Radiation dose evaluation model related to gene expression level and application
By constructing a multivariate linear regression model based on MDM2 and TNFSF4 gene expression levels, the problem of difficulty in quickly and accurately evaluating human radiation dose in the prior art is solved, and accurate evaluation of multiple ray types and applicability of complex scenes is achieved.
Patent Information
- Application Number
- CN202311142892.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2023-09-06
- Publication Date
- 2025-01-10
AI Technical Summary
The prior art is difficult to quickly and accurately evaluate the dose of nuclear radiation exposure in the human body, especially in the case of multiple radiation types and trauma and burn compound injuries, and the existing genetic testing methods are interfered with by trauma and burn factors, making it difficult to detect in large quantities.
A multivariate linear regression model based on MDM2 and TNFSF4 gene expression levels was constructed, and a radiation dose evaluation model was constructed through microarray chip data and SPSS software. This model can accurately determine the radiation dose in the human body based on the detection of gene expression. Unaffected by trauma and burn factors.
Accurate evaluation of radiation doses of various types of radiation is achieved, with high accuracy and good linearity, and is suitable for radiation exposure dose detection in complex sites, especially battlefields and nuclear accident sites.
Smart Images
Figure SMS_5 
Figure SMS_7 
Figure SMS_8
Abstract
Description
Technical Field
[0001] The invention belongs to the field of biotechnology and nuclear radiation, and specifically relates to a radiation dose assessment model related to gene expression level and its application. Background Art
[0002] With the rapid development of nuclear technology and regional instability, the risk of radioactive accidents and nuclear war is increasing. Therefore, it is very necessary to provide a new detection method for accurately and quickly detecting human radiation exposure dose.
[0003] The clinical manifestations and prognosis of patients after nuclear radiation exposure are significantly correlated with the radiation dose. In general, as the radiation dose gradually increases, complications and mortality increase significantly. Therefore, how to accurately assess the radiation dose of individuals in the early stages of nuclear exposure is one of the key points in the treatment of nuclear radiation injuries. However, there have been very few sudden nuclear incidents in the world, and countries clearly lack experience in responding to such incidents.
[0004] The field of radiation biodosimetry has made great progress in the past few years. A lot of work and efforts have been made by predecessors around the exploration of biological markers that can predict radiation doses. The methods that have been reported to assess individual radiation doses can be roughly divided into the following categories: 1. Estimating radiation doses by wearing personal dosimeters and testing environmental samples; 2. Cytogenetic examinations (detecting lymphocyte chromosome aberration rates and micronuclei); 3. Genetic level testing (detecting the expression of specific genes); 4. Protein and metabolic level testing (detecting specific protein content and small molecule biological markers of 50 to 150 Da in body fluids). In the first method, wearing a personal dosimeter is difficult to implement in practical applications and difficult to apply on a large scale, and estimating radiation doses through environmental samples has a large error; the second method is mainly to collect blood within 24 hours after irradiation and culture it in vitro for 48-72 hours to observe the lymphocyte chromosome aberration rate. The relationship between the aberration rate and the dose is a quadratic polynomial equation: y = aD 2+bD+C (where y is the total distortion rate, a is the secondary hit coefficient, b is the primary hit coefficient, c is the spontaneous distortion rate, and D is the irradiation dose). This method has the main disadvantages of long detection time, cumbersome steps, high requirements for personnel, and difficulty in large-scale detection; therefore, the third and fourth methods have gradually become research trends and hotspots this year. However, nuclear radiation damage is common in battlefields, terrorist attacks, nuclear power plant accidents (such as reactor explosions), etc. In addition to nuclear radiation damage, the casualties of such events are likely to have combined injuries such as trauma and burns. Therefore, although some radiation-related genes have been reported, most of these studies are limited to a single ray, and no one has studied whether these radiation-related genes are also responsive to trauma, burns, etc. If the expression of a radiation-sensitive gene is also affected by trauma or burns, then such a gene will not be used to assess the radiation exposure dose of casualties with combined trauma and burns.
