CN113963801A - Urinary system calculus postoperative recurrence risk prediction model, urinary system calculus postoperative recurrence risk assessment system and urinary system calculus postoperative recurrence risk assessment method - Google Patents
Urinary system calculus postoperative recurrence risk prediction model, urinary system calculus postoperative recurrence risk assessment system and urinary system calculus postoperative recurrence risk assessment method Download PDFInfo
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Abstract
The invention discloses a model for predicting postoperative recurrence risk of urinary system calculus, which combines clinical characteristics of a patient related to the postoperative recurrence risk of urinary system calculus with an SNP risk value to predict the postoperative recurrence risk of the patient. The invention also discloses a construction method of the model, which comprises the steps of blood sample collection after operation, screening of clinical characteristics and SNP sites related to the recurrence risk after operation, detection statistics of SNP site mutation information, model training, calculation of SNP risk values, construction of nomogram and the like. The invention also discloses a system and a method for evaluating the postoperative recurrence risk of the urinary system calculus, which comprise the model. According to the method, the clinical characteristic indexes of the urinary system calculus are screened and combined with the SNP site mutation information of the patient, so that a prediction model of the postoperative recurrence risk of the urinary system calculus is established, the accuracy of the postoperative recurrence risk assessment of the urinary system calculus is greatly improved, and the subsequent health problems caused by calculus recurrence are reduced.
Description
Technical Field
The invention relates to the field of gene detection, in particular to risk assessment of postoperative recurrence of urinary system calculus, and more particularly relates to construction of a urinary system calculus postoperative recurrence risk prediction model based on high-throughput sequencing platform SNP detection, and a urinary system calculus postoperative recurrence risk assessment system and method based on the model.
Background
Urinary calculus is a common disease. It is estimated that the lifetime prevalence of urinary stones is 5% to 12%. Generally, adult males suffer from urinary stones in a greater proportion than adult females, with a ratio of 2:1 or 3: 1. There are research results showing that the peak of onset of calculus in adults is thirty to sixty years old, and although calculus is relatively rare before the age of 20, the incidence of calculus has been increasing in recent years in children and adolescents. China is one of areas with high urinary calculus occurrence, and many patients have the trouble of repeated attack of calculus. According to epidemic research completed by professor Zeng Guo Hua in the first hospital affiliated to Guangzhou medical university, the prevalence rate of calculus in the urinary system of adults in China is 5.8%, wherein the prevalence rate of men is 6.5%, and the prevalence rate of women is 5.1%, therefore, about 6120 thousands of patients with calculus in the urinary system of adults in China can be estimated, wherein about 3470 thousands of men and about 2650 thousands of women have calculus in urinary system of 1 person almost in 17 persons. In addition, the prevalence rate of urinary system calculus in south China and the southwest area is obviously higher than that in north China and the northwest area; the urinary system calculus prevalence rate (7.8%) of rural residents is obviously higher than that of urban residents (4.9%). Risk factors for urinary stones include sex, age, diet, disease, family history, etc.
Although high-risk factors such as regional conditions, dietary habits, water quality and the like are gradually revealed and controlled under the investigation of epidemic diseases, the incidence rate and the recurrence rate of the urinary calculus are continuously increased, which indicates that some patients are still influenced by the genetic factors, so that the hereditary urinary calculus patients are not effectively intervened and consulted in time. In recent years, with the progress of related genetic research technologies such as chip technology and sequencing technology, the research on genetic factors of urinary calculus has attracted more and more attention. Genome-wide association analysis (GWAS) is a method for analyzing the association between common genetic variation (single nucleotide polymorphism and copy number) and gene population in the Genome-wide range. The whole research is carried out in the whole genome range, the outline overview of the disease can be carried out at one time, and the method is suitable for the research of the complex disease. To date, GWAS has been implicated in more than about 30 complex diseases or phenotypes including tumor, type I and type ii diabetes, coronary heart disease, hypertension, bipolar disorder, and memory, and has identified a large panel of genes associated with each complex disease and phenotype. Plays an important role in the research of complex diseases. In the aspect of urinary system calculus, GWAS research has found that SNP (single nucleotide polymorphism) sites which are obviously related to urinary system calculus risk exist in some risk candidate genes, such as rs1544410, rs2228570, rs731236 and rs7975232 of vitamin D receptor gene (VDR); rs1126616 of the Osteopontin (OPN) gene; rs3752472 of the Klotho gene; rs1501899 and Arg990Gly of calcium ion sensitive receptor (CasR) gene, and the like.
