miRNA-375 Combined EB-DNA as Differential Diagnostic Biomarker for EBV-IM and EBV-HLH in Children

miRNA-375 Combined EB-DNA as Differential Diagnostic Biomarker for EBV-IM and EBV-HLH in Children | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article miRNA-375 Combined EB-DNA as Differential Diagnostic Biomarker for EBV-IM and EBV-HLH in Children Zhuo Li, Jian Zhou, Yingjie Ren, Lin Lei, Xin Wang, Lijuan Yu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6837655/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In children, Epstein-Barr Virus (EBV) often causes infectious mononucleosis (EBV-IM) but can escalate to a severe condition called EBV-associated hemophagocytic lymphohistiocytosis (EBV-HLH). Differentiating these conditions is vital for proper treatment and prognosis. This study aimed to find plasma biomarkers to distinguish between EBV-IM and EBV-HLH. A retrospective analysis was performed on 17 children with EBV-IM and 23 with EBV-HLH. Selected microRNAs (miRNAs) were measured in plasma using qPCR, and EBV DNA was isolated and quantified. Biomarker effectiveness was assessed using constructing receiver operating characteristic (ROC) curves and the area under the curve (AUC) calculations. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed a significant enrichment of miR-375, miR-148a-3p, and miR-92a-1-5p within the Viral Carcinogenesis pathway. Quantitative PCR validation demonstrated a marked upregulation of miR-375 and miR-148a-3p in patients with EBV-HLH compared to those with EBV-IM, whereas the expression of miR-92a-1-5p did not show a statistically significant difference between the groups. Notably, receiver operating characteristic (ROC) analysis indicated that the combined evaluation of miR-375 and EBV-DNA load achieved superior diagnostic discrimination between EBV-HLH and EBV-IM, with an AUC of 0.951, highlighting its potential high clinical utility. The integrated biomarker panel, consisting of miR-375 and EBV-DNA load, demonstrated high diagnostic accuracy in distinguishing between EBV-HLH and EBV-IM. These findings offer a clinically applicable tool for precise differential diagnosis and therapeutic stratification. Epstein-Barr virus-associated infectious mononucleosis EBV-associated Hemophagocytic Lymphohistiocytosis differential diagnosis biomarker Figures Figure 1 Figure 2 Figure 3 1. Introduction Epstein-Barr Virus (EBV), a ubiquitous gammaherpesvirus infecting over 90% of the global population, establishes lifelong latency in human B lymphocytes following primary infection [ 1 ] . Viral replication initiates in oropharyngeal epithelial cells, with subsequent B-cell entry facilitating systemic dissemination through viremia [ 2 ] . While EBV typically maintains low-copy latent persistence, its oncogenic potential is exemplified by associations with malignancies such as Burkitt lymphoma and nasopharyngeal carcinoma [ 3 ] . Pediatric EBV infections exhibit age-dependent clinical heterogeneity: infants often experience asymptomatic latency, whereas school-aged children frequently develop infectious mononucleosis (EBV-IM), characterized by fever, lymphadenopathy, and atypical lymphocytosis [ 4 , 5 ] . Beyond self-limiting EBV-IM, severe EBV-driven pathologies including chronic active EBV infection (CAEBV) and EBV-associated hemophagocytic lymphohistiocytosis (EBV-HLH) present substantial diagnostic and therapeutic challenges due to their overlapping yet distinct pathophysiological profiles [ 6 , 7 ] . EBV-IM, EBV-HLH, and CAEBV exemplify the spectrum of EBV-related immunopathology. EBV-IM, though benign, manifests through a pathognomonic triad of fever, pharyngitis, and lymphadenopathy, with diagnosis relying on serological detection of heterophile antibodies or EBV-specific IgM [ 8 , 9 ] . In contrast, EBV-HLH represents a cytokine storm syndrome marked by fulminant hyperinflammation, cytopenias, and multi-organ dysfunction, requiring urgent intervention guided by HLH diagnostic criteria [ 10 , 11 , 12 ] . CAEBV, characterized by chronic or recurrent IM-like symptoms with clonal EBV-infected T/NK-cell proliferation, bridges the clinical continuum between EBV-IM and EBV-HLH [ 13 ] . Despite established diagnostic frameworks, differentiation among these entities remains clinically arduous due to nonspecific early presentations and biomarker limitations. Current research prioritizes identifying disease-specific biomarkers to address this diagnostic dilemma. Emerging evidence highlights microRNAs (miRNAs) as key regulators of EBV-host interactions, with distinct expression profiles potentially reflecting pathological states [ 14 , 15 ] . Concurrently, immunophenotypic markers and EBV-DNA load quantification are being explored for their discriminatory capacity [ 16 , 17 ] . This study investigates plasma miRNA signatures combined with virological parameters to develop a novel diagnostic algorithm for distinguishing EBV-IM from EBV-HLH, aiming to optimize clinical decision-making in pediatric EBV-associated diseases. 2. Methods 2.1 Clinical Data Acquisition This retrospective cohort study included 40 pediatric patients, comprising 17 with EBV-IM and 23 with EBV-HLH, who were treated at the First Affiliated Hospital of Xi'an Medical University between January 2017 and December 2021. (1) The diagnostic criteria were defined as follows: For EBV-IM, the criteria included a clinical fever (≥ 38°C for ≥ 3 days), pharyngitis, and cervical lymphadenopathy; laboratory confirmation required the presence of atypical lymphocytes ≥ 10% in peripheral blood, positive EBV serology (EBV-IgM + or VCA-IgG+/EBNA-IgG–), and plasma EBV-DNA levels ≥ 500 copies/ml. For EBV-HLH, the criteria required the fulfillment of at least five of the HLH-2004 diagnostic criteria, virological confirmation with plasma EBV-DNA > 10 3 copies/ml, cytopenia (hemoglobin < 90 g/L, platelets < 100×10⁹/L), and hyperferritinemia (ferritin ≥ 500 µg/L). (2) The exclusion criteria were as follows: For EBV-IM, patients with coinfections (such as CMV, HIV), pre-existing hematologic disorders (such as leukemia, aplastic anemia), or primary immunodeficiencies (such as X-linked lymphoproliferative disease, primary immunodeficiency) were excluded. For EBV-HLH, cases of EBV-IM that did not meet the HLH-2004 criteria, secondary HLH triggers (such as malignancies, macrophage activation syndrome), and confirmed genetic HLH (such as familial hemophagocytic lymphohistiocytosis mutations) were excluded. (3) Ethical Compliance: The study protocol received approval from the Institutional Review Board of the First Affiliated Hospital of Xi'an Medical University (Approval No. XYYFY2021LSK-015), adhering to Declaration of Helsinki principles. Demographic and clinical parameters were retrieved from institutional electronic medical records with informed consent. Key characteristics including age, sex, and disease manifestations are summarized in Table 1 . Table 1 Baseline data for the research. IM EBV-HLH P-Value Age 5.34 ± 1.45 3.63 ± 3.10 0.09 Gender Female:4; Male:13 Female:12; Male:11 Number of patients 17 23 Clinical data Hb 124.29 ± 16.67 90.26 ± 24.11 < 0.0001 Plt 232 ± 69.14 118.78 ± 90.15 0.0001 EB-DNA 2893.88 ± 2780 1571355 ± 3710999 0.09 2.2 Pathway Enrichment Analysis Target pathways of candidate miRNAs (miR-375, miR-148a-3p, miR-92a-1-5p) were identified using DIANA-miRPath v3.0 ( http://www.microrna.gr/miRPathv3 ), with KEGG pathway enrichment analysis performed under default parameters (FDR-adjusted P < 0.05). 2.3 Biospecimen Processing Peripheral blood (5 ml) collected in sodium citrate tubes was centrifuged at 4000 × g for 10 min at 4°C. Plasma aliquots were stored at -80°C until analysis. 