A prognostic model for NK / T-cell lymphoma using ERV as a marker

Abstract

This invention relates to the field of biological diagnostics, and particularly to a prognostic model for NK / T-cell lymphoma using ERV as a biomarker. Through analysis of patient sample data, this application has found a close correlation between NKTCL prognosis and ERV expression levels. The applicant has calculated relevant numerical parameters using methods such as Cox regression and Lasso regression, and constructed a novel prognostic grouping and scoring model capable of significantly distinguishing between high-risk and low-risk patients. The prognostic model provided in this application is the first to be constructed using ERV expression levels, and it quantitatively detects ERV under objective conditions, avoiding the subjectivity of human judgment. Furthermore, this application only requires the detection of ERV expression levels for evaluation, making the method simple.

Description

Technical Field

[0001] This invention relates to the field of biological diagnostics, and in particular to a prognostic model for NK / T-cell lymphoma using ERV as a marker. Background Technology

[0002] Natural killer T-cell lymphoma (NKTCL) is a rare but highly aggressive non-Hodgkin's lymphoma closely associated with Epstein-Barr virus (EBV) infection, primarily occurring in the nasal cavity and upper respiratory tract. While significant progress has been made in NK / T-cell lymphoma research in recent years, highly effective and specific treatments remain lacking, resulting in a poor prognosis. Accurate initial and prognostic assessments are crucial for guiding treatment selection and adjustments; therefore, the development of assessment scoring models plays a vital role in disease control and treatment.

[0003] Endogenous retroviruses (ERVs) are gene sequences that invaded and integrated into the human genome millions of years ago by ancient retroviruses, accounting for approximately 8% of the human genome DNA. Due to mutations, epigenetic modifications, and other reasons, the vast majority of human endogenous retroviruses (HERVs) are inactive or silenced, but some HERVs retain transcriptional activity, and some ERVs play indispensable roles in cell development and cell function. Current research has found that abnormal activation and expression of endogenous retroviruses may affect genome stability and have adverse effects on the body; some autoimmune diseases and tumors are closely related to ERV expression.

[0004] Currently, the staging and prognostic scoring of NK / T-cell lymphoma are based on multiple clinical and biological indicators, such as age, stage, lymph node involvement, clinical subtype, and EBV DNA. These parameters help assess the severity of the patient's condition and predict treatment outcomes. Initial staging often uses the Ann Arbor staging system, and prognostic models commonly selected include the International Prognostic Index (IPI), the NK / T-cell lymphoma prognostic index (PINK, PINK-E), and the nomogram risk index (NRI).

[0005] Current staging and prognostic assessment methods have been applied to some extent in clinical practice, but some shortcomings and deficiencies still exist. For example, the Ann Arbor staging system lacks specificity and does not comprehensively consider all factors, making accurate judgment difficult; similarly, the IPI is not accurate enough in assessing the prognostic grouping of early-stage patients. Summary of the Invention

[0006] In view of this, the present invention constructs a prognostic model by measuring the ERV expression level in tumor tissue.

[0007] To achieve the above-mentioned objectives, the present invention provides the following technical solution:

[0008] In a first aspect, the present invention provides prognostic markers for NK / T-cell lymphoma, including ERV.

[0009] In some specific embodiments of the present invention, the ERV includes one or a combination of at least two of HERVE, HERVEA, HERVFH21, HML5, LTR46, and MER61.

[0010] Secondly, the present invention also provides the application of ERV as a prognostic marker in any of the following:

[0011] (I) Constructing a prognostic risk assessment model for NK / T-cell lymphoma;

[0012] (II) Constructing a model for individualized medication for NK / T-cell lymphoma patients;

[0013] (III) Preparation of a device for assessing the prognostic risk of NK / T cell lymphoma;

[0014] (IV) Construct a system for assessing the prognostic risk of NK / T-cell lymphoma;

[0015] (V) Screening for drugs to prevent, improve, treat and / or adjuvant therapies for NK / T-cell lymphoma;

[0016] (VI) Preparation of drugs for the prevention and / or treatment of NK / T-cell lymphoma;

[0017] (VII) Preparation of reagents and / or kits for the prognostic diagnosis of NK / T cell lymphoma;

[0018] (VIII) Prepare products for assessing immune cell infiltration in NK / T cell lymphoma; and / or

[0019] (IX) Prepare products for evaluating somatic mutations.