In recent years, genetic polymorphism has been adopted to clarify the pathology and physiological mechanism of lithiasis and the risk of lithiasis, so as to adopt corresponding strategy for prevention. Case-control studies are an alternative or adjunct approach to family studies, including whole genome testing or candidate gene testing. The whole gene detection can be widely applied in the future, but the candidate gene detection is more feasible at present. When the candidate gene detection method of case contrast research is applied, the influence factors such as research design, the method for selecting case group and contrast group, the selection of candidate gene, the functional significance of the polymorphism to be researched and data analysis should ensure that the real association can be detected. Furthermore, linkage disequilibrium and haplotype structure are important for the association analysis of the identified related candidate genes. When a given polymorphic allele is found to be associated with disease, one or more alleles may be further explained on the basis of Linkage Disequilibrium (LD) and haplotype structure. Once a haplotype is determined to carry risk alleles, the true influential haplotype allelic variation can be determined by molecular biological functional analysis.
Despite the extensive research conducted over the past decades, the precise cause of urolithiasis is still not well understood, resulting in high incidence rates and high recurrence rates after treatment, and unsatisfactory preventive measures. Therefore, the method has important clinical significance for researching the cause of urolithiasis. Many aspects of urological calculus still need further research elucidation, and although genetic factors have been studied extensively, there is still no chromosome map of patients with idiopathic hypercalcemia. The only widely recognized conclusion based on genetic studies is that this is a disease with multiple gene defects and insertions. Despite considerable economic and social impact, there is still no effective predictive marker for this disease and many patients are diagnosed after they have symptoms.
Urinary calculus is a multifactorial disease with mixed gene and environmental factors, and about 50-70% of patients with calculus have a close relative with calculus. Whether the increased risk is attributed to genetic, environmental, or confounding factors remains questionable. Curran et al found that limiting calcium intake by family members with a history of stone formation may increase the risk of stone formation. Genetic analysis can identify an individual's susceptibility to calculus, and also help understand the mechanisms of calculus formation and predict susceptibility to drugs and nutrients. It is likely that in the near future, genome typing will enable us to develop appropriate treatments and dietary guidelines based on the genetic profile of an individual to describe the individual's risk of being susceptible to calculus. Recent functional studies of several candidate genes support the existence of autosomal dominant inheritance in family members with a family history of calculus. Such as Vitamin D Receptor (VDR), E-cadherin, Calcitonin receptor gene (CTR), and cytokines have been proposed as relevant candidate genes. The contribution of perhaps a few genes is small and therefore the main genetic factors of urological stones have not been elucidated. The hypothesis that minor changes in gene sequence contribute to urinary stone susceptibility can only determine a pair of genotype-phenotype correlations has been clarified. Polymorphisms, such as variations in repetitive sequences, present in these regions of the entire genome are the basis of linkage studies. Thus, SNP has become a tool for the study of complex diseases, making it possible to find candidate genes for calcium ion metabolism responsible for the etiology of lithiasis.
In addition to the pathogenesis of urinary stones, stone recurrence is a core concern. The calcium oxalate calculi (60% -80%) with the highest proportion of urinary calculi are also the types of calculi with the highest recurrence rate. It is statistically estimated that the urinary calculi have a recurrence rate of about 50% within 10 years, about 75% within 20 years, and some patients experience even 10 or more recurrences during their lifetime. Meanwhile, urinary calculus and recurrence thereof are also important characteristics and risk factors of Chronic Kidney Disease (CKD), and the death rate of China related to urinary calculus is higher than that of European and American countries.
Medication and dietary changes help prevent recurrence of urolithiasis, but these precautions may be economically demanding, difficult to administer, or cause side effects, and if we know which patients are at high risk of developing secondary symptomatic urolithiasis, we can better advise the patient whether they need to take some preventive diet or medication to prevent stone recurrence.
