Detection method and kit thereof for distinguishing caebv and ebv-positive or -negative ptcl

Abstract

The present application relates to a detection method and kit for distinguishing CAEBV and EBV positive or negative PTCL. The present application provides the use of a detection reagent of a biomarker in the preparation of a composition or kit for distinguishing chronic active Epstein-Barr virus infection (CAEBV), Epstein-Barr virus positive peripheral T cell lymphoma (EBV+ PTCL) and Epstein-Barr virus negative peripheral T cell lymphoma (EBV- PTCL), wherein the biomarker comprises any 1, 2, 3, 4, 5 of the FAS, SELP, LY86, GZMB and CCL7 genes, preferably a combination comprising all 5 of them. Compared with traditional clinical observation, pathological biopsy and in situ hybridization, the present application has higher disease specificity and sensitivity, and can provide molecular basis for early clinical treatment and drug intervention.

Description

Technical Field

[0001] This invention belongs to the field of molecular diagnostic technology, specifically relating to a kit and its application for differentiating chronic active Epstein-Barr virus (CAEBV) infection, Epstein-Barr virus-positive peripheral T-cell lymphoma (EBV+PTCL), and Epstein-Barr virus-negative peripheral T-cell lymphoma (EBV-PTCL) by detecting the expression and methylation levels of specific genes. Background Technology

[0002] CAEBV, EBV+PTCL, and EBV-PTCL are clinically related but significantly different diseases. CAEBV is a complex Epstein-Barr virus-associated lymphoproliferative disorder caused by persistent and recurrent Epstein-Barr virus infection. It is characterized by systemic inflammation, dysregulation of the immune response, and clonal expansion of EBV-infected T cells or natural killer (NK) cells, ultimately leading to multi-organ damage and posing a significant health risk. According to the World Health Organization (WHO) classification criteria, the diagnosis of CAEBV currently relies on the detection of EBV DNA load in peripheral blood, pathological evidence from affected tissues, etc., lacking direct and rapid molecular diagnostic methods. Furthermore, the etiology of CAEBV is not fully understood, and its clinical differentiation from other EBV-related diseases that may also present with high viral loads presents significant challenges.

[0003] Clinical studies have shown that CAEBV can progress to life-threatening NK / T-cell lineage leukemia and lymphoma. PTCL represents a highly heterogeneous group of non-Hodgkin lymphomas with a generally poor prognosis, and some patients are associated with EBV infection. However, due to insufficient sample size and a lack of experimental models, research on its pathological mechanisms remains limited. Furthermore, since not all PTCL patients are infected with EBV, i.e., there are EBV-PTCL patients, this indicates that EBV+PTCL and EBV-PTCL differ in their pathogenic molecular mechanisms and tumor progression.

[0004] Currently, patients with CAEBV, EBV+PTCL, and EBV-PTCL share similar clinical symptoms. Differential diagnosis of these diseases primarily relies on methods such as pathological biopsy and in situ hybridization. However, these methods suffer from drawbacks such as high invasiveness, complex procedures, or limited diagnostic capabilities. Therefore, developing a non-invasive or minimally invasive, precise differential diagnostic method based on molecular markers has significant clinical need and market value, and can further optimize early diagnosis, risk stratification, and clinical intervention strategies. Summary of the Invention

[0005] The purpose of this invention is to provide a detection kit and method that can quickly and accurately distinguish between CAEBV, EBV+PTCL and EBV-PTCL, so as to overcome the shortcomings of existing differential diagnostic techniques.

[0006] This application may cover the inventions described in the following items.

[0007] 1. Use of a biomarker detection reagent in the preparation of a composition or kit for differentiating chronic active Epstein-Barr virus (CAEBV) infection, Epstein-Barr virus-positive peripheral T-cell lymphoma (EBV+PTCL), and Epstein-Barr virus-negative peripheral T-cell lymphoma (EBV-PTCL), wherein the biomarker comprises any 1, 2, 3, 4, or 5 genes selected from FAS, SELP, LY86, GZMB, and CCL7, preferably a combination of all 5 genes.

[0008] 2. The use described in Item 1, wherein the detection reagent includes reagents for detecting gene expression and / or methylation levels, such as any one or more of the following detection reagents: reagents for detecting FAS and SELP gene expression and reagents for detecting GZMB, LY86 and CCL7 gene methylation levels.

[0009] 3. The use described in item 1 or 2, wherein the detection reagent includes primers and / or probes for detecting gene expression and / or methylation levels.

[0010] 4. The use described in Item 3, wherein the primers and / or probes are selected from the group consisting of SEQ ID NOs:1-9 and 24-35.

[0011] 5. The use described in any one of items 1-4, wherein the detection is performed by a PCR-based method, such as qPCR.

[0012] 6. The use described in any one of items 1-5, wherein the biomarker is derived from a body fluid sample, such as whole blood, serum or plasma.

[0013] 7. The use described in any one of items 1-6, wherein,

[0014] If F(FAS)≥1, F(SELP)<1, R(CCL7)<1, and either GZMB or LY86 has a low methylation level (R<1), then it is determined to be CAEBV.

[0015] If F(FAS)≥3, F(SELP)≥1, and R(GZMB)<1, R(LY86)<1, R(CCL7)<3, then it is determined to be EBV+PTCL;

[0016] If F(FAS)≥1, F(SELP)≥3, and R(GZMB)<1, R(LY86)<3, R(CCL7)<1, then it is determined to be EBV-PTCL.

[0017] 8. A composition or kit for differentiating chronic active Epstein-Barr virus (CAEBV) infection, Epstein-Barr virus-positive peripheral T-cell lymphoma (EBV+PTCL) and Epstein-Barr virus-negative peripheral T-cell lymphoma (EBV-PTCL), wherein the composition or kit comprises a detection reagent for a biomarker as defined in any one of items 1-7.

[0018] 9. A computer system comprising a processor and a storage device storing computer-executable code, wherein when the computer-executable code is executed at the processor, it is configured to: obtain a biomarker detection level as defined in any one of items 1-7, and differentiate chronic active Epstein-Barr virus (CAEBV), Epstein-Barr virus-positive peripheral T-cell lymphoma (EBV+PTCL), and Epstein-Barr virus-negative peripheral T-cell lymphoma (EBV-PTCL) based on a calculation result of the biomarker detection level.

[0019] 10. A computer-readable medium having instructions stored thereon that, when executed by a processor, cause the processor to execute computer-executable code as defined in item 9.

