CN114649091A - Construction method of T lymphoblastic lymphoma prognosis model based on CpG methylation - Google Patents

Abstract

The invention belongs to the technical field of cancer risk assessment, and particularly discloses a construction method of a T lymphoblastic lymphoma prognosis model based on CpG methylation, which comprises the following steps: step 1: screening differential CpG methylation sites; step 2: constructing a CpG methylation label; and step 3: and (5) constructing a Nomogram prognosis model. The invention is based on gene chip technology, through the high-dimensional statistical modeling mode, select CpG methylation site correlated to T-LBL patient's no recurrence life cycle (RFS), and verify it is used for differentiating whether to benefit from BFM scheme +/-HSCT predicting value; the invention can screen patients who can benefit from large-dose chemotherapy +/-HSCT in early stage, and is very important for making a T-LBL individual precise treatment scheme and perfecting a layered treatment basis.

Description

Construction of a prognostic model of T lymphoblastic lymphoma based on CpG methylation

technical field

The invention relates to the technical field of cancer risk assessment, in particular to a method for constructing a T lymphoblastic lymphoma prognosis model based on CpG methylation.

Background technique

T lymphoblastic lymphoma (T-LBL) is a type of hematological tumor derived from immature T lymphocytes with a high degree of malignancy, and the prognosis of patients is very poor. Current guidelines recommend that T-LBL patients receive chemotherapy ± hematopoietic stem cell transplantation (HSCT). The former is relatively well tolerated, but there are hidden dangers of insufficient treatment; the latter regimen has huge toxic and side effects, seriously affecting the quality of life of patients, and even life-threatening. Therefore, if the patients who can benefit from the high-intensity treatment modality of BFM can be identified early, and the precise stratified treatment of T-LBL can be realized, it is particularly important to improve the long-term survival prognosis of T-LBL patients.

At present, T-LBL is mainly stratified clinically through the traditional Ann Arbor staging, IPI score and other prognostic systems that use clinical factors as evaluation indicators. Patient populations benefiting from intensive chemotherapy ± HSCT.

To this end, we propose a method for constructing a prognostic model of T lymphoblastic lymphoma based on CpG methylation to solve the above problems.

SUMMARY OF THE INVENTION

In order to realize precise stratified treatment of T-LBL and improve the long-term survival prognosis of T-LBL patients, the present invention provides a method for constructing a T lymphoblastic lymphoma prognosis model based on CpG methylation.

A method for constructing a T-lymphoblastic lymphoma prognostic model based on CpG methylation, which includes the following steps:

Step 1: Screening of differential CpG methylation sites;

Step 2: Construction of CpG methylation tags;

Step 3: Construction of Nomogram prognostic model.

As a preferred solution, in step 1, the specific steps of the screening of the differential CpG methylation sites are:

S11: Collect a certain number of tissue specimens from T-LBL patients after complete remission of treatment;

S12: Detection of CpG methylation levels in tissue samples from non-relapsed and relapsed patients after treatment by Methylation 850K chip;

S13: Candidate differentially methylated sites were screened by LASSO and SVM-RFE.

As a preferred solution, the candidate differentially methylated sites obtained by screening are divided into a training set and an internal validation set.

As a preferred solution, there are 13 candidate differentially methylated sites.

As a preferred solution, in step 1, the number of tissue specimens from T-LBL patients after complete remission of the treatment is 49 cases.

As a preferred solution, in step 1, the ratio of the tissue samples of the patients without recurrence after the treatment to the tissue samples of the patients with recurrence after the treatment is 28:21.

As a preferred solution, in step 2, the specific steps for the construction of the CpG methylation tag are:

S21: Collect several formalin-fixed paraffin-embedded T-LBL specimens with complete follow-up data as an external validation set;

S22: Use LASSO regression to screen candidate methylation sites to predict the RFS of patients in the training cohort, obtain a risk scoring formula, and use X-tile software to set the scoring threshold;

S23: Receiver operating characteristic (ROC) curve and area under the curve (AUC) were used to describe the prediction accuracy of the model to obtain CpG methylation signatures.

As a preferred solution, the prediction accuracy and stability of the CpG methylation tag are verified by using an internal validation set and an external validation set.

As a preferred solution, in step 3, the specific steps of constructing the Nomogram prognosis model are:

S31: Combine the patient's clinical indicators with CpG methylation signatures, and use multivariate Cox regression analysis to construct a nomogram prognostic model;

S32: Use the training set to train the nomogram prognostic model, and use the internal validation set and external validation set to validate the performance of the nomogram prognostic model for predicting whether patients benefit from high-intensity BFM chemotherapy ± HSCT.