In previous foreign studies, the urinary Stone Recurrence score (ROKS) system has been used to model Stone Recurrence, including more independent risk factors for Recurrence, such as age, sex, family history, Stone side, type, etc. Table 1 is 3 independent studies. The disadvantage of these foreign research prediction models is that they are based only on patient clinical information and do not take into account the genetic background of the individual and are therefore less accurate.
TABLE 1
The national Sichuan university Hospital, Waysi, China discloses a prediction system (Chinese patent application No. CN202011080261.0) for predicting the occurrence of calcium oxalate urinary system calculus, wherein the system combines 3 clinical indexes and 4 relative abundance values of genera, and uses a conventional algorithm to predict the risk of the occurrence of urinary system calculus. However, at present, the research for predicting the recurrence risk based on Chinese population is less, and a prediction model related to the prediction of the recurrence risk of urinary calculus is not available.
Disclosure of Invention
One of the technical problems to be solved by the invention is to provide a model for predicting the postoperative recurrence risk of urinary system calculus, which can accurately predict the postoperative recurrence risk of urinary system calculus.
In order to solve the technical problems, the model for predicting the postoperative recurrence risk of urinary system calculus according to the present invention combines the clinical characteristics of the patient who has undergone urinary system calculus surgery and is related to the postoperative recurrence risk of urinary system calculus with the SNP risk value of the patient to predict the postoperative recurrence risk of urinary system calculus of the patient.
The clinical characteristics related to the postoperative recurrence risk of the urinary system calculus comprise the following 11 clinical characteristics which are obviously related to the postoperative recurrence risk of the urinary system calculus: sex, whether diabetes is present, whether hyperthyroidism is present, the past stone history, the side of the stone, the position of the stone, the size of the stone, whether creatinine is abnormal, whether urinary tract infection is present, whether calcium oxalate stone is present, and whether infectious stone is present.
The SNP risk value is calculated according to the mutation information of 14 SNP loci related to the recurrence risk after urinary system calculus operation, and the 14 SNP loci and the coefficients thereof are shown as the following table:
| numbering | SNP site | Coefficient of |
| 1 | rs10735810 | -0.061126149 |
| 2 | rs11746443 | 0.06320885 |
| 3 | rs13003198 | 0.002622989 |
| 4 | rs13041834 | -0.038376443 |
| 5 | rs1544935 | 0.115736739 |
| 6 | rs2043211 | -0.056319815 |
| 7 | rs2286526 | -0.019386002 |
| 8 | rs3798519 | -0.006515652 |
| 9 | rs4793434 | -0.036131569 |
| 10 | rs56235845 | 0.01600068 |
| 11 | rs6464214 | -0.007046239 |
| 12 | rs7057398 | 0.010441697 |
| 13 | rs755622 | 0.045534189 |
| 14 | rs780093 | -0.039971052 |
The calculation formula of the SNP risk value is as follows:
in the formula, S is the SNP risk value of the sample, beta is the coefficient of the SNP locus, X is the mutation information of the SNP locus corresponding to the sample, and i is the number of the SNP locus.
The mutation information of the SNP site comprises: wild type, value 0; heterozygous mutation, assignment 1; homozygous mutation, value 2.
The recurrence risk comprises one or more of recurrence time, recurrence probability and survival rate.
The second technical problem to be solved by the present invention is to provide a method for constructing the model for predicting the postoperative recurrence risk of urinary calculus, which comprises the following steps:
collecting a blood sample of a patient with urinary system calculus after an operation;
collecting the information of clinical characteristics of the patient possibly related to the postoperative recurrence risk of the urinary system calculus, carrying out single-factor regression analysis, screening out the clinical characteristics related to the postoperative recurrence risk of the urinary system calculus and assigning values;
preliminarily screening candidate SNP loci possibly related to postoperative recurrence risk of urinary system calculus according to literature, detecting and counting mutation information of all candidate SNP loci in each blood sample and assigning values;
randomly dividing the blood sample into a training set and a testing set, taking mutation information of candidate SNP sites in the training set as input data, modeling and verifying by adopting a Lasso-Cox method, selecting an optimal model to obtain SNP sites with nonzero coefficients, and calculating the SNP risk value of each sample according to the following formula:
wherein S is the SNP risk value of the sample, beta is the coefficient of the SNP locus, X is the mutation information of the SNP locus corresponding to the sample, i is the number of the SNP locus with the nonzero coefficient, and m is the total number of the SNP loci with the nonzero coefficient;
and taking the clinical characteristics related to the postoperative recurrence risk of the urinary system calculus and the SNP risk value as variables, constructing a multi-factor regression model, calculating regression coefficients of the variables, calculating the value scores of the variables by taking the maximum regression coefficient as a standard, and constructing a nomogram for predicting the postoperative recurrence risk of the urinary system calculus.