[0020] This invention provides a method for distinguishing CAEBV and PTCL at the molecular level by detecting the expression or methylation levels of five genes, namely FAS, SELP, LY86, GZMB, and CCL7, using PCR methods such as qPCR. Compared with traditional methods such as clinical observation, pathological biopsy, and in situ hybridization, this invention has higher disease specificity and sensitivity, and can provide molecular evidence for early clinical treatment and drug intervention. Attached Figure Description

[0021] Figure 1 : FAS gene expression amplification curve.

[0022] Figure 2 SELP gene expression amplification curve.

[0023] Figure 3 Results of amplification of the FAS gene using the second and third sets of primers and probes.

[0024] Figure 4 Results of amplification of the SELP gene using the second and third sets of primers and probes.

[0025] Figure 5GZMB methylation reaction (M) amplification results: red represents the amplification curve of fully methylated template, and green represents the unmethylated template.

[0026] Figure 6 GZMB unmethylation reaction (U) amplification results: red represents the amplification curve of the unmethylated template, and green represents the fully methylated template.

[0027] Figure 7 LY86 methylation reaction (M) amplification results: red represents the amplification curve of fully methylated template, and green represents the unmethylated template.

[0028] Figure 8 LY86 unmethylation reaction (U) amplification results: red represents the amplification curve of the unmethylated template, and green represents the fully methylated template.

[0029] Figure 9 : CCL7 methylation reaction (M) amplification results, red is the amplification curve of fully methylated template, green is the unmethylated template.

[0030] Figure 10 CCL7 unmethylation reaction (U) amplification results: red represents the amplification curve of the unmethylated template, and green represents the fully methylated template.

[0031] Figure 11 : Results of FAS gene qPCR sensitivity test, gray represents negative control.

[0032] Figure 12A Results of GZMB gene qPCR sensitivity full methylation reaction (M) test.

[0033] Figure 12B Results of GZMB gene qPCR sensitivity nonmethylation reaction (U) test.

[0034] Figure 13 ROC curves for CAEBV vs. non-CAEBV determination results.

[0035] Figure 14 ROC curves for EBV+ PTCL vs. non-EBV+ PTCL determination results.

[0036] Figure 15 ROC curves for EBV-PTCL vs. non-EBV-PTCL determination results.

[0037] Figure 16 : A schematic diagram of the judgment process of an example of the computer-aided diagnostic system of the present invention. Detailed Implementation

[0038] 1. Definition

[0039] Unless otherwise stated, the terms used herein have the meanings commonly understood by those skilled in the art. Some of the terms used herein are defined as described below.

[0040] In this article, the FAS gene refers to the gene encoding the Fas cell surface death receptor, with NCBI Gene ID 355 and mRNA sequence NM_000043.6. It belongs to the tumor necrosis factor (TNF) receptor superfamily and its main function is to participate in mediating apoptosis.

[0041] In this article, the SELP gene refers to the gene encoding P-selectin, NCBI Gene ID 6403, and its mRNA sequence is shown in NM_003005.4. When platelets or endothelial cells are activated, P-selectin rapidly translocates to the cell surface and mediates the initial rolling of leukocytes along the blood vessel wall by binding to ligands on the leukocyte surface. This is a crucial first step in initiating leukocyte recruitment and extravasation at sites of inflammation or tissue damage.

[0042] In this article, the GZMB gene refers to the gene encoding granzyme B, with NCBI Gene ID 3002 and mRNA sequence shown in NM_004131.6. Once inside target cells, granzyme B can cleave and activate caspase, thereby efficiently inducing apoptosis in target cells and is a core effector molecule in cell-mediated cytotoxicity.

[0043] In this article, the LY86 (lymphocyte antigen 86) gene refers to the gene encoding the MD-1 (Myeloid Differentiation 1) protein, with NCBI Gene ID 9450 and mRNA sequence shown in NM_004271.4. The MD-1 protein encoded by this gene forms a complex with the RP105 (CD180) protein, which can recognize pathogen-associated molecular patterns such as bacterial lipopolysaccharides, participate in the regulation of innate immune responses, and play a certain modulatory role in the TLR4 signaling pathway.

[0044] In this article, the CCL7 (CC motif chemokine ligand 7) gene refers to the gene encoding monocyte chemokine ligand 3 (MCP-3), with NCBI Gene ID 6354 and mRNA sequence shown in NM_006273.4. MCP-3, encoded by this gene, is a chemokine that can attract various immune cells, including monocytes, eosinophils, basophils, and T lymphocytes, playing a crucial recruitment role in inflammatory responses, infectious disease defense, and the formation of the tumor microenvironment.

[0045] In this paper, the Ct value (Cycle threshold) refers to the number of cycles required for the fluorescence signal to reach a set threshold in a real-time quantitative PCR reaction. The Ct value has a linear negative correlation with the logarithm of the initial template amount and is a fundamental parameter for qPCR quantitative analysis.

[0046] In this paper, the F-value (relative expression level) refers to the relative expression level of the target gene relative to an internal reference gene (such as GAPDH). It can represent the fold change in the expression level of the target gene in the patient sample relative to the healthy control. For example, it can be calculated using the ΔΔCt method as follows: ΔCt = Ct_target gene - Ct_control gene, ΔΔCt = ΔCt_patient – ​​ΔCt_healthy control, F = 2 -ΔΔCt .

[0047] In this paper, the PMR value (Percentage of Methylated Reference) refers to the methylation reference percentage, which is an indicator used to quantify the level of DNA methylation. The PMR value can be calculated by comparing the Ct values ​​of methylated (M) and unmethylated (U) reactions using the following formula: ΔCt_M = Ct(M) - Ct(M control), ΔCt_U = Ct(U) - Ct(U control), PMR (%) = [2] -ΔCt_M / (2 -ΔCt_M +2 -ΔCt_U The value is calculated as 100% (%), which reflects the proportion of methylated DNA to total DNA in the sample.

[0048] In this paper, the R value (relative methylation ratio) refers to the relative methylation level of a target gene (e.g., a CpG island in a promoter region), which can be calculated using R = PMR(patient) / PMR(healthy). The R value can represent the fold change in the methylation level of a target gene in a patient sample relative to a healthy control.

[0049] In this article, CAEBV (Chronic Active Epstein-Barr Virus Infection) is a complex Epstein-Barr virus-associated lymphoproliferative disorder. According to WHO classification criteria, CAEBV is characterized by persistent and recurrent Epstein-Barr virus infection, systemic inflammatory response, dysregulated immune response, clonal expansion of Epstein-Barr virus-infected T cells or NK cells, ultimately leading to multi-organ damage. CAEBV can be diagnosed based on a comprehensive assessment of persistently elevated Epstein-Barr virus DNA load in peripheral blood (≥400 copies / mL), pathological evidence from affected tissues, and clinical symptoms.

