Use of reagents for detecting pSTAT3 expression levels in differentiating PTCL-NOS from ALK-ALCL

Abstract

The application discloses application of a reagent for detecting pSTAT3 expression level in identification of PTCL-NOS and ALK-ALCL, relates to the technical field of biological medicine, and finds that detection of the phosphorylation level of STAT3-S727 of a sample can be well used for identification of PTCL-NOS and ALK-ALCL expressing CD30, the identification process is simple and rapid, and is high in specificity and sensitivity, thereby providing a new effective approach for differential diagnosis of PTCL-NOS and ALK-ALCL expressing CD30.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of biological medical technology, in particular to the application of a reagent for detecting the expression level of pSTAT3 in identifying PTCL-NOS and ALK-ALCL. BACKGROUND

[0002] Peripheral T-cell lymphoma not otherwise specified (PTCL-NOS) is a disease entity with heterogeneous biological characteristics and clinical manifestations. Since PTCL-NOS is a diagnostic exclusion, it needs to be differentiated from various other types of mature T-cell lymphoma.

[0003] In actual work, a part of PTCL-NOS can diffuse strong expression of CD30, and needs to be differentiated from ALK-negative anaplastic large cell lymphoma (ALK-ALCL). However, when the histological morphology is atypical or the specimen processing problem causes the CD30 staining to be unreliable, it is difficult to differentiate PTCL-NOS from ALK-ALCL.

[0004] At the same time, clinical research has found that it is important for patients to receive reliable treatment to distinguish CD30-expressing PTCL-NOS from ALK-ALCL. CD30-expressing PTCL-NOS is different from ALK-ALCL in terms of prognosis and response to CD30-targeted therapy, which requires pathologists to use new biomarkers to assist in the differential diagnosis of CD30+PTCL-NOS and ALK-ALCL.

[0005] In view of this, the present application is proposed. SUMMARY

[0006] The purpose of the present application is to provide the application of a reagent for detecting the expression level of pSTAT3 in identifying PTCL-NOS and ALK-ALCL.

[0007] The present application is implemented as follows:

[0008] In a first aspect, the embodiments of the present application provide the application of a reagent for detecting the expression level of pSTAT3 in the preparation of a reagent or kit for identifying CD30-expressing PTCL-NOS and ALK-ALCL, wherein the pSTAT3 includes pSTAT3-S727.

[0009] In a second aspect, an embodiment of the present application provides a training method of a model for distinguishing PTCL-NOS and ALK-ALCL expressing CD30, which comprises: obtaining pSTAT3 expression levels in training samples and corresponding annotation results, the pSTAT3 being as described in the foregoing embodiments; inputting the pSTAT3 expression levels in the training samples into a pre-constructed distinguishing model to obtain distinguishing results; the distinguishing model is used to determine whether a sample is PTCL-NOS or ALK-ALCL expressing CD30 according to the pSTAT3 expression level in the sample; and updating parameters of the model according to the annotation results and the distinguishing results.

[0010] In a third aspect, an embodiment of the present application provides a device for distinguishing PTCL-NOS and ALK-ALCL expressing CD30, which comprises an obtaining module and a distinguishing module. The obtaining module is used to obtain a detection result of a pSTAT3 expression level in a sample to be tested, the pSTAT3 being as described in the foregoing embodiments. The distinguishing module is used to input the detection result of the pSTAT3 expression level in the sample to be tested into a distinguishing model trained by the training method described in the foregoing embodiments to obtain a distinguishing result of the sample to be tested.

[0011] In a fourth aspect, an embodiment of the present application provides a training device of a model for distinguishing PTCL-NOS and ALK-ALCL expressing CD30, which comprises an obtaining module, a distinguishing module and a parameter updating module. The obtaining module is used to obtain pSTAT3 expression levels in training samples and corresponding annotation results, the pSTAT3 being as described in the foregoing embodiments. The distinguishing module is used to input the pSTAT3 expression levels in the training samples into a pre-constructed distinguishing model to obtain distinguishing results. The distinguishing model is used to determine whether a sample is PTCL-NOS or ALK-ALCL expressing CD30 according to the pSTAT3 expression level in the sample. The parameter updating module is used to update parameters of the model according to the annotation results and the distinguishing results.

