CN113943817A - Cervical cancer canceration level evaluation model and construction method - Google Patents
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Abstract
The invention relates to the field of molecular biology detection, in particular to a cervical cancer canceration level evaluation model and a construction method thereof; screening the existing cervical cancer gene and the methylation region thereof, designing a probe, and extracting cervical cell DNA from the cervical cancer tumor tissue; carrying out end repair and joint connection after fragmenting cervical cell DNA, constructing a cervical cell DNA methylation library, and carrying out targeted enrichment on the library; performing biogenic analysis on the treated cervical cell DNA methylation library, constructing an evaluation model by using a logistic regression algorithm, and verifying the evaluation model by using a clinical sample; by evaluating the canceration of the cervical cancer through the evaluation model, the coincidence rate can reach 92.5 percent, the clinical sensitivity is 0.888889, the clinical specificity is 0.954545, and the AUC is 0.9271, so that the overtreatment can be effectively reduced, and the morbidity and the mortality of the cervical cancer can be reduced.
Description
Technical Field
The invention relates to the field of molecular biological detection, in particular to a cervical cancer canceration level evaluation model and a construction method thereof.
Background
Cervical cancer is the fourth most common cancer in women worldwide and is the leading cause of cancer death in women in developing countries. Every year, 13 thousands of new cases of cervical cancer exist in China, and account for 28 percent of the total number of new cases of cervical cancer in the world. On 6 th 7 th 2021, the World Health Organization (WHO) issued guidelines for screening and treatment of cervical cancer for prevention of precancerous lesions in the cervix, suggested optimization of diagnostic tools and screening options, facilitated cervical cancer prevention and saved more lives.
Current screening for cervical cancer includes cervical cytology and Human Papilloma Virus (HPV) testing. Cytological examinations included pap smears and TCT liquid-based cell pellets. TCT is more likely to find abnormal cells than pap smears, is more sensitive, but is relatively expensive and the level of diagnosis is more influenced by physician subjectivity. Compared with cytological detection, the HPV DNA detection can be used for typing and quantitatively detecting high-risk types and low-risk types, but 80% of women can be infected with HPV in a lifetime, more than 90% of infected persons can automatically clear viruses within two years, and the HPV DNA detection generates a large amount of false positives (low specificity). Cervical Intraepithelial Neoplasia (CIN) is the stage of precancerous lesions of the cervix, with increasing incidence of carcinogenesis as the degree of lesion increases. Even for individuals with the same grade of precancerous lesions, there is a difference in cancer risk. The natural regression rate of CIN1 was 60-85%, most cases recovered to normal within 2 years, with a progression of 10-15% for CIN2-3 and approximately 0.3% for cancer; the natural regression rate of CIN2 is about 50%, and about 23% of lesions are in a persistent state; CIN3 is capable of progressing to cervical cancer in a proportion of 30%. Therefore, a new method needs to be developed to accurately evaluate the risk of individuals with precancerous lesions, and determine to treat or follow-up the precancerous lesions according to the risk degree, so that the over-treatment can be effectively reduced, the quality of life can be improved, and the morbidity and mortality of the cervical cancer can be reduced, namely, patients with potential lesion progression risk need to be shunted.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to solve the problems that: how to provide a cervical cancer canceration level evaluation model and a construction method thereof to solve the problem of inaccurate cervical cancer evaluation.
In order to solve the problems, the invention adopts the following technical scheme:
DNA methylation is that under the catalysis of methyltransferase, cytosine of two nucleotides of CG of DNA is selectively added with methyl to form 5-methylcytosine; is an epigenetic effect, does not change nucleic acid sequences, but can regulate protein expression by changing the structure of chromosomes, and has specific change patterns in various stages of tumorigenesis and development. The gene methylation analysis is a non-morphological molecular detection method, which can not only avoid the limitation of cell inspection, but also ensure the accuracy of diagnosis.
