55f-3f7c51e3b15e","added_by":"auto","created_at":"2025-04-15 17:26:16","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":7297738,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSerum liver function distribution,Distribution of T, B, and NK cells ,and distribution of IgG, IgM, IgA, and IgE in IM patients by age and sex.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e: Changes in liver function indexes in all children, \u003cstrong\u003eB\u003c/strong\u003e: Changes in liver function indexes in female children, \u003cstrong\u003eC\u003c/strong\u003e: Changes in liver function indexes in male children, \u003cstrong\u003eD\u003c/strong\u003e: Changes in liver function in all children with and without pathogenic bacteria. \u003cstrong\u003eE\u003c/strong\u003e: Changes in lymphocyte indexes in all children, \u003cstrong\u003eF\u003c/strong\u003e: Changes in lymphocyte indexes in female children, \u003cstrong\u003eG\u003c/strong\u003e: Changes in lymphocyte indexes in male children, \u003cstrong\u003eH\u003c/strong\u003e: Changes in lymphocytes with and without pathogenic bacteria in all children.\u003cstrong\u003eI\u003c/strong\u003e: Changes in serum antibodies in all children, \u003cstrong\u003eJ\u003c/strong\u003e: Changes in serum antibodies in female children, \u003cstrong\u003eK\u003c/strong\u003e: Changes in serum antibody indexes in male children,\u003cstrong\u003e L\u003c/strong\u003e: Changes in serum antibodies in all children with and without pathogens. Kruskal-Wallis Test + multiple hypothesis test and comparison between the two groups: Mann-Whitney U test，*p\u0026lt;0.05 **p\u0026lt;0.01 ***p\u0026lt;0.001.spearman correlation analysis in children with IM,*p\u0026lt;0.05 **p\u0026lt;0.01 ***p\u0026lt;0.001.\u003c/p\u003e","description":"","filename":"Figure12.png","url":"https://assets-eu.researchsquare.com/files/rs-6211732/v1/916de1547c5152e2c928a237.png"},{"id":80666432,"identity":"86c1c40e-955a-4bd5-9faa-6e5696ad16ab","added_by":"auto","created_at":"2025-04-15 17:50:16","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":4059828,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution of Bacteria, Viruses, and Fungi in Immunocompromised Patients.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring the admission and treatment of IM patients, different pathogens were identified by collecting throat swabs for culture and MALDI-TOF-MS.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA:\u003c/strong\u003e Percentage of children with co-pathogenic bacteria in different groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB:\u003c/strong\u003e Combination of different pathogens:\u003c/p\u003e\n\u003cp\u003eGram-negative bacterium: Moraxella catarrhalis,Baumann/Acinetobacter calcimolate, Haemophilus haemolyticus, Haemophilus influenzae, Enterobacter cloacae.\u003c/p\u003e\n\u003cp\u003eGram-positive bacterium: Staphylococcus aureus.\u003c/p\u003e\n\u003cp\u003eGram-positive bacteria combined with viruses: Staphylococcus aureus and cytomegalopathy, Staphylococcus aureus and mycoplasma pneumoniae, Staphylococcus aureus and influenza A virus, Staphylococcus aureus and herpes simplex virus, Staphylococcus aureus and cytomegalovirus and rubella virus and herpes simplex virus.\u003c/p\u003e\n\u003cp\u003eViruses: Cytomegalovirus(CMV), CMV and herpes simplex virus. Herpes simplex virus I, Rubella and herpes simplex virus I, Respiratory adenovirus.\u003c/p\u003e\n\u003cp\u003eFungus: Candida albicans.\u003c/p\u003e\n\u003cp\u003eRests: Mycoplasma pneumoniae, RFrheumatoid factors, Antinuclear antibody (ANA), Antistreptococcal \"O\" hemolysin.\u003c/p\u003e","description":"","filename":"Figure13.png","url":"https://assets-eu.researchsquare.com/files/rs-6211732/v1/0f483b52b3b6b6c8e66cc0c1.png"}],"financialInterests":"","formattedTitle":"Landscape of infltrating immune cells and related genes in Infectious Mononucleosis and its Associated Characteristics in Children with Primary Epstein-Barr Virus Infection","fulltext":[{"header":"Introduction","content":"\u003cp\u003eInfectious mononucleosis is a clinical condition characterized by fever, sore throat, and swollen lymph nodes in the neck. Various pathogens contribute to the development of this disease, but this article specifically focuses on the illness caused by primary Epstein-Barr virus (EBV) infection\u003csup\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/sup\u003e. EBV infection can enhance the body's immune response. However, the immune response mechanism in children, especially in young children, is not yet fully understood. Primary Epstein-Barr virus (EBV) infection in childhood often presents atypically, which can result in misdiagnosis or underdiagnosis\u003csup\u003e(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e)\u003c/sup\u003e. Moreover, there is a lack of comprehensive studies examining the spectrum of diseases in children with primary EBV infection and infectious mononucleosis (IM).\u003c/p\u003e \u003cp\u003eThe diagnosis of EBV infection primarily relies on serological and molecular biology tests\u003csup\u003e(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e)\u003c/sup\u003e. It is often necessary to determine the immune status of patients with Epstein-Barr virus (EBV)-infected infectious mononucleosis (IM) in order to diagnose complications associated with EBV infection\u003csup\u003e(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e)\u003c/sup\u003e. Both innate and adaptive immunity are involved in host infection with EBV. However, the overall immune response of the innate immune system during infectious mononucleosis has not been thoroughly studied\u003csup\u003e(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e)\u003c/sup\u003e. Only in humanized mice has it been shown that NK cells are crucial for early control of EBV, particularly in eliminating lysed infected B cells\u003csup\u003e(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e)\u003c/sup\u003e. The immune response to EBV is initiated when the viral load becomes measurable in the oropharynx and peripheral blood\u003csup\u003e(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e)\u003c/sup\u003e. By the time patients begin to experience symptoms related to the acute illness, this response has significantly diminished, transitioning to a signature more closely associated with hemophagocytic syndromes due to the rapid expansion of CD8\u003csup\u003e+\u003c/sup\u003eTcells\u003csup\u003e(11)(12)\u003c/sup\u003e. In this study, a combination of serology and molecular biology techniques was used to track the disease spectrum and changes in immune function during primary Epstein-Barr virus (EBV) infection in children. Additionally, the study aimed to evaluate the potential factors that may influence the development of infectious mononucleosis (IM) and its prognosis.