CN114649091A - Construction method of T lymphoblastic lymphoma prognosis model based on CpG methylation - Google Patents
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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
Technical Field
The invention relates to the technical field of cancer risk assessment, in particular to a construction method of a T lymphoblastic lymphoma prognosis model based on CpG methylation.
Background
T lymphoblastic lymphomas (T-LBLs) are a group of hematological tumors of high malignancy derived from immature T lymphocytes, and have a poor prognosis for patients. Current guidelines recommend that patients with T-LBL receive chemotherapy + -Hematopoietic Stem Cell Transplantation (HSCT) therapy, and the chemotherapy regimen can be divided into a moderate to high intensity HyperCVAD regimen and a high intensity BFM regimen. The tolerance of the former is relatively good, but the hidden trouble of insufficient treatment exists; the latter scheme has great toxic and side effects, seriously influences the life quality of patients and even endangers life. Therefore, accurate stratification of T-LBL therapy is particularly important for improving the prognosis of long-term survival in patients with T-LBL if early identification of patients who would benefit from BFM, a high-intensity treatment regimen, is achieved.
At present, T-LBL is clinically subjected to prognosis stratification mainly through traditional prognosis systems with Ann Arbor staging, IPI scoring and the like taking clinical factors as evaluation indexes, the means are difficult to reflect the biological behavior of tumors, and a patient population capable of benefiting from high-intensity chemotherapy +/-HSCT cannot be identified.
Therefore, we propose a construction method of prognosis model of T lymphoblastic lymphoma based on CpG methylation to solve the above existing problems.
Disclosure of Invention
In order to realize accurate layered treatment of T-LBL and improve the prognosis of long-term survival of a T-LBL patient, the invention provides a construction method of a T lymphoblastic lymphoma prognosis model based on CpG methylation.
A construction method of a prognosis model of T lymphoblastic lymphoma based on CpG methylation 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.
Preferably, in step 1, the screening of the differential CpG methylation sites comprises the following steps:
s11: collecting a quantity of tissue specimens from a T-LBL patient after complete remission of the treatment;
s12: detecting the CpG Methylation level of the tissue specimen of a patient who does not relapse or relapses after treatment by a Methylation 850K chip;
s13: and screening by LASSO and SVM-RFE to obtain candidate differential methylation sites.
Preferably, the candidate differentially methylated sites obtained by screening are divided into a training set and an internal validation set.
Preferably, the candidate differentially methylated sites are 13.
Preferably, in step 1, the number of tissue specimens from a T-LBL patient after complete remission of said treatment is 49.
Preferably, in step 1, the ratio of the tissue sample of the non-relapsed patient to the tissue sample of the relapsed patient after the treatment is 28: 21.
preferably, in step 2, the construction of the CpG methylation signature specifically comprises the following steps:
s21: collecting a plurality of formalin-fixed paraffin-embedded T-LBL specimens with complete follow-up data as an external verification set;
s22: screening candidate methylation sites by using LASSO regression, predicting RFS of patients in a training queue to obtain a risk scoring formula, and setting a scoring threshold by using X-tile software;
s23: CpG methylation signatures were obtained using Receiver Operating Characteristic (ROC) curves and area under the curve (AUC) to describe the prediction accuracy of the model.
Preferably, the prediction accuracy and stability of the CpG methylation signature are verified using an internal validation set and an external validation set.
Preferably, in step 3, the specific steps of constructing the Nomogram prognosis model are as follows:
s31: combining clinical indicators of patients with CpG methylation signatures, and constructing a nomogram prognosis model using multifactor Cox regression analysis;
s32: the nomogram prognostic model is trained using a training set, and validated using an internal validation set and an external validation set for predicting the efficacy of a patient to benefit from high-intensity BFM chemotherapy ± HSCT.
Preferably, the clinical indicators include patient age, stage, ECOG-PS score, and laboratory test results.