The invention provides an application of a reagent in preparing a methylation diagnostic agent for evaluating the canceration level of cervical cancer, which is characterized in that the reagent is used for detecting the methylation level of the following gene sequences or complementary sequences thereof:
chr18:904579-908594，chr1:75597615-75599122，chr8:11534462-11534610，chr8:1-9761810，chr1:75597615-75599122，chr8:1-9762032，chr1:4713491-4713784，chr5:140261645-140262296，chr8:11559214-11559488，chr8:55370129-55372569，chr1:75599392-75599677，chr8:1-9762229，chr1:4713905-4714578，chr5:140261645-140262296，chr8:11559597-11561010，chr8:55370129-55372569，chr1:75600124-75600531，chr8:1-9762493，chr1:4713905-4714578，chr5:140261645-140262296，chr10:118031045-118033137，chr8:55370129-55372569，chr1:75600124-75600531，chr8:1-9763458，chr1:177134149-177134454，chr5:140261645-140262296，chr10:118033273-118034031，chr1:77332756-77334533，chr1:165324192-165326477，chr8:2-65289223，chr5:31193844-31194465，chr5:145716905-145717476，chr16:22824320-22826458，chr19:58238586-58239222，chr2:95690640-95692430，chr20:21683745-21684707，chr9:90112101-90112269，chr5:145717676-145718106，chr11:133938586-133939680，chr20:21684808-21685009，chr8:1-9760528，chr5:145718229-145720094，chr9:90112446-90113948，chr20:21685286-21685954，chr4:21305805-21306181，chr4:20253277-20256867，chr8:1-9760751，chr20:21685286-21685954，chr8:11565128-11567211，chr13:112719981-112721006，chr1:75593662-75594135，chr20:21686139-21686728，chr8:1-9761087，chr13:112721124-112723581，chr8:11565128-11567211。
further, the reagent comprises a sulfite.
The present invention adaptively provides a method for screening gene sequences, comprising:
(1) screening the existing cervical cancer gene and the methylation region thereof, designing a probe, and extracting cervical cell DNA from the cervical cancer tumor tissue;
(2) performing end repair and joint connection after fragmenting the cervical cell DNA, constructing a cervical cell DNA methylation library, and performing library targeted enrichment;
(3) performing a biological analysis on the cervical cell DNA methylation library treated in the step (2), wherein the biological analysis comprises the following steps:
A. performing high-throughput sequencing on the cervical cell DNA methylation library processed in the step (2) to obtain sequencing original data;
B. sequentially carrying out base recognition on the sequencing original data, removing a sequencing joint and deleting low-quality bases, comparing to a human genome hg19, extracting CpG locus information of a gene related to cervical cancer, screening a CpG high-density region, and screening a high-association locus set from the CpG high-density region;
C. calculating a methylation value for each of the set of high association sites, the methylation value MHL being calculated by:
wherein l is a CpG site contained in the candidate MARKER, MHi is the proportion of i continuous CpG sites which are completely methylated, and i is more than or equal to 4; p is the ratio of complete methyl fragments in the fragment of i consecutive CpG sites.
D. And screening the gene sequence by a random forest model.
Specifically, the designing of the probe in the step (1) includes: obtaining CpG sites and front and back 75bp positions of a methylation region of a SEQ ID No.1-45 gene sequence, designing sulfite-treated simulated hypermethylation and simulated hypomethylation sequences according to a forward sequence and a reverse sequence of the sequences, intercepting 120bp sequences from a first base by taking the simulated hypermethylation and simulated hypomethylation sequences as templates to serve as probes, moving n bases backwards again, and intercepting 120bp sequences to serve as probes until the last 120bp sequence.
Further, the linker sequence in step (2) is one or more of the following sequence groups:
1-1.ACACTCTTTCCCTACACGACGCTCTTCCGATCTCGAGCTCA*T，
1-2.P-TGAGCTCGAGATCGGAAGAGCACACGTCT；
2-1.ACACTCTTTCCCTACACGACGCTCTTCCGATCTAGACATGCAG*T，
2-2.P-CTGCATGTCTAGATCGGAAGAGCACACGTCT；
3-1.ACACTCTTTCCCTACACGACGCTCTTCCGATCTGTCTAGCAC*T，
3-2.P-GTGCTAGACAGATCGGAAGAGCACACGTCT；
4-1.ACACTCTTTCCCTACACGACGCTCTTCCGATCTTACGCTAC*T，
4-2.P-GTAGCGTAAGATCGGAAGAGCACACGTCT；
5-1.ACACTCTTTCCCTACACGACGCTCTTCCGATCTCGTCACTAAG*T，
5-2.P-CTTAGTGACGAGATCGGAAGAGCACACGTCT；
6-1.ACACTCTTTCCCTACACGACGCTCTTCCGATCTCTCACGTGC*T，
6-2.P-GCACGTGAGAGATCGGAAGAGCACACGTCT；
7-1.ACACTCTTTCCCTACACGACGCTCTTCCGATCTATGTCTCA*T，
7-2.P-TGAGACATAGATCGGAAGAGCACACGTCT；
8-1.ACACTCTTTCCCTACACGACGCTCTTCCGATCTCACACGTCCA*T，
8-2.P-TGGACGTGTGAGATCGGAAGAGCACACGTCT；
9-1.ACACTCTTTCCCTACACGACGCTCTTCCGATCTACATCTCAG*T，
9-2.P-CTGAGATGTAGATCGGAAGAGCACACGTCT；
10-1.ACACTCTTTCCCTACACGACGCTCTTCCGATCTCGTAGCGT*T，
10-2.P-ACGCTACGAGATCGGAAGAGCACACGTCT。
specifically, the step B specifically includes:
sequentially carrying out base recognition on the sequencing original data, removing a sequencing joint by using cutadapt, deleting low-quality bases, and generating clear reads; wherein the parameters of cutadapt are-q 10, 10-nextseq-trim =10-aATCTCGTATGCCGTCTTCTGCTTG-A AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGTAGATCTCGGTGGTCGCCGTATCATT, and the sequence length is less than 80;
clean reads are aligned to a human genome hg19 by using Bismark software, wherein the Bismark parameter is-un-genome _ folder-N1-p8-L30-most _ valid _ alignment 3-B-samtools _ path-o-path _ to _ bowtie-1-2, a bam file is generated,
the bam file is de-redundant using a default _ Bismark module in the Bismark software,
extracting all CpG position information from the redundancy-removed bam file by using a Bismark _ methyl _ extra module in Bismark software, wherein the Bismark parameter is-p-no _ overlay-align 4-align _ r 24-samtools _ path-bdgraph-buffer _ size 20G-cytosine _ report-genome _ folder-o./-multicore 10 bam;
extracting CpG locus information of the cervical cancer related gene from all the CpG locus information by using an s _ extract _ from _ bed.py python script, wherein the Bismark parameter is python3 s _ extract _ from _ bed.py all.Cpg.txt target.CpG.xls bed;
dividing all connected CpG locus regions less than 100bp in the CpG locus information of the gene related to the cervical cancer into high-density CpG regions;
and screening the high-association-degree locus set according to the association degree of any locus in the locus set and the rest at least one locus, wherein the association degree is more than or equal to 0.8.
The invention also provides a method for constructing the cervical cancer canceration level evaluation model, which screens the gene sequence by using the method of the gene sequence, constructs the evaluation model by using a logistic regression algorithm, and verifies the evaluation model by using a clinical sample.
Further, the invention also provides an evaluation model constructed by the method for constructing the cervical cancer canceration level evaluation model.
In particular, the model may be used to distinguish between CIN2 and above and/or CIN1 and below.
The invention has the beneficial effects that:
1. the invention obtains the abundance of the methylated and unmethylated CpG sites of the gene by a targeted sequencing method, has high capture efficiency, better capture stability and uniformity and reduces the sequencing cost.
2. The invention adopts the joint detection of multiple loci of multiple genes and the evaluation of a unique algorithm model, and the sensitivity and the specificity are higher than those of a single gene.
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In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described in detail with reference to the accompanying drawings, in which:
FIG. 1 is a ROC graph of example 3 of the present invention.
Detailed Description
The present invention will be described in further detail with reference to specific examples.
It should be noted that these examples are only for illustrating the present invention, and not for limiting the present invention, and the simple modification of the method based on the idea of the present invention is within the protection scope of the present invention.
Example 1 DNA methylation detection method establishment
1. Methylated probe design
(1) Methylated mark gene screening
Screening methylation regions with obvious methylation characteristics related to cervical cancer, taking a reference sequence (reference genome version hg 19) of each region, removing sequences of repeated regions, and analyzing the repeated sequences by using a RepeatMask software to obtain the gene sequences of SEQ ID Nos. 1-45 in Table 1.
TABLE 1 SEQ ID No.1-45 Gene List
| ADCYAP1 | CDKN2A | JAM3 | PCDHA4 | TERT |
| AJAP1 | DAPK1 | KCNIP4 | POU4F3 | TIMP3 |
| ANKRD18CP | DLX1 | LHX8 | RARB | WIF1 |
| APC | EPB41L3 | LMX1A | RNASEH2A | ZNF671 |
| ASTN1 | FAM19A4 | MAL | RUBCNL | ZSCAN1 |
| CADM1 | FANCI | MIR124-1 | RXFP3 | |
| CCNA2 | FHIT | MIR124-2 | SLIT2 | |
| CDH1 | GATA4 | MIR124-3 | SOX1 | |
| CDH13 | GFRA1 | PAX1 | SOX17 | |
| CDH6 | HS3ST2 | PCDHA13 | ST6GALNAC5 |
(2) Probe design
Extracting reference sequences (reference genome version hg 19) from CPG sites and positions of front and back 75bp of the methylation regions of the 45 genes, designing sulfite-treated simulated hypermethylation and hypomethylation sequences according to the forward sequence and the reverse sequence of the sequences, intercepting 120bp sequences from the first base by taking the sequences as templates to serve as probes, moving n bases backwards again, and intercepting 120bp sequences to serve as probes until the last 120bp is reached. N varies depending on the GC content of the exon in each region, and the smaller n is too high or too low, the denser the probe design is to achieve improved uniformity of capture.
