Method and system for establishing cancer pain prognosis prediction model

Abstract

The invention provides a method and system for establishing a cancer pain prognosis prediction model, and the method comprises the steps: collecting cases, and selecting variable factors which need to be included in the construction of the prediction model; detecting the genotype of each SNP site by adopting gene sequencing; assigning values to dependent variable factors and independent variable factors; a prediction model is established based on Logistic regression analysis; and evaluating the prediction capability of the prediction model by using the Cox-Snell R score and the Neigore R score. The prediction model is established by detecting genotype combination analysis of SNP loci with a great cancer pain prognosis effect and combining clinical characteristics, biochemical indexes and gene detection results, individual prediction is used for each patient, cancer pain patients with poor prognosis are found timely and accurately, the drug test time is shortened, the identification speed is increased, and the detection efficiency is improved. The efficiency of determining the treatment effect of the cancer pain medicine is improved, and insufficient analgesia and serious adverse reaction are avoided.

Description

Technical Field

[0001] This invention relates to the field of intelligent model technology, and more specifically, to a method and system for establishing a cancer pain prognosis prediction model. Background Technology

[0002] Cancer pain is one of the most common complaints among cancer patients, a major cause of loss of work capacity in nearly 55% of cancer patients, and a high-risk factor for adverse outcomes such as depression and death. Therefore, cancer pain management has become a core task in comprehensive cancer treatment.

[0003] Currently, cancer pain treatment primarily relies on the three-step analgesic ladder recommended by the World Health Organization. After treatment with three classes of analgesics of varying intensities—non-steroidal anti-inflammatory drugs (NSAIDs), weak opioids, and strong opioids—80-90% of cancer pain patients experience pain relief. However, 10-20% of cancer pain patients cannot achieve pain relief using the three-step analgesic ladder; this portion of cancer pain is considered to have a poor prognosis. Poor prognosis in cancer pain refers to moderate to severe pain caused by the tumor itself or tumor treatment-related factors, where patients are still dissatisfied with pain relief and / or cannot tolerate adverse reactions after 1-2 weeks of standardized drug treatment.

[0004] Although the patient population with poor prognosis due to cancer pain is very large, at present, it is still necessary to try different levels of analgesics recommended by the three-step analgesic ladder to identify a poor prognosis. Currently, the clinical method for identifying a poor prognosis in cancer pain is to try three different classes of analgesics with varying intensities: nonsteroidal anti-inflammatory drugs (NSAIDs), weak opioids, and strong opioids. Failure to respond to these three classes of analgesics indicates a poor prognosis, allowing for the implementation of appropriate treatment measures.

[0005] However, the traditional method of identifying patients based on their response to the three-step analgesic ladder is problematic. Previous research shows that this trial-and-error process of repeatedly trying different analgesics is not only time-consuming and laborious, but also requires anywhere from two weeks to several months for accurate identification of patients with poor prognosis in cancer pain. During this period, patients endure poor pain control, and their cancer pain is not relieved in a timely manner. They often experience symptoms such as loss of appetite and sleep disturbances, leading to weakness, malnutrition, and increased suffering. This severely impacts the quality of life and treatment outcomes for patients with poor prognosis in cancer pain.

[0006] Furthermore, because traditional identification methods use the same drug trial strategy for all patients, patients with poor prognosis in cancer pain may receive unnecessary treatment drugs and suffer from possible adverse drug reactions, thus affecting the treatment outcome of patients with poor prognosis in cancer pain. Summary of the Invention

[0007] Therefore, the purpose of this invention is to provide a method and system for establishing a cancer pain prognosis prediction model. This method involves detecting the genotypes of SNP loci that have a significant impact on cancer pain prognosis, performing combined analysis of the genotypes at each SNP locus, and combining the patient's clinical characteristics, biochemical indicators, and gene testing results to establish a cancer pain prognosis prediction model. This model provides individualized prediction for each cancer pain patient, enabling timely and accurate identification of whether cancer pain indicates a poor prognosis. It maximizes the identification of patients with poor cancer pain prognosis, reduces the time patients spend on repeated drug trials, and thus improves the efficiency of identifying patients with poor cancer pain prognosis, avoiding insufficient analgesia and serious adverse reactions.

