Genetic molecular marker related to auxiliary diagnosis of colorectal cancer of multiple humans and application of genetic molecular marker

Abstract

The invention discloses a genetic molecular marker related to colorectal cancer auxiliary diagnosis and application of the genetic molecular marker, and belongs to the field of genetic engineering and oncology. 37 SNPs significantly related to the colorectal cancer onset risk are screened out, the colorectal cancer auxiliary diagnosis kit prepared based on 37 markers is helpful for clinicians to rapidly master the genetic risk of colorectal cancer onset, powerful support is provided for early prevention measures, and the colorectal cancer auxiliary diagnosis kit is suitable for multiple populations and has a wide application prospect. The problem that a conventional genetic risk prediction model is not compatible with people in China and Europe and America is effectively solved.

Description

Technical Field

[0001] The present invention belongs to the field of genetic engineering and oncology, and specifically discloses a genetic molecular marker related to auxiliary diagnosis of colorectal cancer and its application. Background Art

[0002] Colorectal cancer is a common malignant tumor, with the incidence and mortality ranking third and second among all tumors in the world respectively, and it has obvious regional characteristics, with high incidence areas mainly in Australia, New Zealand, Europe, North America and East Asia. In recent years, the incidence and mortality of colorectal cancer have shown an increasing trend in my country. Since early detection of colorectal cancer is difficult and there is a lack of specific and sensitive screening indicators, it has caused serious disease burden and health hazards to Chinese residents. Therefore, it is necessary to identify sensitive and effective molecular markers to assess the risk of colorectal cancer, and to conduct early screening for high-risk populations with conventional colonoscopy and other methods for early diagnosis and treatment of colorectal cancer.

[0003] The occurrence and development of colorectal cancer is a multi-stage and multi-factorial biological process, which is the result of environmental factors (such as smoking, eating habits and other unhealthy lifestyles), genetic factors and the interaction between the two. However, only a few people develop colorectal cancer under the same environmental exposure, suggesting that genetic factors play a key role in the occurrence and development of colorectal cancer. Therefore, identifying genetic molecular markers related to colorectal cancer can help us better assess the genetic risk of colorectal cancer in individuals, thereby achieving early detection, diagnosis and targeted treatment of colorectal cancer.

[0004] Genome-wide association study (GWAS) has been widely used in association studies of various complex diseases. Previous studies have shown that the establishment of a genetic risk prediction model for colorectal cancer based on genetic loci discovered by GWAS (mainly single nucleotide polymorphism, SNP loci) is of great significance for screening high-risk populations and achieving early prevention.

[0005] However, the heterogeneity of genetic background limits the intercommunication of genetic risk prediction models between Asian populations such as my country and European and American populations. In the past, most effective genetic models for colorectal cancer were constructed based on GWAS sites of European and American populations. Their predictive efficiency in the Chinese population is relatively low, and they cannot maximize the effect of genetic prediction models. Therefore, based on the screening of GWAS data of populations in East Asia (China and Japan) and Europe and America (regions such as the United States and the United Kingdom), combined with external data verification, reliable genetic molecular markers for auxiliary diagnosis of colorectal cancer are identified, and a prediction model that can effectively predict the risk of colorectal cancer in multiple races is constructed in combination with exposure factors such as lifestyle. It is particularly urgent and important to implement more effective individualized prevention and scientific intervention in the future. This patent will prepare a genetic marker kit for auxiliary diagnosis of colorectal cancer, hoping to lay the foundation for screening, individualized prevention and treatment of high-risk populations for colorectal cancer in populations of multiple races, including my country, and provide a basis for decision-making. Summary of the invention

[0006] The purpose of the present invention is to provide a genetic molecular marker related to auxiliary diagnosis of colorectal cancer and its application, so as to solve the problem that the previous genetic model has low predictive efficiency in the Chinese population.

[0007] In order to achieve the above object, the technical solution of the present invention is:

[0008] In a first aspect, the present invention claims protection for the use of a genetic molecular marker or a substance for detecting the genetic molecular marker in the following (a1) or (a2) or (a3):

[0009] (a1) Application in the preparation of auxiliary diagnosis products for colorectal cancer;

[0010] (a2) Application in the preparation of a product for predicting the genetic risk of colorectal cancer;

[0011] (a3) Application in the preparation of a genetic risk prediction model for colorectal cancer;

[0012] The genetic molecular markers are rs6424881, rs12140604, rs58383609, rs7623129, rs72942485, rs1909122, rs72748467, rs7729156, rs2071590, rs6948177, rs7810512, rs2015069, rs6470510, rs1537372, rs17578367, rs704017, rs12241008, rs494491 3. the combination of rs174598, rs9634162, rs11169572, rs908664, rs3217840, rs8023022, rs17563, rs1406389, rs12921341, rs6507874, rs7229639, rs73039434, rs2305122, rs6066825, rs1015563, rs6067417, rs355529, rs6121558 and rs6086208.

[0013] Furthermore, the substance used to detect the genetic molecular marker is at least one of the following (b1)-(b3):

[0014] (b1) specific amplification primers for the genetic molecular markers;

[0015] (b2) specific probe primers for the genetic molecular markers;

[0016] (b3) A reagent comprising the specific amplification primer described in (b1) and / or the specific probe primer described in (b2).

