Method and device for predicting treatment effect of methotrexate on psoriasis

Abstract

The invention provides a method and device for predicting the treatment effect of methotrexate on psoriasis. According to the method for predicting the therapeutic effect of MTX on psoriasis, the PRS is calculated according to related information of 33 SNP loci in a blood sample of a psoriasis patient, the clinical therapeutic effect of MTX on psoriasis can be well predicted according to the calculation result of the PRS, and clinical doctors can be guided to detect and analyze MTX therapy adopted by part of patients in Chinese population; according to the present invention, the basis is provided for the improvement of the clinical treatment effect of the MTX, the important guiding significance is provided for the subsequent clinical treatment of the psoriasis patient, the improvement of the drug treatment efficiency of the psoriasis patient is easily achieved, and the prediction result of the treatment effect of the MTX on the psoriasis is accurate by simultaneously incorporating the related information of the 33 SNP loci so as to provide the basis for the clinical treatment effect of the MTX; and clinical doctors can be guided to formulate suitable MTX treatment intervention schemes according to individual differences of different patients.

Description

Technical Field

[0001] The present invention relates to the technical field of drug efficacy prediction, and in particular to a method and device for predicting the therapeutic efficacy of methotrexate on psoriasis. Background Art

[0002] Psoriasis, also known as psoriasis, is a common skin disease in clinical practice. It is mainly caused by the proliferation of keratinocytes in the body, which leads to incomplete keratinization of the skin, and then causes changes in the ratio of the granular layer and the spinous layer, resulting in redness, swelling, and scaling. The global prevalence of psoriasis is about 2-3%. It has a long course of disease, a low cure rate, and a high recurrence rate. The incidence rate is higher in young and middle-aged people, which seriously affects the physical and mental health and quality of life of patients.

[0003] Methotrexate (MTX) is the first-line systemic drug of choice for the treatment of moderate to severe psoriasis. As a classic immunosuppressant, MTX exerts antiproliferative and immunosuppressive effects in the treatment of psoriasis by inhibiting epidermal cells and various immune cells. While exerting its therapeutic effect, MTX has the outstanding characteristics of convenient administration and low medication cost. However, clinical drug research and medication experience on MTX show that only about 50% of psoriasis patients can effectively control their condition after receiving the drug treatment, and the individual response to the drug efficacy varies greatly, and the drug treatment efficiency needs to be further improved. If the therapeutic efficacy of psoriasis patients receiving methotrexate treatment can be predicted, it will have extremely important guiding significance for the subsequent clinical treatment of psoriasis patients and can improve the drug treatment efficiency of psoriasis patients. Therefore, there is an urgent need to find a method that can predict the efficacy of methotrexate in the treatment of psoriasis. Summary of the invention

[0004] The present invention provides a method and device for predicting the therapeutic efficacy of methotrexate on psoriasis. The method can predict the therapeutic efficacy of psoriasis patients receiving MTX treatment, can provide guidance to clinicians to detect and analyze the use of MTX treatment in some patients in the Chinese population, and provides a basis for improving the clinical efficacy of MTX. At the same time, it has extremely important guiding significance for the subsequent clinical treatment of psoriasis patients, and is conducive to improving the drug treatment efficiency of psoriasis patients.

[0005] According to a first aspect of the present invention, there is provided a method for predicting the therapeutic efficacy of methotrexate on psoriasis, the method comprising the following steps:

[0006] Step 1: Obtain relevant information of SNP sites from blood samples of psoriasis patients receiving methotrexate treatment;

[0007] The SNP sites include rs2279831, rs4713429, rs10947131, rs6532049, rs6857600, rs1466480, rs1609943, rs10817781, rs6787588, rs1233444, rs13388699, rs7562996, rs2231344, rs197614, rs6584604, rs6993405, rs 2074474, rs3762614, rs206016, rs206019, rs2571104, rs12245332, rs12487716, rs2312078, rs11807 161, rs6793362, rs494558, rs3886609, rs62399311, rs75175362, rs12541730, rs1742128, rs7778899;

[0008] Step 2: Calculate the polygenic risk score of psoriasis patients receiving methotrexate treatment based on the relevant information of the SNP sites, and evaluate the therapeutic efficacy of psoriasis patients receiving methotrexate treatment based on the calculated polygenic risk score.

[0009] Methotrexate (MTX) exerts its antiproliferative and anti-inflammatory effects in the treatment of psoriasis mainly by inhibiting the polyglutamyl pathway, folate metabolism, methionine synthesis, de novo purine synthesis, and adenosine synthesis pathways.