[0020] In some specific embodiments of the present invention, the ERV includes one or more of HERVE, HERVEA, HERVFH21, HML5, LTR46, and MER61.

[0021] In some specific embodiments of the present invention, the expression level of the ERV is used to construct a prognostic risk assessment model for the NK / T cell lymphoma.

[0022] Thirdly, the present invention also provides a prognostic risk assessment model for NK / T-cell lymphoma, the input variables of which include the expression levels of the prognostic biomarkers.

[0023] In some specific embodiments of the present invention, the risk score S = HERVE gene expression level × HERVE regression coefficient (-9.59E-05) + HERVEA gene expression level × HERVEA regression coefficient (-0.0005749) + HERVFH21 gene expression level × HERVFH21 regression coefficient (0.0003299) + HML5 gene expression level × HML5 regression coefficient (-0.0005405) + LTR46 gene expression level × LTR46 regression coefficient (-2.22E-05) + MER61 gene expression level × MER61 regression coefficient (-5.74E-05).

[0024] In some specific embodiments of the present invention, the optimal cutoff value is -0.7148771;

[0025] The risk score S ≥ -0.7148771 is classified as high-risk, and the risk score S < -0.7148771 is classified as low-risk.

[0026] Fourthly, the present invention also provides a method for constructing a prognostic risk assessment model for NK / T-cell lymphoma, comprising the following steps:

[0027] Step 1: Obtain the gene expression levels of the prognostic markers in patients with NK / T-cell lymphoma to be evaluated;

[0028] Step 2: Input the gene expression level into the prognostic risk assessment model to obtain the risk score S;

[0029] Risk score S = HERVE gene expression level × HERVE regression coefficient (-9.59E-05) + HERVEA gene expression level × HERVEA regression coefficient (-0.0005749) + HERVFH21 gene expression level × HERVFH21 regression coefficient (0.0003299) + HML5 gene expression level × HML5 regression coefficient (-0.0005405) + LTR46 gene expression level × LTR46 regression coefficient (-2.22E-05) + MER61 gene expression level × MER61 regression coefficient (-5.74E-05).

[0030] Fifthly, the present invention also provides a prognostic assessment method for NK / T-cell lymphoma, comprising the following steps:

[0031] Step 1: Obtain the gene expression levels of the prognostic markers in patients with NK / T-cell lymphoma to be evaluated;

[0032] Step 2: Input the gene expression level into the prognostic risk assessment model to obtain the risk score S;

[0033] Risk score S = HERVE gene expression level × HERVE regression coefficient (-9.59E-05) + HERVEA gene expression level × HERVEA regression coefficient (-0.0005749) + HERVFH21 gene expression level × HERVFH21 regression coefficient (0.0003299) + HML5 gene expression level × HML5 regression coefficient (-0.0005405) + LTR46 gene expression level × LTR46 regression coefficient (-2.22E-05) + MER61 gene expression level × MER61 regression coefficient (-5.74E-05);

[0034] Step 3: Based on the risk score S obtained from the prognostic risk assessment model, patients are divided into different prognostic risk groups using the cutoff value.

[0035] In some specific embodiments of the present invention, the cutoff value is -0.7148771;

[0036] The risk score S ≥ -0.7148771 is classified as high-risk, and the risk score S < -0.7148771 is classified as low-risk.