[0017] Furthermore, the specific amplification primers described in (b1) comprise the following primer sequences:

[0018] The primer sequences for rs6424881 are SEQ ID NO.1 and SEQ ID NO.2;

[0019] The primer sequences for rs12140604 are SEQ ID NO. 5 and SEQ ID NO. 6;

[0020] The primer sequences for rs58383609 are SEQ ID NO.9 and SEQ ID NO.10;

[0021] The primer sequences for rs7623129 are SEQ ID NO. 13 and SEQ ID NO. 14;

[0022] The primer sequences for rs72942485 are SEQ ID NO. 17 and SEQ ID NO. 18;

[0023] The primer sequences for rs1909122 are SEQ ID NO. 21 and SEQ ID NO. 22;

[0024] The primer sequences for rs72748467 are SEQ ID NO. 25 and SEQ ID NO. 26;

[0025] The primer sequences for rs7729156 are SEQ ID NO. 29 and SEQ ID NO. 30;

[0026] The primer sequences for rs2071590 are SEQ ID NO.33 and SEQ ID NO.34;

[0027] The primer sequences for rs6948177 are SEQ ID NO. 37 and SEQ ID NO. 38;

[0028] The primer sequences for rs7810512 are SEQ ID NO.41 and SEQ ID NO.42;

[0029] The primer sequences for rs2015069 are SEQ ID NO.45 and SEQ ID NO.46;

[0030] The primer sequences for rs6470510 are SEQ ID NO. 49 and SEQ ID NO. 50;

[0031] The primer sequences for rs1537372 are SEQ ID NO.53 and SEQ ID NO.54;

[0032] The primer sequences for rs17578367 are SEQ ID NO.57 and SEQ ID NO.58;

[0033] The primer sequences for rs704017 are SEQ ID NO.61 and SEQ ID NO.62;

[0034] The primer sequences for rs12241008 are SEQ ID NO.65 and SEQ ID NO.66;

[0035] The primer sequences for rs4944913 are SEQ ID NO.69 and SEQ ID NO.70;

[0036] The primer sequences for rs174598 are SEQ ID NO.73 and SEQ ID NO.74;

[0037] The primer sequences for rs9634162 are SEQ ID NO. 77 and SEQ ID NO. 78;

[0038] The primer sequences for rs11169572 are SEQ ID NO.81 and SEQ ID NO.82;

[0039] The primer sequences for rs908664 are SEQ ID NO.85 and SEQ ID NO.86;

[0040] The primer sequences for rs3217840 are SEQ ID NO.89 and SEQ ID NO.90;

[0041] The primer sequences for rs8023022 are SEQ ID NO.93 and SEQ ID NO.94;

[0042] The primer sequences for rs17563 are SEQ ID NO.97 and SEQ ID NO.98;

[0043] The primer sequences of rs1406389 are SEQ ID NO.101 and SEQ ID NO.102; the primer sequences of rs12921341 are SEQ ID NO.105 and SEQ ID NO.106; the primer sequences of rs6507874 are SEQ ID NO.109 and SEQ ID NO.110; the primer sequences of rs7229639 are SEQ ID NO.113 and SEQ ID NO.114; the primer sequences of rs73039434 are SEQ ID NO.117 and SEQ ID NO.118; the primer sequences of rs2305122 are SEQ ID NO.121 and SEQ ID NO.122; the primer sequences of rs6066825 are SEQ ID NO.125 and SEQ ID NO.126; the primer sequences of rs1015563 are SEQ ID NO.129 and SEQ ID NO. NO.130; the primer sequences of rs6067417 are SEQ ID NO.133 and SEQ ID NO.134; the primer sequences of rs355529 are SEQ ID NO.137 and SEQ ID NO.138;

[0044] The primer sequences of rs6121558 are SEQ ID NO.141 and SEQ ID NO.142; the primer sequences of rs6086208 are SEQ ID NO.145 and SEQ ID NO.146. Furthermore, the specific probe primers described in (b2) include the following primer sequences: the primer sequences of rs6424881 are SEQ ID NO.3 and SEQ ID NO.4;

[0045] The primer sequences for rs12140604 are SEQ ID NO.7 and SEQ ID NO.8;

[0046] The primer sequences for rs58383609 are SEQ ID NO.11 and SEQ ID NO.12;

[0047] The primer sequences for rs7623129 are SEQ ID NO. 15 and SEQ ID NO. 16;

[0048] The primer sequences for rs72942485 are SEQ ID NO. 19 and SEQ ID NO. 20;

[0049] The primer sequences for rs1909122 are SEQ ID NO. 23 and SEQ ID NO. 24;

[0050] The primer sequences for rs72748467 are SEQ ID NO. 27 and SEQ ID NO. 28;

[0051] The primer sequences for rs7729156 are SEQ ID NO.31 and SEQ ID NO.32;

[0052] The primer sequences for rs2071590 are SEQ ID NO.35 and SEQ ID NO.36;

[0053] The primer sequences for rs6948177 are SEQ ID NO.39 and SEQ ID NO.40;

[0054] The primer sequences for rs7810512 are SEQ ID NO. 43 and SEQ ID NO. 44;

[0055] The primer sequences for rs2015069 are SEQ ID NO.47 and SEQ ID NO.48;

[0056] The primer sequences for rs6470510 are SEQ ID NO.51 and SEQ ID NO.52;