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eMicroarrays and Clinical Data Collection\u003c/h2\u003e \u003cp\u003eMicroarray datasets GSE45919 and GSE46519 were downloaded from the Gene Expression Omnibus (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Both datasets are based on the GPL10558 (Illumina HumanHT-12 V4.0 expression beadchip) and GPL570 [HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array. Array platforms. contains includes EBV transcripts from EBV-infected samples 9 control transcripts, and while contains transcripts of from EBV EBV-infected samples 2 control.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eIdentification of Differentially Expressed Genes (DEGs)\u003c/h3\u003e\n\u003cp\u003eTo identify the differences between the EBV infectioned group and the control group, we utilized the R package to standardize the datasets and detect differentially expressed genes (DEGs) in GSE45919 and GSE46519. Genes that met the screening criteria of |log2FC| \u0026ge; 1 and an adjusted P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were classified as DEGs. Volcano plots generated using the package illustrated the differential expression of these DEGs. Additionally, heatmaps were created to display the top 82 DEGs with the highest and lowest expression levels. Finally, we identified DEGs of significant interest in GSE45919 and GSE46519 through Venn diagram analysis.\u003c/p\u003e\n\u003ch3\u003eProtein-Protein Interaction Network Generation and Subnetwork Analysis\u003c/h3\u003e\n\u003cp\u003eThe STRING database (version 12.0; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://string-db.org/\u003c/span\u003e\u003cspan address=\"http://string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was utilized to integrate the DEG-encoded proteins and their associations, facilitating a comprehensive characterization of the query proteins. The protein-protein interaction (PPI) network was imported into Cytoscape software (version 3.9.1) for visualization. CytoHubba, a plugin for Cytoscape, was utilized to calculate the degree of each protein node within the PPI network. The genes with the top 17 node degrees were identified as hub genes.\u003c/p\u003e\n\u003ch3\u003eClinical significance - Prognostic class - Prognostic Lasso coefficient screening\u003c/h3\u003e\n\u003cp\u003eWe employed the Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression for feature selection to identify diagnostic markers in the EBV-infected group. The LASSO algorithm was implemented using the glmnet package (version 4.1.7, Xiantao Academic) to reduce the number of genes in the model and to address multicollinearity issues in the regression analysis. Subsequently, we utilized multivariable logistic regression with a backward elimination method to identify independent diagnostic biomarkers and develop a multimarker diagnostic model.\u003c/p\u003e\n\u003ch3\u003eInfltrating immune cell analysis\u003c/h3\u003e\n\u003cp\u003eBased on ssGSEA algorithm provided by R-package-GSVA, this paper utilizes Immunity article provided markers for 24 immune cells to calculate the immune infiltration of the uploaded data.The ssGSEA algorithm was used to calculate the relative proportions of 24 immune cell types between normal and EBV-infected groups, which were exhibited in a bar plot and a heatmap.To improve the reliability of the deconvolution method, samples with a ssGSEA P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were selected for further analysis. The number of permutations was set at 100. In addition, immune cell profles of the normal and DKD groups were subjected to principal component analysis (PCA).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation analysis\u003c/h2\u003e \u003cp\u003eWe frst conducted a Pearson correlation between the top 3 hub genes and immune cell subtypes of interest, to explore their regulatory networks. Further, we analyzed the association between 3 hub genes and EBV-infected-IM clinical features (PCT,Monocyte and CD56).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eParticipants and Methods\u003c/h3\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStudy Site and Patient Specimens\u003c/h2\u003e \u003cp\u003eA total of 106 patients, comprising 57 males and 49 females, were included in the research conducted at the Hospital. This retrospective study analyzed data collected over a span of three years. All patients diagnosed with IM were categorized into three age groups: Group A (1\u0026ndash;3 years old), Group B (3\u0026ndash;6 years old), and Group C (6\u0026ndash;13 years old). Each group was further divided into subgroups based on gender (A-F/A-M, B-F/B-M, C-F/C-M). We combined variables from hospital admissions to analyze test results and describe primary Epstein-Barr virus (EBV) infection and IM in children. This is a retrospective study.This study was approved by the Scientific Research Ethics Committee of Hangzhou Hospital of Traditional Chinese Medicine (2023KLL101) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e7\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIM is defined by the following criteria: (1) the presence of fever accompanied by symptoms typical of infectious mononucleosis, such as a sore throat, swollen lymph nodes, or enlargement of the liver and spleen, along with a positive VCA-IgM test or the detection of EBV-DNA in the peripheral blood; (2) the presence of at least 50% lymphocytes or at least 10% atypical lymphocytes; and (3) the exclusion of other infections. All three criteria must be satisfied\u003csup\u003e(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e)\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eExperimental Methods\u003c/h2\u003e \u003cp\u003eThe 17-year-old was admitted to the hospital on 21/7/2022. We collected patient materials from19/7/2019 to 17/8/2023.The ELISA method was employed to detect antibodies specific to the EBV. Peripheral venous blood samples (3ml) were collected, centrifuged, and the serum was extracted for further experiments. Serum levels of VCA-IgM, VCA-IgG, EA-IgG, and EBNA-IgG were measured using ELISA kits sourced from China. Peripheral blood and bone marrow samples were treated with Baso Liu A and B solutions (Catalog No. C220101). Real-time quantitative PCR (CSTB) was utilized to determine the EBV-DNA copy number in EBV-infected whole blood. All reagents were obtained from Shengxiang Biology, and amplification was performed using the ABI 7500 instrument. The flow cytometer used was the Beckman Coulter NAVIOS, and the antibodies were provided by Beckman Company. Hemolysin was sourced from QIAGEN Sciences. The number of cells collected and analyzed ranged from 20,000 to 50,000, targeting the markers CD45, SSC, CD3, and CD56.DetectedantibodiesCD3/CD8/CD3/KAPPA/LAMBDA/CD38/CD20/CD56/CD5/CD20/CD29/CD57/FMC-7/CD200/CD79b/CD23/CD103/CD11c/CD22/CD45/158.Specimens were prepared by whole blood erythrolysis method.Biochemical indicators were determined using chemical methods, while immune function was assessed using flow cytometry. Bacterial identification and antimicrobial susceptibility test: Strain identification was performed by matrix-assisted laser desorption ionization flight mass spectrometry on VITEK mass spectrometer.