[0050] In this article, EBV+ PTCL (Epstein-Barr Virus-positive Peripheral T-cell Lymphoma) is a type of peripheral T-cell non-Hodgkin lymphoma associated with Epstein-Barr virus (EBV) infection. This disease is highly heterogeneous and generally has a poor prognosis. EBV+ PTCL can be diagnosed based on pathologically confirmed T-cell lymphoma, positive EBV in situ hybridization (EBER-ISH), and detectable EBV DNA load in peripheral blood.

[0051] In this article, EBV-PTCL (Epstein-Barr Virus-negative Peripheral T-cell Lymphoma) is a type of peripheral T-cell lymphoma that is not associated with Epstein-Barr virus infection. EBV-PTCL can be diagnosed based on pathologically confirmed T-cell lymphoma, negative Epstein-Barr in situ hybridization (EBER-ISH), and the absence of detectable Epstein-Barr virus DNA load in peripheral blood.

[0052] In this article, there are no particular limitations on samples from subjects such as humans or animals, and they may include, for example, tissues and body fluids from organisms, including, for example, blood (whole blood), serum, plasma, urine, cerebrospinal fluid, saliva, sputum, tears, bone marrow aspiration fluid, pleural effusion, ascites, prostatic fluid, etc. For example, they may include peripheral blood mononuclear cells (PBMCs) and cfDNA from the subject.

[0053] In this paper, PBMCs (Peripheral Blood Mononuclear Cells) are a population of cells with a single nucleus in peripheral blood, mainly including lymphocytes (T cells, B cells, NK cells) and monocytes. In some embodiments of the present invention, PBMCs can be used to detect the expression levels of biomarker genes.

[0054] In this paper, cfDNA (cell-free DNA) refers to cell-free DNA fragments present in bodily fluids such as blood, plasma, or serum. cfDNA primarily originates from apoptotic or necrotic cells, and the genetic and epigenetic information it carries can reflect pathological states in vivo. In some embodiments of this invention, cfDNA can be used to detect the methylation level of biomarker genes.

[0055] In this paper, qPCR (Quantitative Polymerase Chain Reaction) is a quantitative technique for real-time monitoring of product accumulation during PCR reactions. By labeling with fluorescent dyes or probes, qPCR can detect changes in fluorescence signals during PCR amplification in real time, achieving precise quantification of the initial template amount. MS-qPCR (Methylation-specific Quantitative PCR) is a quantitative PCR technique specifically designed for detecting DNA methylation. This technique first treats DNA with a conversion reagent (such as bisulfite) to convert unmethylated cytosine (C) to uracil (U), while methylated cytosine remains unchanged. Then, qPCR amplification is performed using primers that specifically recognize methylated or unmethylated sequences, thereby achieving quantitative detection of methylation levels in specific regions.

[0056] In this paper, conversion reagent (such as bisulfite) conversion is a chemical process that deamination of unmethylated cytosine (C) in DNA to uracil (U). Under conditions of conversion reagent such as bisulfite treatment, unmethylated C is converted to U (read as T after PCR amplification), while methylated C is not converted due to the protection of the methyl group. This difference allows methylated and unmethylated sequences to be distinguished and detected using specific primers. Examples of bisulfite conversion reagents include, for example, sodium bisulfite, magnesium bisulfite, ammonium bisulfite, potassium metabisulfite, and sodium metabisulfite, such as a mixed aqueous solution of sodium bisulfite and anhydrous sodium bisulfite.

[0057] 2. Biomarkers and their combinations

[0058] This invention, through systematic bioinformatics analysis and clinical sample verification, screens out five key molecular markers and combinations thereof, which can effectively distinguish healthy individuals, CAEBV, EBV+PTCL, and EBV-PTCL patients at the molecular level. These markers and combinations may include any 1, 2, 3, 4, or 5 genes from FAS, SELP, LY86, GZMB, and CCL7, and in particular, may include combinations of all 5 genes.

[0059] As those skilled in the art know, DNA methylation is one of the epigenetic modifications of genes and plays an important role in biological development and gene expression regulation. When methylation is located in the promoter region of a gene, it usually inhibits gene transcription, while demethylation induces gene reactivation and expression. Therefore, the level of DNA methylation (e.g., promoter region methylation level) can also reflect the activation and expression level of genes. In some embodiments, the expression level (e.g., mRNA level, protein level) of any one or more genes (e.g., genes 1, 2, 3, 4, and 5) from a subject sample can be measured. In some embodiments, the methylation level of any one or more genes (e.g., genes 1, 2, 3, 4, and 5) from a subject sample can be measured. In some embodiments, the expression level (e.g., mRNA level, protein level) and methylation level of any one or more genes (e.g., genes 1, 2, 3, 4, and 5) from this biomarker combination in a subject sample can be measured. For example, in some implementations, the expression levels of the FAS and SELP genes, as well as the methylation levels of the GZMB, LY86, and CCL7 genes, can be measured.

[0060] In some embodiments of the present invention, it has been found that synergistic changes in different signaling pathways of five genes—FAS, SELP, LY86, GZMB, and CCL7—can reflect differences in immune activation status in EBV infection-related diseases (e.g., PTCL). Therefore, these biomarkers and their combinations can advantageously assess the risk of EBV infection, CAEBV, and / or PTCL, and / or facilitate differential diagnosis of healthy individuals, CAEBV, EBV+PTCL, and EBV-PTCL patients. For example, the FAS, GZMB, and LY86 genes play an immune recognition role upstream and downstream of the same allograft rejection pathway, while the SELP and CCL7 genes exert an immune amplification effect upstream and downstream of the interferon gamma response pathway. Compared to healthy individuals, the expression level of the FAS gene in the DNA of PBMC cells of CAEBV and EBV+PTCL patients gradually increases, while GZMB and LY86 in the blood cfDNA of these patients are at a low methylation level, and the methylation rate gradually decreases. The FAS expression level and GZMB and LY86 methylation levels in EBV-PTCL are between those of healthy individuals and CAEBV patients. This indicates that the immune activation state gradually increases in CAEBV and EBV+PTCL patients. High FAS expression promotes apoptosis of infected cells, and the low methylation level of GZMB and LY86 promotes the activation of cytotoxic T cells and the release of inflammatory factors, thereby further improving the clearance efficiency of EBV-infected cells. In contrast, EBV induction is lacking in EBV-PTCL patients, so only tumor-associated antigen induction occurs, and the gene expression and low methylation level are weaker than in positive patients. On the other hand, compared with the SELP gene expression level in healthy individuals, CAEBV showed low expression, EBV+PTCL showed comparable expression levels, while EBV-PTCL showed high expression. This indicates that EBV has an inhibitory effect on this signaling pathway, weakening the migration function of immune cells by reducing SELP expression. In addition, the CCL7 gene in the cfDNA of CAEBV, EBV+PTCL, and EBV-PTCL patients showed low methylation levels compared with healthy individuals, and the methylation rate gradually decreased. This indicates that cytokine-related pathways are continuously activated, but compared with uninfected EBV-PTCL patients, the pathways in the infected group are inhibited by EBV, resulting in a relatively lower level of activation.