Further, in order to evaluate and verify the performance of the model for predicting the postoperative recurrence risk of urinary system calculus, ROC curves of a training set and a test set can be respectively drawn for evaluating and verifying the performance of the model.
The present invention also provides a system for assessing risk of postoperative recurrence of urinary calculus, comprising the above prediction model, the system comprising:
the input module is used for inputting and transmitting the value of clinical characteristics of the patient related to the postoperative recurrence risk of the urinary system calculus and the value of mutation information of the SNP locus to the calculation module;
the calculation module is internally provided with the urinary system calculus postoperative recurrence risk prediction model constructed by the method, and is used for calculating an SNP risk value according to the mutation information value of the SNP locus, calculating the postoperative recurrence time, recurrence probability and survival rate of the urinary system calculus of the patient by combining the value of the clinical characteristics, and transmitting the time, recurrence probability and survival rate to the output module;
and the output module is used for outputting the time of postoperative recurrence of the calculus of the urinary system of the patient, the recurrence probability and the survival rate which are calculated by the calculation module.
The fourth technical problem to be solved by the present invention is to provide a urinary system calculus postoperative recurrence risk assessment method based on the above assessment system, the method comprising the following steps:
follow-up visits to obtain clinical characteristic information of the patient related to postoperative recurrence risk of the urinary system calculus;
collecting the blood sample of the patient, carrying out nucleic acid extraction and multiplex PCR high-throughput sequencing, and analyzing sequencing data to obtain the mutation information of the SNP locus of the patient related to the postoperative recurrence risk of the urinary system calculus;
inputting the values of the clinical characteristic information and the mutation information of the SNP locus into the urinary system calculus postoperative recurrence risk assessment system, and predicting the urinary system calculus postoperative recurrence time, recurrence probability and survival rate of the patient.
The fifth technical problem to be solved by the present invention is to provide an application of the above prediction model in the postoperative recurrence risk assessment of urinary system calculus, wherein the recurrence risk assessment includes the assessment of one or more of recurrence time, recurrence probability, survival rate, etc.
According to the invention, 1001 samples of urinary system calculus patients who visit for about 7 years are utilized to screen out 11 clinical characteristic indexes and 14 SNP sites related to the recurrence risk of urinary system calculus, and a urinary system calculus postoperative recurrence risk prediction model is established, so that the urinary system calculus postoperative recurrence prediction precision is greatly improved (the 7-year recurrence rate AUC respectively reaches 0.75 and 0.727, and the accuracy is higher than that of foreign published documents), and the follow-up health problem caused by calculus recurrence is favorably reduced. In addition, the urinary system calculus postoperative recurrence risk assessment method disclosed by the invention is based on a high-throughput sequencing technology, can be used for detecting 14-48 SNP (single nucleotide polymorphism) sites possibly related to urinary system calculus recurrence risk at one time, and is high in detection efficiency, simple to operate, low in cost and high in accuracy.
Drawings
FIG. 1 is a parameter screening diagram for the Lasso-Cox method of the training set;
FIG. 2 is a training set column line (Nomogram) diagram;
FIG. 3 is a graph of the results of the training set predicting postoperative recurrence rate of urological calculus;
FIG. 4 is a graph of the training set's model effect (AUC) in predicting postoperative recurrence rate of urological calculus;
FIG. 5 is a graph of the effect (AUC) of the test set model for predicting postoperative recurrence rate of urinary calculus.
Detailed Description
In order to more specifically understand the technical content, characteristics and effects of the present invention, the technical solution of the present invention will be further described in detail with reference to the accompanying drawings and specific embodiments.