[0057] The primer sequences for rs1537372 are SEQ ID NO.55 and SEQ ID NO.56;

[0058] The primer sequences for rs17578367 are SEQ ID NO. 59 and SEQ ID NO. 60;

[0059] The primer sequences for rs704017 are SEQ ID NO. 63 and SEQ ID NO. 64;

[0060] The primer sequences for rs12241008 are SEQ ID NO.67 and SEQ ID NO.68;

[0061] The primer sequences for rs4944913 are SEQ ID NO.71 and SEQ ID NO.72;

[0062] The primer sequences for rs174598 are SEQ ID NO.75 and SEQ ID NO.76;

[0063] The primer sequences for rs9634162 are SEQ ID NO. 79 and SEQ ID NO. 80;

[0064] The primer sequences for rs11169572 are SEQ ID NO.83 and SEQ ID NO.84;

[0065] The primer sequences for rs908664 are SEQ ID NO. 87 and SEQ ID NO. 88;

[0066] The primer sequences for rs3217840 are SEQ ID NO.91 and SEQ ID NO.92;

[0067] The primer sequences for rs8023022 are SEQ ID NO.95 and SEQ ID NO.96;

[0068] The primer sequences for rs17563 are SEQ ID NO.99 and SEQ ID NO.100;

[0069] The primer sequences for rs1406389 are SEQ ID NO. 103 and SEQ ID NO. 104;

[0070] The primer sequences for rs12921341 are SEQ ID NO. 107 and SEQ ID NO. 108;

[0071] The primer sequences for rs6507874 are SEQ ID NO.111 and SEQ ID NO.112;

[0072] The primer sequences for rs7229639 are SEQ ID NO.115 and SEQ ID NO.116;

[0073] The primer sequences for rs73039434 are SEQ ID NO. 119 and SEQ ID NO. 120;

[0074] The primer sequences for rs2305122 are SEQ ID NO.123 and SEQ ID NO.124;

[0075] The primer sequences for rs6066825 are SEQ ID NO. 127 and SEQ ID NO. 128;

[0076] The primer sequences for rs1015563 are SEQ ID NO.131 and SEQ ID NO.132;

[0077] The primer sequences for rs6067417 are SEQ ID NO. 135 and SEQ ID NO. 136;

[0078] The primer sequences for rs355529 are SEQ ID NO.139 and SEQ ID NO.140;

[0079] The primer sequences for rs6121558 are SEQ ID NO.143 and SEQ ID NO.144;

[0080] The primer sequences of rs6086208 are SEQ ID NO.147 and SEQ ID NO.148.

[0081] Furthermore, the product described in (a1) or (a2) is a kit.

[0082] In the second aspect, the present invention seeks protection for a method for constructing a genetic risk prediction model for colorectal cancer, which method is to construct a weighted polygenic risk score model (PRS model) based on the aforementioned genetic molecular markers, and to construct a genetic risk prediction model for colorectal cancer based on the PRS model.

[0083] In the third aspect, the present invention claims protection for genetic molecular markers related to auxiliary diagnosis of colorectal cancer, which are rs6424881, rs12140604, rs58383609, rs7623129, rs72942485, rs1909122, rs72748467, rs7729156, rs2071590, rs6948177, rs7810512, rs2015069, rs6470510, rs1537372, rs17578367, rs704017, rs1224 The combination of rs1008, rs4944913, rs174598, rs9634162, rs11169572, rs908664, rs3217840, rs8023022, rs17563, rs1406389, rs12921341, rs6507874, rs7229639, rs73039434, rs2305122, rs6066825, rs1015563, rs6067417, rs355529, rs6121558 and rs6086208.

[0084] In a fourth aspect, the present invention seeks to protect the specific amplification primers and / or specific probe primers of the above-mentioned genetic molecular markers related to auxiliary diagnosis of colorectal cancer, and the primer sequence of the specific amplification primer is as described above; the primer sequence of the specific probe primer is as described above.

[0085] In a fifth aspect, the present invention claims a colorectal cancer auxiliary diagnosis kit, which is used to detect the genotyping of the genetic molecular markers related to the auxiliary diagnosis of colorectal cancer in peripheral blood DNA. Further, the kit comprises the aforementioned specific amplification primers and the aforementioned specific probe primers.

[0086] Furthermore, the kit also includes commonly used reagents for PCR as well as standard substances and control substances.

[0087] In general, the technical solution of the present invention includes:

[0088] (1) Sample collection: determine the sample inclusion and exclusion criteria, and systematically collect blood samples that meet the criteria;

[0089] (2) Genotype detection and site screening: Colorectal cancer cases and healthy controls from East Asian and European populations were selected, and individual genotype data were obtained using a whole-genome high-density SNP chip. SNPs that were significantly associated with the risk of colorectal cancer were identified using a meta-analysis integration strategy;

[0090] (3) Model evaluation: A genetic risk prediction model was constructed based on the identified SNPs with risk effects. The calibration curve and receiver operating characteristic (ROC) curve were used in an external data set to determine the calibration and discrimination of the model, in order to detect the predictive ability of risk SNPs for the risk of colorectal cancer.

[0091] (4) Preparation of auxiliary diagnosis kit for colorectal cancer: construct an auxiliary diagnosis kit based on the SNP genetic markers screened above.