[0061] Therefore, in some embodiments, the data provided by the present invention indicate that the biomarker combination exhibits a synergistic effect. In some embodiments, based on the findings of the present invention, the biomarker combination of the present invention possesses an enhanced synergistic discriminative effect relative to other biomarkers (or combinations thereof) that do not include genes in the combination, such as enhanced specificity and / or sensitivity. In some embodiments, the overall combination of the five genes of the biomarker of the present invention advantageously possesses an enhanced synergistic discriminative effect relative to a combination of partial biomarkers in the combination (e.g., where genes 1, 2, 3, and 4 are located), such as enhanced specificity and / or sensitivity.

[0062] 3. Methods and applications of using biomarker combinations

[0063] In this article, "differentiating between CAEBV, EBV+PTCL, and EBV-PTCL" is used in the broadest sense, which may include differential diagnosis of healthy individuals, patients with CAEBV, EBV+PTCL, and EBV-PTCL, assessment of EBV infection in subjects, risk of having CAEBV and / or PTCL, early warning of CAEBV malignant transformation, dynamic monitoring of disease progression, evaluation of treatment efficacy, patient prognostic stratification, and other related uses described in this article.

[0064] In some implementations, the biomarker combination of the present invention can be used in one or more of the following scenarios: early screening: for patients with EBV infection-related symptoms, early screening of CAEBV and PTCL can be achieved by detecting the biomarker combination; differential diagnosis: for cases where pathological biopsy is difficult to clearly classify, it provides molecular-level auxiliary diagnostic basis; efficacy monitoring: dynamically monitoring changes in biomarker levels during treatment to assess treatment effectiveness; prognostic assessment: assessing the risk of disease progression based on biomarker levels to guide clinical decision-making.

[0065] In some implementations, the present invention determines the optimal thresholds for each biomarker based on the analysis of clinical sample testing data, combined with ROC curve analysis and machine learning model optimization. In some implementations, the judgment criteria can be set by considering the following factors: using multiple indicators for joint judgment to improve diagnostic accuracy and avoid the limitations of a single indicator; determining the thresholds for each indicator based on the 95% reference range of healthy controls to ensure specificity; considering the pathophysiological differences of diseases in the judgment process to reflect the molecular characteristics of diseases; and setting up a repeat testing mechanism to handle boundary samples and improve the reliability of the judgment.

[0066] In some implementation schemes, CAEBV is defined as follows: F(FAS) ≥ 1, F(SELP) < 1, R(CCL7) < 1, and either GZMB or LY86 is hypomethylated (R < 1). CAEBV patients exhibit upregulated FAS gene expression (F ≥ 1), reflecting enhanced immune activation and apoptosis; low SELP gene expression (F < 1), reflecting the inhibitory effect of EBV infection on leukocyte migration; hypomethylation of the CCL7 gene (R < 1), reflecting activation of the chemokine pathway; and hypomethylation of at least one GZMB or LY86 gene (R < 1), reflecting activation of the cytotoxic immune response.

[0067] In some implementation schemes, EBV+PTCL is defined as follows: F(FAS) ≥ 3, F(SELP) ≥ 1, and R(GZMB) < 1, R(LY86) < 1, and R(CCL7) < 3. EBV+PTCL patients exhibit high FAS gene expression (F ≥ 3), reflecting stronger immune activation and apoptotic signals in the tumor microenvironment; restored or upregulated SELP gene expression (F ≥ 1), contrasting with the low expression in CAEBV; hypomethylation of both GZMB and LY86 genes (R < 1), reflecting a sustained cytotoxic immune response; and relatively low CCL7 gene methylation levels (R < 3), but higher than in CAEBV patients. If the above criteria are not met, repeated sampling or testing at other time points can be performed to exclude the influence of sample quality or disease progression stage.

[0068] In some implementation schemes, if F(FAS) ≥ 1, F(SELP) ≥ 3, and R(GZMB) < 1, R(LY86) < 3, and R(CCL7) < 1, the patient is classified as EBV-PTCL. EBV-PTCL patients exhibit upregulated FAS gene expression (F ≥ 1), but to a lesser degree than EBV+PTCL; high SELP gene expression (F ≥ 3) is a characteristic feature of EBV-PTCL, contrasting with the suppression of SELP expression by EBV infection; GZMB gene hypomethylation (R < 1) reflects a cytotoxic immune response; LY86 gene methylation levels are relatively low (R < 3); and CCL7 gene hypomethylation (R < 1) reflects chemokine pathway activation. If the above conditions are not met, the patient is classified as a healthy control, or further testing is recommended to rule out other diseases.

[0069] In some embodiments, the complete detection and determination process may include the following steps: sample collection and processing, collecting peripheral blood samples, separating PBMCs and plasma; optionally, performing EBV viral load detection, if EBV DNA ≥ 400 copies / mL (positive), determining CAEBV / EBV+ PTCL, if EBV DNA < 400 copies / mL (negative), determining EBV-PTCL / healthy control; biomarker detection, for example, detecting FAS, SELP gene expression and GZMB, LY86, CCL7 gene methylation levels; data analysis, for example, calculating F-values ​​and R-values; determination according to the above determination criteria; result output, for example, generating a test report, including the test values ​​of each indicator, determination results, and clinical recommendations. In some embodiments, the present invention may only include the steps of detecting and determining the combination of biomarkers from the subject sample, and may not include the EBV virus detection step.

[0070] Therefore, in some embodiments, the present invention provides a method comprising measuring the expression level and / or methylation level of a combination of biomarkers of the present invention, said method being usable for any one or more of the above purposes. In some embodiments, the present invention provides the use of reagents for detecting the expression level and / or methylation level of a combination of biomarkers of the present invention in the preparation of compositions or kits, said compositions or kits being usable for any one or more of the above purposes. In some embodiments, the present invention provides a composition or kit comprising reagents for detecting the expression level and / or methylation level of a combination of biomarkers of the present invention, said composition or kit being usable for any one or more of the above purposes. In some embodiments, the present invention provides a computer system comprising a processor and a storage device storing computer-executable code, wherein when said computer-executable code is executed at said processor, it is configured to: obtain the expression level and / or methylation level of a combination of biomarkers of the present invention, and provide output results for any one or more of the above purposes based on the calculated biomarker levels. In some embodiments, the present invention provides a computer-readable medium having instructions stored thereon that, when executed by a processor, cause the processor to execute computer-executable code, wherein when the computer-executable code is executed at the processor, it is configured to: obtain expression levels and / or methylation levels of the biomarker combination of the present invention, and provide output results for any one or more of the above purposes based on the calculation results of the biomarker levels.