[0008] This invention provides a method for establishing a cancer pain prognosis prediction model, comprising the following steps:

[0009] S1. Collect cases and select the variables to be included in the cancer pain prognosis prediction model, with cancer pain prognosis as the dependent variable and variables closely related to cancer pain prognosis as independent variables.

[0010] The method for collecting the independent variables includes: selecting gene loci with significant prognostic effects on cancer pain, including: UGT2B7 rs7438135 and rs7439366, ABAT rs1641025, P2RX7 rs1718125, P2RY12 rs3732765, OPRM1 rs1799971, COMT The rs4680 assay includes 7 gene SNP loci. Patient experience indicators were obtained, including: the National Gastrointestinal Scale (NGASR) score, the number of family and social distress factors (FSDs), and the Cognitive Pain Inventory (CPI) score. Clinical and biochemical characteristics were also obtained. Clinical characteristics included: pain intensity, breakthrough pain, bone metastases, neuropathic pain, tenderness, and the number of comorbidities. Biochemical indicators included: serum interleukin-6 (IL-6) and serum tumor necrosis factor-α (TNF-α).

[0011] Specifically, a good prognosis for cancer pain is defined as a patient's pain digit score ≤3, breakthrough pain frequency ≤2, and no intolerable adverse drug reactions after 1–2 weeks of standardized drug treatment; a poor prognosis for cancer pain is defined as a patient's pain digit score ≥4, breakthrough pain frequency ≥3, and / or the presence of intolerable adverse drug reactions after 1–2 weeks of standardized drug treatment. Patient experience indicators are obtained through scale testing; clinical characteristics and biochemical indicators are obtained by reviewing patient medical records.

[0012] Based on the patient's clinical characteristics, biochemical indicators, and gene testing results, the system predicts whether cancer pain is associated with a poor prognosis, enabling individualized predictions for each patient. This maximizes the identification of patients with poor prognoses and reduces the time patients spend on repeated drug trials. The predictive model accurately identifies patients with poor prognoses, avoiding inadequate analgesia and severe adverse reactions.

[0013] S2. Gene sequencing was used to detect the genotype of each selected SNP locus;

[0014] S3. Based on the role of the SNP sites in the prognosis of cancer pain, assign values ​​to the dependent variable factors and some independent variable factors respectively;

[0015] S4. Based on Logistic regression analysis, establish a cancer pain prognosis prediction model;

[0016] S5. Cox & Snell R-squared using Logistic Regression Analysis 2 The scores and Nagelkerke R-squared values. 2 The predictive ability of the cancer pain prognosis prediction model is evaluated by the score of the model.

[0017] Cox & Snell R-squared prediction model 2 ) and Nagelkerke R (Nagelkerke R) 2 In the equation, R-squared is the ratio of the regression sum of squares to the total sum of squares. The higher the R-squared value (the closer it is to 1), the better the fit, the more fully the independent variable factors explain the dependent variable factors, indicating that the prediction model has good predictive performance and high prediction accuracy.

[0018] Furthermore, the method for detecting the genotype of each selected SNP locus using gene sequencing in step S1 includes: designing primer sequences for detecting the SNP loci as follows:

[0019] rs7438135-F1 ACGTTGGATGGCAGCATAGACTAGCCAATC

[0020] rs7438135-R1 ACGTTGGATGGAGCATGGAAAGTTCATAGG

[0021] rs7439366-F1 ACGTTGGATGGGTATTTGTCACTGAGAAGC

[0022] rs7439366-R1 ACGTTGGATGACGCAATTCCCTAAGGACTC

[0023] rs1641025-F1 GCATCTCAATTCCAGGCAATA

[0024] rs1641025-R1 GCCCTGGGATGGTTTAGATT

[0025] rs1718125-F1 AGTCCTACAGGGTTCTC

[0026] rs1718125-R1GGCTGGTGCTCTTTGGTA

[0027] rs3732765-F1 ACGTTGGATGGGGTACTTGATGTCAGCAAC

[0028] rs3732765-R1 ACGTTGGATGTTCTTTGATCTCACGGCTTC

[0029] rs1799971-F1 ACGTTGGATGTGAAGGTGCAGCAGACACAG

[0030] rs1799971-R1 ACGTTGGATGGACCTTACATGTTCCGTGTC

[0031] rs4680-F1 ACGTTGGATGAGTGAGGGTCCAATTTCTCG

[0032] rs4680-R1 ACGTTGGATGGCTACTTCATACCTACTGAG.