2.4 Platelet and Hemoglobin Detection Whole blood samples (3 ml) were collected from each participant into EDTA-K 2 anticoagulant tubes at enrollment. Platelet counts and hemoglobin levels were quantified using an automated hematology analyzer (Sysmex XN-9000) following standardized procedures. (1) Sample preparation: EDTA-anticoagulated venous blood was gently inverted 8–10 times immediately after collection to ensure homogeneity and prevent clot formation. (2) Automated analysis: tubes were placed in the analyzer's sample rack. Whole blood mode was selected, and the automated cycle initiated. The system sequentially aspirated and processed samples. (3) Manual Loading Protocol (if required): The sampling cover was opened, and manual mode activated. Well-mixed blood samples were vertically inserted into the inlet port, ensuring precise needle penetration through the tube cap. Delayed Testing Protocol: Samples requiring delayed analysis were maintained at room temperature (18–26°C) and processed within 4 h post-collection to preserve result validity. This standardized workflow minimized pre-analytical variability and ensured compliance with manufacturer specifications for clinical hematology testing. 2.5 miRNA Quantification Total RNA was extracted from 1 ml plasma using the miRNeasy Serum/Plasma Kit (Qiagen, Germany). Reverse transcription was performed with the miScript II RT Kit (Takara, Japan), followed by qPCR amplification using miRNA-specific primers (RiboBio, China). Cel-miR-39 served as a spike-in control for normalization. Relative expression was calculated via the delta Ct value. 2.6 EBV-DNA detection Plasma EBV-DNA quantification was performed using real-time PCR on an ABI 7500 system (Applied Biosystems, USA) with the Guangzhou Zhongshan Daan Gene PCR kit (Guangzhou, China). The protocol comprised three phases: (1) Sample processing. Plasma aliquots (50 µl) were homogenized with 50 µl of kit-provided magnetic beads; Mixtures were incubated at 100°C for 10 min followed by centrifugation (3,000 × g, 5 min) to isolate total DNA. (2) PCR Amplification. The thermal cycling protocol included: initial denaturation: 96°C for 30 sec, amplification cycle (35 repeats), denaturation: 96°C for 30 sec, annealing: 56°C for 60 sec, extension: 72°C for 60 sec, final extension: 72°C for 5–10 min, erminal hold: 4°C. (3) Quantitative analysis. A four-point standard curve was generated using kit-supplied DNA calibrators (1.0×10⁴ to 1.0×10⁷ gene copies/ml) to determine target concentrations. Amplification data were analyzed with SDS software v2.3 (Applied Biosystems) following MIQE guidelines. 2.7 Receiver operating characteristic (ROC) analysis ROC curve analysis was conducted to assess the diagnostic efficacy of biomarkers for differentiating EBV-IM and EBV-HLH in pediatric populations. The cohort comprised 40 virologically confirmed cases (17 EBV-IM, 23 EBV-HLH) with complete laboratory profiles. Analytical parameters, six discriminators were evaluated: miRNAs: miR-375, miR-148a-3p, hematological indices: hemoglobin (Hb), platelet count (PLT), virological load, EBV-DNA, combinatorial biomarker: miR-375 + EBV-DNA. Statistical protocol, ROC curves were generated using SPSS Statistics 25.0 (SPSSInc., Chicago, Ill., USA) with non-parametric distribution assumptions. Area under the curve (AUC) values with 95% confidence intervals quantified diagnostic discrimination. Optimal cutoff thresholds were determined through maximum Youden index. Diagnostic performance metrics (sensitivity, specificity) were calculated at established cutoffs. 2.8 Statistical methods Comparisons of expression were performed using Students t test, using GraphPad Prism version 9.0. The receiver operating characteristic (ROC) curve was analyzed by SPSS 25.0. P < 0.05 was considered statistically significant ( * , P < 0.05; ** , P < 0.01; *** , P < 0.001). 3. Results 3.1 Clinical Characteristics of the Study Cohort The demographic and laboratory characteristics of 40 pediatric patients, comprising 17 with EBV-IM and 23 with EBV-HLH, are detailed in Table 1 . Baseline comparability was established, as no statistically significant differences were observed between the groups regarding age or sex distribution, thereby confirming the cohort's suitability for subsequent analyses. However, significant differences in laboratory parameters were noted; patients with EBV-HLH exhibited markedly lower hemoglobin levels ( P < 0.001) and platelet counts ( P < 0.001) compared to those with EBV-IM. Conversely, no statistically significant difference was found in plasma EBV-DNA loads between the two groups. 3.2 KEGG Pathway Analysis Reveals Viral Carcinogenesis Association Functional enrichment analysis of the three candidate miRNAs (miR-375, miR-148a-3p, miR-92a-1-5p) was performed through DIANA-miRPath v3.0 [ 18 ] using KEGG pathway databases. Key findings included: Top Enriched Pathway: Viral carcinogenesis exhibited the strongest association with target genes of the miRNA triad (Fig. 2 a). Mechanistic Relevance: This enrichment pattern aligns with established EBV-driven oncogenic mechanisms [ 19 ] , suggesting potential functional involvement of these miRNAs in EBV-HLH pathogenesis. Validation Metrics: Pathway significance was confirmed through hypergeometric testing and target overlap analysis. 3.3 miR-375 and miR-148a-3p were different expressed in EBV-IM and EBV-HLH groups Next, we compared the expression levels of miR-375, miR-148a-3p, and miR-92a-1-5p between EBV-IM and EBV-HLH group. The expression of miR-375 and miR-148a-3p in EBV-HLH patient group was significantly higher than EBV-IM group (Fig. 2 b-c). The expression of miR-92a-1-5p was not statistically significant between the two groups (Fig. 2 d). These data indicated that miR-375 and miR-148a-3p may have the potential to act as distinguished biomarker for EBV-IM and EBV-HLH. 3.4 Combinatorial Biomarker Signature Enhances Diagnostic Precision To optimize disease discrimination between EBV-IM and EBV-HLH, we systematically evaluated single and combined biomarker performance through ROC curve analysis. Individual biomarker performance, miRNA markers: miR-375: AUC 0.760, miR-148a-3p: AUC 0.670 (Figs. 3 a-b). Clinical parameters, Hemoglobin: AUC 0.876, Platelet count: AUC 0.836, EBV-DNA load: AUC 0.836 (Fig. 3 c). Optimal combinatorial model, the miR-375/EBV-DNA dual-marker panel demonstrated synergistic diagnostic efficacy: AUC 0.951 (Fig. 3 d). This enhanced performance aligns with miR-375's established role in modulating EBV latency programs and synergizing with viral replication dynamics. 