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were conducted using IBM SPSS Statistics version 20 (IBM). The Kruskal-Wallis rank-sum test was employed to compare the clinical characteristics within each group in the study of infectious mononucleosis (IM). The Mann-Whitney U test, also known as the Wilcoxon rank-sum test, was utilized to compare clinical indicators between groups with and without comorbid bacterial infections. For correlation analysis, the Pearson correlation coefficient was applied, while the chi-square test was used to compare pre- and post-treatment indicators. The statistical method employed for comparing clinical characteristics across different groups was the Pearson chi-square test. A p-value of less than 0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003ePrincipal Component Analysis and Identification of Differentially Expressed Genes\u003c/h2\u003e \u003cp\u003eWith |log2FC| \u0026ge; 1 and an adjusted P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, we identified 348 differentially expressed genes (DEGs) from the GSE45919 dataset (2281 up-regulated and 2039 down-regulated) and 460 DEGs from the GSE46519 dataset (702 up-regulated and 386 down-regulated). The volcano plots illustrated the significantly different distributions of DEGs in each dataset (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, C). The heat maps displayed the DEGs for each dataset (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eB, D). The Venn diagram revealed that there were 57 continuously up-regulated DEGs and 25 down-regulated DEGs common to both datasets (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eE, F).Furthermore, the genes of 82 DEGs were further analyzed, and the genes with relatively high expression were revealed.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eFunctional enrichment analyses\u003c/h2\u003e \u003cp\u003eGO and KEGG pathway analyses were conducted to evaluate the systematic functional pathway annotations of commonly differentially expressed genes (DEGs). The top 17 GO-enriched biological processes (BPs) related to body systems are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eA,B. According to the KEGG pathways, the common DEGs were enriched in fluid shear stress and atherosclerosis, TNF signaling pathway and NF-kappa B signaling pathway.GSEA showed that the signifcantly enriched pathways in EBV-infectioned were the regulation of cellular DNA damage and mitosis,coagulation cascades pid integrin3 pathway,mapk targets pathway in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eC,D,E.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eIdentification and Analysis of Hub Genes\u003c/h2\u003e \u003cp\u003eWe generated the PPI network based on a combined score\u0026thinsp;\u0026gt;\u0026thinsp;0.4.The PPI network consisted of 82 nodes and 88 edges. Five highly connected subnetworks were identiled by the MCODE plug-in algorithm.The graphical visualization of the generated PPI network was performed by Cytoscape software.we identified 17 hub genes: ICAM1,ISG15,FOS,OAS2,SOD2,VCAM1,TNFRSF9,PCLAF,LAG3,PTX3,IFIT5,HERC6,GBP1,TRIM21,PARP9,GBP3,ZC3HAV1(Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eA-E). In the five highly connected subnetworks, We found that only 3 target genes coexisted with each other, namely ICAM1,ISG15,and OAS2(Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eConstruction of the diagnostic risk model\u003c/h2\u003e \u003cp\u003eTo solve the multicollinearity problem in regression analysis, LASSO analysis was\u003c/p\u003e \u003cp\u003eused to further narrow down the differentially expressed DGEs as candidate diagnostic biomarkers for IM. The LASSO model included 20 genes and basic phenotype information (age and sex). We identiffed 4 genes (ICAM1,OAS2,ISG15 and PARP9) based on lambda.1se (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, C).However, only 3 genes (ICAM1, ISG15 and IFIT5) were found to be different in the normal and EBV-infected groups((Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003eD).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eLandscape of infltrating immune cell\u003c/h2\u003e \u003cp\u003eWe performed an analysis on the infltrating immune cells and immune-related DEGs based on 11 normal controls and 18 EBV-infected sample with a ssGSEA algorithm P-value of \u0026lt;\u0026thinsp;0.05 were entered into the immune infltration analysis. The distribution of 24 immune cells is shown in a bar plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003eA) and heatmap (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003eC).Wilcoxon rank sum test was used to compare the changes of immune cells in each group. The results showed that iDC,NK CD56bright cells,Tcm and Th2 cells were increased in EBV infection group, while Th1, Tem, Eosinophils, Macrophages in the infection group were lower (Figuer 5B).Distinctly different immune cells were identified by ROC analysis, NK CD56bright cells (AUC\u0026thinsp;=\u0026thinsp;0.904), iDC(AUC\u0026thinsp;=\u0026thinsp;0.7934) ,Th1 cells (AUC\u0026thinsp;=\u0026thinsp;0.783),and Tcm(AUC\u0026thinsp;=\u0026thinsp;0.788)(Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eCorrelation analysis between ICAM1, IFIT5 and ISG5 and inffltrating immune cells and EBV-infected-IM clinical features (PCT,Monocyte and CD56)\u003c/b\u003e \u003c/p\u003e \u003cp\u003eCorrelation analysis revealed that ICAM1 was positively correlated with Cytotoxic cells (r\u0026thinsp;=\u0026thinsp;0.870,p\u0026thinsp;\u0026lt;\u0026thinsp;0.01),pDCcells (r\u0026thinsp;=\u0026thinsp;0.847,p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and negatively correlated with Eosinophils (r=-0.779,p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and Neutrophils (r=-0.752, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01).Correlation analysis revealed that IFIT5 was positively correlated with Tcm(r\u0026thinsp;=\u0026thinsp;0.909, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), NK CD56bright cells(r\u0026thinsp;=\u0026thinsp;0.904, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and negatively correlated with Tem (r=-0.856, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01),CD8 T cells(r=-0.809, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01).Correlation analysis revealed that ISG15 was positively correlated with T helper cells(r\u0026thinsp;=\u0026thinsp;0.503,p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), DC cells(r\u0026thinsp;=\u0026thinsp;0.847,p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and negatively correlated with CD8 T cells(r=-0.608, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01),Tcells (r=-0.586, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01)(Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003eA-D).Correlation analysis revealed that ISG15 was positively correlated with CD56(r\u0026thinsp;=\u0026thinsp;0.398,p\u0026thinsp;\u0026lt;\u0026thinsp;0.05)and ICAM1 was positively correlated with PCT(r\u0026thinsp;=\u0026thinsp;0.588,p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and IFIT5 was positively correlated with Monocyte(r\u0026thinsp;=\u0026thinsp;0.381,p\u0026thinsp;\u0026lt;\u0026thinsp;0.05)(Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003eE,F).