[0033] Furthermore, the method for assigning values ​​to the dependent variable factors in step S3 includes:

[0034] Based on whether the patient's cancer pain has a good or bad prognosis, the dependent variable factors were classified into two categories and assigned values, as shown in Table 1.

[0035] Table 1 Dependent Variable Assignment

[0036] .

[0037] Furthermore, the method for assigning values ​​to some independent variable factors in step S3 includes:

[0038] Based on the association with cancer pain prognosis, values ​​were assigned to some independent variables closely related to cancer pain prognosis. These independent variables included: pain intensity, breakthrough pain, bone metastases, neuropathic pain, tenderness, serum interleukin-6 (IL-6), serum tumor necrosis factor-α (TNF-α), UGT2B7 rs7438135 and rs7439366, ABATrs1641025, P2RX7 rs1718125, P2RY12 rs3732765, OPRM1 rs1799971, and COMT rs4680 genotype. The remaining independent variables were used in the model construction with their original values. The specific values ​​assigned to some independent variables are shown in Table 2.

[0039] Table 2. Values ​​Assigned to Some Independent Variables

[0040] .

[0041] Furthermore, the method for establishing a cancer pain prognostic prediction model in step S4 includes:

[0042] Using SPSS statistical analysis software, the information of the included variables was input. Cancer pain prognosis was used as the dependent variable, and variables closely related to cancer pain prognosis were used as independent variables. Based on the logistic regression equation, the forward regression method was used to perform regression analysis on the correlation between the independent variables and the dependent variables to construct a cancer pain prognosis prediction model.

[0043] The logistic regression analysis results in this invention showed that patients' suicide risk assessment scale (NGASR) scores, neuropathic pain, number of comorbidities, serum interleukin-6 (IL-6), and SNP loci genotypes P2RX7 rs1718125, P2RY12 rs3732765, and OPRM1 rs1799971 were correlated with cancer pain prognosis.

[0044] Based on the results of logistic regression analysis, the cancer pain prognosis prediction model was established as follows:

[0045] LogitP=-18.918+1.276*NGASR+2.971*neuropathic+0.984*comorbidity+2.627*IL-6+3.483* rs3732765+2.495* rs1799971+2.724* rs1718125.

[0046] A positive LogitP value indicates a poor prognosis for cancer pain, while a negative LogitP value indicates a good prognosis.

[0047] This invention also provides a system for establishing a cancer pain prognosis prediction model, which executes the method for establishing a cancer pain prognosis prediction model as described above, including:

[0048] Data collection module: Used to collect case data and select variables to be included in the construction of a cancer pain prognosis prediction model, with cancer pain prognosis as the dependent variable and variables closely related to cancer pain prognosis as independent variables. The method for collecting these independent variables includes selecting gene loci that have a significant effect on cancer pain prognosis, including: UGT2B7rs7438135 and rs7439366, ABAT rs1641025, P2RX7 rs1718125, P2RY12 rs3732765, OPRM1rs1799971, and COMT. The rs4680 assay includes 7 gene SNP loci. Patient experience indicators were obtained, including: the National Cancer Institute Suicide Risk Assessment (NGASR) score, the number of family and social distress factors (FSDs), and the Cognitive Pain Inventory (CPI) score. Clinical and biochemical characteristics were also obtained, including: pain intensity, breakthrough pain, bone metastasis of tumors, neuropathic pain, identifiable tender points, and the number of comorbid symptoms. Biochemical indicators included: serum interleukin-6 and serum tumor necrosis factor-α.