4. Discussion This study examined the clinical characteristics and molecular signatures differentiating Epstein-Barr virus-associated infectious mononucleosis (EBV-IM) from the more severe Epstein-Barr virus-associated hemophagocytic lymphohistiocytosis (EBV-HLH) in pediatric patients. Our findings identified miR-375 and miR-148a-3p as potential discriminators, with notable diagnostic synergy observed in the miR-375/EBV-DNA combinatorial model (AUC = 0.951). The study cohort demonstrated comparable baseline demographics, ensuring minimal confounding effects in subsequent analyses. Notably, EBV-HLH patients exhibited significantly lower hemoglobin levels and platelet counts compared to EBV-IM patients, consistent with the known hematologic dysfunction in HLH due to excessive immune activation and hemophagocytosis [ 20 , 21 ] . However, plasma EBV-DNA loads did not differ significantly between the two groups, suggesting that viral load alone may not reliably distinguish EBV-IM from EBV-HLH, reinforcing the need for additional biomarkers [ 12 , 22 ] . KEGG pathway analysis revealed that the three candidate miRNAs (miR-375, miR-148a-3p, and miR-92a-1-5p) were significantly enriched in viral carcinogenesis pathways. This finding aligns with prior evidence linking EBV infection to oncogenic transformation through miRNA-mediated gene regulation [ 23 ] . The strong association of these miRNAs with viral carcinogenesis suggests their potential role in EBV-HLH pathogenesis, possibly by modulating key oncogenic or immune regulatory pathways. We identified miR-375 and miR-148a-3p as significantly upregulated in EBV-HLH compared to EBV-IM, while miR-92a-1-5p showed no differential expression. The elevated expression of miR-375 and miR-148a-3p in EBV-HLH may reflect their involvement in immune dysregulation or EBV latency programs [ 24 ] . Notably, miR-375 has been previously implicated in EBV-associated malignancies, further supporting its potential role in EBV-HLH progression [ 25 ] . These findings suggest that miR-375 and miR-148a-3p could serve as promising biomarkers for distinguishing EBV-HLH from EBV-IM. ROC curve analysis demonstrated that while individual biomarkers (miR-375, miR-148a-3p, hemoglobin, platelet count, and EBV-DNA load) exhibited moderate diagnostic accuracy, the combination of miR-375 with EBV-DNA load significantly improved discrimination between EBV-IM and EBV-HLH (AUC = 0.951). This synergistic effect may stem from miR-375’s role in modulating EBV latency [ 26 , 27 ] , coupled with the direct measurement of viral replication dynamics via EBV-DNA load. The superior performance of this dual-marker panel suggests its potential clinical utility in early and accurate EBV-HLH diagnosis. Despite these promising findings, our study has limitations, including a relatively small sample size and the need for external validation in independent cohorts. Future studies should explore the mechanistic roles of miR-375 and miR-148a-3p in EBV-HLH pathogenesis, particularly their interactions with immune signaling pathways. Additionally, longitudinal studies assessing these biomarkers' predictive value for disease progression and treatment response would further enhance their clinical applicability. In summary, our study identifies distinct clinical and molecular differences between EBV-IM and EBV-HLH, with miR-375 and miR-148a-3p emerging as potential diagnostic biomarkers. The combinatorial use of miR-375 and EBV-DNA load significantly enhances diagnostic precision, offering a promising tool for early and accurate differentiation of these EBV-associated disorders. These findings contribute to a deeper understanding of EBV-HLH pathogenesis and may facilitate improved clinical management of pediatric patients. Declarations Statement on participant consent: All the participants' parents or legal guardians consented for their participate in the study. Author contributions ZL: Study design, Investigation, Data analysis, Writing - original draft, Writing – review & editing. JZ: Data analysis, Writing- original draft, Writing – review & editing. YR: Investigation, Writing – original draft. LL: Data analysis. XW: Writing- review & editing. XH: Writing- review & editing. LY: Conception, Study design, Experiments, Data analysis, Supervision, Writing- review & editing. Data Availability Statement All the original data are available upon reasonable request for correspondence author. Funding Statement This work was supported by the National Natural Science Foundation of China to L.Y. (under Grant No. 82203711), the China Postdoctoral Science Foundation to L.Y. (under Grant No. 2021M701631). Conflict of interest The authors declare there is no conflict of interest. Ethic Approval Statement The study protocol received approval from the Institutional Review Board of the First Affiliated Hospital of Xi'an Medical University (Approval No. XYYFY2021LSK-015), adhering to Declaration of Helsinki principles. References Damania B, Kenney SC, Raab-Traub N., Epstein-Barr virus: Biology and clinical disease. Cell. 2022 Sep 29;185(20):3652-3670. doi: 10.1016/j.cell.2022.08.026. Vietzen H, Berger SM, Kühner LM, et al., Ineffective control of Epstein-Barr-virus-induced autoimmunity increases the risk for multiple sclerosis. Cell. 2023 Dec 21;186(26):5705-5718.e13. doi: 10.1016/j.cell.2023.11.015. Wang J, Su M, Wei N, et al., Chronic active Epstein-Barr virus disease originates from infected hematopoietic stem cells. Blood. 2024 Jan 4;143(1):32-41. doi: 10.1182/blood.2023021074. Yu S, Yang Q, Wu J, et al., Clinical application of Epstein-Barr virus DNA loads in Epstein-Barr virus-associated diseases: A cohort study. J Infect. 2021 Jan;82(1):105-111. doi: 10.1016/j.jinf.2020.11.027. Li Z, Wang X, Jing F, et al., Analysis of two laboratory tests for determination of EBV-IM in children. J Med Virol. 2022 Jun;94(6):2747-2754. doi: 10.1002/jmv.27490. Hurt C, Tammaro D, Diagnostic evaluation of mononucleosis-like illnesses. Am J Med. 2007 Oct;120(10):911.e1-8. doi: 10.1016/j.amjmed.2006.12.011. Oumata N, Zhong Q, Zhang Y, et al., Emerging drugs for Epstein-Barr virus associated-diseases. Eur J Med Chem. 2025 Apr 5;287:117386. doi: 10.1016/j.ejmech.2025.117386. Ebell MH. Epstein-Barr virus infectious mononucleosis. Am Fam Physician. 2004 Oct 1;70(7):1279-87. Zhou J, Zhang J, Zhu D, et al., The diagnostic value of peripheral blood lymphocyte testing in children with infectious mononucleosis. BMC Pediatr. 2024 Nov 16;24(1):746. doi: 10.1186/s12887-024-05228-6. Lai W, Wang Y, Wang J, et al., Epstein-Barr virus-associated hemophagocytic lymphohistiocytosis in adults and adolescents-a life-threatening disease: analysis of 133 cases from a single center. Hematology. 2018 Dec;23(10):810-816. doi: 10.1080/10245332.2018.1491093. Ramos-Casals M, Brito-Zerón P, López-Guillermo A, et al., Adult haemophagocytic syndrome. Lancet. 2014 Apr 26;383(9927):1503-1516. doi: 10.1016/S0140-6736(13)61048-X. Henter JI, Horne A, Aricó M, et al., HLH-2004: Diagnostic and therapeutic guidelines for hemophagocytic lymphohistiocytosis. Pediatr Blood Cancer. 2007 Feb;48(2):124-31. doi: 10.1002/pbc.21039. Cai L, Xing Y, Xia Y, et al., Comparative study of biomarkers for the early identification of Epstein-Barr virus-associated hemophagocytic lymphohistiocytosis in infectious mononucleosis. BMC Infect Dis. 2023 Oct 26;23(1):728. doi: 10.1186/s12879-023-08654-6. Xu Y, Chen Y, Yang Q, et al., Novel plasma microRNA expression features in diagnostic use for Epstein-Barr virus-associated febrile diseases. Heliyon. 2024 Feb 23;10(5):e26810. doi: 10.1016/j.heliyon.2024.e26810. Yu L, Sui B, Fan W, et al., Exosomes derived from osteogenic tumor activate osteoclast differentiation and concurrently inhibit osteogenesis by transferring COL1A1-targeting miRNA-92a-1-5p. J Extracell Vesicles. 2021 Jan;10(3):e12056. doi: 10.1002/jev2.12056. Liu C, Zou A, Wang X, et al., The ability of CD4dimCD8+ T cells to distinguish between Epstein-Barr virus-associated hemophagocytic lymphohistiocytosis and pediatric infectious mononucleosis. Immunol Res. 2025 Jan 30;73(1):43. doi: 10.1007/s12026-025-09597-7. Fang X, Xu S, Cai K, et al., High Epstein-Barr Virus DNA Load in T Cells Predicts Hemophagocytic Lymphohistiocytosis. J Infect Dis. 2025 Feb 27:jiaf065. doi: 10.1093/infdis/jiaf065. Vlachos IS, Zagganas K, Paraskevopoulou MD, et al., DIANA-miRPath v3.0: deciphering microRNA function with experimental support. Nucleic Acids Res. 2015 Jul 1;43(W1):W460-6. doi: 10.1093/nar/gkv403. Wu Y, Sun X, Kang K, et al., Hemophagocytic lymphohistiocytosis: current treatment advances, emerging targeted therapy and underlying mechanisms. J Hematol Oncol. 2024 Nov 7;17(1):106. doi: 10.1186/s13045-024-01621-x. Paolino J, Berliner N, Degar B. Hemophagocytic lymphohistiocytosis as an etiology of bone marrow failure. Front Oncol. 