As a preferred solution, the clinical indicators include patient age, stage, ECOG-PS score and laboratory test results.

Beneficial effects: The present invention is based on the gene chip technology, through high-dimensional statistical modeling, to screen out the CpG methylation sites related to the recurrence-free survival (RFS) of T-LBL patients, and to verify that it is used to identify whether from Predictive value of benefit in BFM regimen ± HSCT, early identification of T-LBL patient populations that could benefit from high-intensity BFM regimen ± HSCT, enabling high-risk patients to receive adequate intensity therapy, while avoiding unnecessary exposure to low-risk patients At the same time, due to the high sensitivity of methylation detection, multiple CpG methylation sites in each target gene region can be detected, and the methylation changes of ctDNA derived from the same type of tumor cells are relatively high. It is stable and is an ideal tumor molecular stratification index; compared with traditional clinical factors, the CpG methylation model obtained by the present invention can reflect the biological behavior of T-LBL; because the first-line stratified treatment mode of T-LBL is not yet clear , and early screening of patients who can benefit from high-dose chemotherapy ± HSCT is very important for the formulation of individualized precision treatment plans for T-LBL and the improvement of the basis for stratified treatment.

Description of drawings

FIG. 1 is a flow chart of the method of the present invention.

FIG. 2 is a demonstration flowchart of Embodiment 5 of the present invention.

Detailed ways

The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are all simplified schematic diagrams, and only illustrate the basic structure of the present invention in a schematic manner, so they only show the structures related to the present invention.

Example 1

As shown in FIG. 1 , the present invention provides a method for constructing a T-lymphoblastic lymphoma prognostic model based on CpG methylation. The method includes the following steps: Step 1: Screening of differential CpG methylation sites; Step 2 : Construction of CpG methylation signature; Step 3: Construction of Nomogram prognostic model. The present invention is based on gene chip technology, through high-dimensional statistical modeling, to screen out the CpG methylation sites related to the recurrence-free survival (RFS) of T-LBL patients, and to verify that it is used to identify whether from the BFM scheme ± Predictive value of benefit in HSCT; the present invention can screen out patients who can benefit from high-dose chemotherapy ± HSCT at an early stage, which is very important for the formulation of individualized precision treatment plans for T-LBL and the improvement of stratified treatment basis.

Example 2

This embodiment includes all the technical features of the above-mentioned embodiment 1, the difference is that in step 1, the specific steps of the screening of the differential CpG methylation sites are:

S11: Collect a certain number of tissue specimens from T-LBL patients after complete remission of treatment;

S12: Detection of CpG methylation levels in tissue samples from non-relapsed and relapsed patients after treatment by Methylation 850K chip;

S13: Candidate differentially methylated sites were screened by LASSO and SVM-RFE.

In this Example 2, the candidate differentially methylated sites obtained by screening were divided into a training set and an internal validation set.

In this Example 2, the candidate differential methylation sites were 13.

In this example 2, in step 1, the number of tissue samples of T-LBL patients after complete remission of the treatment was 49 cases.

In this embodiment 2, in step 1, the ratio of the tissue samples of the patients without recurrence after the treatment to the tissue samples of the patients with recurrence after the treatment is 28:21.

Example 3

This embodiment 3 includes all the technical features of the above-mentioned embodiments, the difference is that, in step 2, the specific steps for constructing the CpG methylation tag are:

S21: Collect several formalin-fixed paraffin-embedded T-LBL specimens with complete follow-up data as an external validation set;

S22: Use LASSO regression to screen candidate methylation sites to predict the RFS of patients in the training cohort, obtain a risk scoring formula, and use X-tile software to set the scoring threshold;

S23: Receiver operating characteristic (ROC) curve and area under the curve (AUC) were used to describe the prediction accuracy of the model to obtain CpG methylation signatures.

In this Example 3, the prediction accuracy and stability of the CpG methylation tag were verified using an internal validation set and an external validation set.

Example 4

This embodiment 4 includes all the technical features of the above-mentioned embodiments, the difference lies in that, in step 3, the specific steps of constructing the Nomogram prognosis model are:

S31: Combine the patient's clinical indicators with CpG methylation signatures, and use multivariate Cox regression analysis to construct a nomogram prognostic model;

S32: Use the training set to train the nomogram prognostic model, and use the internal validation set and external validation set to validate the performance of the nomogram prognostic model for predicting whether patients benefit from high-intensity BFM chemotherapy ± HSCT.