[0037] Sixthly, the present invention also provides the application of the prognostic risk assessment model or the prognostic risk assessment model obtained by the construction method in any of the following:

[0038] (i) To prepare a device for assessing the prognostic risk of NK / T cell lymphoma;

[0039] (ii) Construct a system for assessing the prognostic risk of NK / T-cell lymphoma;

[0040] (iii) Screening for drugs to prevent and / or treat NK / T-cell lymphoma;

[0041] (iv) To prepare drugs for the prevention and / or treatment of NK / T-cell lymphoma;

[0042] (v) Preparation of reagents and / or kits for the prognostic diagnosis of NK / T cell lymphoma;

[0043] (vi) Prepare products for assessing the infiltration of immune cells in NK / T cell lymphoma; and / or

[0044] (vii) Prepare products for evaluating somatic cell mutations.

[0045] In a seventh aspect, the present invention also provides a detection product for detecting the prognostic biomarker, comprising a biomolecule that specifically hybridizes with the prognostic biomarker or its expression product, or a detection reagent targeting the prognostic biomarker.

[0046] In some specific embodiments of the present invention, the biomolecule includes one or more selected from primers, probes, and antibodies; and / or

[0047] The testing products include one or more of chips, reagents, or kits.

[0048] Eighthly, the present invention also provides an apparatus for assessing the prognostic risk of NK / T-cell lymphoma, the apparatus having the prognostic risk assessment model described above or the prognostic risk assessment model obtained by the construction method therein.

[0049] In some specific embodiments of the present invention, the device includes a detection unit and an analysis unit; the detection unit detects the expression level of the prognostic biomarker as described in claim 1 or 2 in the sample; the analysis unit uses the expression level of the prognostic biomarker as an input variable, inputs it into the prognostic risk assessment model or the prognostic risk assessment model obtained by the construction method, and analyzes and assesses the prognostic risk of NK / T cell lymphoma.

[0050] In a ninth aspect, the present invention also provides a computer-readable storage medium comprising a stored program, wherein, when the program is executed, the device on which the storage medium is located executes the method for constructing the prognostic risk assessment model.

[0051] In a tenth aspect, the present invention also provides a processor for running a program, wherein the program executes the method for constructing the prognostic risk assessment model during runtime.

[0052] This application, through analysis of patient sample data, discovered a close correlation between the prognosis of NKTCL and ERV expression levels. The applicant calculated relevant numerical parameters using methods such as Cox regression and Lasso regression, and constructed a novel prognostic grouping and scoring model capable of significantly distinguishing between high-risk and low-risk patients. The prognostic model provided in this application is the first to be constructed using ERV expression levels, and it quantitatively detects ERV under objective conditions, avoiding the subjectivity of human judgment. Furthermore, this application only requires the detection of ERV expression levels for assessment, making the method simple. Attached Figure Description

[0053] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below.

[0054] Figure 1 Figures D, E, F, and G show the prognostic scoring model construction process and its performance on the training and validation sets. A shows the Lasso regression coefficient path diagram based on PFS for the training cohort; B shows the cross-validation curve of the Lasso regression analysis based on PFS for the training cohort; C shows the ROC curve (and the optimal cutoff value, denoted as "X") plotted based on the risk score S obtained from the risk scoring model for the training and validation cohorts, based on PFS; Figures D, E, F, and G show the PFS and OS survival curves plotted based on the risk scores and optimal cutoff values ​​("X") for the training and validation cohorts obtained from the risk scoring model: D shows the PFS survival curve for the training cohort; E shows the PFS survival curve for the validation cohort; F shows the OS survival curve for the training cohort; and G shows the OS survival curve for the validation cohort.