[0005] Therefore, it is necessary to study and discover several genes that have obvious responses to multiple types of radiation and are immune to trauma and burn factors. The expression levels of these genes change significantly after the human body is irradiated. By constructing a radiation dose assessment model based on the expression levels of these genes, the radiation dose of the human body can be accurately determined using this assessment model. Summary of the invention
[0006] The purpose of the present invention is to provide a radiation dose assessment model related to gene expression levels. The assessment model belongs to a multiple linear regression model, and the radiation dose of a human body can be accurately determined based on the detection of gene expression levels.
[0007] To achieve the purpose of the present invention, the following embodiments are provided.
[0008] In one embodiment, a radiation dose assessment model associated with gene expression levels of the present invention, wherein the genes are MDM2 and TNFSF4, is constructed by the following method, which comprises the following steps:
[0009] 1) Obtaining microarray chip data of human peripheral blood samples after being irradiated with different types of radiation;
[0010] 2) Using the expression levels of MDM2 and TNFSF4 genes in the microarray chip data as independent variables and the radiation dose as the dependent variable, a multiple linear regression equation was constructed using SPSS software to obtain the radiation dose assessment model.
[0011] In the radiation dose assessment model of the present invention, the expression levels of MDM2 and TNFSF4 are not affected by trauma and burn factors.
[0012] In the radiation dose assessment model of the present invention, the MDM2 and TNFSF4 genes, the Gene ID of MDM2 in NCBI is 4193, and the Gene ID of TNFSF4 in NCBI is 7292.
[0013] In the radiation dose assessment model of the present invention, the different types of rays in step 1) include X-rays, gamma rays and / or neutrons.
[0014] In another embodiment, the present invention provides use of the radiation dose assessment model of the present invention for detecting or assessing radiation exposure dose of a human body.
[0015] The above-mentioned use of the present invention, the detection or evaluation, includes:
[0016] 1) Detect the gene expression of MDM2 and TNFSF4 by RNA quantitative detection technology such as q-PCR, gene chip or transcriptome sequencing;
[0017] 2) The detected MDM2 and TNFSF4 gene expression levels are input into the radiation dose assessment model to obtain the predicted value of the radiation dose.
[0018] In a specific embodiment, a radiation dose assessment model associated with gene expression levels of the present invention, wherein the genes are MDM2 and TNFSF4, is constructed by the following method, which comprises the following steps:
[0019] 1) obtaining microarray chip data of human peripheral blood samples after being irradiated with different types of radiation, wherein the different types of radiation include X-rays, gamma rays and / or neutrons;
[0020] 2) Using the expression levels of MDM2 and TNFSF4 genes in the microarray chip data as independent variables and the radiation dose as the dependent variable, a multiple linear regression equation was constructed using SPSS software to obtain the radiation dose assessment model.
[0021] Wherein, the expression levels of MDM2 and TNFSF4 are not affected by trauma and burn factors, and the microarray chip data in step 1) comes from the Gene Expression Omnibus (GEO) database.
[0022] Technical effects:
[0023] The present invention constructs a radiation dose assessment model related to gene expression level. The assessment model belongs to a multiple linear regression model, and the radiation exposure dose of the human body can be accurately determined based on the detection of the MDM2 and TNFSF4 gene expression levels using the assessment model, and the model has good linearity and high accuracy. In addition, the model is applicable to a variety of rays for assessing the radiation exposure dose of the human body based on the MDM2 and TNFSF4 expression levels, including X-rays, gamma rays and / or neutron radiation, and in particular, the expression of the MDM2 and TNFSF4 genes involved in the model is not interfered by trauma and burn factors, and has radiation specificity. Therefore, it is also suitable for radiation exposure dose detection of wounded at nuclear exposure sites such as more complex battlefields and reactor explosions. BRIEF DESCRIPTION OF THE DRAWINGS
[0024] Figure 1 These are the results of the R package "limma" screening of differentially expressed genes for each radiation data set. A, C, and E represent heat maps, showing the top 30 up-regulated genes and the top 30 down-regulated genes for X-rays, neutrons, and gamma rays; B, D, and F represent volcano plots, showing the distribution of differentially expressed genes for X-rays, neutrons, and gamma rays.