[0012] In a fifth aspect, an embodiment of the present application provides an electronic device, which comprises a processor and a memory. The memory is used to store a program, when the program is executed by the processor, the processor implements the training method of a model for distinguishing PTCL-NOS and ALK-ALCL expressing CD30 or the distinguishing method of PTCL-NOS and ALK-ALCL expressing CD30 as described in the foregoing embodiments. The distinguishing method comprises the following steps: obtaining a detection result of a pSTAT3 expression level in a sample to be tested; inputting the detection result of the pSTAT3 expression level in the sample to be tested into a distinguishing model trained by the training method described in the foregoing embodiments to obtain a distinguishing result of the sample to be tested.

[0013] In a sixth aspect, an embodiment of the present application provides a computer readable medium, wherein a computer program is stored on the computer readable medium, and the computer program, when executed by a processor, implements the training method of the discrimination model for differentiating PTCL-NOS expressing CD30 from ALK-ALCL or the discrimination method for differentiating PTCL-NOS expressing CD30 from ALK-ALCL as described in the foregoing embodiments; the discrimination method comprises the following steps: obtaining a detection result of the expression level of pSTAT3 in a sample to be tested, inputting the detection result of the expression level of pSTAT3 in the sample to be tested into the discrimination model trained by the training method as described in the foregoing embodiments, and obtaining a discrimination result of the sample to be tested.

[0014] The present application has the following beneficial effects:

[0015] The present application finds that, by detecting the phosphorylation level of STAT3 serine (S727) in a sample, CD30+ PTCL-NOS expressing CD30 and ALK-ALCL can be well identified, the discrimination process is simple and rapid, and has high specificity and good sensitivity, thereby providing a new and effective approach for the differential diagnosis of PTCL-NOS expressing CD30 and ALK-ALCL. BRIEF DESCRIPTION OF DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments. It should be understood that the following drawings only show some of the embodiments of the present application, and therefore should not be considered as limiting the scope. For those skilled in the art, other related drawings can also be obtained without creative labor on the basis of these drawings.

[0017] Figure 1 ROC curve for differentiating CD30+ PTCL-NOS from ALK-ALCL. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions in the embodiments of the present application will be clearly and completely described below. If the specific conditions are not indicated in the embodiments, the conventional conditions or the conditions suggested by the manufacturers are used. If the reagents or instruments used are not indicated by the manufacturers, they are all conventional products that can be purchased in the market.

[0019] The embodiments of the present application provide the application of the reagent for detecting the expression level of pSTAT3 in a sample in the preparation of a reagent or kit for differentiating PTCL-NOS expressing CD30 (CD30+ PTCL-NOS) from ALK-ALCL, and the pSTAT3 includes pSTAT3-S727.

[0020] The present application finds that, by detecting the phosphorylation level of serine (S727) of STAT3 in a sample, CD30+ PTCL-NOS and ALK-ALCL expressing CD30 can be well identified, the identification process is simple and fast, and has high specificity and good sensitivity.

[0021] The type of reagent for detecting the expression level of pSTAT3 in the sample is not specifically limited as long as it can achieve the detection of the expression level of pSTAT3, including but not limited to detection of pSTAT3 antibody.

[0022] Preferably, the sample includes a tissue sample. The tissue sample is preferably a "paraffin-embedded tissue specimen". Before identification, the sample is a sample definitely diagnosed as PTCL-NOS and / or ALK-ALCL, excluding other cases such as (1) other T cell lymphomas expressing CD30, including but not limited to NK / T cell lymphoma (2) angioimmunoblastic T cell lymphoma (3) and the like.

[0023] Preferably, the pSTAT3 further includes pSTAT3-T705.

[0024] The embodiment of the present application provides a training method of a model for identifying PTCL-NOS and ALK-ALCL expressing CD30, which comprises the following steps:

[0025] The expression level of pSTAT3 in the training sample and the corresponding annotation result are obtained, and the pSTAT3 is the pSTAT3 described in any of the preceding embodiments.