[0092] Experimental methods:

[0093] 1. Determine the research object

[0094] (1) Cases of colorectal cancer confirmed by pathology;

[0095] (2) healthy controls;

[0096] This study included 35,145 colorectal cancer cases and 288,934 healthy controls as training sets (including East Asian populations such as China and Japan, as well as European and American populations), 727 cases and 1,452 healthy controls (Chinese population) as external validation sets, and 355,543 subjects (European and American populations) as external cohort test sets to identify and recognize risk loci.

[0097] 2. Extract peripheral blood genomic DNA by phenol-chloroform method and operate according to the conventional method. Usually 20-50ng / μL DNA can be obtained, and the purity (ratio of UV 260OD to 280OD) is 1.6-2.0.

[0098] 3. Detection of whole genome SNPs in the training set and preliminary screening of risk loci

[0099] (1) Whole genome DNA samples were obtained from subjects in the training set, including 35,145 colorectal cancer cases and 288,934 healthy controls;

[0100] (2) Genotyping using genotyping chips such as Illumina Human Omni ZhongHua Bead Chips and Illumina Asian Screening Array (ASA);

[0101] (3) The training set data were filled with genotypes using IMPUTE2 software;

[0102] (4) The distribution differences of each genotype in the training set of colorectal cancer cases and controls were compared by fitting logistic regression analysis, and the meta-analysis P value <5E-08 (linkage disequilibrium r 2 <0.01) was used as the standard for screening inclusion sites.

[0103] 4. Detection of candidate SNPs in validation and test sets

[0104] (1) DNA samples were collected from the validation set (727 cases and 1,452 healthy controls) and the test set (355,543 subjects);

[0105] (2) Genotyping using genotyping chips such as Illumina Asian Screening Array (ASA);

[0106] (3) genotype imputation;

[0107] (4) Based on the sites initially screened in the training set, the validation set and test set data were used to determine their effect in predicting the risk of colorectal cancer.

[0108] 5. Construction and evaluation of genetic risk prediction models

[0109] (1) Based on the above strategy, the SNPs sites closely associated with the risk of colorectal cancer (P < 5E-08) were identified, including rs6424881, rs12140604, rs58383609, rs7623129, rs72942485, rs1909122, rs72748467, rs7729156, rs2071590, rs6948177, rs7810512, rs2015069, rs6470510, rs1537372, rs17578367, rs704017, and r s12241008, rs4944913, rs174598, rs9634162, rs11169572, rs908664, rs3217840, rs8023022, rs17563, rs1406389, rs12921341, rs6507874, rs7229639, rs73039434, rs2305122, rs6066825, rs1015563, rs6067417, rs355529, rs6121558, and rs6086208;

[0110] (2) construct a weighted polygenic risk score model (PRS model);

[0111] (3) The PRS model was used to assess the risk of validation and test set samples, and the calibration curve and ROC curve were used to evaluate the calibration and discrimination of the model to detect the predictive ability of the identified risk SNPs for the risk of colorectal cancer;

[0112] (4) Based on the PRS model, a comprehensive colorectal cancer risk prediction model with better predictive efficiency was constructed by combining factors such as gender, age, and lifestyle scores (including body mass index (BMI), smoking, drinking, waist-to-hip ratio (WHR), physical activity, sedentary time, red meat and processed meat intake, and vegetable and fruit intake).

[0113] 6. Preparation of auxiliary diagnostic kit for colorectal cancer

[0114] The diagnostic kit is based on the above-screened SNPs (rs6424881, rs12140604, rs58383609, rs7623129, rs72942485, rs1909122, rs72748467, rs7729156, rs2071590, rs6948177, rs7810512, rs2015069, rs6470510, rs1537372, rs17578367, rs704017, rs12241008, rs49 The diagnostic reagent comprises the specific amplification and probe primers of the SNP genetic markers; the commonly used reagents for PCR, such as Taq enzyme, dNTP mixed solution, deionized water, etc.; and the standard substance and the control substance.

[0115] 7. Statistical analysis

[0116] The additive model of logistic regression was used to analyze the strength of the association between SNPs and the risk of colorectal cancer, while adjusting for confounding variables (such as gender and age). A meta-analysis of the results of multiple ethnic groups was performed to screen SNPs sites associated with the risk of colorectal cancer from a genome-wide perspective.

[0117] The genetic risk prediction model is fitted by calculating the PRS. The basic steps include:

[0118] (1) The three genotypes of each SNP are quantitatively scored, such as wild homozygous type is "0", heterozygous type is "1", and homozygous mutant type is "2", and the effect scores of all sites are adjusted to positive association;

[0119] (2) The weight coefficient of each SNP is the regression coefficient obtained by meta-analysis, and the constructed equation is: PRS = rs6424881 × 0.08 + rs9634162 × 0.06 + rs58383609 × 0.07 + rs6066825 × 0.07 + rs2015069 × 0.09 + rs8023022 × 0.07 + rs12921341 × 0.06 + rs4 944913×0.08+rs73039434×0.11+rs72748467×0.09+rs2071590×0.06+rs11169572×0.07+rs6948177×0.06+rs7623129×0.06+rs6507874×0.14+rs17563×0.06+rs1015563×0.08+ rs1537372×0.07+rs2305122×0.1+rs1406389×0.11+rs908664×0.06+rs7729156×0.08+rs7229639×0.11+rs174598×0.06+rs6067417×0.06+rs7810512×0.07+rs17578367×0.07+ rs355529×0.1+rs6121558×0.1+rs1909122×0.09+rs704017×0.11+rs12140604×0.09+rs12241008×0.13+rs6470510×0.11+rs6086208×0.09+rs72942485×0.15+rs3217840×0.08;

[0120] (3) The calibration curve and the area under the ROC curve (AUC) were used to evaluate the predictive performance of multiple models such as the PRS model. All statistical analyses were performed using PLINK1.90 and R 4.0.0 software. The statistical significance P value was 0.05, and all tests were two-sided.