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eThe course of diagnosis and treatment and the evaluation of treatment\u003c/h2\u003e \u003cp\u003eThe 17-year-old had no underlying medical conditions. Ten days ago, he had a fever of 37.9\u0026deg;C for no apparent reason, and the fever occurred non-stop after taking ibuprofen.(Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). Admission test analysis: The total number of white blood cells is 22.30 (4\u0026ndash;10) \u0026times;10\u003csup\u003e9\u003c/sup\u003e/l, with a neutrophil count of 12.90 (50\u0026ndash;70)%, a lymphocyte count of 72.20 (20\u0026ndash;40) %, and a monocyte count of 14.30 (3\u0026ndash;10) %. The total platelet count is 80 (100\u0026ndash;300) \u0026times;10\u003csup\u003e9\u003c/sup\u003e/l (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e7\u003c/span\u003eC and D). Alanine aminotransferase (ALT): 389.3 U/l (normal range: 9\u0026ndash;50 U/l), aspartate aminotransferase (AST): 195.7 U/l (normal range: 15\u0026ndash;40 U/l)(Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e7\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003eOn admission during the first week, test results indicated that the levels of cytomegalovirus IgM antibody (luminescence) were positive at 74.44 AU/ml (reference range: 1\u0026ndash;10 AU/ml), and the levels of EB-DNA were measured at 1.5 x 10\u003csup\u003e5\u003c/sup\u003e copies/ml(Table.1). Bone marrow smear results show significant granulocyte hyperplasia with no abnormalities and no significant changes in red blood cell lines(Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e7\u003c/span\u003eE). Lymphocyte morphological irregularities suggest impaired macrokaryocyte maturation (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e7\u003c/span\u003eF). Ultrasound of the lymph nodes showed bilateral inguinal lymphadenopathy, as well as lymphadenopathy in the abdominal cavity, retroperitoneum, mediastinum, and axillary regions (Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA, B, C). Flow cytometry analysis of peripheral blood lymphoma revealed that 62.6% of the mature lymphocyte population was examined, with CD3\u0026thinsp;+\u0026thinsp;CD8\u0026thinsp;+\u0026thinsp;TRBC1 cells accounting for 32.6% ( Figs.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA, B). The flow cytometry analysis of KIR receptor expression and function in NK cells indicated that the CD3 population increased to 92.8% ( Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eA). Additionally, 2.8% of the naive cell population expressed markers consistent with acute leukemia, as determined by immunophenotyping, with CD3\u0026thinsp;+\u0026thinsp;CD8\u0026thinsp;+\u0026thinsp;TRBC1\u0026thinsp;+\u0026thinsp;cells comprising 39.4% (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eB). During the second week of transfer, a re-examination of EBV-DNA revealed a significant decrease to 9.07 \u0026times; 10\u0026sup2; copies/ml (Table\u0026nbsp;1).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of children with IM\u003c/h2\u003e \u003cp\u003eThe research involved 106 children who had Epstein-Barr virus-specific antibodies and underwent plasma immune function tests. The median age of the enrolled children was 5.34 years, with an age range of 1 to 13 years. The male-to-female ratio was 1.16, consisting of 57 males and 49 females. Among the 106 hospitalized patients, 96(90.57%) tested positive for EBV antibodies, while 10 (9.43%) tested negative for EBV antibodies, including those not detected. All 106 hospitalized patients exhibited clinical symptoms associated with infectious mononucleosis (IM).\u003c/p\u003e \u003cp\u003e \u003cb\u003eDistribution of Epstein-Barr virus (EBV)-specific antibodies by age and gender.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe retrospectively studied 106 subjects who had a confirmed diagnosis of IM. Of the 106 hospitalized patients, 96(90.57%) tested positive for EBV antibodies, while 10 (9.43%) tested negative on the EBV antibody test. The serum antibody titers of VCA-IgM antibody were significantly higher in groups B and C compared to group A. The serum antibody titers of VCA-IgM antibody in the C-M group showed a consistent trend, similar to the previous pattern. VCA-IgG was not statistically significant in any of the groups, with a seropositivity rate of only 22 out of 106 (20.75%) during the acute phase of infection. At the same time, the inflammatory indicators of the patients were different, which was manifested as CRP and PCT were significantly higher in group B than in its group, and groups A-F and C-F were significantly higher than those in group B-F(Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eA,B,C).Therefore, VCA-IgM is the primary indicator for children who have recently had an Epstein-Barr virus infection (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eD,E,F). Further study showed that the antibody titers of EA-IgM and VCA-IgM antibodies in the serum were significantly higher than those of non-pathogenic bacteria, indicating that other pathogens may also affect the lymphocyte changes of the body through other forms (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eG,H,I). Atypical lymphocytes were found to be greater than 10% in 106 cases (46.22%).The proportion of atypical lymphocytes in group C is relatively high(Table\u0026nbsp;1).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eDistribution of patients with infectious mononucleosis based on age and gender\u003c/h2\u003e \u003cp\u003eWe gathered data from 106 children and found that the serum mean value of ALT in the 6\u0026ndash;13 years old (C) group was 170.93 U/l, surpassing the upper limit of detection. This disparity was statistically significant compared to the other groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In the ADA test results, the values in the 6\u0026ndash;13 years old (C) group were relatively higher (49.49 U/l) than in the other groups. γ-GT had the highest values of 80.25 U/l and 64.31 U/l in the 6\u0026ndash;13 years old (C) group and the 6\u0026ndash;13 years old male (C-M) group. We observed that CK and CK-MB levels were notably higher in the 1\u0026ndash;3 years group, measuring 1106.63 U/l and 51.15 U/l, respectively. The levels of CK and CK-MB in the 1\u0026ndash;3 years old female (A-F) and 1\u0026ndash;3 years old male (A-M) groups exhibited a significant increase of 160.63 U/l, 46.37 U/l, 235.61 U/l, and 54.93 U/l. There was no significant difference in liver function between comorbid and non-concomitant bacteria in this group (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003eA,B,C).