[0026] The expression level of pSTAT3 in the training sample is input into a pre-constructed identification model to obtain an identification result; and the identification model is used for judging whether a sample is PTCL-NOS or ALK-ALCL expressing CD30 according to the expression level of pSTAT3 in the sample.

[0027] The model is updated according to the annotation result and the identification result.

[0028] Preferably, the annotation result can be that the sample is PTCL-NOS or ALK-ALCL expressing CD30.

[0029] The embodiment of the present application also provides an identification device for PTCL-NOS and ALK-ALCL expressing CD30, which comprises an acquisition module and an identification module, and specifically comprises the following.

[0030] The acquisition module is used for acquiring a detection result of the expression level of pSTAT3 in a sample to be tested, and the pSTAT3 is the pSTAT3 described in any of the preceding embodiments.

[0031] a discrimination module, configured to input the detection result of the expression level of pSTAT3 in the sample to be tested into the discrimination model trained by the training method in the foregoing embodiments, to obtain a discrimination result of the sample to be tested.

[0032] Preferably, the discrimination device further comprises a storage module configured to store the discrimination model.

[0033] Preferably, the detection result is an immunohistochemical detection result, and the discrimination model further comprises calculating and judging the sample to be tested as CD30-expressing PTCL-NOS or ALK-ALCL according to an immunohistochemical score H-Score.

[0034] Preferably, the formula of the H-Score is as follows: H-Score = 100 x ∑(i x Pi); wherein i represents the staining intensity, and Pi represents the percentage of the number of positively stained cells to the total number of tumor cells.

[0035] Preferably, the staining intensity comprises 1, 2 and 3, and the staining intensity can be judged based on conventional methods in the art, for example, 1 represents weak, the staining result is light yellow, 2 represents moderate, the staining result is light brown, and 3 represents strong, the staining result is brown or dark brown.

[0036] Preferably, the discrimination model judges the sample to be tested as CD30-expressing PTCL-NOS or ALK-ALCL based on a positive threshold value: if the H-Score of the sample > the positive threshold value, the sample is judged as ALK-ALCL; and if the H-Score of the sample ≤ the positive threshold value, the sample is judged as CD30-expressing PTCL-NOS.

[0037] Preferably, when pSTAT3 is pSTAT3-S727, the positive threshold value is 140-150, and specifically can be any one of 140, 141, 142, 143, 144, 145, 146, 147, 148, 149 and 150 or a range between any two of them. Preferably, it is 142-147, and more preferably, it is 145.

[0038] When pSTAT3 is pSTAT3-T705, the positive threshold value is 160-170, and specifically can be any one of 160, 161, 162, 163, 164, 165, 166, 167, 168, 169 and 170 or a range between any two of them. Preferably, it is 163-167, and more preferably, it is 165. The discrimination effect is better in the preferred range.

[0039] The embodiment of the present application also provides a training device of a differential model of PTCL-NOS and ALK-ALCL expressing CD30, which comprises an acquisition module, a differential module and a parameter updating module.

[0040] The acquisition module is used for acquiring the pSTAT3 expression level in the training sample and the corresponding annotation result, wherein the pSTAT3 is the pSTAT3 described in any of the preceding embodiments.

[0041] The differential module is used for inputting the pSTAT3 expression level in the training sample into the pre-constructed differential model to obtain a differential result, wherein the differential model is used for judging whether the sample is PTCL-NOS or ALK-ALCL expressing CD30 according to the pSTAT3 expression level in the sample.

[0042] The parameter updating module is used for updating the parameters of the model according to the annotation result and the differential result.

[0043] It should be noted that the modules described in any of the preceding embodiments can be stored in the memory in the form of software or firmware (Firmware) or solidified in the operating system (Operating System, OS) of the electronic device provided in the present application, and can be executed by the processor in the electronic device. Meanwhile, the data, program codes and the like required for executing the above modules can be stored in the memory.

[0044] The embodiment of the present application also provides an electronic device, which comprises a processor and a memory; the memory is used for storing a program, when the program is executed by the processor, the processor implements the training method of the differential model of PTCL-NOS and ALK-ALCL expressing CD30 or the differential method of PTCL-NOS and ALK-ALCL expressing CD30 as described in the preceding embodiments.