[0025] Figure 2 This is the result of weighted gene co-expression network analysis of various radiation data sets by the WGCNA software package, where AB is the X-ray related key module, CD is the neutron related key module, and EF is the gamma ray related key module. Figure 3 The intersection graphs were taken for X-ray, neutron, and γ-ray related DEGs and genes in the X-ray, neutron, and γ-ray related key gene modules.
[0026] Figure 4 The following are the results of transcriptome sequencing analysis of clinical samples from patients with severe trauma and severe burns. A is a volcano plot showing the distribution of differentially expressed genes in patients with severe trauma, and B is a volcano plot showing the distribution of differentially expressed genes in patients with severe burns.
[0027] Figure 5 The intersection graph of trauma- and burn-related DEGs and 33 multi-type radiation-induced genes was obtained.
[0028] Figure 6 Figure 2 is the ROC analysis result of 12 radiation-specific genes, where AC is the X-ray data set, DF is the neutron data set, GI is the γ-ray data set, and J is the bar graph of the average AUC values of the 12 genes in the three radiation types.
[0029] Figure 7Figure 1 is a statistical graph of the correlation between radiation dose prediction value (MLR prediction) and radiation dose, where A is a lollipop graph of the correlation between radiation dose prediction value and the 8 genes with the best radiation diagnosis effect in the X-ray data set; B is a lollipop graph of the correlation between radiation dose prediction value and the 8 genes with the best radiation diagnosis effect in the neutron data set; C is a lollipop graph of the correlation between radiation dose prediction value and the 8 genes with the best radiation diagnosis effect in the γ-ray data set; D is a scatter plot fitting curve of the correlation between radiation dose prediction value and radiation dose in the X-ray data set; E is a scatter plot fitting curve of the correlation between radiation dose prediction value and radiation dose in the neutron data set; F is a scatter plot fitting curve of the correlation between radiation dose prediction value and radiation dose in the γ-ray data set. In the lollipop graph, R represents the Spearman correlation coefficient, and in the correlation scatter plot, R square represents the determination coefficient. DETAILED DESCRIPTION
[0030] The following examples are representative and are used to further understand and illustrate the essence of the present invention, but are not intended to limit the scope of the present invention in any way.
[0031] In this study, we analyzed multiple radiation-related transcriptional microarray datasets and collected 61 peripheral blood samples from healthy subjects, severe trauma patients, and severe burn patients for transcriptome sequencing in order to screen out radiation-specific genes that are applicable to multiple types of radiation and are not affected by trauma and burn factors. A multivariate linear regression model was constructed based on the expression levels of these genes to accurately assess the radiation exposure dose of the human body.
[0032] All statistical analyses in this study were performed using R software (version 4.2.1), except for multiple linear regression analysis using SPSS26 software, in which the Spearman method was used for correlation analysis of lollipop plots. P < 0.05 was considered statistically significant.
[0033] Example 1 Screening of genes co-induced by multiple types of radiation
[0034] 1. Acquisition of radiation-related datasets
[0035] The microarray chip datasets of human peripheral blood irradiated with different types of radiation, GSE90909 (including 11 normal control samples, 41 X-ray irradiation samples and 40 neutron irradiation samples, platform: GPL13497 Agilent-026652 Whole Human Genome Microarray 4x44Kv2) and GSE102971 (including 20 normal control samples and 80 γ-ray irradiation samples, platform: GPL10332 Agilent-026652 Whole Human Genome Microarray 4x44K v2) were obtained from the GEO database (http: / / www.ncbi.nlm.nih.gov / geo / ) as screening sets, and GSE44201 (including 5 normal control samples and 20 γ-ray irradiation samples, platform: GPL10332 Agilent-026652 Whole HumanGenome Microarray 4x44K v2) was used as the validation set.