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eDistribution of IgG, IgM, IgA, and Ig E in children following EBV infection, categorized by age and gender\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe investigated and found that out of 106 children, 6.60% (7/106) had total IgG levels that exceeded the upper limit of detection (1600 mg/dl), 15.09% (16/106) had IgM antibody levels that surpassed the upper limit of detection (230 mg/dl), and 33.96% (36/106) had IgE antibody levels that exceeded the upper limit of detection (165 mg/dl). IgG and IgA levels were higher in group C compared to other groups. However, 14.15% of the children had IgM values higher than the detected levels, and 32.3% of the children had IgE values higher than the detected levels. There was no significant change in immune indexes between concurrent and non-concurrent bacteria (Figure.12D,E,F). When a child with IM is infected with the Epstein-Barr virus, the body's immune cells mainly respond to B and T cells, resulting in cell proliferation and the presence of atypical lymphocytes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eDistribution of T, B, and NK cells in peripheral blood by age and gender\u003c/h2\u003e \u003cp\u003eWe retrospectively counted that 52.83% (56/106) of CD3\u0026thinsp;+\u0026thinsp;T cells in 106 IM peripheral blood samples had relative numbers higher than the upper limit of detection (81.22%). Additionally, 83.96% (89/106) of CD8\u0026thinsp;+\u0026thinsp;T lymphocytes had relative numbers higher than the upper limit of detection, which was 38.24%. Furthermore, 2.83% (3/106) of CD19\u0026thinsp;+\u0026thinsp;B lymphocytes had relative numbers that exceeded the upper limit of detection (18.23%). However, 50.94%(54/106) of CD19\u0026thinsp;+\u0026thinsp;B lymphocytes the number is below the lower limit of detection(5.389%). And the proportion of NK cells (CD56+/CD3-) was lower than the upper limit of detection (6.37%) in 29.24% (31/106)of cases. The relative numbers of CD3\u0026thinsp;+\u0026thinsp;T cells and CD8\u0026thinsp;+\u0026thinsp;T cells were significantly increased, and the number of children in groups B and C was also significantly higher (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003eG,H,I). The proportion of CD4\u0026thinsp;+\u0026thinsp;lymphocytes in Group A was significantly higher than that in groups B and C. Additionally, the lymphocyte count in groups A-M was consistent. Nevertheless, we did find that the relative number of CD19\u0026thinsp;+\u0026thinsp;B lymphocytes in the blood was significantly higher in Group A compared to Group B and C, and this trend was consistent across different age groups and genders. According to the data changes, we found a positive correlation between CD4\u0026thinsp;+\u0026thinsp;and CD19\u0026thinsp;+\u0026thinsp;B by spearman correlation analysis, and the correlation coefficient R was 0.733 (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003eJ).\u003c/p\u003e \u003cp\u003e \u003cb\u003eDistribution of Bacteria, Viruses, and Fungi in Immunocompromised Patients.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThere were 8 cases of gram-negative bacteria, including 2 cases of Moraxella catarrhalis, 1 case of Acinetobacter baumannii, 1 case of Haemophilus haemolyticus, 1 case of Haemophilus influenzae, 1 case of Enterobacter cloacae, 1 case of Klebsiella pneumoniae, and 14 cases of Gram-positive bacteria. Additionally, there were 9 cases of Gram-positive bacteria combined with viruses, 5 cases of Staphylococcus aureus and cytomegalopathy, 1 case of Staphylococcus aureus and Mycoplasma pneumoniae, 1 case of Staphylococcus aureus\u0026thinsp;+\u0026thinsp;influenza A virus, 1 case of Staphylococcus aureus\u0026thinsp;+\u0026thinsp;herpes simplex virus, and 1 case of rubella virus\u0026thinsp;+\u0026thinsp;Herpes simplex virus\u0026thinsp;+\u0026thinsp;cytomegalovirus\u0026thinsp;+\u0026thinsp;Staphylococcus aureus. The total number of viruses was 14: 10 cases of CMV, 1 case of herpes simplex virus, 1 case of cytomegalovirus and herpes simplex virus, 1 case of rubella\u0026thinsp;+\u0026thinsp;herpes simplex virus, and 1 case of respiratory adenovirus. There was also 1 case of Candida albicans fungus. Other findings included Mycoplasma pneumoniae in 3 cases, rheumatoid factor in 1 case, antinuclear antibody (ANA) in 1 case, and antistreptococcal \"O\" hemolysin in 1 case (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003eA). The total number of children was 106, with 52 cases having pathogenic bacteria and 54 cases without pathogens. In group A, there were 19 cases of combinations, totaling 31 cases; in group B, there were 18 cases of combinations, totaling 39 cases; and in group C, there were 15 cases of combinations, totaling 33 cases. Furthermore, there were 8 cases of combined bacteria in group A-F, totaling 16 cases; 11 cases of combined bacteria in A-M combination, totaling 15 cases; 7 cases of combined bacteria in B-F combination, totaling 15 cases; 11 cases of combined bacteria in B-M combination, totaling 24 cases; 6 cases of combined bacteria in C-F combination, totaling 18 cases; and 9 cases of combined bacteria in C-M combination, totaling 15 cases (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eClinical manifestations of children with and without co-orbidiobacteria\u003c/h2\u003e \u003cp\u003eFirst EBV infection is more common in young children, and the vast majority present with asymptomatic or atypical infection. Mild overt manifestations may include symptoms of upper respiratory tract infection, such as fever, nasal congestion, pharyngitis, etc., and can also be accompanied by superficial lymph node enlargement. Symptoms of upper respiratory tract infection such as fever, sore throat, and palpable swelling of superficial lymph nodes in the neck, armpit, or groin are typical clinical manifestations of IM. Chi-square analysis of IM and related diseases showed that the typical clinical manifestations of non-co-bacteria were more obvious than those of co-orbids. According to the table, among the clinical signs and symptoms of IM without pathogenic bacteria, fever, nasal congestion, pharyngeal redness and swelling with tonsils II and signs, cervical lymphatic swelling, and splenomegaly were more obvious than those in children with pathogenic bacteria, but pharyngeal redness and swelling with tonsillar I were more obvious in comorbid pathogens. Comparison of clinical antibiotic administration between the two groups showed that the co-pathogenic bacteria were much higher than the non-co-pathogenic bacteria.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn the present study, we demonstrated the landscape of infltrating immune cells in patients with EBV-infected group and identifed the top 17 hub immune-regulatory genes. Three of the core genes (ICAM1, ISG15, and IFIT5) were signifcantly correlated with EBV. Through multiplex immunofuorescence staining, we verifed the diferentially NK CD56bright cells, iDC, and Th1 subsets. We found that the macrophage count was remarkably elevated, whereas NK CD56bright cells were remarkably.We analyze further that correlation analysis between ICAM1, IFIT5 and ISG5 and inffltrating immune cells and EBV-infected-IM clinical features (PCT,Monocyte and CD56).