2022 Oct 27;12:1016318. doi: 10.3389/fonc.2022.1016318. Gottlieb A, Pham HPT, Saltarrelli JG, et al., Expanded T lymphocytes in the cerebrospinal fluid of multiple sclerosis patients are specific for Epstein-Barr-virus-infected B cells. Proc Natl Acad Sci U S A. 2024 Jan 16;121(3):e2315857121. doi: 10.1073/pnas.2315857121. Yang X, Lu X, Feng L, et al., Enhancing diagnostic precision in EBV-related HLH: a multifaceted approach using 18F-FDG PET/CT and nomogram integration. Cancer Imaging. 2024 Aug 18;24(1):108. doi: 10.1186/s40644-024-00757-w. Awasthi P, Kohli AS, Dwivedi M, et al., Implications of EBV-Encoded and EBV-Related miRNAs in Tumors. Curr Gene Ther. 2025 Jan 8. doi: 10.2174/0115665232327174241211075019. Xu L, Guo X, Guan H. Serious consequences of Epstein-Barr virus infection: Hemophagocytic lymphohistocytosis. Int J Lab Hematol. 2022 Feb;44(1):74-81. doi: 10.1111/ijlh.13736. Prinz C, Mese K, Weber D. MicroRNA Changes in Gastric Carcinogenesis: Differential Dysregulation during Helicobacter pylori and EBV Infection. Genes (Basel). 2021 Apr 19;12(4):597. doi: 10.3390/genes12040597. Blümke J, Bauer M, Vaxevanis C, et al., Identification and characterization of the anti-viral interferon lambda 3 as direct target of the Epstein-Barr virus microRNA-BART7-3p. Oncoimmunology. 2023 Nov 27;12(1):2284483. doi: 10.1080/2162402X.2023.2284483. Jasinski-Bergner S, Blümke J, Bauer M, et al., Novel approach to identify putative Epstein-Barr-virus microRNAs regulating host cell genes with relevance in tumor biology and immunology. Oncoimmunology. 2022 May 1;11(1):2070338. doi: 10.1080/2162402X.2022.2070 Additional Declarations The authors declare no competing interests. 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6837655","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":467600624,"identity":"2ce36100-361a-42ea-b8ac-207c6b1dd17c","order_by":0,"name":"Zhuo Li","email":"","orcid":"https://orcid.org/0000-0001-8774-0471","institution":"Department of Laboratory Medicine, The First Affiliated Hospital of Xi’an Medical University, Xi’an, China","correspondingAuthor":false,"prefix":"","firstName":"Zhuo","middleName":"","lastName":"Li","suffix":""},{"id":467613030,"identity":"8f526b7f-7527-472c-97d3-37315e2c4273","order_by":1,"name":"Jian Zhou","email":"","orcid":"https://orcid.org/0000-0001-7211-1680","institution":"Department of Laboratory Medicine, The First Affiliated Hospital of Xi’an Medical University, Xi’an, China","correspondingAuthor":false,"prefix":"","firstName":"Jian","middleName":"","lastName":"Zhou","suffix":""},{"id":467613031,"identity":"219688e0-4eaf-4ba0-bdc7-608fdd4517d0","order_by":2,"name":"Yingjie Ren","email":"","orcid":"https://orcid.org/0009-0001-8117-6328","institution":"Department of Laboratory Medicine, The First Affiliated Hospital of Xi’an Medical University, Xi’an, China","correspondingAuthor":false,"prefix":"","firstName":"Yingjie","middleName":"","lastName":"Ren","suffix":""},{"id":467613032,"identity":"bcd9facb-98a6-4cc1-8572-c80c216f7a9e","order_by":3,"name":"Lin Lei","email":"","orcid":"https://orcid.org/0000-0002-6131-9782","institution":"Department of Clinical Laboratory Medicine, Xi'an Hospital of Traditional Chinese Medicine, Xi’an, China","correspondingAuthor":false,"prefix":"","firstName":"Lin","middleName":"","lastName":"Lei","suffix":""},{"id":467613033,"identity":"02d438cf-e7c5-4837-a28b-61462bfa2e2e","order_by":4,"name":"Xin Wang","email":"","orcid":"https://orcid.org/0000-0001-8152-4719","institution":"Department of Laboratory Medicine, The First Affiliated Hospital of Xi’an Medical University, Xi’an, China","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Wang","suffix":""},{"id":467613034,"identity":"81ed2ccc-8738-4292-a20f-a380b434eec8","order_by":5,"name":"Lijuan Yu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAt0lEQVRIiWNgGAWjYBACPmaGBAiLvYFILWxwLTwHiNUCZ0kkEKuFneGZxM8dDIkbbj6/JsHwx4Yoh6VJ9p4BarmdUybB2JZGnBYJ3jawljQJxobDRNryF6Tl5pk0oMP+E6dFGmzLDfZjEgxsB4jSkmwt2yZhPPNMDrNFYlsyYS38/GcSb75ts5HtO3784Y0Pf+wIawFGYQKQkHBsYOAxgMUqIcB+AETaAxkPiNMwCkbBKBgFIw4AAGuvM9rmhQlYAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0003-3558-3800","institution":"Sahlgrenska Center for Cancer Research, Department of Oncology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden","correspondingAuthor":true,"prefix":"","firstName":"Lijuan","middleName":"","lastName":"Yu","suffix":""}],"badges":[],"createdAt":"2025-06-06 14:05:24","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":true,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6837655/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6837655/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84392111,"identity":"4ecfba5a-bb8b-4dcd-ba6a-4f80d11c75b4","added_by":"auto","created_at":"2025-06-11 11:42:44","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":146141,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDesign of the experiments.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6837655/v1/4e5b1974bcacca2bd803a83e.png"},{"id":84392115,"identity":"13a8f4ee-6be5-41bd-9653-ca630f57196c","added_by":"auto","created_at":"2025-06-11 11:42:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":624740,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe relative expression of miRNAs in IM and EBV-HLH patient plasma.\u003c/strong\u003e \u003cstrong\u003ea\u003c/strong\u003e KEGG analysis indicates the three miRNAs (miR-375, miR-148a-3p, miR-92a-1-5p) enriched in viral carcinogenesis. \u003cstrong\u003eb\u003c/strong\u003e Delta CT value of miR-375 in EBV-IM and EBV-HLH patient plasma. MiR-375 is upregulated. \u003cstrong\u003ec \u003c/strong\u003eDelta CT value of miR-148a-3p in EBV-IM and EBV-HLH patient plasma. MiR-148a-3p is upregulated. \u003cstrong\u003ed\u003c/strong\u003e Delta CT value of miR-92a-1-5p in EBV-IM and EBV-HLH patient plasma. MiR-92a-1-5p is not significantly difference between groups. Data were analyzed by \u003cem\u003et\u003c/em\u003etest.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6837655/v1/c1f62ecc23e8be3f297de475.png"},{"id":84392377,"identity":"76f6e4de-2ad7-4c0e-a688-15a00fad0490","added_by":"auto","created_at":"2025-06-11 11:50:45","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":512839,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROCs to distinguish IM and EBV-HLH patients.\u003c/strong\u003e \u003cstrong\u003ea \u003c/strong\u003eROC for miR-375 to distinguish IM and EBV-HLH. \u003cstrong\u003eb\u003c/strong\u003e ROC for miR-148a-3p to distinguish IM and EBV-HLH. \u003cstrong\u003ec \u003c/strong\u003eROC for Hb, Plt and EB-DNA to distinguish IM and EBV-HLH. \u003cstrong\u003ed \u003c/strong\u003eROC for combination of miR-375 and EB-DNA to distinguish IM and EBV-HLH. The AUC was calculated with 95% confidence intervals.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-6837655/v1/6430c93543102fd06cfc5bfc.png"},{"id":84392382,"identity":"c82d3329-dda8-4ba8-9ac3-9a8f1030357b","added_by":"auto","created_at":"2025-06-11 11:50:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1096050,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6837655/v1/5b49c06a-1bc7-4a49-a791-c10d4224fd65.