2. Cervical cell DNA extraction
Extracting DNA of cervical scraped cells: after extracting genomic DNA from 300. mu.L of the cells preserved in the preservation solution according to the instructions of a universal column type genome extraction kit (Kangji, CWY 004), the concentration of the extracted genomic DNA is detected by Qubit.
Extracting genome DNA of paraffin tissues or paraffin sections: the concentration was measured by taking 10 μm thick paraffin blocks or 8-10 pieces of paraffin sections and extracting genomic DNA according to the GeneRead DNA FFPE Kit (Qiagen, 180134) instructions followed by the Qubit.
Fresh tissue genomic DNA extraction: 25 mg of fresh tissue is taken, and the concentration is detected by the Qubit after the genome DNA is extracted according to the instructions of a universal column type genome extraction kit (Kangji, CWY 004).
3. Genomic DNA methylation library construction
(1) Methylated anti-pollution linker design and synthesis
The methylation anti-pollution joint is composed of two parts of a universal sequence and an anti-pollution label, wherein the anti-pollution label comprises 8-10 bases (random sequence), all cytosine (C) bases in the joint are methylated and modified, and concretely, the methylation anti-pollution joint is shown in a table 2 (T sulfo modification and P phosphorylation modification), and the joint is synthesized according to the sequence in the table. Centrifuging the synthesized dry powder of the linker at 12000 rpm by a high-speed centrifuge, and adding a certain volume of 10mM Tris-HCl with pH of 8.0 to dilute the mixture to 100 mu M; each of the joints 1 and 2 was annealed at 50. mu.L and 100. mu.M according to the reaction procedure shown in Table 3.
TABLE 2 methylated antipollution linker sequences
| Joint numbering | Linker sequences | Decoration |
| 1_1 | ACACTCTTTCCCTACACGACGCTCTTCCGATCTCGAGCTCA*T | 5-MethyldC |
| 1_2 | P-TGAGCTCGAGATCGGAAGAGCACACGTCT | 5-MethyldC |
| 2_1 | ACACTCTTTCCCTACACGACGCTCTTCCGATCTAGACATGCAG*T | 5-MethyldC |
| 2_2 | P-CTGCATGTCTAGATCGGAAGAGCACACGTCT | 5-MethyldC |
| 3_1 | ACACTCTTTCCCTACACGACGCTCTTCCGATCTGTCTAGCAC*T | 5-MethyldC |
| 3_2 | P-GTGCTAGACAGATCGGAAGAGCACACGTCT | 5-MethyldC |
| 4_1 | ACACTCTTTCCCTACACGACGCTCTTCCGATCTTACGCTAC*T | 5-MethyldC |
| 4_2 | P-GTAGCGTAAGATCGGAAGAGCACACGTCT | 5-MethyldC |
| 5_1 | ACACTCTTTCCCTACACGACGCTCTTCCGATCTCGTCACTAAG*T | 5-MethyldC |
| 5_2 | P-CTTAGTGACGAGATCGGAAGAGCACACGTCT | 5-MethyldC |
| 6_1 | ACACTCTTTCCCTACACGACGCTCTTCCGATCTCTCACGTGC*T | 5-MethyldC |
| 6_2 | P-GCACGTGAGAGATCGGAAGAGCACACGTCT | 5-MethyldC |
| 7_1 | ACACTCTTTCCCTACACGACGCTCTTCCGATCTATGTCTCA*T | 5-MethyldC |
| 7_2 | P-TGAGACATAGATCGGAAGAGCACACGTCT | 5-MethyldC |
| 8_1 | ACACTCTTTCCCTACACGACGCTCTTCCGATCTCACACGTCCA*T | 5-MethyldC |
| 8_2 | P-TGGACGTGTGAGATCGGAAGAGCACACGTCT | 5-MethyldC |
| 9_1 | ACACTCTTTCCCTACACGACGCTCTTCCGATCTACATCTCAG*T | 5-MethyldC |
| 9_2 | P-CTGAGATGTAGATCGGAAGAGCACACGTCT | 5-MethyldC |
| 10_1 | ACACTCTTTCCCTACACGACGCTCTTCCGATCTCGTAGCGT*T | 5-MethyldC |
| 10_2 | P-ACGCTACGAGATCGGAAGAGCACACGTCT | 5-MethyldC |
TABLE 3 annealing reaction procedure
| Step (ii) of | Temperature of | Time |
| 1 | 95℃ | 2min |
| 2 | Reducing the temperature to 25 ℃ at the speed of 0.1 ℃/8s | About 90min |
| 3 | 4℃ | Hold |
(2) Genomic DNA methylation library construction
A. Fragmentation of genomic DNA
DNA obtained from cells or tissues was fragmented to 200bp using a Covaris ultrasound disruptor according to the relevant instructions.