Example 1 establishment of evaluation System for Risk of recurrence after urinary calculus surgery
1. Sample source
In this example, 1001 blood samples of patients with urinary system calculus after surgery were taken, wherein 438 cases of calcium oxalate calculus, 7 cases of calcium phosphate calculus, 72 cases of uric acid calculus, 220 cases of infectious calculus, 7 cases of other calculus, and 257 cases of unknown types. Median time for follow-up visit of all cases was 7 years, and median time for relapse-free survival was 4 years.
2. Screening for clinical features
The follow-up visit collected 21 clinical characteristics of the above described urological stone patients and assigned values, see table 2.
TABLE 2 urinary calculus patients' 21 clinical characteristics
Using 21 clinical features in table 2 as input data, calling the surf () function of the R language survivval package to perform single-factor cox regression analysis, and screening out 11 clinical features with p value <0.1, which are statistically significantly related to the postoperative recurrence risk of urinary system calculus (see table 3).
TABLE 3 11 clinical features associated with risk of postoperative recurrence of urologic calculus
3. Screening SNP loci and calculating risk values
According to literature research, 48 candidate SNP loci are collected, and the conditions of wild type, heterozygous mutation and homozygous mutation of all 48 SNP loci in 1001 patients after urinary calculus operation are measured by using a multiplex PCR and high-throughput sequencing detection method, and the statistical results are shown in Table 4.
TABLE 448 statistics of wild type, homozygous and heterozygous mutations for candidate SNP sites
1001 samples are randomly divided into a training set (500 samples) and a testing set (501 samples), mutation information (wild type: 0, heterozygous mutation: 1, homozygous mutation: 2) of 48 sites in the training set is used as input data, a Lasso-Cox method of a glmnet package in an R language is called for modeling and 10-fold cross validation, a model corresponding to a minimum lambda is selected as 0.03013884 (lambda is a parameter for adjusting model complexity, the parameter selection method refers to FIG. 1, the abscissa is a lambda value of log conversion, the ordinate C-Index is a value for evaluating model prediction accuracy, the larger the C-Index value is, the higher the accuracy is, the minimum lambda value is selected by comprehensively considering the model prediction accuracy and the model complexity), and 14 SNP sites with nonzero coefficients are obtained (see Table 5). According to the formula:
(beta: SNP site coefficient, X: mutation information of SNP site corresponding to sample) calculating SNP risk value S (risk score) of each sample.
TABLE 5 14 SNP sites and their coefficients associated with postoperative recurrence of urinary system calculus
4. Constructing a model for predicting postoperative recurrence risk of urinary calculus
Taking the values of 11 clinical features (see table 3) of the training set (500 cases) in the step 3 and the calculated SNP risk value as input data, calling the cph function and the Surv function of the R language survivval package to construct a multi-factor regression model, and calculating regression coefficients (Coef) of the 11 clinical features and the SNP risk value, see table 6.
Regression coefficients for the 612 variables in the Table
Calling a nomogram function of an R language rms packet to establish a nomogram (namely a model for predicting the postoperative recurrence risk of the urinary calculus, see fig. 2), and predicting the postoperative recurrence time of the urinary calculus and the recurrence probabilities of 3 years, 5 years and 7 years (the recurrence probability is 1-survival rate). The score in the nomogrm Nomogram is a scale of the variable; the scores are the names of variables in the prediction model, such as sex, diabetes, SNP risk value, etc.; the line segment corresponding to the variable represents the value range of the variable; the scores of the variables were transformed using the largest of the regression coefficients of all variables as the standard: the maximum regression coefficient variable is the SNP risk value 2.0520 (see Table 6), the value of the SNP risk value of each sample corresponding to 500 training sets ranges from-0.50 to 0.25, the change is 2.0520X (0.25- (-0.50)) to 1.539 per unit change,
(Coef is a regression coefficient corresponding to a variable, X is a value of the variable, X0Value of the variable when the score is 0); the total score is the sum of corresponding scores after all variables are valued, and represents the number of predicted relapse months; 3. the 5-and 7-year survival rates were obtained by follow-up and corresponded to the total score, wherein the survival rate at a certain time point was the survival rate x at the previous time point (survival number at the current time/(survival number at the current time + deletion number)). FIG. 3 is a graph of the relationship between the total score and the probability of relapse plotted according to the nomogram of FIG. 2, wherein the abscissa is the total score of 11 clinical features and SNP risk values and the ordinate is the probability of relapse.