[0121] Result description:

[0122] (1) Based on the results of multi-ethnic meta-analysis in the training set, 37 genetic loci that were significantly associated with the risk of colorectal cancer (P < 5E-08) were finally identified, including rs6424881, rs12140604, rs58383609, rs7623129, rs72942485, rs1909122, rs72748467, rs7729156, rs2071590, rs6948177, rs7810512, rs2015069, rs6470510, rs1537372, rs17578367, and rs7 See Table 1.

[0123] Table 1. Basic information of 37 SNPs associated with colorectal cancer risk

[0124]

[0125]

[0126] a Based on NCBI genome build 37 (hg19); b risk / reference allele; c From meta-analyses of East Asian and European populations; d Weights for constructing a pleiotropic risk score (PRS).

[0127] (2) Based on the 37 colorectal cancer genetic loci screened, a PRS was constructed in the validation set, and a colorectal cancer risk prediction model was constructed using the logistic regression method. Figure 1 The calibration curve and ROC curve shown in the figure show that the model has good calibration (goodness of fit test P value = 0.988) and discrimination (AUC PRS模型 =0.570; AUC 综合模型 =0.617).

[0128] (3) Based on the 37 colorectal cancer genetic loci screened, a PRS was constructed in the test set cohort, and the population was divided into low, medium, and high PRS groups based on <10%, 10% to 90%, and >90%. It was found that the high PRS population had the highest risk of colorectal cancer; and a colorectal cancer risk prediction model was constructed using the logistic regression method. Figure 2 As shown in the figure, based on the calibration curve and ROC curve, it was found that the model had good calibration (goodness of fit test P value = 0.762) and discrimination (AUC PRS模型 =0.583; AUC 综合模型 =0.690).

[0129] Based on the above results, the inventors prepared a colorectal cancer auxiliary diagnosis kit, which includes specific primers for the identified 37 SNPs and other reagents.

[0130] In general, the colorectal cancer auxiliary diagnosis kit prepared with specific primers for the 37 SNPs can help clinicians quickly understand the genetic risk of patients developing the disease and provide strong support for timely treatment measures.

[0131] Beneficial effects of the present invention:

[0132] (1) SNP genetic markers are different from traditional biomarkers in that they are stable and easy to detect;

[0133] (2) The colorectal cancer auxiliary diagnosis kit helps clinicians quickly understand the genetic risk of colorectal cancer in patients, conduct early diagnosis, individualized prevention and treatment, and provide a decision-making basis for screening and intervention in high-risk groups for colorectal cancer;

[0134] (3) SNPs closely related to the risk of colorectal cancer were screened from the perspective of the whole genome of multiple ethnic groups. This method is applicable to multi-ethnic populations and has broader application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0135] Figure 1 It is the calibration curve (PRS model) and ROC curve (PRS model and comprehensive model [PRS and traditional factors]) of the colorectal cancer genetic risk prediction model in the external validation set (Chinese population) in the embodiment of the present invention;

[0136] Figure 2 These are the cumulative risk curves of different PRS populations in the external test set cohort (European and American population) in the embodiments of the present invention, the calibration curve (PRS model) and the ROC curve (PRS model and comprehensive model [PRS and traditional factors]) of the colorectal cancer genetic risk prediction model. DETAILED DESCRIPTION

[0137] The following operation steps are used to illustrate the present invention, so that the technical content is clearer and easier to understand. The following is an exemplary description, and the modification or replacement of the method, steps or conditions of the present invention without departing from the spirit and essence of the present invention belongs to the scope of the present invention.

[0138] Unless otherwise specified, the experimental methods used below are all conventional methods; the materials and reagents used can be purchased through commercial channels unless otherwise specified.

[0139] Embodiment 1:

[0140] 1. Sample collection and data collation

[0141] Training set: The inventors have collected colorectal cancer samples from the Chinese population from Nanjing and other regions since 2010, totaling 4,410 cases, and 5,411 controls who underwent health examinations in hospitals at the same time; and combined with data from Japan's BBJ (BioBank Japan Project, 7,062 cases and 195,745 controls) and European and American populations GECCO (Genetics and Epidemiology of Colorectal Cancer Consortium, 21,608 cases and 20,278 controls) and PLCO (Prostate, Lung, Colorectal and Ovarian cancer screening trial, 2,065 cases and 67,500 controls).

[0142] Validation set: The inventors collected 727 colorectal cases and 1,452 healthy controls from Jiangsu Provincial People's Hospital and other hospitals in the Chinese population.

[0143] Test set: The inventors obtained 355,543 European and American population cohort data through the UK Biobank system (https: / / www.ukbiobank.ac.uk / ).