[0045] The differential method comprises: acquiring a detection result of the pSTAT3 expression level in a to-be-tested sample; inputting the detection result of the pSTAT3 expression level in the to-be-tested sample into the differential model trained by the training method as described in any of the preceding embodiments to obtain a differential result of the to-be-tested sample. The differential method can specifically correspond to the execution steps of the differential device as described in any of the preceding embodiments, and will not be described in detail.

[0046] The electronic device can also comprise a bus and a communication interface, and the memory, the processor and the communication interface are directly or indirectly electrically connected to each other to realize the transmission or interaction of data. For example, these elements can be electrically connected to each other through one or more buses or signal lines.

[0047] The memory can be, but is not limited to, a random access memory (RAM), a read only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), and the like.

[0048] The processor can be an integrated circuit chip with a signal processing capability. The processor can be a general purpose processor, including a central processing unit (CPU), a network processor (NP), and the like; or can be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic device, discrete gate or transistor logic device, discrete hardware component.

[0049] In actual applications, the electronic device can be a server, a cloud platform, a mobile phone, a tablet computer, a notebook computer, an ultra-mobile personal computer (UMPC), a handheld computer, a netbook, a personal digital assistant (PDA), a wearable electronic device, a virtual reality device, and the like, and thus the type of the electronic device is not limited in the embodiments of the present application.

[0050] In addition, the embodiments of the present application further provide a computer readable medium, and the computer readable medium stores a computer program. The computer program is executed by a processor to implement the training method of the discrimination model of PTCL-NOS expressing CD30 and ALK-ALCL or the discrimination method of PTCL-NOS expressing CD30 and ALK-ALCL.

[0051] The identification method comprises: obtaining a detection result of the expression level of pSTAT3 in the to-be-tested sample; inputting the detection result of the expression level of pSTAT3 in the to-be-tested sample into the identification model trained by the training method, to obtain an identification result of the to-be-tested sample. The identification method can specifically correspond to the execution steps of the identification device, and details are not described again.

[0052] The computer readable medium can be a general storage medium such as a mobile disk, a hard disk, etc.

[0053] The features and performances of the present application are further described in detail below in combination with embodiments.

[0054] Embodiment 1

[0055] An identification method for distinguishing CD30-expressing PTCL-NOS from ALK-ALCL, comprising the following steps.

[0056] The expression level of phosphorylated STAT3-S727 in the tissue sample is analyzed by immunohistochemical method: the sample is detected by anti-phosph-STAT3 (S727) of Abeam Company, and the immunohistochemical result is interpreted by using H-score.

[0057] The calculation formula of H-score is as follows: H-score = 100 x ∑(i x Pi); wherein, i represents the staining intensity, the staining intensity includes: 1, 2 and 3, 1 represents weak, the staining result is light yellow, 2 represents moderate, the staining result is light brown, and 3 represents strong, the staining result is brown or dark brown; Pi represents the percentage of positive cells, the value range is 0-100%.

[0058] The positive threshold of judgment is 145, when the calculation score of H-score > the positive threshold, the sample is judged as ALK-ALCL, and when the calculation score of H-score ≤ the positive threshold, it is judged as CD30+PTCL-NOS.

[0059] Embodiment 2

[0060] An identification method for distinguishing CD30-expressing PTCL-NOS from ALK-ALCL, comprising the following steps.

[0061] The expression level of phosphorylated STAT3-S727 in the tissue sample is analyzed by immunohistochemical method: the sample is detected by anti-phosph-STAT3 (S727) of Abeam Company, and the immunohistochemical result is interpreted by using H-score.

[0062] The positive threshold of pSTAT3(S727) judgment is 145, when the calculated score of H-score > the positive threshold, the sample is judged as ALK-ALCL, and when the calculated score of H-score < the positive threshold, the sample is judged as CD30+ PTCL-NOS.