And (3) fragmenting the DNA of the tissues or cells in the step (2) to be about 200bp by using a Covaris ultrasonic disruptor (Covaris, S220) according to parameters of Peak Incident Power 175W, Duty Factor 10%, cycle per Burst 200 and Treatment Time 180S.
B. End repairing and connecting joint
Lambda DNA was added to the fragmented DNA at a ratio of 2000:1, followed by end repair and linker ligation using Rapid Max DNA Lib Prep Kit for Illumina (Abclonal, RK20217) Kit. Wherein the linker is the methylation modified linker synthesized in the step (1).
C. Library methylation processing
Methylation of the product of the previous step was carried out using the Epitect Plus DNA Bisufite Kit (Qiagen, 59124) methylation Kit according to the instructions.
Enrichment by PCR
The methylated product of the previous step was amplified using KAPA HIFI Hi-Fi methylation library amplification reagents (KAPA, KK 2802) and purified using the Magbead DNA purification kit (Kan., CW 2508M).
E. Library quality inspection
And (5) detecting the concentration of the product in the last step by using qubit.
4. Library targeted enrichment
The genomic DNA of the cells or tissues of the sample to be tested is taken as a library for targeted capture according to the reagent of example 1 and the method of example 2 of patent 201810580442.6.
5. Analysis of letter of birth
And (4) carrying out high-throughput sequencing on the target sequence capture library obtained in the step (4) by a second-generation sequencing platform such as Nextseq500, X Ten, Novaseq and the like to obtain sequencing original data, and carrying out the following analysis.
(1) Base recognition
And converting and splitting an Illumina sequencer off-line binary BCF format file into a single sample readable file fastq format according to a sample index sequence by using Illumina official software BCF2fastq (version 2.15.0.4).
(2) Data quality control
Sequencing adapters were removed using cutatapt (version 1.16) and low quality bases were deleted to generate clean reads. Wherein the parameters of the cutadapt (version 1.16) are (-q 10, 10-nextseq-trim =10-a ATCTCGTATGCCGTCTTCTGCTTG-A AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGTAGATCTCGGTGGTCGCCGTATCATT), and the sequence length is less than 80.
(3) Data comparison
Clean reads are aligned to the human genome hg19 by using methylation alignment special software Bismark (v0.17.0), wherein Bismark alignment parameters are (-un-genome _ folder-N1-p8-L30-most _ valid _ alignment 3-B-nanoparticles _ path-o-path _ to _ bowtie-1-2), and a bam file is generated.
And using a default _ Bismark module in the Bismark to remove redundancy of the compared bam file.
CpG position information is extracted from the redundancy-removed bam file by using a Bismark _ methyl _ extra module in a Bismark, and parameters are (-p-no _ overlap-align 4-align _ r 24-samtools _ path-bedGraph-buffer _ size 20G-cytosine _ report-genome _ folder-o./-multicore 10 bam).
(4) CpG site extraction
All the CpG position information is extracted from the bismark _ methyl _ extra module, and only the CpG position of the gene related to the cervical cancer is focused on, so that the required part of the CpG position information is extracted from all the CpG position information. The written s _ extract _ from _ bed. py python script is used to extract the part of CpG site information that we want from all CpG site information. The parameter is (python 3 s _ extract _ from _ bed. py all. cpg. txt target. cpg. xls bed).
(5) Screening for CpG high Density regions
The CpG high density area, namely all connected CpG locus areas in the area are less than 100bp, and the high density CpG area is marked out.
(6) Screening high association locus set for CpG high-density area
Simultaneous methylation or non-methylation of both sites is considered a methylation association between the two sites. The degree of two-site methylation association is defined as: the ratio of the number of simultaneously methylated or simultaneously unmethylated support sequences at both sites to the total of the two cover sequences. The high association degree site set is defined as that the association degree of any site in the site set with at least one other site is more than or equal to 0.8. Interference sites are filtered by further partitioning groupings of sites (called site sets) by this rule.
(7) One methylation score is calculated for each set of sites.
The methylation score is calculated by the formula
Wherein l is the CpG sites contained in the candidate MARKER, MHi is the proportion of complete methylation of i continuous CpG sites, P is the proportion of complete methyl fragments in the fragments of the i continuous CpG sites, and i starts from 4 when the methylation score is calculated in order to more effectively distinguish cervicitis/CINI from CINII/CINIII/cervical cancer.