[0010] Pharmacogenomics is a discipline that studies the relationship between gene polymorphisms and individual differences in drug action. Through the analysis of gene polymorphisms, we can find genetic markers that can partially explain individual differences in medication use, thereby predicting drug responses and achieving personalized treatment for specific population stratifications.

[0011] Single nucleotide polymorphism (SNP) is the most common heritable variation in humans, accounting for more than 90% of all known polymorphisms.

[0012] Genome-wide association study uses millions of SNPs in the genome as molecular genetic markers to identify genetic markers associated with diseases / traits by comparing differences in genetic variation and their frequencies between case and control groups. It has made important contributions to revealing the highly polygenic structure and complex characteristics of common diseases.

[0013] The inventors of the present application used pharmacogenomics and whole genome association analysis to analyze the DNA in the blood of psoriasis patients receiving MTX treatment. After the initial screening stage, 41 differential sites (SNP sites) related to the MTX therapeutic response were screened out. Then, these 41 SNPs were subjected to genotyping analysis. Through verification, 33 SNP sites related to the MTX therapeutic response were identified as genetic markers for predicting the clinical therapeutic efficacy of MTX for psoriasis. Based on the above findings, the method for predicting the therapeutic efficacy of MTX for psoriasis provided by the present invention obtains relevant information of the 33 SNP sites in the blood samples of psoriasis patients, and calculates the polygenic risk score (PRS) of psoriasis patients receiving MTX treatment according to the relevant information of the above SNP sites. The calculation result of the polygenic risk score can well predict the clinical therapeutic efficacy of MTX for psoriasis, and can guide clinicians to detect and analyze the use of MTX for treatment of some patients in the Chinese population, which provides a basis for improving the clinical efficacy of MTX. At the same time, it has extremely important guiding significance for the subsequent clinical treatment of psoriasis patients, which is beneficial to improving the drug treatment efficiency of psoriasis patients. Moreover, compared with only using a single SNP site, the relevant information of the above 33 SNP sites is simultaneously included for prediction, so that the prediction result of the therapeutic efficacy of MTX for psoriasis is more accurate, which helps to guide clinicians to formulate suitable MTX treatment intervention plans according to the individual differences of different patients.

[0014] Preferably, in step 2, the polygenic risk score of psoriasis patients receiving methotrexate treatment is calculated using the following formula: Where PRS represents the polygenic risk score, m represents the total number of SNP sites included in the calculation, i represents the i-th psoriasis patient, β i represents the effect of the SNP site of the i-th psoriasis patient on the disease, j represents the genotype of the SNP site, ω ij Represents the frequency of each genotype at the SNP locus in individual psoriasis patients.

[0015] Preferably, β i =log 10 OR, where the OR value stands for odds ratio, that is, odds ratio.

[0016] Preferably, the value of j is selected from one of 0, 1, and 2, wherein j=0 indicates no mutation, j=1 indicates a heterozygous mutation, and j=2 indicates a homozygous mutation.

[0017] Preferably, when PRS≥0.05, it is judged that methotrexate has no therapeutic effect on patients with psoriasis; when PRS≤0.033, it is judged that methotrexate has a therapeutic effect on patients with psoriasis.

[0018] Preferably, the relevant information of the above-mentioned SNP site includes the genotype of the SNP site.

[0019] Preferably, in step 2, the method further comprises the step of evaluating the therapeutic efficacy of each psoriasis patient receiving methotrexate treatment in combination with the probability of effectiveness of the psoriasis patient receiving methotrexate treatment. The calculation formula of the probability of effectiveness of the methotrexate treatment for each psoriasis patient is as follows: Where X represents PRS, P(effective X) represents the probability that each psoriasis patient will be effective after receiving methotrexate treatment, and f p (X is valid) represents the probability that PRS ≤ X in the valid group of the PRS data set, f p (X) represents the probability that the PRS value in the PRS data set is ≤X, P(effective) represents the probability that the methotrexate treatment is effective in the PRS data set, and P(ineffective) represents the probability that the methotrexate treatment is ineffective in the PRS data set.

[0020] According to a second aspect of the present invention, a device for predicting the therapeutic effect of methotrexate on psoriasis is provided, the device comprising an information acquisition module and a data analysis module; the information acquisition module is used to obtain relevant information of SNP sites from blood samples of psoriasis patients receiving methotrexate treatment; the SNP sites include rs2279831, rs4713429, rs10947131, rs6532049, rs6857600, rs1466480, rs1609943, rs10817781, rs6787588, rs1233444, rs13388699, rs7562996, rs2231344, rs197614, rs6584604, rs69934 05, rs2074474, rs3762614, rs206016, rs206019, rs2571104, rs12245332, rs12487716, rs2312078, rs11807161, rs6793362, rs494558, rs3886609, rs62399311, rs75175362, rs12541730, rs1742128, rs7778899; The data analysis module is used to calculate the polygenic risk score of psoriasis patients receiving methotrexate treatment based on the relevant information of the SNP sites, and evaluate the therapeutic efficacy of psoriasis patients receiving methotrexate treatment based on the calculated polygenic risk score.