[0049] Gene sequencing module: Used to detect the genotype of each selected SNP locus using gene sequencing;

[0050] Data processing module: used to assign values ​​to the dependent variable and some independent variable factors based on the role of the SNP loci in cancer pain prognosis;

[0051] Predictive Model Building Module: Used to build a cancer pain prognosis prediction model based on Logistic regression analysis;

[0052] Model performance validation module: Used to evaluate the predictive ability of the cancer pain prognosis prediction model using the Cox-Snell R-squared score and the Nagorno-R-squared score of Logistic regression analysis.

[0053] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0054] The method and system for establishing a cancer pain prognosis prediction model provided by this invention detects the genotypes of SNP loci that have a significant impact on the prognosis of cancer pain in patients, performs combination analysis on the genotypes of each SNP locus, and combines the patient's clinical characteristics, biochemical indicators, and gene testing results to establish a cancer pain prognosis prediction model. Individualized prediction is used for each cancer pain patient. Through the cancer pain prognosis prediction model, it is possible to timely and accurately identify whether cancer pain belongs to a poor prognosis, maximize the identification of patients with poor prognosis, reduce the time patients spend on repeated drug trials, accelerate the identification of poor prognosis, improve the efficiency of determining poor prognosis of cancer pain, and avoid insufficient analgesia and serious adverse reactions. Attached Figure Description

[0055] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention.

[0056] In the attached diagram:

[0057] Figure 1 This is a graph showing the results of a Logistic regression analysis of the correlation between independent and dependent variables in an embodiment of the present invention.

[0058] Figure 2 This is a graph showing the statistical analysis results of the cancer pain prognosis prediction model according to an embodiment of the present invention;

[0059] Figure 3 This is a flowchart illustrating a method for establishing a cancer pain prognosis prediction model according to the present invention. Detailed Implementation

[0060] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and products consistent with some aspects of this disclosure as detailed in the appended claims.

[0061] The terminology used in this disclosure is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The singular forms “a,” “the,” and “the” as used in this disclosure and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any and all possible combinations of one or more of the associated listed items.

[0062] It should be understood that although the terms first, second, third, etc., may be used in this disclosure to describe various information, such information should not be limited to these terms. These terms are used only to distinguish information of the same type from one another. For example, without departing from the scope of this disclosure, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to determination."

[0063] The embodiments of the present invention will be described in further detail below.

[0064] This invention provides a method for establishing a cancer pain prognosis prediction model, see [link to relevant documentation]. Figure 3 As shown, it includes the following steps:

[0065] S1. Collect cases and select the variables to be included in the cancer pain prognosis prediction model, with cancer pain prognosis as the dependent variable and variables closely related to cancer pain prognosis as independent variables.

[0066] This embodiment includes 50 cancer pain patients treated in the pain department of a hospital, of whom 25 had poor prognosis.

[0067] The gene loci with significant prognostic effects on cancer pain were selected. These gene loci include: UGT2B7 rs7438135 and rs7439366, ABAT rs1641025, P2RX7 rs1718125, P2RY12 rs3732765, OPRM1 rs1799971, and COMT rs4680, totaling seven gene SNP loci.

[0068] Patients are tested using scales to obtain their experience indicators, which include: suicide risk assessment scale (NGASR) score, number of family social distress factors (FSD), and pain cognition scale (CPI) score.

[0069] By reviewing the patient's medical records, clinical characteristics and biochemical indicators were obtained;

[0070] The clinical features include: pain intensity, breakthrough pain, bone metastases, neuropathic pain, tenderness, and number of comorbidities.

[0071] The biochemical indicators include: serum interleukin-6 (IL-6) and serum tumor necrosis factor-α (TNF-α).