[0036] 2. Acquisition of radiation-related differentially expressed genes (DEGs)
[0037] The R package "limma" was used to screen out X-ray, neutron, and γ-ray related DEGs. The screening conditions for differentially expressed genes were set as: absolute value of the difference fold |FoldChange|>0.5, adjusted P value adj.P-value<0.05, and X-ray, neutron, and γ-ray related DEGs were obtained. Finally, 168 X-ray related DEGs, 180 neutron related DEGs, and 360 γ-ray related DEGs were screened out. Heat map ( Figure 1 A, C, E) and volcano plots ( Figure 1 B, D, and F) show the distribution of differentially expressed genes irradiated by X-rays, neutrons, and γ-rays, respectively.
[0038] 3. Weighted gene co-expression network analysis (WGCNA) and key gene module acquisition
[0039] The WGCNA package in R was used to perform weighted gene co-expression network analysis on each radiation dataset, and finally the X-ray related key gene module (X ray_grey60 color block, 75 genes, Figure 2 AB), neutron-related key gene module (Neutron_lightgreen color block, 97 genes, Figure 2 CD), γ-ray related key gene module (Gammaray_blue block, 181 genes, Figure 2 EF).
[0040] 4. Acquisition of genes co-induced by multiple types of radiation
[0041] By drawing a Venn diagram, the DEGs related to X-rays, neutrons, and γ-rays and the genes in the key gene modules related to X-rays, neutrons, and γ-rays were intersected, and finally 33 multi-type ray co-induced genes were obtained ( Figure 3 ). The 33 genes are AEN, APOBEC3H, ASCC3, BAX, BBC3, CCNG1, CD70, DCP1B, DDB2, EDA2R, FDXR, GADD45A, GDF15, GLS2, MAMDC4, MDM2, PAPPA, PCNA, PHLDA3, PHPT1, PLK2, POLH, PVT1, SESN1, TAC3, TMEM30A, TNFRSF10B, TNFRSF10D, TNFSF4, TRIAP1, TRIM22, XPC, and ZNF541.
[0042] Example 2 Screening for Differentially Expressed Genes (DEGs) Related to Trauma and Burns
[0043] 1. Collection and transcriptome sequencing of clinical samples from patients with severe trauma and severe burns
[0044] 1.1 Recruitment of patients with severe trauma and burns and healthy volunteers
[0045] All procedures of this study were approved by the Ethics Committee of the First Affiliated Hospital of Army Medical University. All subjects (or agents) signed the informed consent form. For patients who were unable to sign for themselves due to injury or impaired consciousness, a legally authorized agent (usually a close relative) signed on their behalf. For patients with severe trauma and severe burns, the recruitment criteria were as follows: Inclusion criteria: 1) Age 18-65 years old, regardless of gender; 2) Previously healthy, without major underlying diseases; 3) Injury or burn time between 12-24 hours, trauma severity ISS score > 16 points, or total burn area > 30%; 4) Voluntary participation in the study and signing the informed consent form. Exclusion criteria: 1) Age < 18 or > 65 years old; 2) Women who have recent fertility plans, are pregnant, breastfeeding, or are menstruating; 3) Incapable of civil conduct; 4) Unable to understand the potential risks and benefits of the study and unable to follow up and evaluate as required; 5) History of drug or alcohol abuse; 6) History of trauma or burns within six months; 7) Any more complex medical problems that may interfere with research behavior or increase risks, such as upper respiratory tract infection, inflammatory state, malignant tumor, blood disease, AIDS, viral hepatitis, cardiovascular and cerebrovascular disease, mental illness, skin disease, autoimmune disease, etc. For healthy volunteers, the recruitment criteria are as follows: Inclusion criteria: 1) Age 18-65 years old, regardless of gender; 2) Good health, no major underlying diseases; 3) Voluntary participation in the study and signing of the informed consent form. The exclusion criteria for healthy volunteers are the same as those for patients with severe trauma and severe burns.