And calling a survivalROC function of the survivalROC package of the R language to draw a time-dependent ROC (receiver operating characteristic) curve to evaluate the model effect (the AUC value is the area formed by the ROC curve and a coordinate axis, and the closer the AUC value is to 1.0, the better the model performance is). The effect of predicting 3-year recurrence probability in the training set was AUC of 0.645, the effect of predicting 5-year recurrence probability was AUC of 0.723, and the effect of predicting 7-year recurrence probability was AUC of 0.75, see fig. 4.
The stability of the model prediction was verified using the test set in step 3 (501 cases). Firstly, calculating the SNP risk value of each sample in a test set according to the mutation information (wild type: 0, heterozygous mutation: 1, homozygous mutation: 2) of 14 SNP sites obtained in the step 3 and the corresponding coefficient beta (see Table 5); then, the SNP risk value and 11 clinical characteristic values (see table 3) obtained in the step 2 are used as input data, and the recurrence probability of the urinary system calculus after operation for 3 years, 5 years and 7 years is predicted by using a nomogram; the model effect was verified by plotting a ROC curve, and the AUC for predicting the 3-year recurrence probability was 0.631, the AUC for predicting the 5-year recurrence probability was 0.708, and the AUC for predicting the 7-year recurrence probability reached 0.727 were obtained, see fig. 5.
5. Urinary system calculus postoperative recurrence risk assessment system
And (4) developing a system for predicting the postoperative recurrence risk assessment of the urinary system calculus by using the urinary system calculus postoperative recurrence risk prediction model obtained in the step (4). The system can predict the time and probability of postoperative calculus recurrence of a patient who has undergone urinary calculus surgery by collecting 11 pieces of clinical characteristic information and 14 pieces of SNP site mutation information of the patient related to the risk of postoperative recurrence of urinary calculus.
The system for predicting the risk of recurrence of the urinary calculus after the operation comprises an input module, a calculation module and an output module, wherein:
an input module for inputting and transmitting 11 clinical characteristic values (including sex, diabetes, hyperthyroidism, past medical history, calculus side, calculus position, calculus size, creatinine abnormality, urinary tract infection, calcium oxalate calculus and infectious calculus) related to the postoperative recurrence risk of the urinary system calculus of the patient to the calculation module, and mutation information values (wild type: 0, heterozygous mutation: 1, homozygous mutation: 2) of 14 SNP loci; the information of the clinical characteristics is obtained according to follow-up; the mutation information of the SNP locus is obtained by adopting a multiplex PCR and high-throughput sequencing method;
the calculation module is connected behind the input module, is internally provided with a trained urinary system calculus postoperative recurrence risk prediction model and is used for calculating an SNP risk value according to the mutation information value of the 14 SNP sites, calculating the calculus recurrence time of the operated urinary system calculus patient, the survival rate and the calculus recurrence probability of 3 years, 5 years and 7 years after operation by combining the values of the 11 clinical characteristics, and transmitting the calculation result to the output module; the calculation module uses a glmnet packet, a survival packet, an rms packet and a survivalROC packet in the R language;
the output module is connected behind the calculation module and used for outputting the time of postoperative recurrence of the patient urinary system calculus, the recurrence probability and the survival rate which are calculated by the calculation module; the recurrence risk after the urinary calculus operation can be predicted according to the recurrence probability value.
Example 21 evaluation of risk of postoperative recurrence in patients with urinary calculus
The information and values of 11 clinical features of the patient with urinary calculus in this example are shown in table 7.