[0021] Preferably, in the above data analysis module, the calculation formula for the polygenic risk score of psoriasis patients receiving methotrexate treatment is as follows: Where PRS represents the polygenic risk score, m represents the total number of SNP sites included in the calculation, i represents the i-th psoriasis patient, β i represents the effect of the SNP site of the i-th psoriasis patient on the disease, j represents the genotype of the SNP site, ω ij Represents the frequency of each genotype at the SNP locus in individual psoriasis patients.

[0022] Preferably, β i =log 10 OR, where the OR value stands for odds ratio, that is, odds ratio.

[0023] Preferably, the value of j is selected from one of 0, 1, and 2, wherein j=0 indicates no mutation, j=1 indicates a heterozygous mutation, and j=2 indicates a homozygous mutation.

[0024] Preferably, when PRS≥0.05, it is judged that methotrexate has no therapeutic effect on patients with psoriasis; when PRS≤0.033, it is judged that methotrexate has a therapeutic effect on patients with psoriasis.

[0025] Preferably, the relevant information of the above-mentioned SNP site includes the genotype of the SNP site.

[0026] Preferably, the data analysis module is also used to calculate the probability of each psoriasis patient receiving effective methotrexate treatment based on the relevant information of the SNP site. The formula for calculating the probability of each psoriasis patient receiving effective methotrexate treatment is as follows: Where X represents PRS, P(effective X) represents the probability that each psoriasis patient will be effective after receiving methotrexate treatment, and f P (X is valid) represents the probability that PRS ≤ X in the valid group of the PRS data set, f p (X) represents the probability that the PRS value in the PRS data set is ≤X, P(effective) represents the probability that the methotrexate treatment is effective in the PRS data set, and P(ineffective) represents the probability that the methotrexate treatment is ineffective in the PRS data set. DETAILED DESCRIPTION

[0027] The following is a further clear and complete description of the technical features in the technical solution provided by the present invention in conjunction with the specific implementation methods. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0028] Example 1

[0029] This example aims to use the Psoriasis Area and Severity Index (PASI) to evaluate the therapeutic efficacy of 569 psoriasis patients treated with methotrexate (MTX) alone. The specific steps are as follows:

[0030] 1. 569 patients with moderate to severe psoriasis were selected

[0031] The inclusion criteria for patients were: (1) meeting the Western medicine diagnostic criteria for psoriasis according to the SIGN collaborative group guidelines; (2) aged 18 to 70 years; (3) the morphology of the rash was plaque psoriasis; (4) no systemic treatment within the past 4 weeks and no topical medication within the past week;

[0032] The exclusion criteria for patients were as follows: (1) pregnant or lactating women, or those who planned to become pregnant within six months; (2) patients with severe primary diseases of other systems; (3) patients with severe disorders of water, electrolytes and acid-base balance; (4) patients with primary or secondary immunodeficiency and hypersensitivity; (5) patients with clinical test indicators of alanine aminotransferase or aspartate aminotransferase increased by more than 1.5 times the upper limit of normal, creatinine increased by more than 1.5 times the upper limit of normal, any of the main indicators of blood routine tests was lower than the lower limit of normal, or other laboratory test abnormalities that the researchers judged to be unsuitable for participation in this trial; (6) patients who were known to be allergic to the drugs used in this study or to related drug ingredients.

[0033] 2. MTX was used to treat the above 569 patients with moderate to severe psoriasis

[0034] The Psoriasis Area and Severity Index (PASI) was used to assess the clinical severity of each patient. Eligible psoriasis patients were given a standardized monotherapy regimen of MTX. The oral dose of MTX started at 7.5 mg per week and increased by 2.5 mg every 2 to 4 weeks to 15 mg per week. Dermatologists followed up with the patients at the beginning of treatment (baseline), 4th week, 8th week, and 12th week, and recorded the patients' PASI scores. During the medication period, in order to minimize the impact of other drugs on the efficacy of MTX, only topical moisturizers were used for skin care. Blood samples were collected from patients who completed 12 weeks of medication follow-up.