[0046] 1.2 Collection and storage of samples from subjects
[0047] After the subjects (or agents) have obtained their fully informed consent, peripheral venous blood samples were collected from the subjects using PAXgene Blood RNA Tube (Qiagen). After collecting 2.5 ml of blood from each person, the blood was gently inverted and mixed 8-10 times according to the user manual of PAXgene Blood RNATube and allowed to stand at room temperature for more than 2 hours, and then stored in a -80°C refrigerator. In this study, peripheral blood samples were collected from 11 healthy volunteers, 13 severely burned patients, and 37 severely traumatized patients.
[0048] 1.3 RNA extraction and quantification
[0049] After the frozen blood samples were thawed at room temperature, Total RNA was extracted using PAX Blood RNA Kit (Codon X Biotechnology, Cat: RE105) according to the manufacturer's protocol and treated with RNase-free DNase I kit to remove genomic DNA contamination. RNA integrity was assessed using 1.0% agarose gel. Spectrophotometer (IMPLEN, CA, USA) and The quality and quantity of RNA were assessed by Fluorometer 2.0 (Invitrogen). High-quality RNA samples were then submitted to Sangon Biotech (Shanghai) Co., Ltd. for library preparation and sequencing.
[0050] 1.4 mRNA library construction
[0051] Total RNA was accurately quantified using the Qubit RNA Assay Kit (Life, Cat: Q32855), and library construction was performed using 1 g of total RNA per sample. Hieff NGS was used according to the manufacturer's recommendations. TM MaxUp Dual-mode mRNALibrary Prep Kit for (YEASEN, Cat: 12301ES96) to generate sequencing libraries and add index codes to the attribute sequences of each sample. The successfully constructed libraries were quality checked by gel electrophoresis, and the recovered DNA was accurately quantified using the Qubit DNA detection kit (Life, Cat: Q10212).
[0052] 1.5 Sequencing
[0053] Paired-end sequencing was performed using an MGISEQ-T7 sequencer (BGI Genomics, Shenzhen, China).
[0054] 1.6 Analysis and quality control of sequencing data and calculation of gene expression
[0055] FastQC software was used to evaluate the quality of the raw data of sequencing, and Trimmomatic was used to filter the raw reads to obtain relatively accurate valid data. Then HISAT2 was used to align the clean reads to the reference genome. RSeQC was used to perform statistics on the comparison results. Qualimap software was used to perform uniformity distribution and genome structure detection. BEDTools was used to perform statistical analysis on gene coverage. StringTie software was used to calculate the gene expression value of the transcript.
[0056] 2. Screening of trauma and burn-related DEGs
[0057] The R package "DESeq2" was used to determine the differentially expressed genes (DEGs) between healthy people and patients with severe trauma. The screening conditions for differential genes were set as: absolute value of difference fold |FoldChange|>0.5, adjusted P value adj.P-value<0.05, and finally 6635 trauma-related DEGs were obtained. The R package "DESeq2" was used to determine the differentially expressed genes (DEGs) between healthy people and patients with severe burns. The screening conditions for differential genes were set as: absolute value of difference fold |FoldChange|>0.5, adjusted P value adj.P-value<0.05, and finally 7703 burn-related DEGs were obtained. The volcano plot shows the trauma ( Figure 4 A) Burns Figure 4 B) Distribution of differentially expressed genes.
[0058] Example 3 Acquisition of radiation-specific genes not affected by trauma and burn factors
[0059] The intersection of 6635 trauma-related DEGs and 7703 burn-related DEGs with the 33 multi-type radiation-induced genes obtained in Example 1 was taken ( Figure 5 ), and finally obtained 12 multi-type radiation-induced genes that have no intersection with trauma and burns. These genes are radiation-specific genes that are not affected by trauma and burns. The functions of these radiation-specific genes are mainly related to DNA damage repair and apoptosis. Table 1 lists the annotation information of these 12 radiation-specific genes.