[0071] 4. Detection of biomarkers

[0072] In some embodiments, gene expression detection can be performed using PCR-based methods, such as TaqMan probe-based qPCR. In some embodiments, the detection reagent may include: primer pairs specifically amplifying the target gene or fragment thereof (e.g., the relevant gene or fragment described herein), TaqMan fluorescent probes, internal reference gene (e.g., GAPDH) primers and probes, and qPCR reaction premix. In some embodiments, primers and probes may be designed to span exon-exon junctions to avoid interference from genomic DNA contamination.

[0073] In some implementations, methylation level detection can be performed using methylation-specific quantitative PCR (MS-qPCR). In some implementations, the detection reagents include: a conversion reagent (e.g., bisulfite), methylation-specific primers (M primers), non-methylation-specific primers (U primers), SYBR Green fluorescent dye, and qPCR premix. In some implementations, the M primers are designed to target the methylated sequence after bisulfite conversion (preserving CG), and the U primers are designed to target the non-methylated sequence (CG converted to TG).

[0074] In some embodiments, detection using the biomarker combination of the present invention may include one or more of the following steps: Sample collection: collecting peripheral blood samples from subjects, isolating PBMCs for RNA extraction and cfDNA extraction; RNA extraction and reverse transcription: extracting total RNA from PBMCs and reverse transcribing it into cDNA for gene expression detection; cfDNA extraction and transformation: extracting cfDNA from plasma, transforming it, and using it for gene methylation detection; qPCR detection: performing qPCR amplification using specific primers and probes to obtain the Ct value of each gene; Data analysis: calculating the F value (relative expression level) and R value (relative methylation ratio) and analyzing the results to provide a judgment.

[0075] In some embodiments, specific primers and TaqMan probes can be designed based on the mRNA sequence of the biomarker gene according to the present invention. Using cDNA from a subject sample as a template, PCR amplification is performed to obtain the target fragment of the gene, and the expression level is determined by PCR. In some embodiments, methylation-specific PCR primers can be designed based on the promoter region sequence of the biomarker gene according to the present invention. For example, methylation-specific primers (M) and non-methylation-specific primers (U) can be designed for each gene. Samples (e.g., cfDNA) from subjects are collected, treated with a transformation reagent (e.g., bisulfite), and qPCR is performed. The Ct value of each reaction well is recorded, and the methylation ratio (PMR) and relative methylation ratio (R) are calculated.

[0076] 5. Composition or kit

[0077] In some embodiments, the compositions or kits of the present invention may contain one or more of the following components: a primer-probe mixture for gene (e.g., FAS gene, SELP gene) detection, a primer mixture for gene (e.g., GZMB gene, LY86 gene, CCL7 gene) methylation detection, a qPCR premix, a SYBR Green premix, a bisulfite conversion reagent, and nuclease-free water. In some embodiments, the kit may include control samples, such as: a positive control, which may contain standards for each target gene, used to verify the effectiveness of the PCR reaction system; a negative control, which may be a template-free control (NTC), used to detect the presence of contamination; an internal control, such as the GAPDH gene, used to standardize and monitor RNA quality; and a methylation control, such as fully methylated and fully demethylated DNA standards, used to verify bisulfite conversion efficiency. In some embodiments, the kits of the present invention may contain one or more of the following reagents: a detection reagent for detecting the mRNA expression levels of the FAS gene and SELP gene, and / or a detection reagent for detecting the methylation levels of the CpG islands in the upstream promoters of the GZMB gene, LY86 gene, and CCL7 gene cfDNA. In some embodiments, preferably, the detection reagent includes primers and probes for qPCR and primers and probes for MS-qPCR. In some embodiments, the detection reagent may further include: 1) a conversion reagent for detecting methylation, such as bisulfite; 2) PCR reagents, such as polymerase; and / or 3) nucleic acid extraction reagents.

[0078] 6. Computer systems and readable media

[0079] In some embodiments, the present invention includes a computer system, computer-executable code, and computer-readable medium for implementing the biomarker data analysis and disease determination of the present invention.

[0080] In some implementation schemes, the computer system can be configured as a standalone workstation, a network server, a cloud computing platform, etc.

[0081] In some embodiments, the present invention provides a computer device including a processor and a memory having instructions stored thereon that, when executed by the processor, cause the processor to perform the steps of the method described herein, including, for example, obtaining a detection result and performing a determination step.

[0082] In some embodiments, the present invention provides a computer-readable storage medium storing instructions that, when executed by a processor, cause the processor to perform the steps of the method described herein, including, for example, obtaining a detection result and performing a determination step.

[0083] In some embodiments, one or more steps of the algorithm described herein may be performed using a computer program. In some embodiments, the invention includes steps executed by a computer program. In some embodiments, the invention includes a computer-readable storage medium having executable instructions stored thereon that, when executed by one or more processors, cause the one or more processors to perform one or more steps of the method of the invention.

[0084] In some implementation schemes, the functional modules of the computer-executable code may include one or more of the following modules: a data import module: importing raw Ct value data from the qPCR instrument export file; a data quality control module: performing quality control checks on the imported data, including inter-well Ct value difference analysis, control verification, outlier detection, etc.; a calculation and analysis module: executing core calculation algorithms, including F-value calculation, PMR value calculation, R-value calculation, etc.; a disease determination module: automatically determining disease subtyping based on determination criteria, outputting the determination results and the determination basis for each indicator; a report generation module: generating standardized test reports; and a data management module: storing, querying, statistically analyzing, and exporting test data, complying with medical data security and privacy protection requirements, etc.

[0085] In some implementations, the computer-readable medium can be a tangible computer-readable medium, including, for example, a solid-state drive, a hard disk drive, a USB flash drive, a flash memory card, an optical disk, a read-only memory, an erasable programmable read-only memory, etc.; or an intangible computer-readable medium, including, for example, a network-transmitted digital signal, a cloud storage remote storage medium, etc. In some implementations, the computer-readable medium can be configured as locally installed software, a web application, a mobile application, etc.

[0086] 7. Beneficial effects

[0087] This invention provides a non-invasive or minimally invasive, precise diagnostic approach based on molecular markers, which has significant clinical needs and market value, and can further optimize early diagnosis, risk stratification, and clinical intervention strategies. Compared with traditional methods such as clinical sign observation, pathological biopsy, and in situ hybridization, this invention has higher disease specificity and sensitivity, and can provide molecular evidence for early clinical treatment and drug intervention.