In this Example 4, the clinical indicators include patient age, stage, ECOG-PS score and laboratory test results.

Example 5

The difference between this example 5 and the above examples is that: the tissue samples of 49 T-LBL patients after complete remission after first-line treatment were collected, and the CpG methylation levels of 28 patients without recurrence and 21 patients with recurrence after treatment were detected by Methylation 850K chip. , candidate differentially methylated sites were obtained by LASSO and SVM-RFE screening. Collected 500 formalin-fixed paraffin-embedded T-LBL specimens from domestic multi-centers with complete follow-up data; LASSO regression was used to screen candidate methylation sites, which were used to predict RFS of patients in the training cohort to obtain a risk score The formula, using the X-tile software to set the scoring threshold, uses the receiver operating characteristic (ROC) curve and the area under the curve (AUC) to describe the predictive accuracy of the model; the CpG A is verified in the internal and external validation sets. The prediction accuracy and stability of the basement signature; in addition, the clinical indicators such as age, stage, ECOG-PS score, and laboratory test results were combined with the CpG methylation signature, and multivariate Cox regression analysis was used to construct a nomogram prognostic model to verify Power of the nomogram to predict whether a patient would benefit from high-intensity BFM chemotherapy ± HSCT.

The present invention is based on gene chip technology, through high-dimensional statistical modeling, to screen out the CpG methylation sites related to the recurrence-free survival (RFS) of T-LBL patients, and to verify that it is used to identify whether from the BFM scheme ± Predictive value of benefit in HSCT, early identification of T-LBL patient populations that can benefit from high-intensity BFM regimen ± HSCT, enabling high-risk patients to receive adequate intensity treatment, while avoiding low-risk patients receiving unnecessary treatment, achieving Accurate stratified treatment of T-LBL; at the same time, due to the high sensitivity of methylation detection, multiple CpG methylation sites in each target gene region can be detected, and the methylation changes of ctDNA derived from the same type of tumor cells are relatively stable. It is an ideal tumor molecular stratification index; compared with traditional clinical factors, the CpG methylation model obtained by the present invention can reflect the biological behavior of T-LBL; since the first-line stratified treatment mode of T-LBL is still unclear, early screening To identify patients who can benefit from high-dose chemotherapy ± HSCT, it is very important to formulate individualized precision treatment plans for T-LBL and improve the basis for stratified treatment.

Those skilled in the art can understand that the description of the above device is only an example, and does not constitute a limitation on the terminal device. It may include more or less components than the above description, or combine some components, or different components, for example, you can Including input and output devices, network access devices, buses, etc.

The processor used to realize the above functions may be a central processing unit (Central Processing Unit, CPU), or other general-purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The above-mentioned processor is the control center of the above-mentioned terminal equipment, and uses various interfaces and lines to connect various parts of the entire user terminal.

The memory used to realize the above-mentioned functions, this memory can be used to store computer programs and/or modules, and the above-mentioned processor realizes the above-mentioned by running or executing the computer programs and/or modules stored in the memory, and calling the data stored in the memory. Various functions of terminal equipment. The memory can mainly include a stored program area and a stored data area, wherein the stored program area can store the operating system, application programs required for at least one function (such as information collection template display function, product information release function, etc.), etc.; Store the data created according to the use of the berth status display system (such as product information collection templates corresponding to different product types, product information that different product providers need to publish, etc.), etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory such as hard disk, internal memory, plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card , a flash card (Flash Card), at least one magnetic disk storage device, flash memory device, or other volatile solid-state storage device.

If the modules/units integrated in the terminal equipment used to realize the above functions are implemented in the form of software functional units and sold or used as independent products, they may be stored in a computer-readable storage medium. Based on this understanding, the present invention realizes all or part of the modules/units in the system of the above-mentioned embodiments, and can also be completed by instructing the relevant hardware through a computer program, and the above-mentioned computer program can be stored in a computer-readable storage medium, the computer When the program is executed by the processor, the functions of the above system embodiments can be implemented. Wherein, the computer program includes computer program code, and the computer program code may be in the form of source code, object code, executable file or some intermediate forms, and the like. Computer-readable media may include: any entity or device capable of carrying computer program code, recording media, USB flash drives, removable hard disks, magnetic disks, optical discs, computer memory, read-only memory (ROM, Read-Only Memory), random access Memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc.