[0144] 2. Whole-genome scanning of SNPs in peripheral blood DNA in the training set

[0145] Among the 35,145 colorectal cancer cases and 288,934 healthy controls that met the requirements, genotyping was performed using Illumina Human Omni ZhongHua Bead Chips, Illumina Asian Screening Array (ASA) and other genotyping chips. The specific steps are as follows:

[0146] (1) Preparation of lysis buffer: Prepare 40 portions of 219.72 g sucrose, 2.02 g magnesium chloride, and 20 mL Triton X-100 (Amresco 0694), mix, and dilute to 2000 mL with TrisHCl solution.

[0147] (2) Add lysis buffer to the peripheral blood in a 2 mL cryotube and mix thoroughly by inverting.

[0148] (3) Removal of red blood cells: Fill a 5 mL centrifuge tube to the 4 mL mark with lysis buffer, mix well, centrifuge at 4000 rpm for 10 minutes, and discard the supernatant. Add 4 mL of lysis buffer to the precipitate, mix well again, wash once, centrifuge at 4000 rpm for 10 minutes, and discard the supernatant.

[0149] (4) Prepare the extract: 122.5 mL of 0.2 M sodium chloride, 14.4 mL of 0.5 M ethylenediaminetetraacetic acid, 15 mL of 10% (g / 100 ml) sodium dodecyl sulfate and 148.1 mL of double distilled water per 300 mL.

[0150] (5) DNA extraction: Add 1 mL of extraction solution and 8 μL of proteinase K to the obtained precipitate, shake thoroughly on a shaker to mix, and incubate in a 37°C water bath overnight.

[0151] (6) Protein removal: Add 1 mL of saturated phenol and mix thoroughly, centrifuge at 4000 rpm for 10 minutes, and transfer the supernatant to a new 5 mL centrifuge tube. Add an equal volume of chloroform and isoamyl alcohol mixture (chloroform: isoamyl alcohol = 24:1, v / v) to the supernatant, mix thoroughly, centrifuge at 4000 rpm for 10 minutes, and take the supernatant and place it in two 1.5 mL centrifuge tubes respectively.

[0152] (7) DNA precipitation: Add 60 μL of 3 M sodium acetate to the supernatant and add an equal volume of ice-cold anhydrous ethanol to the supernatant. Gently shake up and down until a white flocculent precipitate is visible. Centrifuge at 12,000 rpm for 10 min.

[0153] (8) DNA washing: Add 1 mL of ice-cold anhydrous ethanol to the precipitate, centrifuge at 12,000 rpm for 10 min, discard the supernatant, and finally evaporate to dryness in a clean and dry environment.

[0154] (9) Concentration measurement: Usually 20-50 ng / μL DNA can be obtained, with a purity (ratio of UV 260OD to 280OD) of 1.6-2.0.

[0155] (10) Whole genome scanning: Whole genome scanning is performed on genotyping chips such as Illumina Human Omni ZhongHua Bead Chips and Illumina Asian Screening Array (ASA).

[0156] (11) Genotype imputation was performed using IMPUTE2 software;

[0157] (12) Data analysis and processing: By fitting the logistic regression model, SNPs with significant differences were screened in the case group and the healthy control group of the training set, and the meta-analysis P value was less than 5E-08 (linkage disequilibrium r 2 <0.01) was used as the standard for screening inclusion sites.

[0158] 3. Genotyping of candidate SNPs in validation and test sets

[0159] Conduct corresponding tests on research subjects who meet the requirements.

[0160] (1) DNA extraction method is the same as above.

[0161] (2) The whole genome scanning and genotype imputation methods are the same as above.

[0162] 4. Using PRS to build a genetic risk prediction model

[0163] Based on the 37 risk SNPs identified in the training set, the PRS model was constructed with the meta-analysis regression coefficient as the weight. The calibration curve and ROC curve were used to evaluate the predictive ability of the risk loci for the risk of colorectal cancer. The results showed that the 37 SNPs identified (rs6424881, rs12140604, rs58383609, rs7623129, rs72942485, rs1909122, rs72748467, rs7729156, rs2071590, rs6948177, rs7810512, rs2015069, rs6470510, rs1537372, rs17578367, rs704017, rs12241008, rs4944913, rs174598, rs9634162, rs11169572, The PRS model had good calibration (goodness-of-fit test P value = 0.988 and 0.762) and discrimination (AUC = 0.054) in both the validation set and the test set. PRS模型 =0.570 and 0.583; AUC 综合模型 = 0.617 and 0.690, see Figure 1-2 ), suggesting that these 37 genetic loci can better predict the genetic risk of colorectal cancer.

[0164] 5. Preparation of auxiliary diagnostic kit for colorectal cancer

[0165] The kit contains a batch of SNPs-specific amplification primers and specific probe primers (see Table 2), as well as commonly used PCR reagents such as Taq enzyme, dNTP mixture, deionized water, etc., as well as standard and control substances. Compared with other diagnostic instruments, this kit has the advantages of being simple, stable, and able to predict disease status and genetic risks. Therefore, it is recommended that this kit be put into clinical work to provide a decision-making basis for screening and intervention of multi-ethnic high-risk populations for colorectal cancer.

[0166] Table 2. Genotyping primer information for 37 colorectal cancer risk-associated SNPs

[0167]

[0168]

[0169]

[0170]

[0171] F: Forward Primer, upstream primer; R: Reverse Primer, downstream primer; FAM and VIC: fluorescence signals of different alleles.