[0088] This invention has fully verified the unique value of a combination of five key molecular markers in identifying CAEBV, EBV+PTCL, and EBV-PTCL. This combination preferably includes: two gene expression level markers, the FAS gene and the SELP gene, and three gene methylation level markers, the GZMB gene, the LY86 gene, and the CCL7 gene.

[0089] The beneficial effects of the present invention preferably include one or more of the following aspects:

[0090] 1. High specificity and sensitivity: By jointly detecting two different types of molecular markers, namely expression level and methylation level, a multi-dimensional discrimination system was constructed, which significantly improved the accuracy and reliability of differential diagnosis.

[0091] 2. Simple and rapid operation: qPCR technology, due to its high sensitivity, accurate quantification, and ease of operation, has become the gold standard for gene expression analysis. This invention designs specific primers and probes to establish a qPCR detection system targeting key regulatory genes in the aforementioned diseases, providing a non-invasive or minimally invasive precision diagnostic solution based on molecular markers. Based on mature qPCR and MS-qPCR platforms, the detection process is standardized, can be performed in routine molecular laboratories, and results can be obtained within hours, facilitating clinical application.

[0092] 3. Wide range of sample sources: It can be used for relatively easy-to-obtain samples such as peripheral blood, which reduces the invasiveness of the test and facilitates dynamic monitoring and risk assessment of patients.

[0093] 4. Clear clinical application prospects: It provides important molecular evidence for early warning of malignant transformation of CAEBV, precise classification of lymphoma, and the formulation of individualized treatment strategies.

[0094] The present invention will be specifically described below with reference to embodiments, but the present invention is not limited thereto.

[0095] Example 1: Detection of relative expression levels of FAS and SELP genes based on qPCR method

[0096] (1) Primer and probe sequences

[0097]

[0098]

[0099] (2) Method for constructing standard quality plasmid template sequences containing the target gene for quality control: clone the target fragment of the FAS or SELP gene into the pUC57 vector;

[0100] The FAS insert sequence (amplified region) is as follows (SEQ ID NO:10):

[0101]

[0102] The SELP insert sequence (amplified region) is as follows (SEQ ID NO:11):

[0103]

[0104] (3) Detection method and reaction system design

[0105] a. Sample Collection and Processing: Collect 2-3 mL of peripheral blood sample from the subject in an EDTA anticoagulant tube, then extract PBMC cells using Ficoll Paque PLUS (Cytiva, USA) as an optional reagent, and process according to the reagent instructions; subsequently, extract total RNA from PBMCs using TRIzol (Invitrogen, USA) as an optional reagent, and process according to the reagent instructions; reverse transcribe the extracted RNA into cDNA using the PrimeScript RT reagent Kit (Takara Bio, Japan) as an optional kit, and process according to the kit instructions;

[0106] b. Prepare the qPCR reaction solution according to the following system. When performing quality control reactions, use plasmid standards as templates; use sample cDNA as templates for clinical testing. Cycling conditions: 95℃ for 10 minutes (pre-denaturation); 95℃ for 15 seconds, 60℃ for 1 minute, 40 cycles.

[0107]

[0108] (4) Data Analysis

[0109] Taking the FAS gene as an example, the relative expression level of FAS is calculated using the ΔΔCt method:

[0110] a. Standardize to GAPDH, ΔCt = Ct_FAS - Ct_GAPDH;

[0111] b. Compared with healthy controls, ΔΔCt = ΔCt_patient - ΔCt_control;

[0112] c. Relative expression level F = 2 -ΔΔCt ;

[0113] d. Judgment criteria: F≥3 indicates high FAS expression; 1≤F≤3 indicates comparable expression levels; F<1 indicates low expression.

[0114] (5) Primer-probe pair ratio and experimental results

[0115] Two additional sets of primer and probe sequences were designed for the amplification regions of the FAS and SELP genes, respectively, as listed below:

[0116] FAS-F2: CGGAGTTGGGGAAGCTCTTT (SEQ ID NO:12)

[0117] FAS-R2: TTGGTGTTGCTGGTGAGTGT (SEQ ID NO:13)

[0118] FAS-P2: CAGACTGCGTGCCCTGCCAA (SEQ ID NO:14)

[0119] FAS-F3: GAGCTCGTCTCTGATCTCGC (SEQ ID NO:15)

[0120] FAS-R3: CTCCTTCCCTCTCTGGCAGG (SEQ ID NO:16)

[0121] FAS-P3: TTCTCCCGCGGGTTGGTGGA (SEQ ID NO:17)

[0122] SELP-F2: ACAAACGCTGCATTTGACCC (SEQ ID NO:18)

[0123] SELP-R2: GAAGGTCCACGGTGACATGT (SEQ ID NO:19)

[0124] SELP-P2: GACCAGCCTGTTGGACCCGC (SEQ ID NO:20)

[0125] SELP-F3: GGACAGACTCCCCACCATG (SEQ ID NO:21)

[0126] SELP-R3: TCTTGGCATGCGTTTGTGC (SEQ ID NO:22)

[0127] SELP-P3: CCAGAGCAGGGCAGCCTGGA (SEQ ID NO:23)

[0128] The qPCR primer and probe amplification test was performed according to the experimental steps described in step (3), and the results are as follows: Figure 3 As shown.

[0129] The amplification results showed that the amplification efficiency of the above primers and probes was not significant. Therefore, based on the experimental results, the primer and probe system listed in step (1) was selected as the final detection scheme.

[0130] Example 2: Detection of methylation levels of GZMB, LY86, and CCL7 genes based on methylation qPCR method

[0131] (1) Primer sequence

[0132]

[0133]

[0134] (2) Method for constructing standard quality plasmid template sequences containing target genes for quality control: The fragments of fully methylated sequence templates (original sequences) and fully unmethylated sequence templates (all C bases of the original sequence are changed to T bases) of the amplified regions of GZMB, LY86 and CCL7 genes are cloned into the pUC57 vector respectively;

[0135] The GZMB fully methylated insert sequence (amplified region) is as follows (SEQ ID NO:36):

[0136] The GZMB fully unmethylated insert sequence (amplified region) is as follows (SEQ ID NO:37):

[0137] The fully methylated insert sequence (amplified region) of LY86 is as follows (SEQ ID NO:38):

[0138] The fully unmethylated insert sequence (amplified region) of LY86 is as follows (SEQ ID NO:39):

[0139] The fully methylated CCL7 insert sequence (amplified region) is as follows (SEQ ID NO:40):

[0140] The CCL7 fully unmethylated insert sequence (amplified region) is as follows (SEQ ID NO:41):

[0141] (3) Detection method and reaction system design

[0142] a. cfDNA extraction: Collect 9.5 mL of peripheral blood sample from the subject using a cfDNA preservation tube (Cell-Free DNA BCT tube, Streck, USA). Perform plasma separation according to the preservation tube instructions and extract cfDNA using a commercially available cfDNA extraction kit (Circulating cfDNA Kits, QIAGEN, Germany).