\u003c/p\u003e \u003cp\u003eThe young child was admitted to the hospital with a diagnosis of infectious mononucleosis (IM), presenting with persistent fever, a sore throat, and significant cervical lymphadenopathy, which ruled out other hematologic disorders. The child exhibited a notable increase in liver injury markers, lymph node enlargement, and alterations in the immune cell count. Based on this information, we reviewed relevant studies and found that liver impairment was more pronounced in patients aged 6 to 13 years. However, damage to cardiac enzymes, specifically creatine kinase (CK) and CK-MB, was more significant in the younger age group of 1 to 3 years, which aligns with the findings of Chijioke O \u003csup\u003e(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e)\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn this review, the diagnosis of infectious mononucleosis (IM) was confirmed in 90.57% of cases, with 46.22% of patients exhibiting atypical lymphocytes in relevant examinations\u003csup\u003e(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e)\u003c/sup\u003e. Liver injury caused by IM is a common early complication, resulting from the infiltration of lymphocytes infected with the EBV\u003csup\u003e(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e)\u003c/sup\u003e. Retrospective studies have indicated that liver function impairment is more severe in patients older than 7 years of age. Symptomatic treatment with liver protection has shown significant improvement. For patients aged 1 to 3 years, myocardial enzyme injury was more pronounced, with elevated levels of CK and CK-MB observed in the peripheral blood of both girls and boys (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). While cardiac complications are uncommon in infectious mononucleosis, the first association between acute pericarditis and EBV infection was described by researchers\u003csup\u003e(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e)\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eInnate and adaptive immunity play crucial roles in mediating host responses to EBV infections. NK cells are significant contributors to the pathophysiology of infectious mononucleosis\u003csup\u003e(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e)\u003c/sup\u003e. In an analysis of NK cell counts in the peripheral blood of 106 patients with infectious mononucleosis, we observed that 29.24% (31 out of 106) of the children exhibited a decrease in NK cell counts. As noted in the literature, NK cell depletion following EBV infection does not significantly differ from the depletion observed prior to infection\u003csup\u003e(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e)\u003c/sup\u003e. EBV-activated lymphocytes initiate adaptive immunity, and NK cells are emerging as critical players in the context of infectious mononucleosis, as they preferentially kill EBV-infected cells when the virus enters the lytic cycle, underscoring the importance of NK cells\u003csup\u003e(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e)\u003c/sup\u003e. NK cells are continuously depleted during the immune response to EBV, leading to a reduced population that reflects the overall immune status of the body\u003csup\u003e(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e)\u003c/sup\u003e. Interestingly, unlike the depleting effects observed with co-pathogenic bacterial infections, the depletion of NK cells in EBV-infected individuals does not appear to have a significant impact\u003csup\u003e(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e)\u003c/sup\u003e. During primary EBV infection, there is a notable increase in the number of peripheral blood T cells. This population consists of a mixture of CD8\u0026thinsp;+\u0026thinsp;cytotoxic T cells, NK cells, and CD4\u0026thinsp;+\u0026thinsp;helper T cells, with a significantly higher proportion of these cells observed in children aged 1 to 3 years compared to other age groups\u003csup\u003e(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e)\u003c/sup\u003e. Although there is no significant increase in the total number of CD4\u0026thinsp;+\u0026thinsp;T cells during infectious mononucleosis, evidence suggests that CD4\u0026thinsp;+\u0026thinsp;T cells become activated and play a role in controlling infected B cells\u003csup\u003e(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e)\u003c/sup\u003e. Studies indicate that during acute infection, CD4\u0026thinsp;+\u0026thinsp;T cells can recognize multiple lytic antigens\u003csup\u003e(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e)\u003c/sup\u003e. Our data reveal that the number of B lymphocytes decreased in 51% (49 out of 106) of the children, with a significant decline noted in those aged 6 to 13 years. This finding aligns with previous studies conducted on healthy children\u003csup\u003e(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e)\u003c/sup\u003e. As a child's immune system matures with age, the importance of B lymphocytes in defending against viral infections becomes increasingly evident\u003csup\u003e(\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e)\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eResearch has shown that the predominant pathogenic bacteria in children with IM and tonsillitis are Staphylococcus aureus and anaerobic bacteria\u003csup\u003e(\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e)\u003c/sup\u003e. However, there is a significant lack of studies analyzing pathogenic bacteria in children with IM across different age groups. Our findings indicate that 29.82% (17/57) of IM cases were complicated by cytomegalovirus and Staphylococcus aureus, 8.77% (5/57) were associated with the herpes simplex virus, 4.02% (4/57) tested positive for anti-streptococcal type O, 5.26% (3/57) were allergic to household dust mites, 5.26% (3/57) tested positive for rheumatic factor, and 3.51% (2/57) tested positive for the rubella virus. Therefore, after children are infected with EBV and diagnosed with IM, and following appropriate symptomatic treatment, it is essential to remain vigilant for the possibility of co-infection with other strains, particularly cytomegalovirus and Staphylococcus aureus, as these are the most frequently encountered pathogens\u003csup\u003e(\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e)\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn summary, we analyzed 106 hospitalized children with IM and observed significant differences in EBV-specific antibodies, lymphocyte count, and immune function in the peripheral blood of children across various age groups and genders. These differences may make children more vulnerable to infections caused by other viruses and bacteria, ultimately leading to less effective treatment and prognosis for infectious mononucleosis IM.