[0035] 3. Evaluation of MTX treatment efficacy

[0036] The PASI score was used to assess the clinical severity of each psoriasis patient. Compared with the baseline level before medication, the percentage change of PASI after 12 weeks of medication was used as an indicator to measure the clinical therapeutic efficacy of MTX. The calculation formula of the percentage change of PASI was as follows:

[0037]

[0038] Patients with a PASI percentage change of less than 30% or with no improvement after medication were clearly documented in the absence of a score were classified as non-responders (<PASI 30%); patients with a PASI percentage change of more than 75% or with a significant medication effect were clearly documented in the absence of a score were assigned to good responders (≥PASI 75%).

[0039] The experimental results showed that among all 569 patients with moderate to severe psoriasis who received MTX alone, 234 were included in the good response group and 147 were included in the non-response group.

[0040] Example 2

[0041] This example aims to conduct a two-stage genome-wide association analysis on the drug efficacy response of 569 patients with moderate to severe psoriasis who received MTX alone in Example 1 to determine the gene loci that can be used to predict the efficacy of MTX in the treatment of psoriasis. The specific steps are as follows:

[0042] 1. Genotyping and quality control

[0043] A two-stage genome-wide association study was conducted on the drug efficacy response of the above-mentioned MTX in the treatment of psoriasis; genomic DNA was extracted from patient blood samples using the Qiagen Flexi Gene DNA Kit (QIAGEN, Hilden, Germany); the genotyping test in the initial screening stage used high-throughput genotyping chips designed for the Chinese population, IlluminaHuman Omni ZhongHua-8 Beadchips V1.3 and V1.4 (Illumina, San Diego, CA, USA), which can capture 878291 and 932207 single nucleotide polymorphism (SNPs) sites in the whole genome, respectively, and are commonly used tools for studying the genetic structure of the Chinese population. The data generated by the genotyping chips need to be quality controlled from both the sample and SNP aspects.

[0044] The following unqualified samples were eliminated according to the following criteria: (1) samples with SNPs missing rate > 2%; (2) samples with a mean heterozygosity rate greater than 3 standard deviations; (3) outliers generated in principal component analysis (PCA), that is, individuals falling on one of the two variation axes and whose ancestry is more than 6 standard deviations from the mean; (4) samples with inconsistent gender detection, that is, samples with a calculated X chromosome homozygosity estimate (XHE) of SNPs carried on the X chromosome that is inconsistent with clinical data (XHE>0.80 for males and <0.20 for females).

[0045] The following SNPs that failed quality control were eliminated according to the following criteria: (1) SNPs with a site deletion rate >2% in all samples; (2) SNPs with a Hardy-Weinberg equilibrium p value <1×10 -4 ; (3) Minor allele frequency (MAF) < 1%; (4) All SNPs on chromosomes X and Y.

[0046] During the validation phase, genotyping was performed using a Sequenom MassARRAY system (San Diego, CA, USA) with matrix-assisted laser desorption / ionization time-of-flight mass spectrometry; locus-specific PCR and detection primers were designed using MassarrayAssay Design 3.0 and spectra were analyzed using Massarray Typer (version 4.0).

[0047] Quality control in the validation phase was accomplished through the following two aspects: (1) removing samples with a single SNP or genotype missing rate less than 90%; and (2) removing SNP sites with poor spectral quality and clustering map quality.

[0048] 2. Statistical Analysis

[0049] GraphPad Prism version 5.0 software (GraphPad Software Inc, San Diego, California USA) was used to perform statistical description and statistical analysis on the baseline of the cohort included in this study. Independent sample t test, Mann-Whitney test and χ test were used according to the data type and statistical requirements. 2 test or FISHER exact test.

[0050] Based on the analysis method of categorical variables-response conditions (i.e., divided into non-response group and good response group), the PLINK software was used to perform a statistical analysis of the association between genomic SNPs and anti-methotrexate treatment response in patients with psoriasis using Fisher's Exact test method, where P < 0.05 was considered statistically significant, and the P value was Bonferroni corrected for multiple group comparisons.

[0051] 3. Comparison of clinical characteristics of patients with different efficacy

[0052] A total of 569 psoriasis patients in the clinical cohort of MTX monotherapy completed the 12-week medication follow-up, of which 381 patients were divided into the non-response group (147 patients) and the good response group (234 patients) according to the PASI percentage change value. Among the 461 patients in the initial screening stage, 183 were classified as the good response group and 90 were classified as the non-response group; among the 108 independent samples in the validation stage, 51 were classified as the good response group and 57 were classified as the non-response group.