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003emiRNA-375 Combined EB-DNA as Differential Diagnostic Biomarker for EBV-IM and EBV-HLH in Children\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eEpstein-Barr Virus (EBV), a ubiquitous gammaherpesvirus infecting over 90% of the global population, establishes lifelong latency in human B lymphocytes following primary infection \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Viral replication initiates in oropharyngeal epithelial cells, with subsequent B-cell entry facilitating systemic dissemination through viremia \u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. While EBV typically maintains low-copy latent persistence, its oncogenic potential is exemplified by associations with malignancies such as Burkitt lymphoma and nasopharyngeal carcinoma \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Pediatric EBV infections exhibit age-dependent clinical heterogeneity: infants often experience asymptomatic latency, whereas school-aged children frequently develop infectious mononucleosis (EBV-IM), characterized by fever, lymphadenopathy, and atypical lymphocytosis \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Beyond self-limiting EBV-IM, severe EBV-driven pathologies including chronic active EBV infection (CAEBV) and EBV-associated hemophagocytic lymphohistiocytosis (EBV-HLH) present substantial diagnostic and therapeutic challenges due to their overlapping yet distinct pathophysiological profiles \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eEBV-IM, EBV-HLH, and CAEBV exemplify the spectrum of EBV-related immunopathology. EBV-IM, though benign, manifests through a pathognomonic triad of fever, pharyngitis, and lymphadenopathy, with diagnosis relying on serological detection of heterophile antibodies or EBV-specific IgM \u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. In contrast, EBV-HLH represents a cytokine storm syndrome marked by fulminant hyperinflammation, cytopenias, and multi-organ dysfunction, requiring urgent intervention guided by HLH diagnostic criteria \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. CAEBV, characterized by chronic or recurrent IM-like symptoms with clonal EBV-infected T/NK-cell proliferation, bridges the clinical continuum between EBV-IM and EBV-HLH \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Despite established diagnostic frameworks, differentiation among these entities remains clinically arduous due to nonspecific early presentations and biomarker limitations.\u003c/p\u003e \u003cp\u003eCurrent research prioritizes identifying disease-specific biomarkers to address this diagnostic dilemma. Emerging evidence highlights microRNAs (miRNAs) as key regulators of EBV-host interactions, with distinct expression profiles potentially reflecting pathological states \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Concurrently, immunophenotypic markers and EBV-DNA load quantification are being explored for their discriminatory capacity \u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. This study investigates plasma miRNA signatures combined with virological parameters to develop a novel diagnostic algorithm for distinguishing EBV-IM from EBV-HLH, aiming to optimize clinical decision-making in pediatric EBV-associated diseases.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Clinical Data Acquisition\u003c/h2\u003e \u003cp\u003eThis retrospective cohort study included 40 pediatric patients, comprising 17 with EBV-IM and 23 with EBV-HLH, who were treated at the First Affiliated Hospital of Xi'an Medical University between January 2017 and December 2021. (1) The diagnostic criteria were defined as follows: For EBV-IM, the criteria included a clinical fever (\u0026ge;\u0026thinsp;38\u0026deg;C for \u0026ge;\u0026thinsp;3 days), pharyngitis, and cervical lymphadenopathy; laboratory confirmation required the presence of atypical lymphocytes\u0026thinsp;\u0026ge;\u0026thinsp;10% in peripheral blood, positive EBV serology (EBV-IgM\u0026thinsp;+\u0026thinsp;or VCA-IgG+/EBNA-IgG\u0026ndash;), and plasma EBV-DNA levels\u0026thinsp;\u0026ge;\u0026thinsp;500 copies/ml. For EBV-HLH, the criteria required the fulfillment of at least five of the HLH-2004 diagnostic criteria, virological confirmation with plasma EBV-DNA\u0026thinsp;\u0026gt;\u0026thinsp;10\u003csup\u003e3\u003c/sup\u003e copies/ml, cytopenia (hemoglobin\u0026thinsp;\u0026lt;\u0026thinsp;90 g/L, platelets\u0026thinsp;\u0026lt;\u0026thinsp;100\u0026times;10⁹/L), and hyperferritinemia (ferritin\u0026thinsp;\u0026ge;\u0026thinsp;500 \u0026micro;g/L). (2) The exclusion criteria were as follows: For EBV-IM, patients with coinfections (such as CMV, HIV), pre-existing hematologic disorders (such as leukemia, aplastic anemia), or primary immunodeficiencies (such as X-linked lymphoproliferative disease, primary immunodeficiency) were excluded. For EBV-HLH, cases of EBV-IM that did not meet the HLH-2004 criteria, secondary HLH triggers (such as malignancies, macrophage activation syndrome), and confirmed genetic HLH (such as familial hemophagocytic lymphohistiocytosis mutations) were excluded. (3) Ethical Compliance: The study protocol received approval from the Institutional Review Board of the First Affiliated Hospital of Xi'an Medical University (Approval No. XYYFY2021LSK-015), adhering to Declaration of Helsinki principles. Demographic and clinical parameters were retrieved from institutional electronic medical records with informed consent. Key characteristics including age, sex, and disease manifestations are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline data for the research.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEBV-HLH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.34\u0026thinsp;\u0026plusmn;\u0026thinsp;1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.63\u0026thinsp;\u0026plusmn;\u0026thinsp;3.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale:4; Male:13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale:12; Male:11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of patients\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClinical data\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHb\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e124.29\u0026thinsp;\u0026plusmn;\u0026thinsp;16.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90.26\u0026thinsp;\u0026plusmn;\u0026thinsp;24.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePlt\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e232\u0026thinsp;\u0026plusmn;\u0026thinsp;69.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e118.78\u0026thinsp;\u0026plusmn;\u0026thinsp;90.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEB-DNA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2893.88\u0026thinsp;\u0026plusmn;\u0026thinsp;2780\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1571355\u0026thinsp;\u0026plusmn;\u0026thinsp;3710999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Pathway Enrichment Analysis\u003c/h2\u003e \u003cp\u003eTarget pathways of candidate miRNAs (miR-375, miR-148a-3p, miR-92a-1-5p) were identified using DIANA-miRPath v3.