[0060] Table 1 Annotation information of 12 radiation-specific genes
[0061]
[0062]
[0063] Example 4 Evaluation of the diagnostic effect of radiation-specific genes on radiation
[0064] The data were classified into "non-irradiated" and "irradiated". ROC analysis was performed on 12 radiation-specific genes using the R package "pROC". The analysis results were visualized using the R package "ggplot2". The area under the curve (AUC) was used to evaluate the diagnostic efficiency of each radiation-specific gene for radiation. Figure 6 AC is the ROC curve and AUC value of each gene in the X-ray data set, Figure 6 DF is the ROC curve and AUC value of each gene in the neutron dataset. Figure 6GI is the ROC curve and AUC value of each gene in the γ-ray data set. The average AUC values of the 12 genes in the three types of radiation were calculated and sorted from high to low. The results showed that 8 genes, including DDB2, PVT1, CCNG1, TNFRSF10B, MDM2, BBC3, BAX, and TNFSF4, had the best diagnostic effect on radiation ( Figure 6 J).
[0065] Example 5 Multiple Linear Regression Analysis
[0066] In order to explore the establishment of a radiation dose assessment model related to gene expression levels, the inventors performed a multiple linear regression analysis. Specifically, the expression levels of the first 8 genes screened in Example 4 were used as independent variables, and the radiation dose was used as the dependent variable, and multiple linear regression analysis was performed using SPSS26 software. The results of the multiple linear regression analysis showed (Table 2, multicollinear genes have been removed). In the X-ray data set, the multiple linear regression model can explain 89.8% of the variation in radiation dose, among which MDM2 (β = 0.693, P = 0.000) and TNFSF4 (β = 0.212, P = 0.021) gene expression levels can significantly positively predict radiation dose. In the neutron data set, the multiple linear regression model can explain 85.3% of the variation in radiation dose, among which MDM2 (β = 0.423, P = 0.005) and TNFSF4 (β = 0.395, P = 0.006) gene expression levels can significantly positively predict radiation dose. In the γ-ray data set, the multivariate linear regression model could explain 93.7% of the variation in radiation dose, among which the expression levels of MDM2 (β=0.624, P=0.000) and TNFSF4 (β=0.584, P=0.000) genes could significantly positively predict radiation dose.
[0067] Table 2 Results of multiple linear regression analysis of three types of rays
[0068]
[0069]
[0070] Example 6 Construction of radiation dose assessment model based on MDM2 and TNFSF4 expression levels
[0071] According to the results of multiple linear regression analysis of Example 5, whether it is X-ray, neutron or gamma ray radiation, the β coefficient of MDM2 and TNFSF4 genes is the highest, so it is most suitable for constructing an assessment model for predicting radiation dose. Therefore, the present inventors constructed radiation dose assessment models for X-rays, neutrons and gamma rays based on the expression levels of MDM2 and TNFSF4 genes, respectively. In the assessment model equation, the independent variable x1 is the MDM2 gene expression, the independent variable x2 is the TNFSF4 gene expression, and the dependent variable y is the radiation exposure dose prediction value, which is linearly related to the actual radiation exposure dose y', that is: y'=By+C (B, C are both constants). Wherein the radiation dose assessment model for X-rays is: y=6.096*x1+0.75*x2; the radiation dose assessment model for neutrons is: y=1.282*x1+0.451*x2; the radiation dose assessment model for gamma rays is: y=15.152*x1+6.232*x2.