Has the advantages that: the invention is based on gene chip technology, through the high-dimensional statistical modeling mode, select CpG methylation sites related to T-LBL patient recurrence-free survival (RFS), and verify it is used for distinguishing whether to benefit from BFM scheme + -HSCT prediction value, early identify can from high-strength BFM scheme + -HSCT T profit T-LBL patient group, make high-risk patient accept the treatment of sufficient intensity, avoid low-risk patient to accept the unnecessary treatment at the same time, realize the accurate layered treatment of T-LBL; meanwhile, because the methylation detection sensitivity is high, a plurality of CpG methylation sites in each target gene region can be detected, and the methylation change of ctDNA from the same type of tumor cells is relatively stable, so that the target gene is an ideal tumor molecule layering index; compared with the traditional clinical factors, the CpG methylation model obtained by the invention can reflect the biological behavior of T-LBL; as the first-line layered treatment mode of the T-LBL is not clear, patients who can benefit from high-dose chemotherapy +/-HSCT can be screened out in early stage, and the method is very important for making an individual precise treatment scheme of the T-LBL and perfecting the layered treatment basis.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
FIG. 2 is a flow chart illustrating an embodiment 5 of the present invention.
Detailed Description
The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic views illustrating only the basic structure of the present invention in a schematic manner, and thus show only the constitution related to the present invention.
Example 1
As shown in FIG. 1, the invention provides a construction method of a prognosis model of T lymphoblastic lymphoma 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.
Example 2
This example contains all the technical features of example 1 above, except that in step 1, the specific steps of screening for differential CpG methylation sites are:
s11: collecting a quantity of tissue specimens from a patient having T-LBL after complete remission of the treatment;
s12: detecting the CpG Methylation level of the tissue specimen of a patient who does not relapse or relapses after treatment by a Methylation 850K chip;
s13: and screening by LASSO and SVM-RFE to obtain candidate differential methylation sites.
In this example 2, candidate differentially methylated sites from the screen were assigned to the training set and the internal validation set.
In this example 2, the candidate differentially methylated sites are 13.
In this example 2, the number of tissue specimens from T-LBL patients after complete remission of the treatment in step 1 was 49.
In this example 2, in step 1, the ratio of the tissue specimen of the non-relapsed patient after the treatment to the tissue specimen of the relapsed patient after the treatment is 28: 21.
example 3
This example 3 contains all the technical features of the above examples, except that in step 2, the construction of the CpG methylation signature specifically comprises the following steps:
s21: collecting a plurality of formalin-fixed paraffin-embedded T-LBL specimens with complete follow-up data as an external verification set;
s22: screening candidate methylation sites by using LASSO regression, predicting RFS of patients in a training queue to obtain a risk scoring formula, and setting a scoring threshold by using X-tile software;
s23: CpG methylation signatures were obtained using Receiver Operating Characteristic (ROC) curves and area under the curve (AUC) to describe the prediction accuracy of the model.
In this example 3, the CpG methylation signature was validated for prediction accuracy and stability using an internal validation set and an external validation set.
Example 4
Example 4 includes all technical features of the above examples, and differs in that, in step 3, the specific steps of constructing the Nomogram prognosis model are as follows:
s31: combining clinical indicators of patients with CpG methylation signatures, and constructing a nomogram prognosis model using multifactor Cox regression analysis;
s32: the nomogram prognostic model is trained using a training set, and validated using an internal validation set and an external validation set for predicting the efficacy of a patient to 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
This example 5 differs from the above examples in that: collecting tissue specimens of 49 patients with T-LBL after complete remission of first-line treatment, detecting the CpG Methylation level of 28 patients without relapse and 21 patients with relapse after treatment by a Methylation 850K chip, and screening by LASSO and SVM-RFE to obtain candidate differential Methylation sites. Collecting 500 cases of formalin-fixed paraffin-embedded T-LBL specimens which are in multiple centers and have complete follow-up data in China; screening candidate methylation sites by using LASSO regression, predicting RFS of patients in a training queue to obtain a risk scoring formula, setting a scoring threshold by using X-tile software, and describing prediction accuracy of a model by using a Receiver Operating Characteristic (ROC) curve and an area under the curve (AUC); verifying the prediction accuracy and stability of the CpG methylation label in an internal verification set and an external verification set; in addition, clinical indicators such as age, staging, ECOG-PS scores, laboratory test results, etc. are combined with CpG methylation signatures, and a multi-factor Cox regression analysis is used to construct a nomogram prognostic model to verify the efficacy of nomogram for predicting whether a patient would benefit from high-intensity BFM chemotherapy ± HSCT.