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.microrna.gr/miRPathv3\u003c/span\u003e\u003cspan address=\"http://www.microrna.gr/miRPathv3\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), with KEGG pathway enrichment analysis performed under default parameters (FDR-adjusted \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Biospecimen Processing\u003c/h2\u003e \u003cp\u003ePeripheral blood (5 ml) collected in sodium citrate tubes was centrifuged at 4000 \u0026times; \u003cem\u003eg\u003c/em\u003e for 10 min at 4\u0026deg;C. Plasma aliquots were stored at -80\u0026deg;C until analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Platelet and Hemoglobin Detection\u003c/h2\u003e \u003cp\u003eWhole blood samples (3 ml) were collected from each participant into EDTA-K\u003csub\u003e2\u003c/sub\u003e anticoagulant tubes at enrollment. Platelet counts and hemoglobin levels were quantified using an automated hematology analyzer (Sysmex XN-9000) following standardized procedures. (1) Sample preparation: EDTA-anticoagulated venous blood was gently inverted 8\u0026ndash;10 times immediately after collection to ensure homogeneity and prevent clot formation. (2) Automated analysis: tubes were placed in the analyzer's sample rack. Whole blood mode was selected, and the automated cycle initiated. The system sequentially aspirated and processed samples. (3) Manual Loading Protocol (if required): The sampling cover was opened, and manual mode activated. Well-mixed blood samples were vertically inserted into the inlet port, ensuring precise needle penetration through the tube cap. Delayed Testing Protocol: Samples requiring delayed analysis were maintained at room temperature (18\u0026ndash;26\u0026deg;C) and processed within 4 h post-collection to preserve result validity. This standardized workflow minimized pre-analytical variability and ensured compliance with manufacturer specifications for clinical hematology testing.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 miRNA Quantification\u003c/h2\u003e \u003cp\u003eTotal RNA was extracted from 1 ml plasma using the miRNeasy Serum/Plasma Kit (Qiagen, Germany). Reverse transcription was performed with the miScript II RT Kit (Takara, Japan), followed by qPCR amplification using miRNA-specific primers (RiboBio, China). Cel-miR-39 served as a spike-in control for normalization. Relative expression was calculated via the delta Ct value.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 EBV-DNA detection\u003c/h2\u003e \u003cp\u003ePlasma EBV-DNA quantification was performed using real-time PCR on an ABI 7500 system (Applied Biosystems, USA) with the Guangzhou Zhongshan Daan Gene PCR kit (Guangzhou, China). The protocol comprised three phases: (1) Sample processing. Plasma aliquots (50 \u0026micro;l) were homogenized with 50 \u0026micro;l of kit-provided magnetic beads; Mixtures were incubated at 100\u0026deg;C for 10 min followed by centrifugation (3,000 \u0026times; g, 5 min) to isolate total DNA. (2) PCR Amplification. The thermal cycling protocol included: initial denaturation: 96\u0026deg;C for 30 sec, amplification cycle (35 repeats), denaturation: 96\u0026deg;C for 30 sec, annealing: 56\u0026deg;C for 60 sec, extension: 72\u0026deg;C for 60 sec, final extension: 72\u0026deg;C for 5\u0026ndash;10 min, erminal hold: 4\u0026deg;C. (3) Quantitative analysis. A four-point standard curve was generated using kit-supplied DNA calibrators (1.0\u0026times;10⁴ to 1.0\u0026times;10⁷ gene copies/ml) to determine target concentrations. Amplification data were analyzed with SDS software v2.3 (Applied Biosystems) following MIQE guidelines.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Receiver operating characteristic (ROC) analysis\u003c/h2\u003e \u003cp\u003eROC curve analysis was conducted to assess the diagnostic efficacy of biomarkers for differentiating EBV-IM and EBV-HLH in pediatric populations. The cohort comprised 40 virologically confirmed cases (17 EBV-IM, 23 EBV-HLH) with complete laboratory profiles. Analytical parameters, six discriminators were evaluated: miRNAs: miR-375, miR-148a-3p, hematological indices: hemoglobin (Hb), platelet count (PLT), virological load, EBV-DNA, combinatorial biomarker: miR-375\u0026thinsp;+\u0026thinsp;EBV-DNA. Statistical protocol, ROC curves were generated using SPSS Statistics 25.0 (SPSSInc., Chicago, Ill., USA) with non-parametric distribution assumptions. Area under the curve (AUC) values with 95% confidence intervals quantified diagnostic discrimination. Optimal cutoff thresholds were determined through maximum Youden index. Diagnostic performance metrics (sensitivity, specificity) were calculated at established cutoffs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Statistical methods\u003c/h2\u003e \u003cp\u003eComparisons of expression were performed using Students \u003cem\u003et\u003c/em\u003e test, using GraphPad Prism version 9.0. The receiver operating characteristic (ROC) curve was analyzed by SPSS 25.0. \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant (\u003csup\u003e*\u003c/sup\u003e, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; \u003csup\u003e**\u003c/sup\u003e, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01; \u003csup\u003e***\u003c/sup\u003e, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Clinical Characteristics of the Study Cohort\u003c/h2\u003e\n \u003cp\u003eThe demographic and laboratory characteristics of 40 pediatric patients, comprising 17 with EBV-IM and 23 with EBV-HLH, are detailed in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. Baseline comparability was established, as no statistically significant differences were observed between the groups regarding age or sex distribution, thereby confirming the cohort\u0026apos;s suitability for subsequent analyses. However, significant differences in laboratory parameters were noted; patients with EBV-HLH exhibited markedly lower hemoglobin levels (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and platelet counts (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) compared to those with EBV-IM. Conversely, no statistically significant difference was found in plasma EBV-DNA loads between the two groups.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 KEGG Pathway Analysis Reveals Viral Carcinogenesis Association\u003c/h2\u003e\n \u003cp\u003eFunctional enrichment analysis of the three candidate miRNAs (miR-375, miR-148a-3p, miR-92a-1-5p) was performed through DIANA-miRPath v3.0 \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e using KEGG pathway databases. Key findings included: Top Enriched Pathway: Viral carcinogenesis exhibited the strongest association with target genes of the miRNA triad (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ea). Mechanistic Relevance: This enrichment pattern aligns with established EBV-driven oncogenic mechanisms \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e, suggesting potential functional involvement of these miRNAs in EBV-HLH pathogenesis. Validation Metrics: Pathway significance was confirmed through hypergeometric testing and target overlap analysis.\u003c/p\u003e\n \u003ch2\u003e3.3 miR-375 and miR-148a-3p were different expressed in EBV-IM and EBV-HLH groups\u003c/h2\u003e\n \u003cp\u003eNext, we compared the expression levels of miR-375, miR-148a-3p, and miR-92a-1-5p between EBV-IM and EBV-HLH group. The expression of miR-375 and miR-148a-3p in EBV-HLH patient group was significantly higher than EBV-IM group (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eb-c). The expression of miR-92a-1-5p was not statistically significant between the two groups (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ed). These data indicated that miR-375 and miR-148a-3p may have the potential to act as distinguished biomarker for EBV-IM and EBV-HLH.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 Combinatorial Biomarker Signature Enhances Diagnostic Precision\u003c/h2\u003e\n \u003cp\u003eTo optimize disease discrimination between EBV-IM and EBV-HLH, we systematically evaluated single and combined biomarker performance through ROC curve analysis. Individual biomarker performance, miRNA markers: miR-375: AUC 0.760, miR-148a-3p: AUC 0.670 (Figs.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ea-b). Clinical parameters, Hemoglobin: AUC 0.876, Platelet count: AUC 0.836, EBV-DNA load: AUC 0.836 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ec). Optimal combinatorial model, the miR-375/EBV-DNA dual-marker panel demonstrated synergistic diagnostic efficacy: AUC 0.951 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ed). This enhanced performance aligns with miR-375\u0026apos;s established role in modulating EBV latency programs and synergizing with viral replication dynamics.