[0172] The above is only an embodiment of the present invention, and the common knowledge such as the known specific structure and characteristics in the scheme is not described in detail here. It should be pointed out that for those skilled in the art, several modifications and improvements can be made without departing from the structure of the present invention, which should also be regarded as the protection scope of the present invention, and these will not affect the effect of the implementation of the present invention and the practicality of the patent.

Claims

1. Use of a genetic molecular marker or a substance for detecting the genetic molecular marker in the following (a1) or (a2) or (a3): (a1) Application in the preparation of auxiliary diagnosis products for colorectal cancer; (a2) Application in the preparation of a product for predicting the genetic risk of colorectal cancer; (a3) Application in the preparation of a genetic risk prediction model for colorectal cancer; The genetic molecular markers are rs6424881, rs12140604, rs58383609, rs7623129, rs72942485, rs1909122, rs72748467, rs7729156, rs2071590, rs6948177, rs7810512, rs2015069, rs6470510, rs1537372, rs17578367, rs704017, rs12241008, rs494491 3. the combination of rs174598, rs9634162, rs11169572, rs908664, rs3217840, rs8023022, rs17563, rs1406389, rs12921341, rs6507874, rs7229639, rs73039434, rs2305122, rs6066825, rs1015563, rs6067417, rs355529, rs6121558 and rs6086208.

2. The use according to claim 1, characterized in that: The substance used to detect the genetic molecular marker is at least one of the following (b1)-(b3): (b1) specific amplification primers for the genetic molecular markers; (b2) specific probe primers for the genetic molecular markers; (b3) A reagent comprising the specific amplification primer described in (b1) and / or the specific probe primer described in (b2).

3. The use according to claim 2, characterized in that: The specific amplification primers described in (b1) comprise the following primer sequences: The primer sequences for rs6424881 are SEQ ID NO.1 and SEQ ID NO.2; The primer sequences for rs12140604 are SEQ ID NO. 5 and SEQ ID NO. 6; The primer sequences for rs58383609 are SEQ ID NO.9 and SEQ ID NO.10; The primer sequences for rs7623129 are SEQ ID NO. 13 and SEQ ID NO. 14; The primer sequences for rs72942485 are SEQ ID NO. 17 and SEQ ID NO. 18; The primer sequences for rs1909122 are SEQ ID NO. 21 and SEQ ID NO. 22; The primer sequences for rs72748467 are SEQ ID NO. 25 and SEQ ID NO. 26; The primer sequences for rs7729156 are SEQ ID NO. 29 and SEQ ID NO. 30; The primer sequences for rs2071590 are SEQ ID NO.33 and SEQ ID NO.34; The primer sequences of rs6948177 are SEQ ID NO.37 and SEQ ID NO.38; the primer sequences of rs7810512 are SEQ ID NO.41 and SEQ ID NO.42; the primer sequences of rs2015069 are SEQ ID NO.45 and SEQ ID NO.46; the primer sequences of rs6470510 are SEQ ID NO.49 and SEQ ID NO.50; the primer sequences of rs1537372 are SEQ ID NO.53 and SEQ ID NO.54; the primer sequences of rs17578367 are SEQ ID NO.57 and SEQ ID NO.58; the primer sequences of rs704017 are SEQ ID NO.61 and SEQ ID NO.62; The primer sequences for rs12241008 are SEQ ID NO.65 and SEQ ID NO.66; the primer sequences for rs4944913 are SEQ ID NO.69 and SEQ ID NO.70; the primer sequences for rs174598 are SEQ ID NO.73 and SEQ ID NO.74; The primer sequences of rs9634162 are SEQ ID NO.77 and SEQ ID NO.78; the primer sequences of rs11169572 are SEQ ID NO.81 and SEQ ID NO.82; the primer sequences of rs908664 are SEQ ID NO.85 and SEQ ID NO.86; The primer sequences for rs3217840 are SEQ ID NO.89 and SEQ ID NO.90; the primer sequences for rs8023022 are SEQ ID NO.93 and SEQ ID NO.94; the primer sequences for rs17563 are SEQ ID NO.97 and SEQ ID NO.98; The primer sequences of rs1406389 are SEQ ID NO.101 and SEQ ID NO.102; the primer sequences of rs12921341 are SEQ ID NO.105 and SEQ ID NO.106; the primer sequences of rs6507874 are SEQ ID NO.109 and SEQ ID NO.110; the primer sequences of rs7229639 are SEQ ID NO.113 and SEQ ID NO.114; the primer sequences of rs73039434 are SEQ ID NO.117 and SEQ ID NO.118; the primer sequences of rs2305122 are SEQ ID NO.121 and SEQ ID NO.122; the primer sequences of rs6066825 are SEQ ID NO.125 and SEQ ID NO.126; the primer sequences of rs1015563 are SEQ ID NO.129 and SEQ ID NO. NO.130; the primer sequences of rs6067417 are SEQ ID NO.133 and SEQ ID NO.134; the primer sequences of rs355529 are SEQ ID NO.137 and SEQ ID NO.138; the primer sequences of rs6121558 are SEQ ID NO.141 and SEQ ID NO.142; The primer sequences of rs6086208 are SEQ ID NO.145 and SEQ ID NO.