The invention is based on gene chip technology, through the high-dimensional statistical modeling mode, select CpG methylation sites related to T-LBL patient recurrence-free survival (RFS), and verify it is used for distinguishing whether to benefit from BFM scheme + -HSCT prediction value, early identify can from high-strength BFM scheme + -HSCT T profit T-LBL patient group, make high-risk patient accept the treatment of sufficient intensity, avoid low-risk patient to accept the unnecessary treatment at the same time, realize the accurate layered treatment of T-LBL; meanwhile, because the methylation detection sensitivity is high, a plurality of CpG methylation sites in each target gene region can be detected, and the methylation change of ctDNA from the same type of tumor cells is relatively stable, so that the target gene is an ideal tumor molecule layering index; compared with the traditional clinical factors, the CpG methylation model obtained by the invention can reflect the biological behavior of T-LBL; as the first-line layered treatment mode of the T-LBL is not clear, patients who can benefit from high-dose chemotherapy +/-HSCT can be screened out in early stage, and the method is very important for making an individual precise treatment scheme of the T-LBL and perfecting the layered treatment basis.
Those skilled in the art will appreciate that the above description of devices is by way of example only and does not constitute a limitation of terminal devices and may include more or fewer components than those described above, or some of the components may be combined, or different components may include, for example, input output devices, network access devices, buses, etc.
The Processor used for implementing the above functions may be a Central Processing Unit (CPU), other general-purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, a discrete hardware component, and the like. The general-purpose processor may be a microprocessor or the processor may be any conventional processor or the like, which is the control center of the terminal equipment and connects the various parts of the entire user terminal using various interfaces and lines.
The processor implements various functions of the terminal device by running or executing the computer programs and/or modules stored in the memory and calling data stored in the memory. The memory mainly comprises a storage program area and a storage data area, wherein the storage program area can store an operating system, application programs (such as an information acquisition template display function, a product information publishing function and the like) required by at least one function and the like; the storage data area may store data created according to the use of the berth-state display system (e.g., product information acquisition templates corresponding to different product types, product information that needs to be issued by different product providers, etc.), and the like. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
The terminal device integrated module/unit used to implement the above functions may be stored in a computer-readable storage medium if it is implemented in the form of a software functional unit and sold or used as a separate product. Based on such understanding, all or part of the modules/units in the system according to the above embodiment may be implemented by a computer program, which may be stored in a computer-readable storage medium and used by a processor to implement the functions of the embodiments of the system. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying computer program code, recording medium, U.S. disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution media, and the like.
In the description herein, references to the description of the terms "one embodiment," "certain embodiments," "an illustrative embodiment," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the 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.
While there have been shown and described what are at present considered the fundamental principles and essential features of the invention and its advantages, it will be apparent to those skilled in the art that the invention is not limited to the details of the foregoing exemplary embodiments, but is capable of other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Furthermore, it should be understood that although the present description refers to embodiments, not every embodiment may contain only a single embodiment, and such description is for clarity only, and those skilled in the art should integrate the description, and the embodiments may be combined as appropriate to form other embodiments understood by those skilled in the art.
Claims (10)
1. A construction method of a prognosis model of T lymphoblastic lymphoma based on CpG methylation is characterized by comprising 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.
2. The method for constructing a prognostic model of CpG-methylated T lymphoblastic lymphoma according to claim 1, wherein the T lymphoblastic lymphoma is divided into three classes,
in step 1, the specific steps of screening the differential CpG methylation sites are as follows:
s11: collecting a quantity of tissue specimens from a patient having T-LBL after complete remission of the treatment;
s12: detecting the CpG Methylation level of the tissue specimen of a patient who does not relapse or relapses after treatment by a Methylation 850K chip;
s13: and screening by LASSO and SVM-RFE to obtain candidate differential methylation sites.