\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study examined the clinical characteristics and molecular signatures differentiating Epstein-Barr virus-associated infectious mononucleosis (EBV-IM) from the more severe Epstein-Barr virus-associated hemophagocytic lymphohistiocytosis (EBV-HLH) in pediatric patients. Our findings identified miR-375 and miR-148a-3p as potential discriminators, with notable diagnostic synergy observed in the miR-375/EBV-DNA combinatorial model (AUC\u0026thinsp;=\u0026thinsp;0.951).\u003c/p\u003e \u003cp\u003eThe study cohort demonstrated comparable baseline demographics, ensuring minimal confounding effects in subsequent analyses. Notably, EBV-HLH patients exhibited significantly lower hemoglobin levels and platelet counts compared to EBV-IM patients, consistent with the known hematologic dysfunction in HLH due to excessive immune activation and hemophagocytosis \u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. However, plasma EBV-DNA loads did not differ significantly between the two groups, suggesting that viral load alone may not reliably distinguish EBV-IM from EBV-HLH, reinforcing the need for additional biomarkers \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eKEGG pathway analysis revealed that the three candidate miRNAs (miR-375, miR-148a-3p, and miR-92a-1-5p) were significantly enriched in viral carcinogenesis pathways. This finding aligns with prior evidence linking EBV infection to oncogenic transformation through miRNA-mediated gene regulation \u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. The strong association of these miRNAs with viral carcinogenesis suggests their potential role in EBV-HLH pathogenesis, possibly by modulating key oncogenic or immune regulatory pathways.\u003c/p\u003e \u003cp\u003eWe identified miR-375 and miR-148a-3p as significantly upregulated in EBV-HLH compared to EBV-IM, while miR-92a-1-5p showed no differential expression. The elevated expression of miR-375 and miR-148a-3p in EBV-HLH may reflect their involvement in immune dysregulation or EBV latency programs \u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. Notably, miR-375 has been previously implicated in EBV-associated malignancies, further supporting its potential role in EBV-HLH progression \u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. These findings suggest that miR-375 and miR-148a-3p could serve as promising biomarkers for distinguishing EBV-HLH from EBV-IM.\u003c/p\u003e \u003cp\u003eROC curve analysis demonstrated that while individual biomarkers (miR-375, miR-148a-3p, hemoglobin, platelet count, and EBV-DNA load) exhibited moderate diagnostic accuracy, the combination of miR-375 with EBV-DNA load significantly improved discrimination between EBV-IM and EBV-HLH (AUC\u0026thinsp;=\u0026thinsp;0.951). This synergistic effect may stem from miR-375\u0026rsquo;s role in modulating EBV latency \u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e, coupled with the direct measurement of viral replication dynamics via EBV-DNA load. The superior performance of this dual-marker panel suggests its potential clinical utility in early and accurate EBV-HLH diagnosis.\u003c/p\u003e \u003cp\u003eDespite these promising findings, our study has limitations, including a relatively small sample size and the need for external validation in independent cohorts. Future studies should explore the mechanistic roles of miR-375 and miR-148a-3p in EBV-HLH pathogenesis, particularly their interactions with immune signaling pathways. Additionally, longitudinal studies assessing these biomarkers' predictive value for disease progression and treatment response would further enhance their clinical applicability.\u003c/p\u003e \u003cp\u003eIn summary, our study identifies distinct clinical and molecular differences between EBV-IM and EBV-HLH, with miR-375 and miR-148a-3p emerging as potential diagnostic biomarkers. The combinatorial use of miR-375 and EBV-DNA load significantly enhances diagnostic precision, offering a promising tool for early and accurate differentiation of these EBV-associated disorders. These findings contribute to a deeper understanding of EBV-HLH pathogenesis and may facilitate improved clinical management of pediatric patients.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eStatement on participant consent:\u003c/strong\u003e All the participants\u0026apos; parents or legal guardians consented for their participate in the study.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZL: Study design, Investigation, Data analysis, Writing - original draft, Writing \u0026ndash; review \u0026amp; editing. JZ: Data analysis, Writing- original draft, Writing \u0026ndash; review \u0026amp; editing. YR: Investigation, Writing \u0026ndash; original draft. LL: Data analysis. XW: Writing- review \u0026amp; editing. XH: Writing- review \u0026amp; editing. LY: Conception, Study design, Experiments, Data analysis, Supervision, Writing- review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the original data are available upon reasonable request for correspondence author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China to L.Y. (under Grant No. 82203711), the China Postdoctoral Science Foundation to L.Y. (under Grant No. 2021M701631).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare there is no conflict of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthic Approval Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol received approval from the Institutional Review Board of the First Affiliated Hospital of Xi\u0026apos;an Medical University (Approval No. XYYFY2021LSK-015), adhering to Declaration of Helsinki principles.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eDamania B, Kenney SC, Raab-Traub N., Epstein-Barr virus: Biology and clinical disease. Cell. 2022 Sep 29;185(20):3652-3670. doi: 10.1016/j.cell.2022.08.026.\u003c/li\u003e\n \u003cli\u003eVietzen H, Berger SM, K\u0026uuml;hner LM, et al., Ineffective control of Epstein-Barr-virus-induced autoimmunity increases the risk for multiple sclerosis. Cell. 2023 Dec 21;186(26):5705-5718.e13. doi: 10.1016/j.cell.2023.11.015.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eWang J, Su M, Wei N, et al., Chronic active Epstein-Barr virus disease originates from infected hematopoietic stem cells. Blood. 2024 Jan 4;143(1):32-41. doi: 10.1182/blood.2023021074.\u003c/li\u003e\n \u003cli\u003eYu S, Yang Q, Wu J, et al., Clinical application of Epstein-Barr virus DNA loads in Epstein-Barr virus-associated diseases: A cohort study. J Infect. 2021 Jan;82(1):105-111. doi: 10.1016/j.jinf.2020.11.027.\u003c/li\u003e\n \u003cli\u003eLi Z, Wang X, Jing F, et al., Analysis of two laboratory tests for determination of EBV-IM in children. J Med Virol. 2022 Jun;94(6):2747-2754. doi: 10.1002/jmv.27490.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eHurt C, Tammaro D, Diagnostic evaluation of mononucleosis-like illnesses. Am J Med. 2007 Oct;120(10):911.e1-8. doi: 10.1016/j.amjmed.2006.12.011.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eOumata N, Zhong Q, Zhang Y, et al., Emerging drugs for Epstein-Barr virus associated-diseases. Eur J Med Chem. 2025 Apr 5;287:117386. doi: 10.1016/j.ejmech.2025.117386.\u003c/li\u003e\n \u003cli\u003eEbell MH. Epstein-Barr virus infectious mononucleosis. Am Fam Physician. 2004 Oct 1;70(7):1279-87.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eZhou J, Zhang J, Zhu D, et al., The diagnostic value of peripheral blood lymphocyte testing in children with infectious mononucleosis. BMC Pediatr. 