[0053] 4. 33 loci were identified to be associated with MTX efficacy response

[0054] The whole genome typing chips used in the initial screening stage included two versions, Illumina Human Omni ZhongHua-8Bead-ChipV.1.3 and V.1.4; the results of the genome control method calculation showed that the population structure genome expansion coefficients (λgc) obtained from the two chip typing data were 1.071 and 1.067, respectively, indicating that no population stratification occurred; after unifying the positive and negative chains of the two chip design files and merging the consistent sites, after quality control, 814,315 successfully typed sites were retained for association analysis.

[0055] In the initial screening stage, 41 loci with no heterogeneity in the two chip data (Q>0.1) and weak correlation with the therapeutic response (P<1×10 -4 , OR <0.8 or >1.2) were used for subsequent verification, as shown in Table 1, where the relevant information of the above SNP loci can be found in the NCBI database (https: / / www.ncbi.nlm.nih.gov), CHR is the chromosome number; BP is the physical position; A1 is the minor allele; A2 is the major allele; F_A is the minimum allele frequency in cases; F_U is the minimum allele frequency in controls; CHISQ is the chi-square value; OR is odds ratio, odds ratio; Q is the p-value of Cochrane’s Q statistic.

[0056] By analyzing the typing results of 42 sites, it was found that 33 sites were related to the therapeutic efficacy of MTX in patients with moderate to severe psoriasis (P < 0.05), as shown in Table 2.

[0057] Table 1 41 differential loci associated with MTX efficacy response were screened out in the initial screening stage

[0058]

[0059]

[0060] Table 2 Verification and identification of 33 differential loci associated with MTX efficacy response

[0061]

[0062]

[0063] Example 3

[0064] This embodiment provides a method for predicting the therapeutic efficacy of methotrexate on psoriasis, the method comprising the following steps:

[0065] Step 1: Obtain relevant information of SNP sites from blood samples of psoriasis patients receiving methotrexate treatment;

[0066] The SNP sites include rs2279831, rs4713429, rs10947131, rs6532049, rs6857600, rs1466480, rs1609943, rs10817781, rs6787588, rs1233444, rs13388699, rs7562996, rs2231344, rs197614, rs6584604, rs6993405, rs 2074474, rs3762614, rs206016, rs206019, rs2571104, rs12245332, rs12487716, rs2312078, rs11807 161, rs6793362, rs494558, rs3886609, rs62399311, rs75175362, rs12541730, rs1742128, rs7778899;

[0067] Step 2: Calculate the polygenic risk score of psoriasis patients receiving methotrexate treatment based on the relevant information of the SNP loci, and evaluate the therapeutic efficacy of psoriasis patients receiving methotrexate treatment based on the calculated polygenic risk score;

[0068] The polygenic risk score for patients with psoriasis receiving methotrexate treatment was calculated as follows: Where PRS represents the polygenic risk score, m represents the total number of SNP sites included in the calculation, i represents the i-th psoriasis patient, β i represents the effect of the SNP site of the i-th psoriasis patient on the disease, j represents the genotype of the SNP site, ω ij Represents the frequency of each genotype at the SNP locus in individual psoriasis patients.

[0069] When PRS≥0.05, it is judged that methotrexate has no therapeutic effect on patients with psoriasis; when PRS≤0.033, it is judged that methotrexate has therapeutic effect on patients with psoriasis.

[0070] Furthermore, β i =log 10 OR, where the OR value stands for odds ratio, that is, odds ratio.

[0071] Furthermore, the value of j is selected from one of 0, 1, and 2, wherein j=0 indicates no mutation, j=1 indicates a heterozygous mutation, and j=2 indicates a homozygous mutation.

[0072] Furthermore, the relevant information of the above-mentioned SNP site includes the genotype of the SNP site.

[0073] Furthermore, in step 2, there is also a step of evaluating the therapeutic efficacy of each psoriasis patient receiving methotrexate treatment in combination with the probability of effectiveness of methotrexate treatment for each psoriasis patient. The calculation formula for the probability of effectiveness of methotrexate treatment for each psoriasis patient is as follows: Where X represents PRS, P(effective X) represents the probability that each psoriasis patient will be effective after receiving methotrexate treatment, and f p (X is valid) represents the probability that PRS ≤ X in the valid group of the PRS data set, f p (X) represents the probability that the PRS value in the PRS data set is ≤X, P(effective) represents the probability that the methotrexate treatment is effective in the PRS data set, and P(ineffective) represents the probability that the methotrexate treatment is ineffective in the PRS data set.