[0063] The positive threshold of pSTAT3(Y705) judgment is 165, when the calculated score of H-score > the positive threshold, the sample is judged as ALK-ALCL, and when the calculated score of H-score < the positive threshold, the sample is judged as CD30+ PTCL-NOS.

[0064] When the judgment results of pSTAT3(S727) and pSTAT3(Y705) are both ALK-ALCL, the final result is determined as ALK-ALCL, otherwise as CD30+ PTCL-NOS.

[0065] Test Example 1

[0066] Using the identification methods of Examples 1 and 2, four test groups were set up, and 22 cases of ALK-ALCL and 34 cases of CD30-expressing PTCL-NOS (10 cases of CD30+ CD30-expressing PTCL-NOS) with clear pathological diagnosis were detected, and the immunohistochemical results were judged using H-score. The results are shown in Table 1 and Figure 1

[0067] Table 1 Identification results

[0068]

[0069] pSTAT3-Y705 is based on the detection of pSTAT3-Y705 expression level to judge ALK-ALCL and 34 cases of CD30-expressing PTCL-NOS, for specific process, refer to the corresponding steps in Example 2;

[0070] pSTAT3-S727: corresponding to Example 1;

[0071] pSTAT3-Y705|S727: the same as Example 2, the difference is that when any judgment result of pSTAT3(S727) or pSTAT3(Y705) is positive, the final result is determined as ALK-ALCL, otherwise as negative.

[0072] pSTAT3-Y705&S727: corresponding to Example 2;

[0073] ​From the results, when 165 is used as the pSTAT-Y705 positive threshold, the sensitivity of the differential diagnosis of ALK-ALCL and CD30+ PTCL-NOS expressing CD30 is 0.68, and the specificity is 1; when 145 is used as the pSTAT3-S727 positive threshold, the sensitivity of the differential diagnosis of ALK-ALCL and CD30+ PTCL-NOS expressing CD30 is 0.86, and the specificity is 0.9. Compared with pSTAT3-S727 alone, the combined use of pSTAT3-Y705 and pSTAT3-S727 (pSTAT3-Y705 & S727) can improve the sensitivity of the differential diagnosis, but reduces the specificity; or improves the specificity of the differential diagnosis while reducing the sensitivity. Therefore, pSTAT3-S727 alone can achieve the purpose of differentiating ALK-ALCL and CD30+ PTCL-NOS expressing CD30.

[0074] The preferred embodiments of the present application have been described above with the aid of drawing. Obviously, the present application can be implemented not only in the form of the preferred embodiments, but also in the form of other embodiments. Therefore, the preferred embodiments of the present application are not limited to the above-described embodiments, but cover any and all modifications and equivalents included in the scope of the present application.

Claims

1. Use of a reagent for detecting the expression level of pSTAT3 in a sample in the manufacture of a reagent or kit for discriminating between PTCL-NOS expressing CD30 and ALK-ALCL, characterized in that, The pSTAT3 includes pSTAT3-S727.

2. Use according to claim 1, characterized in that, The pSTAT3 also includes pSTAT3-T705.

3. A training method of a model for differentiating PTCL-NOS expressing CD30 from ALK-ALCL, characterized by, It comprises: Obtaining the expression level of pSTAT3 in the training sample and the corresponding annotation result, the pSTAT3 being the pSTAT3 as claimed in claim 1 or 2; Inputting the expression level of pSTAT3 in the training sample into a pre-constructed identification model to obtain an identification result; the identification model is used to judge whether the sample is CD30-expressing PTCL-NOS or ALK-ALCL according to the expression level of pSTAT3 in the sample; According to the annotation result and the identification result, the model is updated in parameters.

4. A differential device for PTCL-NOS expressing CD30 and ALK-ALCL, characterized by, It comprises: An acquisition module is configured to acquire a detection result of the expression level of pSTAT3 in a to-be-tested sample, the pSTAT3 being the pSTAT3 as claimed in claim 1 or 2; An identification module is configured to input the detection result of the expression level of pSTAT3 in the to-be-tested sample into an identification model trained by the training method as claimed in claim 3 to obtain an identification result of the to-be-tested sample.