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Scientific Research Ethics Committee of Hangzhou Hospital of Traditional Chinese Medicine (2023KLL101). The committee agreed on the use of clinical specimen data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to acknowledge the infrastructure support provided by Hangzhou TCM Hospital Affiliated with Zhejiang Chinese Medicine University and Department of Clinical Laboratory, Huai\u003csup\u003e，\u003c/sup\u003ean Second People\u0026rsquo;s Hospital, The Affiliated Huai\u003csup\u003e，\u003c/sup\u003ean Hospital of Xuzhou Medical University. The clinically collected data is accurate and reliable. Informed consent was obtained from all participants and Informed consent must have been obtained from a parent and/or legal guardian.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll relevant data are within the paper and its Supporting information files.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStudy design, S.L; Sample and data collection, Y.Z; Case collection L.X, Q,Y. Data analysis, L.X, Q,Y. S.L wrote the first draft of the manuscript and participated in the collection and evaluation of literature. S.L supervised and critically revised the manuscript. All authors contributed to the article and approved the submitted version. The clinically collected data is true and reliable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank all author institution for their strong support.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eBalfour H H , Dunmire S K , Hogquist K A .Infectious mononucleosis[J].Clinical \u0026amp; Translational Immunology, 2015, 4(2).DOI:10.1038/cti.2015.\u003c/li\u003e\n \u003cli\u003eDowd JB, Palermo T, Brite J, McDade TW, Aiello A. Seroprevalence of Epstein-Barr virus infection in U.S. children ages 6\u0026ndash;19, 2003\u0026ndash;2010. PLoS One. 2013;8:e64921.\u003c/li\u003e\n \u003cli\u003eBravender T .Epstein-Barr virus, cytomegalovirus, and infectious mononucleosis.[J].Adolescent Medicine State of the Art Reviews, 2010, 21(2):251.\u003c/li\u003e\n \u003cli\u003eShi T , Huang L , Chen Z ,et al.Characteristics of primary Epstein〣arr virus infection disease spectrum and its reactivation in children, in Suzhou, China[J].Journal of Medical Virology, 2021.DOI:10.1002/jmv.26941.\u003c/li\u003e\n \u003cli\u003eČalkić L, Bajramović‐Omeragić L, Mujezinović A. Infectious mononucleosis (Epstein‐Barr virus infection) and chronic hepatitis. Med Glas (Zenica). 2019;16(2).\u003c/li\u003e\n \u003cli\u003eBruu AL, Hjetland R, Holter E, et al. Evaluation of 12 commercial tests for detection of Epstein‐Barr virus‐specific and heterophile antibodies. Clin Diagn Lab Immunol. 2000;7(3):451‐456.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eNowalk A, Green M. Epstein‐Barr virus. 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Epstein‐Barr virus (EBV) infection in Chinese children: a retrospective study of age‐specific prevalence. PLOS One. 2014;9(6):e99857.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eCui J, Yan W, Xu S, et al. Anti‐Epstein‐Barr virus antibodies in Beijing during 2013‐2017: what we have found in the different patients. PLOS One. 2018;13(3):e0193171.\u003c/li\u003e\n \u003cli\u003eJennifer Beam Dowd,Tia Palermo,Jennifer Brite, Epstein-Barr seroprevalence among US children, ages 6\u0026ndash;19, 2003\u0026ndash;2010.[J]. 2013.\u003c/li\u003e\n \u003cli\u003eOkuno K, Takashima K, Kanai K, Ohashi M, Hyuga R, Sugihara H, Kuwamoto S, Kato M, Sano H, Sairenji T, Kanzaki S, Hayashi K. Epstein-Barr virus can infect rabbits by the intranasal or peroral route: an animal model for natural primary EBV infection in humans. J Med Virol. 2010; 82:977\u0026ndash; 986.10.1002/jmv.21597 [PubMed: 20419811].\u003c/li\u003e\n \u003cli\u003eCarbone A , Gloghini A , Dotti G .EBV-Associated Lymphoproliferative Disorders: Classification and Treatment[J].The Oncologist, 2008, 13(5):577-585.DOI:10.1634/theoncologist.2008-0036.\u003c/li\u003e\n \u003cli\u003eWang X , Wang P , Wang A ,et al.Hydroa Vacciniforme-like Lymphoproliferative disorder in an adult invades the liver and bone marrow with clear pathological evidence: a case report and literature review[J].BMC Infectious Diseases, 2021, 21(1).DOI:10.1186/s12879-020-05697-x.\u003c/li\u003e\n \u003cli\u003eParvaneh N, Filipovich AH, Borkhardt A. Primary immunodeficiencies predisposed to Epstein-Barr virus-driven haematological diseases. Br J Haematol. 2013; 162:573\u0026ndash;586.10.1111/bjh.12422 [PubMed: 23758097].\u003c/li\u003e\n \u003cli\u003ePakpoor J , Disanto G , Gerber J E ,et al.The risk of developing multiple sclerosis in individuals seronegative for Epstein-Barr virus: a meta-analysis.[J].Multiple Sclerosis Journal, 2013, 19(2):162-166.DOI:10.1177/1352458512449682.\u003c/li\u003e\n \u003cli\u003eStrowig T , Brilot F , Arrey F ,et al.Tonsilar NK cells restrict B cell transformation by the Epstein-Barr virus via IFN-gamma.[J].John Wiley \u0026amp; Sons, 2008.DOI:10.1371/journal.ppat.0040027.\u003c/li\u003e\n \u003cli\u003eWang,Fred.Nonhuman primate models for Epstein-Barr virus infection[J].Current Opinion in Virology, 2013, 3(3):233-237.DOI:10.1016/j.coviro.2013.03.003.\u003c/li\u003e\n \u003cli\u003eLong H M , Chagoury O L , Leese A M ,et al.MHC II tetramers visualize human CD4+ T cell responses to Epstein-Barr virus infection and demonstrate atypical kinetics of the nuclear antigen EBNA1 response.[J].Journal of Experimental Medicine, 2013, 210(5):933-949.DOI:10.1084/jem.20121437.\u003c/li\u003e\n \u003cli\u003eDunmire S K , Grimm J M , Schmeling D O ,et al.The Incubation Period of Primary Epstein-Barr Virus Infection: Viral Dynamics and Immunologic Events[J].PLOS Pathogens, 2015, 11(12):e1005286.DOI:10.1371/journal.ppat.1005286.\u003c/li\u003e\n \u003cli\u003eClute SC, Watkin LB, Cornberg M, Naumov YN, Sullivan JL, Luzuriaga K, Welsh RM, Selin LK. Cross-reactive influenza virus-specific CD8+ T cells contribute to lymphoproliferation in EpsteinBarr virus-associated infectious mononucleosis. J Clin Invest. 2005; 115:3602\u0026ndash;3612.10.1172/ JCI25078 [PubMed: 16308574]\u003c/li\u003e\n \u003cli\u003eCohen JI, Fauci AS, Varmus H, Nabel GJ. Epstein-Barr virus: an important vaccine target for cancer prevention. Sci Transl Med. 2011; 3:107fs107.10.1126/scitranslmed.3002878.\u003c/li\u003e\n \u003cli\u003eYong-Wei W , Ying-Chao W , Pediatrics D O .Progress in Diagnosis and Treatment of Epstein-Barr Virus Associated Disease in Children[J].Medical Recapitulate, 2013.\u003c/li\u003e\n \u003cli\u003eAndrew D ,Hislop,Umaimainthan,et al.Impaired Epstein-Barr virus-specific CD8+ T-cell function in X-linked lymphoproliferative disease is restricted to SLAM family-positive B-cell targets.[J].Blood, 2010.DOI:10.1182/blood-2009-09-238832.