[0072] S2. Genotypes of selected SNP loci were detected using gene sequencing; the primer sequences designed for detecting SNP loci are as follows:

[0073] rs7438135-F1 ACGTTGGATGGCAGCATAGACTAGCCAATC

[0074] rs7438135-R1 ACGTTGGATGGAGCATGGAAAGTTCATAGG

[0075] rs7439366-F1 ACGTTGGATGGGTATTTGTCACTGAGAAGC

[0076] rs7439366-R1 ACGTTGGATGACGCAATTCCCTAAGGACTC

[0077] rs1641025-F1 GCATCTCAATTCCAGGCAATA

[0078] rs1641025-R1 GCCCTGGGATGGTTTAGATT

[0079] rs1718125-F1 AGTCCTACAGGGTTCTC

[0080] rs1718125-R1GGCTGGTGCTCTTTGGTA

[0081] rs3732765-F1 ACGTTGGATGGGGTACTTGATGTCAGCAAC

[0082] rs3732765-R1 ACGTTGGATGTTCTTTGATCTCACGGCTTC

[0083] rs1799971-F1 ACGTTGGATGTGAAGGTGCAGCAGACACAG

[0084] rs1799971-R1 ACGTTGGATGGACCTTACATGTTCCGTGTC

[0085] rs4680-F1 ACGTTGGATGAGTGAGGGTCCAATTTCTCG

[0086] rs4680-R1 ACGTTGGATGGCTACTTCATACCTACTGAG.

[0087] S3. Based on the role of the SNP sites in the prognosis of cancer pain, assign values ​​to the dependent variable factors and some independent variable factors respectively;

[0088] The method for assigning values ​​to the dependent variable factors is as follows:

[0089] The dependent variable was assigned a binary classification value based on whether the patient's cancer pain had a good or poor prognosis, as shown in Table 1.

[0090] Table 1 Dependent Variable Assignment

[0091] .

[0092] The method for assigning values ​​to some independent variable factors is as follows:

[0093] Based on the association with cancer pain prognosis, values ​​were assigned to some independent variables closely related to cancer pain prognosis. These independent variables included: pain intensity, breakthrough pain, bone metastases, neuropathic pain, tenderness, serum interleukin-6 (IL-6), serum tumor necrosis factor-α (TNF-α), UGT2B7 rs7438135 and rs7439366, ABATrs1641025, P2RX7 rs1718125, P2RY12 rs3732765, OPRM1 rs1799971, and COMT rs4680 genotype. The remaining independent variables were used in the model construction with their original values. In this embodiment, the values ​​assigned to some independent variables are shown in Table 2.

[0094] Table 2. Values ​​Assigned to Some Independent Variables

[0095] .

[0096] S4. Based on Logistic regression analysis, establish a cancer pain prognosis prediction model;

[0097] In this embodiment, the method for establishing a cancer pain prognosis prediction model is as follows: using SPSS statistical analysis software, inputting the information of the included variable factors, taking cancer pain prognosis as the dependent variable factor, and taking variables closely related to cancer pain prognosis as independent variable factors, based on the Logistic regression equation, using the forward method to perform regression analysis on the correlation between independent variable factors and dependent variable factors, and constructing a cancer pain prognosis prediction model.

[0098] S5. Evaluate the predictive ability of the cancer pain prognosis prediction model using the scores of the Cox-Snell R-squared and the Nagorno-R-squared from logistic regression analysis.

[0099] This invention also provides a system for establishing a cancer pain prognosis prediction model, which executes the method for establishing a cancer pain prognosis prediction model as described above, including:

[0100] Data collection module: Used to collect case data and select variables to be included in the construction of a cancer pain prognosis prediction model, with cancer pain prognosis as the dependent variable and variables closely related to cancer pain prognosis as independent variables. The method for collecting these independent variables includes selecting gene loci that have a significant effect on cancer pain prognosis, including: UGT2B7rs7438135 and rs7439366, ABAT rs1641025, P2RX7 rs1718125, P2RY12 rs3732765, OPRM1rs1799971, and COMT. The rs4680 assay includes 7 gene SNP loci. Patient experience indicators were obtained, including: the National Cancer Institute Suicide Risk Assessment (NGASR) score, the number of family and social distress factors (FSDs), and the Cognitive Pain Inventory (CPI) score. Clinical and biochemical characteristics were also obtained, including: pain intensity, breakthrough pain, bone metastasis of tumors, neuropathic pain, identifiable tender points, and the number of comorbid symptoms. Biochemical indicators included: serum interleukin-6 and serum tumor necrosis factor-α.