[0074] Example 4

[0075] The present embodiment provides a device for predicting the therapeutic effect of methotrexate on psoriasis, the device comprising an information acquisition module and a data analysis module; the information acquisition module is used to obtain relevant information of SNP sites from blood samples of psoriasis patients receiving methotrexate treatment; the SNP sites include rs2279831, rs4713429, rs10947131, rs6532049, rs6857600, rs1466480, rs1609943, rs10817781, rs6787588, rs1233444, rs13388699, rs7562996, rs2231344, rs197614, rs6584604, rs6993405, rs2074474, rs3762614, rs2 06016, rs206019, rs2571104, rs12245332, rs12487716, rs2312078, rs11807161, rs6793362, rs494558, rs3886609, rs62399311, rs75175362, rs12541730, rs1742128, rs7778899; the data analysis module is used to calculate the polygenic risk score of psoriasis patients receiving methotrexate treatment according to the relevant information of the SNP sites, and evaluate the therapeutic efficacy of psoriasis patients receiving methotrexate treatment according to the calculated polygenic risk score; among them, in the above data analysis module, the calculation formula of the polygenic risk score of psoriasis patients receiving methotrexate treatment is as follows: Where PRS represents the polygenic risk score, m represents the total number of SNP sites included in the calculation, i represents the i-th psoriasis patient, β i represents the effect of the SNP site of the i-th psoriasis patient on the disease, j represents the genotype of the SNP site, ω ij Represents the frequency of each genotype at the SNP locus in individual psoriasis patients.

[0076] When PRS≥0.05, it is judged that methotrexate has no therapeutic effect on patients with psoriasis; when PRS≤0.033, it is judged that methotrexate has therapeutic effect on patients with psoriasis.

[0077] Furthermore, β i =log 10 OR, where the OR value stands for odds ratio, that is, odds ratio.

[0078] Furthermore, the value of j is selected from one of 0, 1, and 2, wherein j=0 indicates no mutation, j=1 indicates a heterozygous mutation, and j=2 indicates a homozygous mutation.

[0079] Furthermore, the relevant information of the above-mentioned SNP site includes the genotype of the SNP site.

[0080] Furthermore, the data analysis module is also used to calculate the probability of each psoriasis patient receiving effective methotrexate treatment based on the relevant information of the SNP site. The formula for calculating the probability of each psoriasis patient receiving effective methotrexate treatment is as follows: Where X represents PRS, P(effective X) represents the probability that each psoriasis patient will be effective after receiving methotrexate treatment, and f P (X is valid) represents the probability that PRS ≤ X in the valid group of the PRS data set, f p (X) represents the probability that the PRS value in the PRS data set is ≤X, P(effective) represents the probability that the methotrexate treatment is effective in the PRS data set, and P(ineffective) represents the probability that the methotrexate treatment is ineffective in the PRS data set.

[0081] Example 5

[0082] The present embodiment aims to construct a polygenic risk score (PRS) database for patients with moderate to severe psoriasis, and predict the therapeutic efficacy of psoriasis patients receiving methotrexate treatment by combining the method for predicting the therapeutic efficacy of methotrexate for psoriasis provided in Example 3 or the device for predicting the therapeutic efficacy of methotrexate for psoriasis provided in Example 4. Specifically, PRSice-2 software is used to calculate PRS, and a PRS database of 381 patients is constructed (the construction method refers to Choi SW, and O'Reilly PF. "PRSice-2: Polygenic Risk Score Software for Biobank-Scale Data." GigaScience 8, no. 7 (July 1, 2019).). PRSice-2 software uses single nucleotide polymorphism (SNPs) clustering (refining SNPs according to linkage disequilibrium and p-value) to set p-value thresholds, namely the "C+T" method (for p-value threshold setting methods, refer to Choi SW, Mak TSH, O'Reilly PA guide to performing Polygenic Risk Score analyses. bioRxiv 2018, doi: 10.1101 / 416545), and retains most of the features of its previous generation PRSice method (for previous generation methods, refer to Euesden J, Lewis CM, O'Reilly PF. PRSice: Polygenic risk score software. Bioinformatics 2015; 31: 1466–8.), including automatic chain flipping and clustering (for methods, refer to Wray NR, Lee SH, Mehta D, et al. Research review: Polygenic methods and their application to psychiatric traits. J Child Psychol Psychiatry 2014; 55: 1068–87.), and calculate and evaluate PRS at a few (“fast score”) or multiple (“high resolution score”) p-value thresholds. The new generation PRSice-2 calculation formula is as follows: Where PRS represents the polygenic risk score, m represents the total number of SNP sites included in the calculation, i represents the i-th psoriasis patient, β i represents the effect of the SNP site of the i-th psoriasis patient on the disease, j represents the genotype of the SNP site, ω ij Represents the frequency of each genotype at the SNP locus in individual psoriasis patients.