[0055] Figure 2 The results show the effects of the prognostic scoring model and Ann Arbor staging grouping; where A shows the PFS survival curves of all patients based on our constructed model grouping; B shows the PFS survival curves of all patients based on Ann Arbor staging grouping; C shows the OS survival curves of all patients based on the model grouping constructed in this application; and D shows the OS survival curves of all patients based on Ann Arbor staging grouping. Detailed Implementation

[0056] This invention discloses a prognostic model for NK / T-cell lymphoma using ERV as a marker. Those skilled in the art can refer to the content of this document and appropriately modify the process parameters to achieve the model. It is particularly important to note that all similar substitutions and modifications are obvious to those skilled in the art and are considered to be included in this invention. The methods and applications of this invention have been described through preferred embodiments. Those skilled in the art can clearly modify or appropriately change and combine the methods and applications described herein without departing from the content, spirit, and scope of this invention to realize and apply the technology of this invention.

[0057] The purpose of this invention is to provide a prognostic assessment method for NK / T-cell lymphoma. For practical application in specific embodiments, the method includes the following steps:

[0058] 1: Obtain gene expression data from patients with NK / T-cell lymphoma to be evaluated;

[0059] 2. Input the patient's gene expression data into the prognostic risk scoring model and calculate the risk score. Risk score S = HERVE gene expression level * HERVE regression coefficient (-9.59E-05) + HERVEA gene expression level * HERVEA regression coefficient (-0.0005749) + HERVFH21 gene expression level * HERVFH21 regression coefficient (0.0003299) + HML5 gene expression level * HML5 regression coefficient (-0.0005405) + LTR46 gene expression level * LTR46 regression coefficient (-2.22E-05) + MER61 gene expression level * MER61 regression coefficient (-5.74E-05);

[0060] 3: Based on the risk score output by the prognostic risk scoring model, and according to the cutoff value of -0.7148771, patients are divided into different prognostic risk groups.

[0061] The prognostic model provided by this invention is the first to be constructed using ERV expression levels, and it quantitatively detects ERV under objective conditions, avoiding the subjectivity of human judgment. In addition, we only need to detect ERV expression levels for evaluation, making the method simple.

[0062] The raw materials and reagents used in the NK / T cell lymphoma prognostic model with ERV as a marker provided by this invention are all commercially available.

[0063] The present invention will be further illustrated below with reference to the embodiments:

[0064] Example 1

[0065] The applicant collected tumor samples from newly diagnosed NK / T-cell lymphoma patients, cryopreserved them using RNAlater or dry tubes, and then performed RNA-seq transcriptome sequencing on the tumor samples. Library construction was performed using the TruSeq RNA Sample Prep Kit, and paired-end sequencing was performed using the Illumina Hiseq X10 platform, following the manufacturer's (Illumina) instructions. The obtained raw RNA-seq reads were aligned to the human reference genome hg19, and all RNA-seq data were normalized using the R package DESeq2.

[0066] The applicant included 91 patients with NK / T-cell lymphoma as the training set. Based on the expression levels of 60 ERV genes and progression-free survival (PFS) in this group of patients, univariate Cox regression analysis was performed using the R package `survival`. A p-value < 0.05 was considered statistically significant. Genes with p < 0.05 were then subjected to Lasso regression analysis using the R package `glmnet`, yielding six ERV genes and their corresponding Lasso regression coefficients: HERVE, HERVEA, HERVFH21, HML5, LTR46, and MER61, with corresponding regression coefficients of -9.59E-05, -0.0005749, 0.0003299, -0.0005405, -2.22E-05, and -5.74E-05, respectively.

[0067] Table 1

[0068]

[0069]

[0070]

[0071]