[0143] b. Bisulfite treatment: 10-100 ng of cfDNA was treated using a bisulfite conversion kit (such as EZ DNA Methylation-GoldKits, Zymo Research, USA); the treated cfDNA was dissolved in an appropriate amount of elution buffer and stored at -20℃ for later use.

[0144] c. Prepare the dye-based qPCR reaction solution according to the following system. When performing quality control reactions, use plasmid standards as templates; use cfDNA as templates for clinical testing. Each sample has wells for methylation (M), unmethylation (U), fully methylated control (M control), and fully unmethylated (U control). Cycling conditions: 95℃ for 10 minutes (pre-denaturation); 95℃ for 15 seconds, 60℃ for 1 minute, 40 cycles.

[0145]

[0146] (4) Data Analysis

[0147] a. Record the Ct value for each reaction well and calculate the methylation ratio (Percentage of Methylated Reference, PMR).

[0148] b. ΔCt_M = Ct(M) - Ct(M vs.)

[0149] c. ΔCt_U = Ct(U) - Ct(U control)

[0150] d. PMR (%) = [2 -ΔCt_M / (2 -ΔCt_M +2 -ΔCt_U )] × 100%

[0151] e. Relative methylation ratio R = PMR (patient) / PMR (healthy)

[0152] f. Judgment criteria: R≥3 can be considered relatively high methylation; 1≤R<3 can be considered equivalent methylation levels; R<1 can be considered relatively low methylation.

[0153] Example 3: qPCR sensitivity test

[0154] (1) Following the procedures of Examples 1 and 2 above, using the synthetic plasmid standards of each of the five genes as templates, and performing serial dilutions (concentration range 10) 2 ~10 5 (copies / mL), and then qPCR amplification was performed separately;

[0155] (2) Amplification results are as follows Figure 11 As shown, taking FAS and GZMB as examples, it demonstrates that the qPCR reaction system for testing genes can achieve a minimum of 10 2 With a sensitivity of copies / mL, the linear range can reach four orders of magnitude.

[0156] Example 4: Judgment Process Based on Data Analysis Model

[0157] The clinical samples used in this article were peripheral blood samples collected from hospitalized patients. The patients had completed various clinical pathological tests and provided diagnostic results before hospitalization, and the collection and use of samples complied with the relevant regulations.

[0158] Specifically, the diagnostic criteria for CAEBV patients follow the diagnostic and classification criteria recommended by the WHO (World Health Organization), namely, meeting the following four diagnostic criteria: ① persistent or recurrent IM (infectious mononucleosis)-like symptoms for >3 months; ② elevated EBV DNA in peripheral blood or lesion tissue, generally considered to be ≥10^6 bp in peripheral blood. 2.5③ Evidence of EBV infection in tissue or peripheral blood T / NK cells, such as positive EBER; ④ Exclusion of other possible diseases. Patients who do not meet the above diagnostic criteria are excluded. Patients with PTCL should be diagnosed and classified according to the WHO and Chinese Society of Clinical Oncology guidelines for lymphoma: ① Clinical manifestations: Commonly seen in middle-aged and elderly individuals, often presenting as superficial lymphadenopathy, with half of cases accompanied by B symptoms. Extranodal involvement commonly includes the skin and subcutaneous tissue, liver, spleen, digestive tract, thyroid gland, and bone marrow; ② Pathological diagnosis: Histopathological findings show a mixed background of abundant high endothelial small vessel proliferation, epithelioid histiocytic proliferation, and inflammatory cell infiltration; tumor cells exhibit diverse and varied morphology, consisting of small, medium, or large cells, with the majority being medium to large cells. The cytoplasm is pale, the nuclei are pleomorphic and irregular, with abundant or vesicular chromatin, prominent nucleoli, and frequent mitotic figures; common immunophenotypes include CD3 (+), CD4 (+) > CD8 (+), CD5 (+), CD45RO (+), CD7 (-), CD8 (-); Tumor cells often express T-cell-related antigens such as CD3ε and CD2, while losing one or more other mature T-cell antigens (CD5 or CD7), suggesting clonal proliferation of T cells; bone marrow nucleated cell proliferation is often significantly active, mainly lymphocyte proliferation, with adult-onset T-cell leukemia cells often >10%, and can be as high as 80% or more, while granulocytes, erythroblasts, and megakaryocytes are often reduced; ③ Exclude other independently subtypes of T-cell lymphoma. Among patients diagnosed with PTCL, if the immunophenotype is EBV(+), or the pathological tissue is positive for EBERs in situ hybridization, or the peripheral blood EBV load is >200 copies / mL, or the plasma / serum EBV-DNA is positive, then they are classified as EBV-positive PTCL patients; otherwise, they are EBV-negative PTCL patients. Patients who do not meet the above diagnostic criteria are excluded.

[0159] It should be noted that both CAEBV and PTCL are clinically rare diseases. The global annual incidence of CAEBV is estimated to be less than one in a million, while EBV-associated T-cell lymphoma accounts for less than 5% of non-Hodgkin lymphomas and is a rare subtype of PTCL. This extremely low incidence makes it difficult for a single research center to accumulate a large sample cohort within a limited time.

[0160] (1) Experimental design and data acquisition

[0161] a. Study subjects and grouping: Cohort A (CAEBV group): n = 20 cases; Cohort B (EBV+PTCL group): n = 20 cases; Cohort C (EBV-PTCL group): n = 20 cases;

[0162] b. Data Acquisition: Using the aforementioned qPCR method, the relative expression levels (E values) of FAS and SELP genes, and the relative methylation ratios (R values) of the promoters of GZMB, LY86, and CCL7 genes were detected in each sample. Finally, each sample was given a data vector containing 5 feature values: [FAS_F, SELP_F, GZMB_R, LY86_R, CCL7_R].

[0163] (2) Model building

[0164] a. Dataset Split: The total dataset (60 samples) was randomly divided into a training set (70%) (42 samples) for model training and parameter tuning, and a test set (30%) (18 samples) as "unknown" data for final evaluation of the model's generalization ability. Specifically, the 42-sample training cohort included 13 CAEBV patients, 16 EBV+ PTCL patients, and 13 EBV-PTCL patients; the 18-sample test cohort included 7 CAEBV patients, 4 EBV+ PTCL patients, and 7 EBV-PTCL patients.