[0101] Gene sequencing module: Used to detect the genotype of each selected SNP locus using gene sequencing;

[0102] Data processing module: used to assign values ​​to the dependent variable and some independent variable factors based on the role of the SNP loci in cancer pain prognosis;

[0103] Predictive Model Building Module: Used to build a cancer pain prognosis prediction model based on Logistic regression analysis;

[0104] Model performance validation module: Used to evaluate the predictive ability of the cancer pain prognosis prediction model using the Cox-Snell R-squared score and the Nagorno-R-squared score of Logistic regression analysis.

[0105] The Cox & Snell R-squared model for predicting cancer pain prognosis established in this embodiment 2 ) fractions and Nagelkerke R squares 2 The scores were 0.68 and 0.906, respectively, indicating that the cancer pain prognosis prediction model has good predictive performance.

[0106] See Figure 1 As shown in the figure, the results of the logistic regression analysis in this embodiment showed that the patient's suicide risk assessment scale (NGASR) score, neuropathic pain, number of comorbidities, serum interleukin-6 (IL-6), and SNP locus genotypes P2RX7 rs1718125, P2RY12 rs3732765, and OPRM1 rs1799971 were associated with cancer pain prognosis.

[0107] Based on the results of logistic regression analysis, the following prognostic prediction model for cancer pain was established:

[0108] LogitP=-18.918+1.276*NGASR+2.971*neuropathic+0.984*comorbidity+2.627*IL-6+3.483* rs3732765+2.495* rs1799971+2.724* rs1718125.

[0109] A positive LogitP value indicates a poor prognosis for cancer pain, while a negative LogitP value indicates a good prognosis.

[0110] See Figure 2 The Omnibus test results for the model coefficients shown indicate that this cancer pain prognosis prediction model has significant statistical value.

[0111] The technical solution of the present invention has been described in conjunction with preferred embodiments. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions resulting from these changes or substitutions will all fall within the scope of protection of the present invention.

[0112] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for establishing a cancer pain prognosis prediction model, characterized in that, The method comprises the following steps: S1, collecting cases, selecting variable factors to be included in the construction of a cancer pain prognosis prediction model, taking cancer pain prognosis as the dependent variable factor, and taking variables closely related to cancer pain prognosis as the independent variable factor; The collection method of the independent variable factor includes: selecting a gene locus with a large effect on cancer pain prognosis, the gene locus includes: UGT2B7 rs7438135 and rs7439366, ABAT rs1641025, P2RX7 rs1718125, P2RY12 rs3732765, OPRM1 rs1799971, COMT rs4680, a total of 7 gene SNP loci; obtaining patient experience indicators, the experience indicators include: suicide risk assessment scale NGASR score, number of family social distress factors FSD, pain cognition scale CPI score; obtaining clinical characteristics and biochemical indicators, the clinical characteristics include: pain intensity, breakthrough pain, bone metastasis of tumor, neuropathic pain, clear tenderness point, number of combined symptoms, the biochemical indicators include: serum interleukin-6, serum tumor necrosis factor-α; S2, detecting the genotype of each SNP locus selected by gene sequencing; S3, assigning values to the dependent variable factor and part of the independent variable factor in combination with the effect of the SNP locus on cancer pain prognosis; S4, establishing a cancer pain prognosis prediction model based on Logistic regression analysis; S5, using the score value of Cox-Snell R square and the score value of Nagelkerke R square of Logistic regression analysis to evaluate the prediction ability of the cancer pain prognosis prediction model.