TABLE 71 clinical characteristics of patients with urinary system calculus
| Numbering | Clinical features | Information (value) |
| 1 | Sex | Man (1) |
| 2 | Diabetes mellitus | NO (0) |
| 3 | Hyperthyroidism | NO (0) |
| 4 | History of existing calculus | NO (0) |
| 5 | Calculus side | Is (1) |
| 6 | Location of calculus | NO (0) |
| 7 | Size of calculus | Is (1) |
| 8 | Creatinine abnormality | NO (0) |
| 9 | Urinary tract infection | NO (0) |
| 10 | Calcium oxalate calculus | Is (1) |
| 11 | Infectious calculi | NO (0) |
Using the venous blood sampling sample of the patient to carry out nucleic acid extraction and multiplex PCR high-throughput sequencing, and obtaining the genetic SNP information of the patient through biological information analysis as follows:
TABLE 81 SNP site mutation information of urinary system calculus patients
| Numbering | SNP site | Information (value) |
| 1 | rs10735810 | Wild type (0) |
| 2 | rs11746443 | Wild type (0) |
| 3 | rs13003198 | Wild type (0) |
| 4 | rs13041834 | Wild type (0) |
| 5 | rs1544935 | Wild type (0) |
| 6 | rs2043211 | Heterozygote type (1) |
| 7 | rs2286526 | Wild type (0) |
| 8 | rs3798519 | Heterozygote type (1) |
| 9 | rs4793434 | Heterozygote type (1) |
| 10 | rs56235845 | Wild type (0) |
| 11 | rs6464214 | Wild type (0) |
| 12 | rs7057398 | Wild type (0) |
| 13 | rs755622 | Wild type (0) |
| 14 | rs780093 | Heterozygote type (1) |
Calculating the SNP risk value of the patient: -0.138938088.
The above clinical characteristic information value and SNP risk value of the patient were input into the urinary system calculus postoperative recurrence risk assessment system of example 1, and the patient was predicted to have a urinary system calculus postoperative recurrence of 87 months, with a 3-year recurrence rate of 41%, a 5-year recurrence rate of 67%, and a 7-year recurrence rate of 74%, see table 9.
TABLE 91 prediction of recurrence risk in urological calculus patients
The above embodiments are merely possible and preferred embodiments of the present invention, which are intended to illustrate the present invention and not to limit the scope of the claims of the present invention.
Claims (13)
1. The model for predicting the postoperative recurrence risk of urinary system calculus is characterized in that the clinical characteristics of a patient who has undergone urinary system calculus operation and is related to the postoperative recurrence risk of urinary system calculus are combined with the SNP risk value of the patient by the prediction model, so that the postoperative recurrence risk of urinary system calculus of the patient is predicted.
2. The predictive model of claim 1, wherein the clinical features include the following 11 clinical features that are significantly associated with risk of recurrence after urological calculus: sex, whether diabetes is present, whether hyperthyroidism is present, the past stone history, the side of the stone, the position of the stone, the size of the stone, whether creatinine is abnormal, whether urinary tract infection is present, whether calcium oxalate stone is present, and whether infectious stone is present.
3. The prediction model of claim 1 or 2, wherein the SNP risk value is calculated from the mutation information of 14 SNP sites according to the following formula:
wherein S is the SNP risk value of the sample, beta is the coefficient of the SNP locus, X is the mutation information of the SNP locus corresponding to the sample, and i is the number of the SNP locus;
the 14 SNP sites and the coefficients thereof are shown in the following table:
The mutation information of the SNP site comprises: wild type, value 0; heterozygous mutation, assignment 1; homozygous mutation, value 2.
4. The predictive model of claim 3, comprising a nomogram having variables comprising the 11 clinical features and SNP risk values.
5. The method for constructing a model for predicting the risk of postoperative recurrence of a urinary system calculus as claimed in any one of claims 1 to 4, comprising the steps of:
collecting a blood sample of a patient with urinary system calculus after an operation;
collecting the information of clinical characteristics of the patient possibly related to the postoperative recurrence risk of the urinary system calculus, carrying out single-factor regression analysis, screening out the clinical characteristics related to the postoperative recurrence risk of the urinary system calculus and assigning values;
preliminarily screening candidate SNP loci possibly related to postoperative recurrence risk of urinary system calculus according to literature, detecting and counting mutation information of all candidate SNP loci in each blood sample and assigning values;
randomly dividing the blood sample into a training set and a testing set, taking mutation information of candidate SNP sites in the training set as input data, modeling and verifying by adopting a Lasso-Cox method, selecting an optimal model to obtain SNP sites with nonzero coefficients, and calculating the SNP risk value of each sample according to the following formula:
wherein S is the SNP risk value of the sample, beta is the coefficient of the SNP locus, X is the mutation information of the SNP locus corresponding to the sample, i is the number of the SNP locus with the nonzero coefficient, and m is the total number of the SNP loci with the nonzero coefficient;
and taking the clinical characteristics related to the postoperative recurrence risk of the urinary system calculus and the SNP risk value as variables, constructing a multi-factor regression model, calculating regression coefficients of the variables, calculating the value scores of the variables by taking the maximum regression coefficient as a standard, and constructing a nomogram for predicting the postoperative recurrence risk of the urinary system calculus.