[0072] Example 7: Verification of the accuracy of the radiation dose assessment model based on the expression levels of MDM2 and TNFSF4
[0073] According to the radiation dose assessment model constructed in Example 6, the radiation dose prediction value (MLRprediction) of each sample was calculated, and the correlation analysis between the radiation dose prediction value and the actual radiation dose was performed. The analysis results were visualized by correlation lollipop plots and correlation scatter plots using the R package "ggplot2". The results showed that for all three types of radiation, the correlation between the radiation dose prediction value and the actual radiation dose was better than the correlation between the single gene expression level and the actual radiation dose ( Figure 7 AC). The correlation scatter plot shows that in the X-ray data set, the linear fit goodness of fit between the radiation dose prediction value and the radiation dose is R square = 0.908 ( Figure 7 D); in the neutron data set, the linear fit goodness of radiation dose prediction value and radiation dose Rsquare=0.868( Figure 7 E); in the γ-ray data set, the linear fit goodness of fit between the radiation dose prediction value and the radiation dose is R square = 0.941 ( Figure 7 F). Since the closer R square is to 1, the better the linear relationship is, the above results confirm the accuracy of the radiation dose assessment model constructed by the inventors.
[0074] It should be noted that the focus of this study is not on constructing a specific radiation dose assessment model equation, but on discovering two key genes, MDM2 and TNFSF4. The radiation dose assessment model constructed based on their expression has very good dose assessment accuracy for X-rays, neutrons and gamma rays, and the expression of MDM2 and TNFSF4 genes is not affected by trauma and burn factors. Therefore, this model is suitable for radiation exposure dose detection of casualties in more complex battlefields, reactor explosions and other nuclear exposure sites. Since the dose assessment equation is affected by many factors, such as experimental methods and detection platforms, specific experimental methods and detection platforms can be used to construct appropriate dose assessment equations to accurately calculate radiation doses.
[0075] Example 8 Application of the radiation dose assessment model based on the expression levels of MDM2 and TNFSF4 for rapid population classification at large-scale nuclear exposure sites
[0076] 1. After nuclear exposure occurs, the medical station collects blood from people suspected of being exposed to radiation;
[0077] 2. Extract total RNA from the collected blood and perform reverse transcription;
[0078] 3. Amplify MDM2 and TNFSF4 genes by real-time fluorescence quantitative PCR technology and determine the expression levels of the above genes;
[0079] 4. Input the detected MDM2 and TNFSF4 gene expression levels into the radiation dose assessment model to obtain the predicted value of radiation exposure dose;
[0080] 5. Rapidly classify and treat the population based on the predicted results of radiation exposure doses. For example, people with a predicted radiation exposure dose of less than 2Gy may not receive medical intervention for the time being, while people with a predicted radiation exposure dose of more than 2Gy may receive medical intervention.
[0081] Among them, the PCR technology in step 3 can use the well-known primers of MDM2 and TNFSF4 genes or use the conventional method to design a primer pair (MDM2 F: TGTGAAAGAAGCAGTAGCAGT, MDM2 R: GTAACTTGATATACACCAGCATCAA, TNFSF4 F: CAACTCCTTGATGGTGGCCT, TNFSF4 R: TGGAAGTCATCCAGGGAGGT).
Claims
1. A radiation dose assessment model associated with gene expression levels, wherein the genes are MDM2 and TNFSF4, and the model is constructed by the following method, which comprises the following steps: 1) Obtaining microarray chip data of human peripheral blood samples after being irradiated with different types of radiation; 2) Using the expression levels of MDM2 and TNFSF4 genes in the microarray chip data as independent variables and the radiation dose as the dependent variable, a multiple linear regression equation was constructed using SPSS software to obtain the radiation dose assessment model.
2. The radiation dose assessment model according to claim 1, wherein the expression levels of MDM2 and TNFSF4 are not affected by trauma and burn factors. 3 . The radiation dose assessment model according to claim 1 , wherein the different types of radiation in step 1) include X-rays, gamma rays and / or neutrons.
4. Use of the radiation dose assessment model according to any one of claims 1 to 3 for detecting or assessing human radiation exposure dose.
5. The use according to claim 4, wherein the detection or evaluation comprises: 1) Detect the gene expression of MDM2 and TNFSF4 by RNA quantitative detection technology such as q-PCR, gene chip or transcriptome sequencing; 2) The detected MDM2 and TNFSF4 gene expression levels are input into the radiation dose assessment model to obtain the predicted value of the radiation dose.