[0072] In a training set of 91 patients, a prognostic risk scoring model was constructed based on the gene expression levels and lasso regression coefficients of the six ERV genes. The risk score S = Σ(gene expression level × corresponding regression coefficient). Patients were divided into different prognostic risk groups based on the output risk score. In the model of this application, the risk score S = HERVE gene expression level * HERVE regression coefficient (-9.59E-05) + HERVEA gene expression level * HERVEA regression coefficient (-0.0005749) + HERVFH21 gene expression level * HERVFH21 regression coefficient (0.0003299) + HML5 gene expression level * HML5 regression coefficient (-0.0005405) + LTR46 gene expression level * LTR46 regression coefficient (-2.22E-05) + MER61 gene expression level * MER61 regression coefficient (-5.74E-05). Based on the patient's progression-free survival (PFS) and the risk score S obtained from the 91 patients, a Receiver Operating Characteristic curve (ROC curve) was plotted using the R package survivalROC. The optimal cutoff value was calculated to be -0.7148771. Based on the optimal cutoff value, the patients were divided into two groups: the high-risk group had a risk score S ≥ -0.7148771, and the low-risk group had a risk score S < -0.7148771.

[0073] Prognosis was analyzed based on progression-free survival (PFS) and overall survival (OS) of patients, and survival curves were plotted. It was found that there were significant differences in prognosis between the two groups in both PFS and OS, and the high-risk group showed poor prognosis.

[0074] Example 2

[0075] The applicant included another 93 patients with newly diagnosed NK / T-cell lymphoma as a validation set. The expression levels of the six ERV genes of the patients were input into the constructed prognostic risk scoring model to calculate the risk score S. The risk score S = HERVE gene expression level * HERVE regression coefficient (-9.59E-05) + HERVEA gene expression level * HERVEA regression coefficient (-0.0005749) + HERVFH21 gene expression level * HERVFH21 regression coefficient (0.0003299) + HML5 gene expression level * HML5 regression coefficient (-0.0005405) + LTR46 gene expression level * LTR46 regression coefficient (-2.22E-05) + MER61 gene expression level * MER61 regression coefficient (-5.74E-05).

[0076] Table 2

[0077]

[0078]

[0079]

[0080]

[0081] Based on the optimal cutoff value of -0.7148771, patients were divided into different prognostic risk groups: a risk score S ≥ -0.7148771 was assigned to the high-risk group, and a risk score S < -0.7148771 was assigned to the low-risk group. Results showed that, in the validation set, the high-risk group also exhibited significantly poor prognoses in both PFS and OS, demonstrating the stability of the model.

[0082] Our combined analysis of all patients in the validation and training sets revealed that our proposed grouping method significantly outperforms the grouping method based on the Ann Arbor staging system in assessing patient prognosis. According to our grouping, the p-values ​​for both progression-free survival and overall survival were less than 0.0001, indicating significant patient stratification. In contrast, the Ann Arbor staging system did not show significant stratification for progression-free survival, and its stratification effect for overall survival was also inferior to our proposed model, demonstrating the clear advantage of our proposed model.

[0083] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. Prognostic markers for NK / T-cell lymphoma, characterized in that, Including ERV; Preferably, the ERV includes one or a combination of at least two of HERVE, HERVEA, HERVFH21, HML5, LTR46, and MER61.

2. Application of ERV as a prognostic biomarker in any of the following: (I) Constructing a prognostic risk assessment model for NK / T-cell lymphoma; (II) Constructing a model for individualized medication for NK / T-cell lymphoma patients; (III) Preparation of a device for assessing the prognostic risk of NK / T cell lymphoma; (IV) Construct a system for assessing the prognostic risk of NK / T-cell lymphoma; (V) Screening for drugs to prevent, improve, treat and / or adjuvant therapies for NK / T-cell lymphoma; (VI) Preparation of drugs for the prevention and / or treatment of NK / T-cell lymphoma; (VII) Preparation of reagents and / or kits for the prognostic diagnosis of NK / T cell lymphoma; (VIII) Prepare products for assessing immune cell infiltration in NK / T cell lymphoma; and / or (IX) Prepare products for evaluating somatic cell mutations; Preferably, the ERV includes one or more of HERVE, HERVEA, HERVFH21, HML5, LTR46, and MER61; Preferably, the expression level of the ERV is used to construct a prognostic risk assessment model for the NK / T cell lymphoma.