5. The differential device of PTCL-NOS expressing CD30 and ALK-ALCL according to claim 4, characterized in that, The identification device further comprises a storage module configured to store the identification model.

6. The differential device of PTCL-NOS expressing CD30 and ALK-ALCL according to claim 4 or 5, characterized in that, The detection result is an immunohistochemical detection result, and the identification model further comprises calculating and judging whether the to-be-tested sample is CD30-expressing PTCL-NOS or ALK-ALCL according to an immunohistochemical score H-Score.

7. The differential device of PTCL-NOS expressing CD30 and ALK-ALCL according to claim 6, characterized in that, The formula for calculating the H-Score is as follows: H-Score = 100 x ∑(i x Pi); wherein i represents the staining intensity, and Pi represents the percentage of positive cells, with a value ranging from 0 to 100%.

8. The differential device of PTCL-NOS expressing CD30 and ALK-ALCL according to claim 7, characterized in that, The staining intensity includes 1, 2 and 3, 1 representing weak, with a light yellow staining result, 2 representing moderate, with a light brown staining result, and 3 representing strong, with a brown or dark brown staining result.

9. The differential device of PTCL-NOS expressing CD30 and ALK-ALCL according to claim 7, characterized in that, The identification model judges whether the to-be-tested sample is CD30-expressing PTCL-NOS or ALK-ALCL based on a positive threshold value: if the H-Score of the sample is greater than the positive threshold value, the sample is judged to be ALK-ALCL; and if the H-Score of the sample is less than or equal to the positive threshold value, the sample is judged to be CD30-expressing PTCL-NOS.

10. The differential device of PTCL-NOS expressing CD30 and ALK-ALCL according to claim 9, characterized in that, When the pSTAT3 is pSTAT3-S727, the positive threshold value is 140-150.

11. The differential device of PTCL-NOS expressing CD30 and ALK-ALCL according to claim 10, characterized in that, When the pSTAT3 is pSTAT3-S727, the positive threshold value is 142-147.

12. The differential device of PTCL-NOS expressing CD30 and ALK-ALCL according to claim 11, characterized in that, When the pSTAT3 is pSTAT3-S727, the positive threshold value is 145.

13. The differential device of PTCL-NOS expressing CD30 and ALK-ALCL according to claim 9, characterized in that, When the pSTAT3 is pSTAT3-T705, the positive threshold value is 160-170.

14. The differential device of PTCL-NOS expressing CD30 and ALK-ALCL according to claim 13, characterized in that, When the pSTAT3 is pSTAT3-T705, the positive threshold value is 163-167.

15. The differential device of PTCL-NOS expressing CD30 and ALK-ALCL according to claim 14, characterized in that, When the pSTAT3 is pSTAT3-T705, the positive threshold value is 165.

16. A training device for a model to discriminate between PTCL-NOS expressing CD30 and ALK-ALCL, characterized in that, It comprises: An acquisition module is configured to acquire a detection result of the expression level of pSTAT3 in a to-be-tested sample, the pSTAT3 being the pSTAT3 as claimed in claim 1 or 2; The identification module is configured to input the pSTAT3 expression level in the training sample into a pre-constructed identification model to obtain an identification result, and the identification model is configured to determine whether the sample is PTCL-NOS or ALK-ALCL according to the pSTAT3 expression level in the sample. The parameter updating module is configured to update parameters of the model according to the labeling result and the identification result.

17. An electronic device, comprising: The electronic device comprises a processor and a memory, and the memory is configured to store a program, and when the program is executed by the processor, the processor implements the training method of the identification model for PTCL-NOS and ALK-ALCL of CD30 expression or the identification method for PTCL-NOS and ALK-ALCL of CD30 expression as claimed in claim 3. The identification method comprises the following steps: obtaining a detection result of the pSTAT3 expression level in a sample to be tested; inputting the detection result of the pSTAT3 expression level in the sample to be tested into the identification model trained by the training method as claimed in claim 3 to obtain an identification result of the sample to be tested.

18. The electronic device of claim 17, wherein, The identification model further comprises calculating and determining whether the sample to be tested is PTCL-NOS or ALK-ALCL according to an immunohistochemical score H-Score.