(8) Marker screening and modeling prediction
The 55 MARKERs with the highest association with phenotype were screened using a random forest model. A classification model is constructed using a logistic regression algorithm. The effect of the classification model was verified using new clinical samples.
Example 2
Methylation detection classification/prediction model was established using the method of example 1
Samples of 80 persons with pathological results of CIN2 and above and those with pathological results of CIN1 and below (all with patient consent) were collected and tested according to the method of example 1 to establish a methylation classification model.
The probes and methods of example 1 target-enriched target region libraries were high throughput sequenced and the results show that: the probe, the reagent and the method in the embodiment 1 have high capture rate of the target area, and the capture rate is more than 50%; the effective average sequencing depth of the target region is more than 1000X; the 20x coverage reached over 99% (see table 4 for some data).
TABLE 4 results of quality control data of samples tested by the method of the present invention
55 MARKERs were selected by extracting CpG sites from the high throughput sequencing data for 80 samples, screening for CpG high density regions, screening for high association site sets for CpG high density regions, and calculating a methylation value (MHL) for each site set, see Table 5. And constructing a logistic regression model by taking the MHL value of MARKER obtained by screening as an independent variable and taking the phenotype value as a dependent variable, wherein the phenotype value of the crowd with CIN2 or above is coded as 1, and the phenotype code of the crowd with CIN1 or below is coded as 0.
Table 5 random forest model 55 MARKERs with highest association with phenotype were screened
Example 3
40 examples sample validation the classification model/prediction model of example 2
22 samples of the population with CIN1 or less and 18 samples of the population with the pathological results of CIN2 or more (all with informed consent of patients) are collected and detected according to the method of the embodiment 1 and the methylation classification model established in the embodiment 2, the detection results are shown in the table 6, and the results show that the results of 37 samples are consistent with the pathological results, and the coincidence rate is 92.5%.
TABLE 637 sample test results
| Prediction/truth | CIN1- | CIN2+ | Total of |
| CIN1- | 21 | 2 | 23 |
| CIN2+ | 1 | 16 | 17 |
| Total of | 22 | 18 | 40 |
Wherein:
the clinical sensitivity (sensitivity) was 16/(16+2) = 0.888889,
the clinical specificity (specificity) was 21/(21+1) = 0.954545, and the ROC curve is shown in fig. 1, in which AUC is 0.9271.
Finally, it is noted that the above-mentioned embodiments illustrate rather than limit the invention, and that, while the invention has been described with reference to preferred embodiments thereof, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Sequence listing
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Claims (10)
1. Use of an agent for the preparation of a methylation diagnostic agent for assessing the level of cervical cancer carcinogenesis, wherein the agent is used for detecting the level of methylation of the following gene sequences or their complements:
chr18:904579-908594，chr1:75597615-75599122，chr8:11534462-11534610，chr8:1-9761810，chr1:75597615-75599122，chr8:1-9762032，chr1:4713491-4713784，chr5:140261645-140262296，chr8:11559214-11559488，chr8:55370129-55372569，chr1:75599392-75599677，chr8:1-9762229，chr1:4713905-4714578，chr5:140261645-140262296，chr8:11559597-11561010，chr8:55370129-55372569，chr1:75600124-75600531，chr8:1-9762493，chr1:4713905-4714578，chr5:140261645-140262296，chr10:118031045-118033137，chr8:55370129-55372569，chr1:75600124-75600531，chr8:1-9763458，chr1:177134149-177134454，chr5:140261645-140262296，chr10:118033273-118034031，chr1:77332756-77334533，chr1:165324192-165326477，chr8:2-65289223，chr5:31193844-31194465，chr5:145716905-145717476，chr16:22824320-22826458，chr19:58238586-58239222，chr2:95690640-95692430，chr20:21683745-21684707，chr9:90112101-90112269，chr5:145717676-145718106，chr11:133938586-133939680，chr20:21684808-21685009，chr8:1-9760528，chr5:145718229-145720094，chr9:90112446-90113948，chr20:21685286-21685954，chr4:21305805-21306181，chr4:20253277-20256867，chr8:1-9760751，chr20:21685286-21685954，chr8:11565128-11567211，chr13:112719981-112721006，chr1:75593662-75594135，chr20:21686139-21686728，chr8:1-9761087，chr13:112721124-112723581，chr8:11565128-11567211。
2. use of the agent according to claim 1 for the preparation of a methylation diagnostic agent for assessing the level of cervical cancer carcinogenesis, wherein the agent comprises a sulfite.
3. A method for screening the gene sequence of claim 1, comprising:
(1) screening the existing cervical cancer gene and the methylation region thereof, designing a probe, and extracting cervical cell DNA from the cervical cancer tumor tissue;
(2) performing end repair and joint connection after fragmenting the cervical cell DNA, constructing a cervical cell DNA methylation library, and performing library targeted enrichment;
(3) and (3) performing biogenic analysis on the cervical cell DNA methylation library treated in the step (2) to obtain the gene sequence.