In the description of this specification, reference to the terms "one embodiment," "some embodiments," "exemplary embodiment," "example," "specific example," or "some examples", etc. A particular feature, structure, material, or characteristic described in this embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

The basic principles and main features of the present invention and the advantages of the present invention have been shown and described above, and it will be apparent to those skilled in the art that the present invention is not limited to the details of the above-described exemplary embodiments, but without departing from the spirit or essential aspects of the present invention. In the case of the characteristic features, the present invention can be implemented in other specific forms. Therefore, the embodiments are to be regarded in all respects as illustrative and not restrictive, and the scope of the invention is to be defined by the appended claims rather than the foregoing description, which are therefore intended to fall within the scope of the claims. All changes within the meaning and scope of the equivalents of , are included in the present invention. Any reference signs in the claims shall not be construed as limiting the involved claim.

In addition, it should be understood that although this specification is described in terms of embodiments, not each embodiment only includes an independent technical solution, and this description in the specification is only for the sake of clarity, and those skilled in the art should take the specification as a whole , the technical solutions in each embodiment can also be appropriately combined to form other implementations that can be understood by those skilled in the art.

Claims (10)

1. a method for constructing a T-lymphoblastic lymphoma prognostic model based on CpG methylation, characterized in that the method comprises the following steps: Step 1: Screening of differential CpG methylation sites; Step 2: Construction of CpG methylation tags; Step 3: Construction of Nomogram prognostic model. 2. the construction method of the T lymphoblastic lymphoma prognostic model based on CpG methylation according to claim 1, is characterized in that, In step 1, the specific steps of the screening of the differential CpG methylation sites are: S11: Collect a certain number of tissue specimens from T-LBL patients after complete remission of treatment; S12: Detection of CpG methylation levels in tissue samples from non-relapsed and relapsed patients after treatment by Methylation 850K chip; S13: Candidate differentially methylated sites were screened by LASSO and SVM-RFE. 3. the construction method of the T lymphoblastic lymphoma prognostic model based on CpG methylation according to claim 2, is characterized in that, The candidate differentially methylated sites obtained by screening were divided into training set and internal validation set. 4. the construction method of the T lymphoblastic lymphoma prognostic model based on CpG methylation according to claim 3, is characterized in that, The candidate differential methylation sites were 13. 5. the construction method of the T lymphoblastic lymphoma prognostic model based on CpG methylation according to claim 4, is characterized in that, In step 1, the number of tissue samples from T-LBL patients after complete remission of the treatment was 49 cases. 6. the construction method of the T lymphoblastic lymphoma prognostic model based on CpG methylation according to claim 5, is characterized in that, In step 1, the ratio of the tissue samples of the patients without recurrence after the treatment to the tissue samples of the patients with recurrence after the treatment is 28:21. 7. the construction method of the T lymphoblastic lymphoma prognostic model based on CpG methylation according to claim 6, is characterized in that, In step 2, the specific steps for the construction of the CpG methylation tag are: S21: Collect several formalin-fixed paraffin-embedded T-LBL specimens with complete follow-up data as an external validation set; S22: Use LASSO regression to screen candidate methylation sites to predict the RFS of patients in the training cohort, obtain a risk scoring formula, and use X-tile software to set the scoring threshold; S23: Receiver operating characteristic (ROC) curve and area under the curve (AUC) were used to describe the prediction accuracy of the model to obtain CpG methylation signatures. 8. the construction method of the T lymphoblastic lymphoma prognostic model based on CpG methylation according to claim 7, is characterized in that, The prediction accuracy and stability of this CpG methylation signature were verified using an internal validation set and an external validation set. 9. the construction method of the T lymphoblastic lymphoma prognosis model based on CpG methylation according to claim 8, is characterized in that, In step 3, the specific steps of constructing the Nomogram prognosis model are: S31: Combine the patient's clinical indicators with CpG methylation signatures, and use multivariate Cox regression analysis to construct a nomogram prognostic model; S32: Use the training set to train the nomogram prognostic model, and use the internal validation set and external validation set to validate the performance of the nomogram prognostic model for predicting whether patients benefit from high-intensity BFM chemotherapy ± HSCT. 10. The method for constructing a T-lymphoblastic lymphoma prognostic model based on CpG methylation according to claim 9, characterized in that, The clinical indicators include patient age, stage, ECOG-PS score and laboratory test results.

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