\u003c/li\u003e\n \u003cli\u003eAzzi T, Lunemann A, Murer A, Ueda S, Beziat V, Malmberg KJ, Staubli G, Gysin C, Berger C, Munz C, Chijioke O, Nadal D. Role for early-differentiated natural killer cells in infectious mononucleosis. Blood. 2014; 124:2533\u0026ndash;2543.10.1182/blood-2014-01-553024 [PubMed: 25205117]\u003c/li\u003e\n \u003cli\u003eJenson, Hal B .Acute complications of Epstein-Barr virus infectious mononucleosis.[J].Current Opinion in Pediatrics, 2000, 12(3):263.DOI:10.1097/00008480-200006000-00016.\u003c/li\u003e\n \u003cli\u003eDanstrup C S , Klug T E .Low rate of co-infection in complicated infectious mononucleosis[J].Danish medical journal, 2019, 66(9).\u003c/li\u003e\n \u003cli\u003eKlug T E .Incidence and microbiology of peritonsillar abscess: the influence of season, age, and gender[J].European Journal of Clinical Microbiology, 2014, 33(7).DOI:10.1007/s10096-014-2052-8.\u003c/li\u003e\n \u003cli\u003eSakahashi H , Takazawa A , Toyama A ,et al.Active infective endocarditis due to methicillin-resistant Staphylococcus aureus in the acute phase of infectious mononucleosis[J].Jpn J Thorac Cardiovasc Surg, 2002, 50(6):249-251.DOI:10.1007/BF03032154.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eYoung children exhibit unique distributions and heightened levels of Epstein-Barr virus (EBV)-specific antibodies and viral DNA in their plasma.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003evirus capsid antigen(VCA), nuclear antigen(NA),Early antigens(EA),EBNA, Epstein-Barr nuclear antigen.Cytomegalovirus(CMV),Herpes simplex virus type 1(HSV1),Mycoplasma pneumoniae (MP).\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eTime\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 75px;\"\u003e\n \u003cp\u003e23-Jul\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 69px;\"\u003e\n \u003cp\u003e28-Jul\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 80px;\"\u003e\n \u003cp\u003e29-Jul\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 80px;\"\u003e\n \u003cp\u003e1-Aug\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 80px;\"\u003e\n \u003cp\u003e24-Aug\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 80px;\"\u003e\n \u003cp\u003e1-Oct\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eTest items\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eCMV-IgM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eHSV-1IgM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eMP-IgM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eNot detected\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eEB-VCA-IgG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eEB-VCA-IgM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eEBNA1-IgG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eEA-IgM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eEBV DNA (\u0026lt;4.0\u0026times;10\u003csup\u003e2\u003c/sup\u003e copies/mI）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e1.5 \u0026times;10\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e2.57\u0026times;10\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e9.02\u0026times;10\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eBelow the lower detection limit\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eEBV-T DNA (\u0026lt;4.0\u0026times;10\u003csup\u003e2\u003c/sup\u003e copies/mI）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e4.48\u0026times;10\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eBelow the lower detection limit\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eEBV-B DNA (\u0026lt;4.0\u0026times;10\u003csup\u003e2\u003c/sup\u003e copies/mI）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e7.19\u0026times;10\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eBelow the lower detection limit\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eEBV-NK DNA (\u0026lt;4.0\u0026times;10\u003csup\u003e2\u003c/sup\u003e copies/mI）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e8.54\u0026times;10\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eBelow the lower detection limit\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":false,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Epstein-Barr virus (EBV), Infectious mononucleosis, pathogenic bacteria","lastPublishedDoi":"10.21203/rs.3.rs-6211732/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6211732/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Infectious mononucleosis (IM) is a clinical condition characterized by a sore throat, swollen lymph nodes in the neck, and fever. Our aim is to investigate the incidence, characteristics, and potential risk factors of IM to facilitate early risk prediction.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e We downloaded data from the Gene Expression Omnibus (GEO) database. The limma package in R software was used to identify differentially expressed genes (DEGs). We used Xiantao Academic's software to analyze differences in the immune microenvironment between EBV-infected patients and controls. we examined the correlation between diagnostic markers and inffltrating immune cells to better understand the molecular immune mechanism.Finally, We conducted a retrospective analysis of all immunocompetent patients diagnosed with EBV infection at a tertiary Traditional Chinese Medicine over a three-year period. We evaluated their demographic, clinical, and laboratory characteristics.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eWe demonstrated the landscape of infltrating immune cells in patients with EBV-infected and identifed the top 17 hub immune regulatory genes. Five of the core genes (OAS2,PARP9,IFIT5,ISG15,ICAM1) were signifcantly correlated with the estimated EBV-infected. We verifed that macrophage numbers were remarkably elevated, whereas Treg and Th17 cells were remarkably reduced in the EBV-infected. Th1 and Th2 cells were abundant in the EBV-infected. pDC,DC and NK56 cells were abundant in the EBV-infected. The expression of immune cells is also demonstrated by clinical patient results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e The changes in the number and function of immune cells in children with IM in different sexes and ages have certain reference significance for evaluating the overall treatment of IM and their ability to be combined with other pathogenic bacteria.\u003c/p\u003e","manuscriptTitle":"Landscape of infltrating immune cells and related genes in Infectious Mononucleosis and its Associated Characteristics in Children with Primary Epstein-Barr Virus Infection","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-15 17:26:10","doi":"10.21203/rs.3.rs-6211732/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8c04713c-1f8d-43bd-a90e-0ca4da55dbfc","owner":[],"postedDate":"April 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-05-11T16:09:28+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-15 17:26:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6211732","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6211732","identity":"rs-6211732","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}