[0083] The polygenic risk scores (PRS) of 381 patients with moderate to severe psoriasis were calculated and the results are shown in Table 3, where gender 1 is male and 2 is female; regarding effectiveness, 1 is effective (PASI ≥ 75%) and 2 is ineffective (PASI < 30%).

[0084] Table 3 PRS calculation results of 381 patients with moderate to severe psoriasis

[0085]

[0086]

[0087]

[0088]

[0089]

[0090] Table 4 PRS statistics of effective and ineffective groups

[0091]

[0092] The data in Table 3 were subjected to an independent sample T test, and the results are shown in Table 4. As shown in Table 4, the PRS score of the effective group was significantly lower than that of the ineffective group (P = 7.513E-034). When PRS ≤ 0.033, the accuracy of judging the effective group was 79.3% (200 / 252), and when PRS ≥ 0.05, the accuracy of judging the ineffective group was 79.79% (75 / 94).

[0093] Example 6

[0094] The purpose of this example is to obtain information on the 33 SNP sites screened in Example 2, and to predict the genotyping and risk of the samples to be tested by designing upstream and downstream primers and single-base extension primers (UEP primers) used for PCR amplification of each SNP site. The upstream and downstream primers and UEP primers used for PCR amplification of each SNP site are shown in Table 5.

[0095] Table 5 Upstream and downstream primers and UEP primers used for PCR amplification of 33 loci

[0096]

[0097]

[0098] The steps for predicting the genotyping and risk of the samples to be tested are as follows:

[0099] 1. Design upstream and downstream primers and UEP primers for PCR amplification of 33 SNP sites

[0100] The upstream and downstream primers and UEP primers used for PCR amplification of each SNP site are shown in Table 5.

[0101] 2. DNA Extraction

[0102] DNA was extracted from the blood of patients with moderate to severe psoriasis using a commercial DNA extraction kit. The extracted DNA could be used immediately or stored at -20°C.

[0103] 3. PCR Amplification

[0104] The DNA extracted above was amplified by PCR using the primers in Table 5 according to the following procedure: pre-denaturation at 94°C for 5 min; denaturation at 94°C for 30 s, annealing at 56°C for 30 s, extension at 72°C for 45 s, and 35 cycles; finally extension at 72°C for 10 min; standing at 4°C to obtain PCR amplification products.

[0105] 4. Purification of PCR Amplification Products

[0106] The PCR amplification products obtained above were purified using a commercial PCR purification product kit.

[0107] 5. Detection of PCR amplification products

[0108] The purified amplification products were analyzed by mass spectrometry using AGENA mass spectrometer.

[0109] 6. Calculate PRS

[0110] The PRSice-2 software was used to calculate the PRS of each group of samples, and the existing PRS database was used to calculate the probability of score distribution to predict the therapeutic efficacy of MTX for patients with moderate to severe psoriasis. At the same time, the Bayesian theorem was used to calculate the probability:

[0111] Assuming that the PRS score of the new sample is X, according to Bayesian quantification, the effective posterior probability of the new sample after using MTX is: Where X represents PRS, P(effective X) represents the probability that each psoriasis patient will be effective after receiving methotrexate treatment, and f P (X is valid) represents the probability that PRS ≤ X in the valid group of the PRS data set, f p (X) represents the probability that the PRS value in the PRS data set is ≤X, P(effective) represents the probability that the methotrexate treatment is effective in the PRS data set, and P(ineffective) represents the probability that the methotrexate treatment is ineffective in the PRS data set.

[0112] The above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit the protection scope of the present invention. Although the present invention is described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the technical solutions of the present invention can be modified or equivalently replaced, but these modifications or replacements are all within the protection scope of the present invention.

Claims

1. A method for predicting the therapeutic efficacy of methotrexate on psoriasis, characterized in that: The following steps are involved: Step 1, obtaining relevant information of SNP sites from blood samples of psoriasis patients receiving methotrexate treatment; The SNP sites include rs2279831, rs4713429, rs10947131, rs6532049, rs6857600, rs1466480, rs1609943, rs10817781, rs6787588, rs1233444, rs13388699, rs7562996, rs2231344, rs197614, rs6584604, rs6993405, s2074474, rs3762614, rs206016, rs206019, rs2571104, rs12245332, rs12487716, rs2312078, rs11807 161, rs6793362, rs494558, rs3886609, rs62399311, rs75175362, rs12541730, rs1742128, rs7778899; Step 2: Calculate the polygenic risk score of psoriasis patients receiving methotrexate treatment based on the relevant information of the SNP site, and evaluate the therapeutic efficacy of psoriasis patients receiving methotrexate treatment based on the calculated polygenic risk score.