3. The method for constructing a prognostic model of CpG-methylated T lymphoblastic lymphoma according to claim 2, wherein the T lymphoblastic lymphoma is divided into three classes,
and classifying the candidate differential methylation sites obtained by screening into a training set and an internal verification set.
4. The method for constructing a prognostic model of CpG-methylated T lymphoblastic lymphoma according to claim 3, wherein the T lymphoblastic lymphoma is divided into three classes,
the candidate differentially methylated sites are 13.
5. The method for constructing a prognostic model of CpG-methylated T lymphoblastic lymphoma according to claim 4, wherein the T lymphoblastic lymphoma is divided into three classes,
in step 1, the number of tissue specimens from T-LBL patients after complete remission of the treatment was 49.
6. The method for constructing a prognostic model of CpG-methylated T lymphoblastic lymphoma according to claim 5, wherein the T lymphoblastic lymphoma is divided into three classes,
in step 1, the ratio of the tissue specimen of the non-relapsed patient after treatment to the tissue specimen of the relapsed patient after treatment is 28: 21.
7. the method for constructing a prognostic model of CpG-methylated T lymphoblastic lymphoma according to claim 6, wherein the T lymphoblastic lymphoma is divided into three classes,
in step 2, the construction of the CpG methylation label specifically comprises the following steps:
s21: collecting a plurality of formalin-fixed paraffin-embedded T-LBL specimens with complete follow-up data as an external verification set;
s22: screening candidate methylation sites by using LASSO regression, predicting RFS of patients in a training queue to obtain a risk scoring formula, and setting a scoring threshold by using X-tile software;
s23: CpG methylation signatures were obtained using Receiver Operating Characteristic (ROC) curves and area under the curve (AUC) to describe the prediction accuracy of the model.
8. The method for constructing a prognostic model of CpG-methylated T lymphoblastic lymphoma according to claim 7, wherein the T lymphoblastic lymphoma is divided into three classes,
the prediction accuracy and stability of the CpG methylation signature were verified using the internal validation set and the external validation set.
9. The method for constructing a prognostic model of CpG methylation-based T lymphoblastic lymphoma according to claim 8, wherein the T lymphoblastic lymphoma is divided into two parts,
in step 3, the specific steps of the construction of the Nomogram prognostic model are as follows:
s31: combining clinical indicators of patients with CpG methylation signatures, and constructing a nomogram prognosis model using multifactor Cox regression analysis;
s32: the nomogram prognostic model is trained using a training set, and validated using an internal validation set and an external validation set for predicting the efficacy of a patient to benefit from high-intensity BFM chemotherapy ± HSCT.
10. The method for constructing a prognostic model of CpG-methylated T lymphoblastic lymphoma according to claim 9, wherein the T lymphoblastic lymphoma is divided into three classes,
the clinical indices include patient age, stage, ECOG-PS score, and laboratory test results.
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| CN111402949A (en) * | 2020-04-17 | 2020-07-10 | 北京恩瑞尼生物科技股份有限公司 | Construction method of unified model for diagnosis, prognosis and recurrence of hepatocellular carcinoma patient |
| CN111850108A (en) * | 2020-06-05 | 2020-10-30 | 广东省人民医院 | DNA methylation composition related to death risk of coronary heart disease patient and screening method and application thereof |
| CN112908477A (en) * | 2021-01-28 | 2021-06-04 | 黑龙江省医院 | Prognosis risk assessment system for gastric cancer patient |
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| CN111402949A (en) * | 2020-04-17 | 2020-07-10 | 北京恩瑞尼生物科技股份有限公司 | Construction method of unified model for diagnosis, prognosis and recurrence of hepatocellular carcinoma patient |
| CN111850108A (en) * | 2020-06-05 | 2020-10-30 | 广东省人民医院 | DNA methylation composition related to death risk of coronary heart disease patient and screening method and application thereof |
| CN112908477A (en) * | 2021-01-28 | 2021-06-04 | 黑龙江省医院 | Prognosis risk assessment system for gastric cancer patient |
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