4. The method for screening gene sequences according to claim 1, wherein the genetic analysis comprises:
A. performing high-throughput sequencing on the cervical cell DNA methylation library processed in the step (2) to obtain sequencing original data;
B. sequentially carrying out base recognition on the sequencing original data, removing a sequencing joint and deleting low-quality bases, comparing to a human genome hg19, extracting CpG locus information of a gene related to cervical cancer, screening a CpG high-density region, and screening a high-association locus set from the CpG high-density region;
C. calculating a methylation value for each of the set of high association sites, the methylation value MHL being calculated by:
wherein l is a CpG site contained in the candidate MARKER, MHi is the proportion of i continuous CpG sites which are completely methylated, and i is more than or equal to 4;
D. and screening the gene sequence through a random forest model.
5. The method for screening the gene sequence of claim 1 according to claim 3, wherein the designing of the probe in step (1) comprises: obtaining CpG sites and front and back 75bp positions of a methylation region of a SEQ ID No.1-45 gene sequence, designing sulfite-treated simulated hypermethylation and simulated hypomethylation sequences according to a forward sequence and a reverse sequence of the sequences, intercepting 120bp sequences from a first base by taking the simulated hypermethylation and simulated hypomethylation sequences as templates to serve as probes, moving n bases backwards again, and intercepting 120bp sequences to serve as probes until the last 120bp sequence.
6. The method for screening gene sequences according to claim 1, wherein the linker sequence in step (2) is one or more groups selected from the group consisting of:
1-1.ACACTCTTTCCCTACACGACGCTCTTCCGATCTCGAGCTCA*T，
1-2.P-TGAGCTCGAGATCGGAAGAGCACACGTCT；
2-1.ACACTCTTTCCCTACACGACGCTCTTCCGATCTAGACATGCAG*T，
2-2.P-CTGCATGTCTAGATCGGAAGAGCACACGTCT；
3-1.ACACTCTTTCCCTACACGACGCTCTTCCGATCTGTCTAGCAC*T，
3-2.P-GTGCTAGACAGATCGGAAGAGCACACGTCT；
4-1.ACACTCTTTCCCTACACGACGCTCTTCCGATCTTACGCTAC*T，
4-2.P-GTAGCGTAAGATCGGAAGAGCACACGTCT；
5-1.ACACTCTTTCCCTACACGACGCTCTTCCGATCTCGTCACTAAG*T，
5-2.P-CTTAGTGACGAGATCGGAAGAGCACACGTCT；
6-1.ACACTCTTTCCCTACACGACGCTCTTCCGATCTCTCACGTGC*T，
6-2.P-GCACGTGAGAGATCGGAAGAGCACACGTCT；
7-1.ACACTCTTTCCCTACACGACGCTCTTCCGATCTATGTCTCA*T，
7-2.P-TGAGACATAGATCGGAAGAGCACACGTCT；
8-1.ACACTCTTTCCCTACACGACGCTCTTCCGATCTCACACGTCCA*T，
8-2.P-TGGACGTGTGAGATCGGAAGAGCACACGTCT；
9-1.ACACTCTTTCCCTACACGACGCTCTTCCGATCTACATCTCAG*T，
9-2.P-CTGAGATGTAGATCGGAAGAGCACACGTCT；
10-1.ACACTCTTTCCCTACACGACGCTCTTCCGATCTCGTAGCGT*T，
10-2.P-ACGCTACGAGATCGGAAGAGCACACGTCT。
7. the method according to claim 4 for screening the gene sequences according to claim 1, wherein step B is specifically:
sequentially carrying out base recognition on the sequencing original data, removing a sequencing joint by using cutadapt, deleting low-quality bases, and generating clear reads; wherein the parameters of cutadapt are-q 10, 10-nextseq-trim =10-aATCTCGTATGCCGTCTTCTGCTTG-A AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGTAGATCTCGGTGGTCGCCGTATCATT, and the sequence length is less than 80;
clean reads are aligned to a human genome hg19 by using Bismark software, wherein the Bismark parameter is-un-genome _ folder-N1-p8-L30-most _ valid _ alignment 3-B-samtools _ path-o-path _ to _ bowtie-1-2, a bam file is generated,
the bam file is de-redundant using a default _ Bismark module in the Bismark software,
extracting all CpG position information from the redundancy-removed bam file by using a Bismark _ methyl _ extra module in Bismark software, wherein the Bismark parameter is-p-no _ overlay-align 4-align _ r 24-samtools _ path-bdgraph-buffer _ size 20G-cytosine _ report-genome _ folder-o./-multicore 10 bam;
extracting CpG locus information of the cervical cancer related gene from all the CpG locus information by using an s _ extract _ from _ bed.py python script, wherein the Bismark parameter is python3 s _ extract _ from _ bed.py all.Cpg.txt target.CpG.xls bed;
dividing all connected CpG locus regions less than 100bp in the CpG locus information of the gene related to the cervical cancer into high-density CpG regions;
and screening the high-association-degree locus set according to the association degree of any locus in the locus set and the rest at least one locus, wherein the association degree is more than or equal to 0.8.