[0165] b. Feature selection based on the training set: One-way ANOVA is used to select the most valuable feature combinations for tri-class diagnosis, avoiding overfitting;

[0166] c. Model training and selection: Try various machine learning algorithms and find the optimal model through cross-validation: Finally, select the support vector machine, which is suitable for small sample and high-dimensional data, as the analysis model, and use grid search or random search to optimize the model hyperparameters;

[0167] d. Model evaluation based on the test set: The final selected optimal model will be used to make predictions on an independent test set; since it is a three-class classification problem, a "one-to-many" strategy will be adopted for evaluation, that is, the performance of classifying each class will be calculated separately.

[0168] (3) Performance evaluation: The binary ROC curves of each marker and the combined model are plotted as follows, and the AUC value is calculated.

[0169] The specific verification sample results are as follows:

[0170] ① Validation result matrix: Non-CAEBV (11 cases) vs. CAEBV (7 cases):

[0171]

[0172] According to the Mann-Whitney U test, the p-value is 0.0044 < 0.01, indicating that the result is significant.

[0173] ② EBV+ PTCL (4 cases) vs. non-EBV+ PTCL (14 cases), validation result matrix:

[0174]

[0175] According to the Mann-Whitney U test, the p-value = 0.0007 < 0.001, indicating significance.

[0176] ③ EBV-PTCL (7 cases) vs. non-EBV-PTCL (11 cases), validation result matrix:

[0177]

[0178] According to the Mann-Whitney U test, the p-value is 0.0015 < 0.01, indicating that the result is significant.

[0179] The data results show that the model integrating combined biomarker data has significantly better diagnostic efficacy than any single biomarker. (See details...) Figure 13-15 This demonstrates the synergistic effect of biomarker combination diagnosis.

[0180] Example 5: Clinical Sample Testing and Judgment Process

[0181] For details regarding the source of clinical samples and diagnostic criteria, please refer to the description in Example 4. The clinical sample information for this example is summarized below:

[0182]

[0183] (1) Collect peripheral blood samples from patients according to the procedures in Examples 1 and 2 above and perform PBMC separation and extraction of RNA, DNA and cfDNA to obtain nucleic acid samples from the corresponding patients;

[0184] (2) Commercial kits can be used to detect EBV viral load first. The kit can be EBV nucleic acid detection kit (PCR-fluorescent probe method) (Sansure Biotech). If viral load is detected (≥400 copies / mL), it is determined to be CAEBV or EBV+PTCL, and the next round of judgment process is carried out; if it is not detected, it is determined to be a healthy person or EBV-PTCL, and the next round of judgment process is carried out.

[0185] (3) If the virus is detected, the relative expression levels of the two genes and the relative methylation levels of the three genes are tested. If F(FAS)≥1, F(SELP)<1, R(CCL7)<1, and any gene between GZMB and LY86 is at a low methylation level (R<1), it is determined to be CAEBV. If F(FAS)≥3, F(SELP)≥1, and R(GZMB)<1, R(LY86)<1, R(CCL7)<3, it is determined to be EBV+PTCL. If other conditions are not met, the sampling is repeated or other time points are selected for testing.

[0186] (4) If no virus is detected, the relative expression levels of the two genes and the relative methylation levels of the three genes are also tested. If F(FAS)≥1, F(SELP)≥3, and R(GZMB)<1, R(LY86)<3, R(CCL7)<1, then it is determined to be EBV-PTCL; if the condition is not met, it is determined to be a healthy control, or other tests are performed.

[0187] Following the above-mentioned determination process, 6 clinical samples were tested and determined, and the results are shown in the table below (clinical sample test results), which are consistent with the clinical diagnosis.

[0188]

[0189] The literature and data from relevant databases mentioned herein (e.g., gene and / or protein sequence data) are incorporated herein in their entirety by reference. Those skilled in the art can make various combinations and modifications of the embodiments described in detail herein, and such combinations and modifications should be understood to be included within the scope of protection claimed in this application.

Claims

1. Use of a biomarker detection reagent in the preparation of a composition or kit for differentiating chronic active Epstein-Barr virus (CAEBV) infection, Epstein-Barr virus-positive peripheral T-cell lymphoma (EBV+PTCL), and Epstein-Barr virus-negative peripheral T-cell lymphoma (EBV-PTCL), wherein the biomarker comprises any 1, 2, 3, 4, or 5 genes selected from FAS, SELP, LY86, GZMB, and CCL7, preferably a combination of all 5 genes.

2. The use according to claim 1, wherein the detection reagent includes reagents for detecting gene expression and / or methylation levels, such as any one or more of the following detection reagents: reagents for detecting FAS and SELP gene expression and reagents for detecting GZMB, LY86 and CCL7 gene methylation levels.

3. The use according to claim 1 or 2, wherein the detection reagent comprises primers and / or probes for detecting gene expression and / or methylation levels.

4. The use according to claim 3, wherein the primers and / or probes are selected from the group consisting of SEQ ID NOs:1-9 and 24-35.

5. The use according to any one of claims 1-4, wherein the detection is performed by a PCR-based method, such as qPCR.

6. The use according to any one of claims 1-5, wherein the biomarker is derived from a body fluid sample, such as whole blood, serum or plasma.

7. The use according to any one of claims 1-6, wherein, If F(FAS)≥1, F(SELP)<1, R(CCL7)<1, and either GZMB or LY86 has a low methylation level (R<1), then it is determined to be CAEBV. If F(FAS)≥3, F(SELP)≥1, and R(GZMB)<1, R(LY86)<1, R(CCL7)<3, then it is determined to be EBV+PTCL; If F(FAS)≥1, F(SELP)≥3, and R(GZMB)<1, R(LY86)<3, R(CCL7)<1, then it is determined to be EBV-PTCL.

8. A composition or kit for differentiating chronic active Epstein-Barr virus (CAEBV) infection, Epstein-Barr virus-positive peripheral T-cell lymphoma (EBV+PTCL) and Epstein-Barr virus-negative peripheral T-cell lymphoma (EBV-PTCL), wherein the composition or kit comprises a detection reagent for a biomarker as defined in any one of claims 1-7.

9. A computer system comprising a processor and a storage device storing computer-executable code, wherein when the computer-executable code is executed at the processor, it is configured to: obtain a biomarker detection level as defined in any one of claims 1-7, and differentiate between chronic active Epstein-Barr virus (CAEBV) infection, Epstein-Barr virus-positive peripheral T-cell lymphoma (EBV+PTCL), and Epstein-Barr virus-negative peripheral T-cell lymphoma (EBV-PTCL) based on a calculation result of the biomarker detection level.

10. A computer-readable medium having instructions stored thereon that, when executed by a processor, cause the processor to execute the computer-executable code as defined in claim 9.

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