2. The method for predicting the therapeutic efficacy of methotrexate on psoriasis according to claim 1, characterized in that: In step 2, the polygenic risk score of psoriasis patients receiving methotrexate treatment is calculated as follows: Where PRS represents the polygenic risk score, m represents the total number of SNP sites included in the calculation, i represents the i-th psoriasis patient, β i represents the effect of the SNP site of the i-th psoriasis patient on the disease, j represents the genotype of the SNP site, ω ij Represents the frequency of each genotype at the SNP locus in individual psoriasis patients.

3. The method for predicting the therapeutic effect of methotrexate on psoriasis according to claim 2, characterized in that: When PRS≥0.05, it is judged that methotrexate has no therapeutic effect on patients with psoriasis; when PRS≤0.033, it is judged that methotrexate has therapeutic effect on patients with psoriasis.

4. The method for predicting the therapeutic efficacy of methotrexate on psoriasis according to claim 1, characterized in that: The relevant information of the SNP site includes the genotype of the SNP site.

5. The method for predicting the therapeutic efficacy of methotrexate on psoriasis according to claim 1, characterized in that: In the step 2, there is also a step of evaluating the therapeutic efficacy of each psoriasis patient receiving methotrexate treatment in combination with the probability of effectiveness of the psoriasis patient receiving methotrexate treatment. The calculation formula for the probability of effectiveness of each psoriasis patient receiving methotrexate treatment is as follows: Where X represents PRS, P(effective X) represents the probability that each psoriasis patient will be effective after receiving methotrexate treatment, and f p (X|valid) represents the probability that PRS ≤ X in the valid group of the PRS dataset, f p (X) represents the probability that the PRS value in the PRS data set is ≤X, P(effective) represents the probability that the methotrexate treatment is effective in the PRS data set, and P(ineffective) represents the probability that the methotrexate treatment is ineffective in the PRS data set.

6. A device for predicting the therapeutic effect of methotrexate on psoriasis, characterized in that: The device includes an information acquisition module and a data analysis module; The information acquisition module is used to obtain relevant information of SNP sites from blood samples of psoriasis patients receiving methotrexate treatment; The SNP sites include rs2279831, rs4713429, rs10947131, rs6532049, rs6857600, rs1466480, rs1609943, rs10817781, rs6787588, rs1233444, rs13388699, rs7562996, rs2231344, rs197614, rs6584604, rs6993405, s2074474, rs3762614, rs206016, rs206019, rs2571104, rs12245332, rs12487716, rs2312078, rs11807 161, rs6793362, rs494558, rs3886609, rs62399311, rs75175362, rs12541730, rs1742128, rs7778899; The data analysis module is used to calculate the polygenic risk score of psoriasis patients receiving methotrexate treatment based on the relevant information of the SNP site, and to evaluate the therapeutic efficacy of psoriasis patients receiving methotrexate treatment based on the calculated polygenic risk score.

7. The device for predicting the therapeutic efficacy of methotrexate on psoriasis according to claim 6, characterized in that: In the data analysis module, the polygenic risk score for psoriasis patients receiving methotrexate treatment is calculated as follows: Where PRS represents the polygenic risk score, m represents the total number of SNP sites included in the calculation, i represents the i-th psoriasis patient, β i represents the effect of the SNP site of the i-th psoriasis patient on the disease, j represents the genotype of the SNP site, ω ij Represents the frequency of each genotype at the SNP locus in individual psoriasis patients.

8. The device for predicting the therapeutic efficacy of methotrexate on psoriasis according to claim 7, characterized in that: When PRS≥0.05, it is judged that methotrexate has no therapeutic effect on patients with psoriasis; when PRS≤0.033, it is judged that methotrexate has therapeutic effect on patients with psoriasis.

9. The device for predicting the therapeutic efficacy of methotrexate on psoriasis according to claim 6, characterized in that: The relevant information of the SNP site includes the genotype of the SNP site.

10. The device for predicting the therapeutic efficacy of methotrexate on psoriasis according to claim 6, characterized in that: The data analysis module is also used to calculate the probability of each psoriasis patient receiving effective methotrexate treatment based on the relevant information of the SNP site. The formula for calculating the probability of each psoriasis patient receiving effective methotrexate treatment is as follows: Where X represents PRS, P(effective X) represents the probability that each psoriasis patient will be effective after receiving methotrexate treatment, and f P (X is valid) represents the probability that PRS ≤ X in the valid group of the PRS data set, f p (X) represents the probability that the PRS value in the PRS data set is ≤X, P(effective) represents the probability that the methotrexate treatment is effective in the PRS data set, and P(ineffective) represents the probability that the methotrexate treatment is ineffective in the PRS data set.

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