2024 Nov 16;24(1):746. doi: 10.1186/s12887-024-05228-6.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eLai W, Wang Y, Wang J, et al., Epstein-Barr virus-associated hemophagocytic lymphohistiocytosis in adults and adolescents-a life-threatening disease: analysis of 133 cases from a single center. Hematology. 2018 Dec;23(10):810-816. doi: 10.1080/10245332.2018.1491093.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eRamos-Casals M, Brito-Zer\u0026oacute;n P, L\u0026oacute;pez-Guillermo A, et al., Adult haemophagocytic syndrome. Lancet. 2014 Apr 26;383(9927):1503-1516. doi: 10.1016/S0140-6736(13)61048-X.\u003c/li\u003e\n \u003cli\u003eHenter JI, Horne A, Aric\u0026oacute;\u0026nbsp;M, et al., HLH-2004: Diagnostic and therapeutic guidelines for hemophagocytic lymphohistiocytosis. Pediatr Blood Cancer. 2007 Feb;48(2):124-31. doi: 10.1002/pbc.21039.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eCai L, Xing Y, Xia Y, et al., Comparative study of biomarkers for the early identification of Epstein-Barr virus-associated hemophagocytic lymphohistiocytosis in infectious mononucleosis. BMC Infect Dis. 2023 Oct 26;23(1):728. doi: 10.1186/s12879-023-08654-6.\u003c/li\u003e\n \u003cli\u003eXu Y, Chen Y, Yang Q, et al., Novel plasma microRNA expression features in diagnostic use for Epstein-Barr virus-associated febrile diseases. Heliyon. 2024 Feb 23;10(5):e26810. doi: 10.1016/j.heliyon.2024.e26810.\u003c/li\u003e\n \u003cli\u003eYu L, Sui B, Fan W, et al., Exosomes derived from osteogenic tumor activate osteoclast differentiation and concurrently inhibit osteogenesis by transferring COL1A1-targeting miRNA-92a-1-5p. J Extracell Vesicles. 2021 Jan;10(3):e12056. doi: 10.1002/jev2.12056.\u003c/li\u003e\n \u003cli\u003eLiu C, Zou A, Wang X, et al., The ability of CD4dimCD8+ T cells to distinguish between Epstein-Barr virus-associated hemophagocytic lymphohistiocytosis and pediatric infectious mononucleosis. Immunol Res. 2025 Jan 30;73(1):43. doi: 10.1007/s12026-025-09597-7.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eFang X, Xu S, Cai K, et al., High Epstein-Barr Virus DNA Load in T Cells Predicts Hemophagocytic Lymphohistiocytosis. J Infect Dis. 2025 Feb 27:jiaf065. doi: 10.1093/infdis/jiaf065.\u003c/li\u003e\n \u003cli\u003eVlachos IS, Zagganas K, Paraskevopoulou MD, et al., DIANA-miRPath v3.0: deciphering microRNA function with experimental support. Nucleic Acids Res. 2015 Jul 1;43(W1):W460-6. doi: 10.1093/nar/gkv403.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eWu Y, Sun X, Kang K, et al., Hemophagocytic lymphohistiocytosis: current treatment advances, emerging targeted therapy and underlying mechanisms. J Hematol Oncol. 2024 Nov 7;17(1):106. doi: 10.1186/s13045-024-01621-x.\u003c/li\u003e\n \u003cli\u003ePaolino J, Berliner N, Degar B. Hemophagocytic lymphohistiocytosis as an etiology of bone marrow failure. Front Oncol. 2022 Oct 27;12:1016318. doi: 10.3389/fonc.2022.1016318.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eGottlieb A, Pham HPT, Saltarrelli JG, et al., Expanded T lymphocytes in the cerebrospinal fluid of multiple sclerosis patients are specific for Epstein-Barr-virus-infected B cells. Proc Natl Acad Sci U S A. 2024 Jan 16;121(3):e2315857121. doi: 10.1073/pnas.2315857121.\u003c/li\u003e\n \u003cli\u003eYang X, Lu X, Feng L, et al., Enhancing diagnostic precision in EBV-related HLH: a multifaceted approach using 18F-FDG PET/CT and nomogram integration. Cancer Imaging. 2024 Aug 18;24(1):108. doi: 10.1186/s40644-024-00757-w.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eAwasthi P, Kohli AS, Dwivedi M, et al., Implications of EBV-Encoded and EBV-Related miRNAs in Tumors. Curr Gene Ther. 2025 Jan 8. doi: 10.2174/0115665232327174241211075019.\u003c/li\u003e\n \u003cli\u003e\u0026nbsp;Xu L, Guo X, Guan H. Serious consequences of Epstein-Barr virus infection: Hemophagocytic lymphohistocytosis. Int J Lab Hematol. 2022 Feb;44(1):74-81. doi: 10.1111/ijlh.13736.\u003c/li\u003e\n \u003cli\u003ePrinz C, Mese K, Weber D. MicroRNA Changes in Gastric Carcinogenesis: Differential Dysregulation during\u0026nbsp;Helicobacter pylori\u0026nbsp;and EBV Infection. Genes (Basel). 2021 Apr 19;12(4):597. doi: 10.3390/genes12040597.\u003c/li\u003e\n \u003cli\u003eBl\u0026uuml;mke J, Bauer M, Vaxevanis C, et al., Identification and characterization of the anti-viral interferon lambda 3 as direct target of the Epstein-Barr virus microRNA-BART7-3p. Oncoimmunology. 2023 Nov 27;12(1):2284483. doi: 10.1080/2162402X.2023.2284483.\u003c/li\u003e\n \u003cli\u003eJasinski-Bergner S, Bl\u0026uuml;mke J, Bauer M, et al., Novel approach to identify putative Epstein-Barr-virus microRNAs regulating host cell genes with relevance in tumor biology and immunology. Oncoimmunology. 2022 May 1;11(1):2070338. doi: 10.1080/2162402X.2022.2070\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Xi'an Medical University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"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-associated infectious mononucleosis, EBV-associated Hemophagocytic Lymphohistiocytosis, differential diagnosis, biomarker","lastPublishedDoi":"10.21203/rs.3.rs-6837655/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6837655/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn children, Epstein-Barr Virus (EBV) often causes infectious mononucleosis (EBV-IM) but can escalate to a severe condition called EBV-associated hemophagocytic lymphohistiocytosis (EBV-HLH). Differentiating these conditions is vital for proper treatment and prognosis. This study aimed to find plasma biomarkers to distinguish between EBV-IM and EBV-HLH. A retrospective analysis was performed on 17 children with EBV-IM and 23 with EBV-HLH. Selected microRNAs (miRNAs) were measured in plasma using qPCR, and EBV DNA was isolated and quantified. Biomarker effectiveness was assessed using constructing receiver operating characteristic (ROC) curves and the area under the curve (AUC) calculations. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed a significant enrichment of miR-375, miR-148a-3p, and miR-92a-1-5p within the Viral Carcinogenesis pathway. Quantitative PCR validation demonstrated a marked upregulation of miR-375 and miR-148a-3p in patients with EBV-HLH compared to those with EBV-IM, whereas the expression of miR-92a-1-5p did not show a statistically significant difference between the groups. Notably, receiver operating characteristic (ROC) analysis indicated that the combined evaluation of miR-375 and EBV-DNA load achieved superior diagnostic discrimination between EBV-HLH and EBV-IM, with an AUC of 0.951, highlighting its potential high clinical utility. The integrated biomarker panel, consisting of miR-375 and EBV-DNA load, demonstrated high diagnostic accuracy in distinguishing between EBV-HLH and EBV-IM. These findings offer a clinically applicable tool for precise differential diagnosis and therapeutic stratification.\u003c/p\u003e","manuscriptTitle":"miRNA-375 Combined EB-DNA as Differential Diagnostic Biomarker for EBV-IM and EBV-HLH in Children","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-11 11:42:40","doi":"10.21203/rs.3.rs-6837655/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":"c33d86e5-eac7-4851-a00b-9886b1ec6c2e","owner":[],"postedDate":"June 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-28T23:39:44+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-11 11:42:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6837655","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6837655","identity":"rs-6837655","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}