8. A method for constructing an assessment model of the level of cervical cancer canceration, which comprises screening the gene sequence according to claim 1 by the method for screening the gene sequence according to any one of claims 3 to 7, constructing an assessment model by using a logistic regression algorithm, and verifying the assessment model by using a clinical specimen.
9. The cervical cancer canceration level evaluation model constructed by the method for constructing a cervical cancer canceration level evaluation model according to claim 8.
10. The cervical cancer canceration level assessment model according to claim 9, for distinguishing between CIN2 and above and/or CIN1 and below.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115612744A (en) * | 2022-12-14 | 2023-01-17 | 中国医学科学院肿瘤医院 | Human papilloma virus typing and related gene methylation integrated detection model and construction method thereof |
| CN117625795A (en) * | 2024-01-25 | 2024-03-01 | 北京迈基诺基因科技股份有限公司 | Probe set, kit and detection system for methylation detection of lung cancer and application |
| CN117701718A (en) * | 2024-02-04 | 2024-03-15 | 湖南宏雅基因技术有限公司 | Gene methylation marker for diagnosing cervical cancer, primer pair and application thereof |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105177168A (en) * | 2015-10-27 | 2015-12-23 | 山东大学齐鲁医院 | Reagent kit for screening early-stage cervical cancers based on cervical liquid-based cytology and DNA methylation |
| CN108103193A (en) * | 2017-12-18 | 2018-06-01 | 东莞博奥木华基因科技有限公司 | Methylation detecting method based on cervical carcinoma host cell |
| CA3121886A1 (en) * | 2018-12-04 | 2020-06-11 | Hkg Epitherapeutics Limited | Dna methylation biomarkers for early detection of cervical cancer |
| CN111635940A (en) * | 2020-06-03 | 2020-09-08 | 王思贤 | Kit and pharmaceutical composition for cervical cancer detection, screening, typing, diagnosis or prognosis evaluation |
| CN112375822A (en) * | 2020-06-01 | 2021-02-19 | 广州市基准医疗有限责任公司 | Methylation biomarker for detecting breast cancer and application thereof |
-
2021
- 2021-12-20 CN CN202111565778.3A patent/CN113943817B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105177168A (en) * | 2015-10-27 | 2015-12-23 | 山东大学齐鲁医院 | Reagent kit for screening early-stage cervical cancers based on cervical liquid-based cytology and DNA methylation |
| CN108103193A (en) * | 2017-12-18 | 2018-06-01 | 东莞博奥木华基因科技有限公司 | Methylation detecting method based on cervical carcinoma host cell |
| CA3121886A1 (en) * | 2018-12-04 | 2020-06-11 | Hkg Epitherapeutics Limited | Dna methylation biomarkers for early detection of cervical cancer |
| WO2020115728A2 (en) * | 2018-12-04 | 2020-06-11 | Hkg Epitherapeutics Limited | Dna methylation biomarkers for early detection of cervical cancer |
| CN112375822A (en) * | 2020-06-01 | 2021-02-19 | 广州市基准医疗有限责任公司 | Methylation biomarker for detecting breast cancer and application thereof |
| CN111635940A (en) * | 2020-06-03 | 2020-09-08 | 王思贤 | Kit and pharmaceutical composition for cervical cancer detection, screening, typing, diagnosis or prognosis evaluation |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115612744A (en) * | 2022-12-14 | 2023-01-17 | 中国医学科学院肿瘤医院 | Human papilloma virus typing and related gene methylation integrated detection model and construction method thereof |
| CN117625795A (en) * | 2024-01-25 | 2024-03-01 | 北京迈基诺基因科技股份有限公司 | Probe set, kit and detection system for methylation detection of lung cancer and application |
| CN117701718A (en) * | 2024-02-04 | 2024-03-15 | 湖南宏雅基因技术有限公司 | Gene methylation marker for diagnosing cervical cancer, primer pair and application thereof |
| CN117701718B (en) * | 2024-02-04 | 2024-05-07 | 湖南宏雅基因技术有限公司 | Gene methylation marker for diagnosing cervical cancer, primer pair and application thereof |
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