6. The method of claim 5, further comprising the steps of:
drawing an ROC curve of a training set, calculating an AUC value, and evaluating the performance of the urinary system calculus postoperative recurrence risk prediction model;
and drawing an ROC curve of the test set, calculating an AUC value, and verifying the performance of the urinary system calculus postoperative recurrence risk prediction model.
7. The method of claim 5, wherein the clinical characteristics associated with risk of postoperative recurrence of urological stones comprise: sex, whether diabetes is present, whether hyperthyroidism is present, the past stone history, the side of the stone, the position of the stone, the size of the stone, whether creatinine is abnormal, whether urinary tract infection is present, whether calcium oxalate stone is present, and whether infectious stone is present.
8. The method of claim 5, wherein SNP sites with non-zero coefficients comprise: rs10735810, rs11746443, rs13003198, rs13041834, rs1544935, rs2043211, rs2286526, rs3798519, rs4793434, rs56235845, rs 646464214, rs7057398, rs755622 and rs 780093.
9. Urinary system calculus postoperative recurrence risk assessment system, its characterized in that includes:
the input module is used for inputting and transmitting the value of clinical characteristics of the patient related to the postoperative recurrence risk of the urinary system calculus and the value of mutation information of the SNP locus to the calculation module;
the calculation module is internally provided with a prediction model constructed by the method of any one of claims 5 to 8 and used for calculating the SNP risk value according to the mutation information value of the SNP locus, calculating the postoperative recurrence time, recurrence probability and survival rate of the urinary calculus of the patient by combining the value of the clinical characteristics, and transmitting the time, recurrence probability and survival rate to the output module;
and the output module is used for outputting the time of postoperative recurrence of the calculus of the urinary system of the patient, the recurrence probability and the survival rate which are calculated by the calculation module.
10. The system of claim 9, wherein the clinical features comprise: gender, whether diabetes is present, whether hyperthyroidism is present, past stone history, number of stone sides, stone location, stone size, whether creatinine is abnormal, whether urinary tract infection is present, whether calcium oxalate stone is present, and whether infectious stones are present.
11. The system of claim 9, wherein the SNP sites include the following 14 SNP sites: rs10735810, rs11746443, rs13003198, rs13041834, rs1544935, rs2043211, rs2286526, rs3798519, rs4793434, rs56235845, rs 646464214, rs7057398, rs755622 and rs 780093.
12. A method for assessing risk of postoperative recurrence of a urinary system stone according to any of claims 9-11, comprising the steps of:
follow-up visits to obtain clinical characteristic information of the patient related to postoperative recurrence risk of the urinary system calculus;
collecting the blood sample of the patient, carrying out nucleic acid extraction and multiplex PCR high-throughput sequencing, and analyzing sequencing data to obtain the mutation information of the SNP locus of the patient related to the postoperative recurrence risk of the urinary system calculus;
inputting the values of the clinical characteristic information and the mutation information of the SNP locus into the urinary system calculus postoperative recurrence risk assessment system according to any one of claims 9-11, and predicting the postoperative recurrence time, recurrence probability and survival rate of the urinary system calculus of the patient.
13. Use of the predictive model of any one of claims 1 to 4 for assessing the risk of postoperative recurrence of a urinary system calculus.
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| PCT/CN2022/126094 WO2023071877A1 (en) | 2021-10-28 | 2022-10-19 | Prediction model, and evaluation system and method for postoperative recurrence risk of urolithiasis |
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