19. The electronic device of claim 18, wherein, The calculation formula of the H-Score is as follows: H-Score = 100 x ∑ (i x Pi); wherein i represents the staining intensity, and Pi represents the percentage of positive cells, and the value range is 0-100%.

20. The electronic device of claim 19, wherein, The staining intensity comprises 1, 2 and 3, 1 represents weak, and the staining result is light yellow, 2 represents moderate, and the staining result is light brown, and 3 represents strong, and the staining result is brown or dark brown.

21. The electronic device of claim 17, wherein, The identification model determines whether the sample to be tested is PTCL-NOS or ALK-ALCL based on a positive threshold value: if the H-Score of the sample is greater than the positive threshold value, the sample is determined to be ALK-ALCL; and if the H-Score of the sample is less than or equal to the positive threshold value, the sample is determined to be PTCL-NOS of CD30 expression.

22. The electronic device of claim 21, wherein, When pSTAT3 is pSTAT3-S727, the positive threshold value is 140-150.

23. The electronic device of claim 22, wherein, When pSTAT3 is pSTAT3-S727, the positive threshold value is 142-147.

24. The electronic device of claim 23, wherein, When pSTAT3 is pSTAT3-S727, the positive threshold value is 145.

25. The electronic device of claim 21, wherein, When pSTAT3 is pSTAT3-T705, the positive threshold value is 160-170.

26. The electronic device of claim 25, wherein, When pSTAT3 is pSTAT3-T705, the positive threshold value is 163-167.

27. The electronic device of claim 26, wherein, When pSTAT3 is pSTAT3-T705, the positive threshold value is 165.

28. A computer readable medium characterized by: The computer readable medium stores a computer program, and the computer program is executed by the processor to implement the training method of the identification model for PTCL-NOS and ALK-ALCL of CD30 expression or the identification method for PTCL-NOS and ALK-ALCL of CD30 expression as claimed in claim 3; The identification method comprises the following steps: obtaining a detection result of a pSTAT3 expression level in the to-be-tested sample; inputting the detection result of the pSTAT3 expression level in the to-be-tested sample into the discriminant model trained by the training method of claim 3 to obtain a discriminant result of the to-be-tested sample.

29. The computer readable medium of claim 28, wherein, The discriminant model further comprises calculating and judging the to-be-tested sample as the CD30-expressing PTCL-NOS or ALK-ALCL according to an immunohistochemical score H-Score.

30. The computer readable medium of claim 29, wherein, The formula of the H-Score is as follows: H-Score = 100 x ∑(i x Pi); wherein i represents a staining intensity, and Pi represents a percentage of positive cells, and the value range of Pi is 0-100%.

31. The computer readable medium of claim 30, wherein, The staining intensity comprises 1, 2 and 3, wherein 1 represents weak, and the staining result is light yellow, 2 represents moderate, and the staining result is light brown, and 3 represents strong, and the staining result is brown or dark brown.

32. The computer readable medium of claim 30, wherein, The discriminant model judges the to-be-tested sample as the CD30-expressing PTCL-NOS or ALK-ALCL based on a positive threshold value: if the H-Score of the sample is greater than the positive threshold value, the sample is judged as ALK-ALCL; and if the H-Score of the sample is less than or equal to the positive threshold value, the sample is judged as the CD30-expressing PTCL-NOS.

33. The computer readable medium of claim 32, wherein, When the pSTAT3 is pSTAT3-S727, the positive threshold value is 140-150.

34. The computer readable medium of claim 33, wherein, When the pSTAT3 is pSTAT3-S727, the positive threshold value is 142-147.

35. The computer readable medium of claim 34, wherein, When the pSTAT3 is pSTAT3-S727, the positive threshold value is 145.

36. The computer readable medium of claim 32, wherein, When the pSTAT3 is pSTAT3-T705, the positive threshold value is 160-170.

37. The computer readable medium of claim 36, wherein, When the pSTAT3 is pSTAT3-T705, the positive threshold value is 163-167.

38. The computer readable medium of claim 37, wherein, When the pSTAT3 is pSTAT3-T705, the positive threshold value is 165.

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