2. The method of claim 1, wherein the cancer pain prognosis prediction model is established by using the cancer pain prognosis prediction model establishment program. The method for detecting the genotype of each SNP locus selected by gene sequencing in the S1 step includes: designing the primer sequence used for detecting the SNP locus as follows: rs7438135-F1 ACGTTGGATGGCAGCATAGACTAGCCAATC rs7438135-R1 ACGTTGGATGGAGCATGGAAAGTTCATAGG rs7439366-F1 ACGTTGGATGGGTATTTGTCACTGAGAAGC rs7439366-R1 ACGTTGGATGACGCAATTCCCTAAGGACTC rs1641025-F1 GCATCTCAATTCCAGGCAATA rs1641025-R1 GCCCTGGGATGGTTTAGATT rs1718125-F1 AGTCCTACAGGGTTTCTC rs1718125-R1 GGCTGGTGCTCTTTGGTA rs3732765-F1 ACGTTGGATGGGGTACTTGATGTCAGCAAC rs3732765-R1 ACGTTGGATGTTCTTTGATCTCACGGCTTC rs1799971-F1 ACGTTGGATGTGAAGGTGCAGCAGACACAG rs1799971-R1 ACGTTGGATGGACCTTACATGTTCCGTGTC rs4680-F1 ACGTTGGATGAGTGAGGGTCCAATTTCTCG rs4680-R1 ACGTTGGATGGCTACTTCATACCTACTGAG.

3. The method of claim 1, wherein the method is for establishing a prognosis prediction model for cancer pain. The method of assigning the dependent variable factors in the S3 step comprises: According to the prognosis of cancer pain, the dependent variable factors are assigned in two categories.

4. The method of claim 1, wherein the cancer pain prognosis prediction model is established by using a machine learning algorithm. The method of assigning the partial independent variable factors in the S3 step comprises: According to the correlation with the prognosis of cancer pain, the partial independent variable factors closely related to the prognosis of cancer pain are assigned, including: pain intensity, breakthrough pain, bone metastasis of tumor, neuropathic pain, definite tenderness point, serum interleukin-6, serum tumor necrosis factor-α, UGT2B7 rs7438135 and rs7439366, ABAT rs1641025, P2RX7 rs1718125, P2RY12 rs3732765, OPRM1 rs1799971, COMT rs4680 locus genotypes.

5. The method of claim 1, wherein the method is characterized by, The method of establishing the prognosis prediction model of cancer pain in the S4 step comprises: Using SPSS statistical analysis software, input the included variable factor information, take the prognosis of cancer pain as the dependent variable factor, take the variables closely related to the prognosis of cancer pain as the independent variable factors, use the forward method to perform regression analysis on the correlation between the independent variable factors and the dependent variable factors based on the Logistic regression equation, and construct the prognosis prediction model of cancer pain.

6. A system for establishing a cancer pain prognosis prediction model, characterized by, The method of establishing the prognosis prediction model of cancer pain according to any one of claims 1-5 comprises: A data collection module is configured to collect cases, select variable factors to be included in the construction of the prognosis prediction model of cancer pain, take the prognosis of cancer pain as the dependent variable factor, and take variables closely related to the prognosis of cancer pain as the independent variable factors; the collection method of the independent variable factors comprises: selecting gene loci with a large effect on the prognosis of cancer pain, and the gene loci include: UGT2B7 rs7438135 and rs7439366, ABAT rs1641025, P2RX7 rs1718125, P2RY12 rs3732765, OPRM1 rs1799971, and COMT rs4680, a total of 7 gene SNP loci; obtaining experience indicators of patients, the experience indicators including: suicide risk assessment scale NGASR score, number of family social distress factors FSD, and pain cognition scale CPI score; obtaining clinical characteristics and biochemical indicators, the clinical characteristics including: pain intensity, breakthrough pain, bone metastasis of tumor, neuropathic pain, definite tenderness point, and number of combined symptoms, and the biochemical indicators including: serum interleukin-6 and serum tumor necrosis factor-α. a gene sequencing module for detecting genotypes of each SNP site selected by gene sequencing; a data processing module for assigning values to dependent variable factors and partial independent variable factors respectively in combination with effects of the SNP sites on the prognosis of cancer pain; a module for establishing a prediction model for the prognosis of cancer pain based on Logistic regression analysis; a module for verifying performance of the prediction model for the prognosis of cancer pain by using score values of Cox-Snell R square and Nagelkerke R square of the Logistic regression analysis.

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