3. A prognostic risk assessment model for NK / T-cell lymphoma, characterized in that, Its input variables include the expression levels of prognostic biomarkers as described in claim 1 or 2; As a preferred option, the risk score S = HERVE gene expression level × HERVE regression coefficient (-9.59E-05) + HERVEA gene expression level × HERVEA regression coefficient (-0.0005749) + HERVFH21 gene expression level × HERVFH21 regression coefficient (0.0003299) + HML5 gene expression level × HML5 regression coefficient (-0.0005405) + LTR46 gene expression level × LTR46 regression coefficient (-2.22E-05) + MER61 gene expression level × MER61 regression coefficient (-5.74E-05).

4. The prognostic risk assessment model as described in claim 3, characterized in that, The optimal cutoff value is -0.7148771; The risk score S ≥ -0.7148771 is classified as high-risk, and the risk score S < -0.7148771 is classified as low-risk.

5. The method for constructing the prognostic risk assessment model for NK / T-cell lymphoma as described in claim 3 or 4, characterized in that, Includes the following steps: Step 1: Obtain the gene expression levels of the prognostic markers in patients with NK / T-cell lymphoma to be evaluated; Step 2: Input the gene expression level into the prognostic risk assessment model to obtain the risk score S; Risk score S = HERVE gene expression level × HERVE regression coefficient (-9.59E-05) + HERVEA gene expression level × HERVEA regression coefficient (-0.0005749) + HERVFH21 gene expression level × HERVFH21 regression coefficient (0.0003299) + HML5 gene expression level × HML5 regression coefficient (-0.0005405) + LTR46 gene expression level × LTR46 regression coefficient (-2.22E-05) + MER61 gene expression level × MER61 regression coefficient (-5.74E-05).

6. The application of the prognostic risk assessment model as described in claim 3 or 4, or the prognostic risk assessment model obtained by the construction method as described in claim 5, in any of the following: (i) To prepare a device for assessing the prognostic risk of NK / T cell lymphoma; (ii) Construct a system for assessing the prognostic risk of NK / T-cell lymphoma; (iii) Screening for drugs to prevent and / or treat NK / T-cell lymphoma; (iv) To prepare drugs for the prevention and / or treatment of NK / T-cell lymphoma; (v) Preparation of reagents and / or kits for the prognostic diagnosis of NK / T cell lymphoma; (vi) Prepare products for assessing the infiltration of immune cells in NK / T cell lymphoma; and / or (vii) Prepare products for evaluating somatic cell mutations.

7. A detection product for detecting the prognostic marker as described in claim 1, characterized in that, This includes biomolecules that specifically hybridize with the prognostic biomarkers as described in claim 1 or their expression products, or detection reagents that use the prognostic biomarkers as described in claim 1 as the target for detection; Preferably, the biomolecule includes one or more selected from primers, probes, and antibodies; and / or The testing products include one or more of chips, reagents, or kits.

8. A device for assessing the prognostic risk of NK / T-cell lymphoma, characterized in that, The device has a built-in prognostic risk assessment model as described in claim 3 or 4, or a prognostic risk assessment model obtained by the construction method as described in claim 5. Preferably, the device includes a detection unit and an analysis unit; the detection unit detects the expression level of the prognostic biomarker as described in claim 1 in the sample; the analysis unit uses the expression level of the prognostic biomarker as described in claim 1 as an input variable, inputs it into the prognostic risk assessment model as described in claim 3 or 4 or the prognostic risk assessment model obtained by the construction method as described in claim 5, and analyzes and assesses the prognostic risk of NK / T cell lymphoma.

9. A computer-readable storage medium, characterized in that, The storage medium includes a stored program, wherein, when the program is executed, it controls the device where the storage medium is located to execute the method for constructing the prognostic risk assessment model as described in claim 5.

10. A processor, characterized in that, The processor is used to run a program, wherein the program executes the method for constructing the prognostic risk assessment model as described in claim 5.

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