146.

4. The use according to claim 2, characterized in that: The specific probe primer described in (b2) comprises the following primer sequence: The primer sequences for rs6424881 are SEQ ID NO. 3 and SEQ ID NO. 4; The primer sequences for rs12140604 are SEQ ID NO.7 and SEQ ID NO.8; The primer sequences for rs58383609 are SEQ ID NO.11 and SEQ ID NO.12; The primer sequences for rs7623129 are SEQ ID NO. 15 and SEQ ID NO. 16; The primer sequences for rs72942485 are SEQ ID NO. 19 and SEQ ID NO. 20; The primer sequences for rs1909122 are SEQ ID NO. 23 and SEQ ID NO. 24; The primer sequences for rs72748467 are SEQ ID NO. 27 and SEQ ID NO. 28; The primer sequences for rs7729156 are SEQ ID NO.31 and SEQ ID NO.32; The primer sequences for rs2071590 are SEQ ID NO.35 and SEQ ID NO.36; The primer sequences for rs6948177 are SEQ ID NO.39 and SEQ ID NO.40; The primer sequences for rs7810512 are SEQ ID NO. 43 and SEQ ID NO. 44; The primer sequences for rs2015069 are SEQ ID NO.47 and SEQ ID NO.48; The primer sequences for rs6470510 are SEQ ID NO.51 and SEQ ID NO.52; The primer sequences for rs1537372 are SEQ ID NO.55 and SEQ ID NO.56; The primer sequences for rs17578367 are SEQ ID NO. 59 and SEQ ID NO. 60; The primer sequences for rs704017 are SEQ ID NO. 63 and SEQ ID NO. 64; The primer sequences for rs12241008 are SEQ ID NO.67 and SEQ ID NO.68; The primer sequences for rs4944913 are SEQ ID NO.71 and SEQ ID NO.72; The primer sequences for rs174598 are SEQ ID NO.75 and SEQ ID NO.76; The primer sequences for rs9634162 are SEQ ID NO. 79 and SEQ ID NO. 80; The primer sequences for rs11169572 are SEQ ID NO.83 and SEQ ID NO.84; The primer sequences for rs908664 are SEQ ID NO. 87 and SEQ ID NO. 88; The primer sequences for rs3217840 are SEQ ID NO.91 and SEQ ID NO.92; The primer sequences for rs8023022 are SEQ ID NO.95 and SEQ ID NO.96; The primer sequences for rs17563 are SEQ ID NO.99 and SEQ ID NO.100; The primer sequences for rs1406389 are SEQ ID NO. 103 and SEQ ID NO. 104; The primer sequences for rs12921341 are SEQ ID NO. 107 and SEQ ID NO. 108; The primer sequences for rs6507874 are SEQ ID NO.111 and SEQ ID NO.112; The primer sequences for rs7229639 are SEQ ID NO.115 and SEQ ID NO.116; The primer sequences for rs73039434 are SEQ ID NO. 119 and SEQ ID NO. 120; The primer sequences for rs2305122 are SEQ ID NO.123 and SEQ ID NO.124; The primer sequences for rs6066825 are SEQ ID NO. 127 and SEQ ID NO. 128; The primer sequences for rs1015563 are SEQ ID NO.131 and SEQ ID NO.132; The primer sequences for rs6067417 are SEQ ID NO. 135 and SEQ ID NO. 136; The primer sequences for rs355529 are SEQ ID NO.139 and SEQ ID NO.140; The primer sequences for rs6121558 are SEQ ID NO.143 and SEQ ID NO.144; The primer sequences of rs6086208 are SEQ ID NO.147 and SEQ ID NO.

148.

5. The use according to claim 1, characterized in that: The product described in (a1) or (a2) is a kit.

6. A method for constructing a genetic risk prediction model for colorectal cancer, characterized in that: The method is to construct a weighted polygenic risk score model based on the genetic molecular markers described in claim 1, and to construct a genetic risk prediction model for colorectal cancer based on the weighted polygenic risk score model.

7. Genetic molecular markers related to auxiliary diagnosis of colorectal cancer, characterized in that: The markers are rs6424881, rs12140604, rs58383609, rs7623129, rs72942485, rs1909122, rs72748467, rs7729156, rs2071590, rs6948177, rs7810512, rs2015069, rs6470510, rs1537372, rs17578367, rs704017, rs12241008, rs4944913, The combination of rs174598, rs9634162, rs11169572, rs908664, rs3217840, rs8023022, rs17563, rs1406389, rs12921341, rs6507874, rs7229639, rs73039434, rs2305122, rs6066825, rs1015563, rs6067417, rs355529, rs6121558, and rs6086208.

8. The specific amplification primers and / or specific probe primers of genetic molecular markers related to auxiliary diagnosis of colorectal cancer according to claim 7, characterized in that: The primer sequence of the specific amplification primer is as shown in the primer sequence described in claim 3; The primer sequence of the specific probe primer is shown as the primer sequence described in claim 4.

9. A colorectal cancer auxiliary diagnosis kit, characterized in that: The kit is used for detecting the genotyping of the genetic molecular markers described in claim 7 in peripheral blood DNA.

10. The colorectal cancer auxiliary diagnosis kit according to claim 9, characterized in that: The kit comprises the specific amplification primer described in claim 2 and the specific probe primer described in claim 3.

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